Dayton Engineering
Sciences Symposium


List of Submitted Abstracts

* Note that appearance on this list does not guarantee that the abstract has been or will be accepted. All submitted abstracts will be reviewed for suitability and technical content.

Oral Presentations

Additive Manufacturing

Abstract ID: DESS2024-009

Mechanical Response of Triply Periodic Minimal Surface Gyroid Structures Under Combined Loading

Capt Jay Patel
Air Force Institute of Technology
Maj John Brewer
Air Force Institute of Technology
Dr. Elizabeth Bartlett
Air Force Research Laboratory

Awaiting public release.

Abstract ID: DESS2024-038

Enhancing Compressive Properties of SLS-Printed Thermoplastic Lattice Structures Using Thermoset Reinforcement Coatings

Aime Regis Rugerinyange
Miami University
Dr. Muhammad P. Jahan
Miami University
Dr. Yingbin Hu
Miami University
Bruce Hardman
Miami University

Selective laser sintering (SLS) technique has emerged as an important method in additive manufacturing, facilitating the manufacturability of complex lattice structures, known for their high stiffness-to-weight ratios. However, enhancing the mechanical robustness of these structures remains a significant challenge, limiting their practical applications in demanding sectors such as aerospace and automotive industries. This study addresses the critical research gap in enhancing mechanical properties of SLS-printed Nylon 12 (PA12) lattice structures through the innovative integration of matrix material with thermoset resins to form layered composites, therefore, significantly enhancing the compressive strength and energy absorption capabilities of the printed lattice structures. To tackle the challenge of uneven thermoset reinforcement layers observed with traditional dip-coating methods, a continuous rotation coating technique was developed, achieving a uniform resin distribution across the lattice struts. The newly developed coating method resulted in a 12% increase in compressive yield strength of Nylon 12 lattice structures compared to the traditional dip-coating approach, contributing to an overall 260% increase relative to unreinforced PA12. The study also explores incorporating nanofillers into the matrix, to further enhance the mechanical properties by improving energy dissipation under compressive load. Findings from this study provide a pathway to improve the compressive properties of SLS-printed thermoplastic lattice structures for their use in specific engineering applications requiring lightweight, high-stiffness materials.

Abstract ID: DESS2024-041

High-Fidelity Process Modeling for Metal Laser Powder Bed Fusion Additive Manufacturing

Rahul Singha Rathun
University of Dayton
Rahul Singha Rathun
University of Dayton
Andrew Schrader
University of Dayton
Robert Lowe
University of Dayton
Abdullah Al Amin
University of Dayton

The Laser Powder Bed Fusion (LPBF) process is one of the most widely used additive manufacturing (AM) processes. In terms of functional part manufacturing, LPBF faces several challenges arising from the defects generated by the process itself. The first step towards eliminating such defects from metal AM parts is to understand the science of the process in greater detail. In this perspective, computational modeling, as opposed to sophisticated experimentation, can reveal insightful details. In this research, Navier-Stokes, Volume of Fluid, and Energy equations solved using a numerical technique called the Finite Volume Method (FVM) allow building a high-fidelity computational model of the AM process. Building on our past successful research of melt pool geometry prediction for NIST experiments, we are extending our capability to include a more realistic heating source, incorporating Ray tracing, considering the laser beam reflection on the liquid metal surface. In this work, we developed a user-defined function (UDF) for a commercial FVM solver, ANSYS, considering Aluminum alloy (Al6061). The simulation can provide a realistic temperature contour to study the melting and solidification process, phase transition, and melt pool geometry for a single-line scan. We incorporated complex physical interactions such as a Gaussian profile of the laser beam, recoil pressure, Marangoni convection, melting-solidification, evaporation, and heat loss equations in our computational model. Output results such as temperature and melt pool geometry are compared with available NIST experiments. The results and solution times are also compared with in-house developed open-source C++ FVM solver called AM-CFD. The objective is to compare the accuracy vs speed of computational models for the AM process approaching part-scale simulation.

Abstract ID: DESS2024-042

Acoustic Field-Assisted Inkjet Printing of Graphene-Reinforced Polydimethylsiloxane (PDMS) Composites

Chang Liu
Miami University
Chang Liu
Miami University
Yingbin Hu
Miami University
Muhammad Pervej Jahan
Miami University

Polydimethylsiloxane (PDMS), a silicon-based organic polymer, is widely used in biomedical, optical, and mechanical applications due to its excellent biocompatibility and mechanical properties. To enhance these attributes, graphene (Gr) serves as an effective reinforcement due to its remarkable strength and rigidity. However, the incorporation of Gr often leads to voids and gaps that can compromise the performance of the matrix material. This study explores acoustic field (AF)-assisted inkjet printing as a method to mitigate these issues. It was found that, while the addition of Gr increased surface roughness by 50%, the application of AF effectively minimized this roughness by promoting even dispersion of Gr flakes, repelling air bubbles, and ensuring a smooth printing process. Tensile tests revealed that pure PDMS exhibited a tensile strength (TS) of 3.35 MPa, which increased by 15.8% with AF integration. The Gr-PDMS composite demonstrated a 16.4% improvement in TS, although it experienced a slight reduction in elongation due to voids. Notably, the Gr-PDMS composite with the integration of AF achieved the highest TS of 4.27 MPa and toughness of 7.16 MJ/m³. Moreover, both Gr and AF positively impacted the storage modulus of PDMS, particularly at lower temperatures, while maintaining a loss tangent below 0.13, indicating predominantly elastic behavior. These findings underscore the advantages of combining Gr reinforcement with AF-assisted printing for advanced PDMS composites. Finally, it can be concluded that the integration of AF reduces voids and enhances the adhesion between Gr flakes and the PDMS matrix, thus, significantly improving the tensile and dynamic mechanical properties of Gr-reinforced PDMS composites.

Aerospace Engineering

Abstract ID: DESS2024-006

Multi-band Mid-IR Imaging of a Supersonic Combustor

Nathan Childs
Wright State University
Mitch Wolff
Wright State University
Timothy Ombrello
Air Force Research Laboratory

Mid-Infrared imaging was used to interrogate supersonic combustor exhaust flow. Previous similar investigations were challenged by the reduced transmission and elevated emission from windows while attempting to image in the wavelength range of 1.5-5 µm, precluding measurements of CO2 and CO emission. The current effort has imaged the exhaust directly without the use of windows, which allowed for measurements of radiation intensity from H2O, Hydrocarbons, CO2, and CO. Calibrations to absolute in-band radiance were performed, and temperatures were computed from multiple spectral bands targeting specific combustion species.

Abstract ID: DESS2024-024

Design of a Reusable Additively Manufactured Methalox Rocket Engine and Feed System

Reece Davis
University of Akron
Jackson Godsey
University of Akron

Designing a liquid rocket engine is a complex task that requires careful consideration of numerous calculations, manufacturing methods, and overall reliability and safety. This complexity is especially pronounced in regeneratively cooled cryogenic rocket engines intended for integration into high-altitude flight vehicles. As the space industry increasingly adopts liquid oxygen/liquid methane (methalox) rocket engines due to their clean-burning properties and increased efficiency, collegiate rocket design teams often face challenges when recreating these engines at a smaller scale. The most demanding challenges include the high complexity and cost of the design and manufacturing methods required. Liquid rocket engines developed by the University of Akron’s Rocket Design Team have previously been manufactured using traditional methods. Additive manufacturing (AM) offers advantages over traditional manufacturing methods by lowering costs, reducing lead times, minimizing material waste, and enabling more intricate designs. Additionally, AM is particularly beneficial for manufacturing critical rocket engine components such as injectors, regeneratively cooled combustion chambers, and regeneratively cooled nozzles. The University of Akron’s Rocket Design Team aims to utilize the advantages of AM to improve the design, manufacturing, and testing of a methalox rocket engine. Furthermore, a pressure-fed feed system, and supporting test stand infrastructure, will be developed to achieve the desired mass flow rates and engine design criteria while being resilient to cryogenic temperatures and high operating pressures.

Abstract ID: DESS2024-034

Design of an Optically Accessible Combustor for Investigating Ignition

Bryce Ullman
Wright State University
Brent Rankin
Air Force Research Laboratory
Mitch Wolff
Wright State University

This work focuses on the design and implementation of an optically accessible combustor for ignition testing. Ensuring ignition is critical for safety, especially during emergency scenarios where auxiliary power units (APUs) may need to start in air with low temperatures and reduced pressures. The combustor section allows for direct observation and analysis of ignition behavior under various conditions, providing critical insights into the challenges of ignition. This rig design will utilize atmospheric conditions to improve knowledge of ignition.

Abstract ID: DESS2024-037

Envisioning a Future of 3D Printed Wind Tunnel Models for Experimental Aeroelasticity in Design

Kevin Mchugh
Air Force Research Laboratory
Nicholas Jones
PC Krause & Associates
Philip Buskohl
Air Force Research Laboratory
Alexander Pankonien
Air Force Research Laboratory

Additive manufacturing has lowered barriers associated with rapidly realizing complex shapes, such as the airfoils of wind tunnel models. To construct a flexible, aeroelastic wind tunnel model, printed airfoils are typically affixed to an underlying structure made with conventional fabrication techniques and wrapped in traditional “shrink-wrap” skins. However, the resultant design is not representative of the topologies typically found in aircraft, complicate actuation and sensing integration, and are not reproducible. This work details several years of exploring aeroelastic wind tunnel experimental methodologies that leverage novel printed materials to build the underlying load-bearing structure and outer mold line skins. By printing integrated components simultaneously, integration complexity overall fabrication cost and time are both reduced and designs are fully reproducible. Results are shown for wings of several material types for both print accuracy and static aeroelastic response. Through the lens of this initial study, we look forward to a future where more automation is introduced into the experimental process, reaching towards the possibility for experimental data assimilation into a conceptual vehicle design process.

Abstract ID: DESS2024-040

Ignition Probability Comparison Between Nanosecond-Pulsed High-Frequency and DC Arc Discharges

Katherine Opacich
National Research Council
Joshua A. T. Gray
University of South Carolina
Joshua S. Heyne
Washington State University
Timothy M. Ombrello
Air Force Research Laboratory

Ignition probability and kernel development were measured in methane–air mixtures at equivalence ratios of 0.47 – 0.55 with a flow velocity of 5 m/s using nanosecond-pulsed high-frequency discharge (NPHFD) and DC arc discharge exciters. The average power, total energy, and total discharge duration were held constant across both exciters to provide an accurate one-to-one comparison. The metric to define a successful ignition event, and hence ignition probability was based on an area growth rate criterion that must be met within a given amount of time. In total, four minimum growth rate values and four characteristic time intervals were utilized to distinguish between an ignition success and failure. At the most lenient definition of a successful ignition event (lowest growth rate criterion in the longest characteristic time interval) the conventional exciter outperformed the NPHFD exciter in terms of ignition probability at leaner equivalence ratios. The poor performance of the NPHFD exciter at lean conditions was due to localized quenching of the flame kernel early in its development from the entrainment of cold reactants between pulses and onset of jetting motion that further split the reacting region. As the growth rate requirement for a successful ignition event was increased, the ignition probability curves for all test conditions shifted to higher equivalence ratios, indicating that a more reactive mixture was needed to sufficiently grow the kernels to meet the criteria. However, the NPHFD exciter was less sensitive to increasing the growth rate required for a successful ignition event due to its ability to generate flame kernels with rapid growth rates at higher equivalence ratios.

Artificial Intelligence

Abstract ID: DESS2024-003

Integrating Machine Learning and Spectral Shift Analysis for Structural Damage Detection: A Case Study on Horizontal Axis Wind Turbine Blades

Ameen Elsinwi
Air Force Research Laboratory

Damage identification and localization is an important aspect of structural integrity assessment. This work presents a nondestructive method for identifying structural damage using machine learning and spectral shifts. This manuscript introduces a comprehensive approach to damage detection, employing artificial neural networks and spectral shift analysis to assess structural health. This study specifically applies this methodology to Horizontal Axis Wind Turbine (HAWT) blades, demonstrating its effectiveness in identifying and localizing damage. The proposed methodology involves training an artificial neural network (ANN) on a spectral shifts dataset for both healthy and damaged structures. This enables the network to learn complex patterns indicative of structural anomalies. Additionally, the analysis incorporates spectral shifts in the resonance frequencies of the structure under consideration, offering a nuanced insight into changes in its mechanical properties. The findings of this work underscore the potential of integrating artificial intelligence and spectral analysis in structural health monitoring, with implications for enhancing the reliability and efficiency of damage detection systems in the context of HAWT blades. The proposed technique was able to predict the damage location on a turbine blade with a minimum accuracy of 94%. Experimental results and prediction model construction are presented and discussed.

Abstract ID: DESS2024-029

Leveraging AI in Engineering Education: Enhancing Personalized Learning and Community Impact

Molly Savage
University of Dayton
Nathan Powell
University of Dayton

This presentation explores the evolving role of AI in engineering education, focusing on both community-engaged experiential learning and self-directed learning contexts. We examine how AI can enhance personalized, student-led learning and contribute to impactful, sustainable outcomes through the integration of ChatGPT into engineering projects. Specific highlights include how AI can guide curriculum design, hardware selection, and software development, as demonstrated in the creation of a solar panel tracker using an Arduino Uno R4 Wi-Fi microcontroller. Additionally, we investigate how AI tools can be used in analysis of organizational scalability and can help develop strategies for scaling social, economic, and environmental impact in community engaged initiatives. By uniting AI's role in both personalized education and broader community engaged efforts, we illustrate how AI can drive innovation, sustainability, and deeper student engagement, ultimately shaping the future of engineering education.

Abstract ID: DESS2024-045

Performance of Regression Machine Learning Algorithms for Predictive Pilot Manual Control in a Standard Rate Turn Maneuver

Timothy Reissman
University of Dayton
Diya Liz Babu, Tam Nguyen, Megan Reissman
University of Dayton

This study addresses the accuracies expected from achieving data-driven models for predictions of aircraft pilot behavior utilizing current machine learning (ML) methods. To gather the necessary information for such algorithms, routine flight maneuvers are flown by certified pilots using flight simulators, providing a controlled environment for data collection and analysis. Specifically, this research explores using the temporal aspects of data gathered for predicting future pilot manual control inputs during the flight maneuvers or overall task performance. Of special interest in this approach is analyzing how early in a flight maneuver can an ML algorithm accurately forecast pilot manual control behavior or performance. Our proposed solution involves the utilization of ML regression methods, assessing both single- and multi-output models.

Abstract ID: DESS2024-047

Case Study: Application of Artificial Intelligence in development of an industrial control system for an innovative alternate energy fueling system

Sean Cahill
University of Dayton
Francisco Martinez
University of Alicante

Abstract— This case study presentation discusses a real-world application of AI in the alternate energy sector. Artificial intelligence (AI) has advanced rapidly and is becoming a cornerstone technology that drives innovation and efficiency in various industries. Millennium Reign Energy LLC (MRE) was founded in 2008 and is based in Dayton Ohio. The company has developed a cost-effective hydrogen generator that splits water into hydrogen and oxygen. This technology has been bundled into an innovative hydrogen fueling system that combines generation, storage, and distribution into a single appliance that resembles a traditional gas station fuel pump. Their systems are being used by the Honda Performance Manufacturing Center (PMC) in Marysville, Ohio, the site of Honda's hydrogen car plant. PLCs, IIoT and data analytics are becoming a keystone of industrial automation in the Industry 4.0 environment. However, programming PLCs, setting up data collection protocols and developing data analysis algorithms can be time-consuming and complex, often requiring specialized knowledge. Additionally, in industry, there is a constant demand to minimize costs, maximize the return on investment and guarantee results. Project economics and results focus often lead the developer or integrator to prove-out solutions before committing to large software investments or possibly switching software platforms multiple times. This approach creates a need to quickly learn new software interfaces and create data processing applications. This study presents how AI was leveraged to design an industrial control system (ICS) that evolved across multiple PLC platforms and associated components while minimizing software investments. This case study presentation explores the rapidly evolving intersection of Artificial Intelligence (AI) with industrial controls, focusing on its potential to maximize development flexibility and transform PLC programming.

Biomechanics / Biomedical Engineering

Abstract ID: DESS2024-030

Clinical Translation of Virtual Reality Motion Capture for Upper Extremity Therapy Using Machine Learning

Skyler Barclay
University of Dayton
Megan E. Reissman, Timothy Reissman, Allison L. Kinney
University of Dayton

Introduction: For most clinics, it is not feasible to have a full infrared camera-based motion capture setup (IR mocap) that enable the collection of full body kinematic data including joint angles. Without IR mocap, outcome measures mainly include qualitative and spatiotemporal metrics which may limit ability to track progress. Virtual reality motion capture (VR mocap) approach using VR trackers offers a possible low cost and clinician friendly way to collect this information. However, this approach would require automation of the data analysis to be clinically feasible. Our research aims to combine VR mocap with machine learning to output kinematic metrics such as joint angles without the use of a full motion capture setup and analysis. Methods: For this study, 18 people (7 people with a spinal cord injury, 7 age and gender matched controls, and 4 people with cerebral palsy, post traumatic brain injury, or multiple sclerosis) participated. Upper extremity movements were captured using synced VR mocap and IR mocap systems. Without machine learning (ML), VR mocap requires information from the IR mocap. With the large dataset, multiple ML models have been created in order to predict joint angles, hand velocity, and total arm energy. The dataset included position and orientation of each tracker as well as segment lengths of each subject and was normalized between 0 and 1. The final regression algorithms tested included gradient boosting, random forest, k-nearest neighbors, support vector, and gaussian process. The best hyperparameters for each algorithm were then run on all metrics and a 5-fold cross validation was used to find the average normalized mean absolute error (MAE). Results/Discussion: A 5-fold cross validation yielded normalized MAE values for each combination of metric and algorithm. The best performing model for each individual metric was chosen. Random forest regression performed best for shoulder angles, frontal wrist angles, hand velocity, and total arm energy. Gradient boosting regression models performed best for elbow and sagittal wrist angles. Gradient boosting and random forest regression had average normalized MAE values (between 0 and 1 with 0 being no error) of 0.0425 and 0.0420 respectively during the 5-fold cross validation. This indicates a small error between the actual and predicted values. The final models were used to predict time series data for an in-sample participant as well as an out-of-sample participant. Out-of-sample error values were consistently larger than in-sample data. Conclusion: The current model can predict the overall shape of the joint angles, however it does not accurately represent the peaks of the datasets. This is possibly due to only using VR tracker data from the same timepoint for prediction. Moving forward, time shifted data will be added to give the model data from several timepoints around the prediction timepoint, with the goal to decrease the out-of-sample error.

Abstract ID: DESS2024-031

Kinematic and Performance Characterization of People with a Spinal Cord Injury Using Virtual Reality

Skyler Barclay
University of Dayton
Trent Brown, Rebekah Revadelo
University of Dayton
Allison L. Kinney, Timothy Reissman, Megan E. Reissman
University of Dayton
Tessa M. Hill, Ann Smith
Dayton Childrens Hospital

Introduction: To investigate the kinematic and performance characteristics of people with a spinal cord injury (SCI) this study will compare movement tasks within a virtual reality (VR) environment across people with an SCI and control participants. The movement task was provided through a commercially available game Beat Saber. The task was to cut virtual blocks through the middle and in the direction indicated by an arrow on the block. It was hypothesized that arm kinematics for SCI participants would have decreased elbow extension, decreased shoulder angles, and increased wrist deviation compared to controls. It was hypothesized that movement performance of block cutting will be less centered and straight compared to controls. Methods: Participants included individuals with an SCI (n=7, 2 female, 30.37±15.86 years, 74.31±28.35 kg, 12.44±8.18 years since injury) and healthy age/gender matched controls (n=7, 2 female, 30.27±15.73 years, 64.64±22.01 kg). Participants played a customized VR game and focused on unilateral, mirrored, or opposing movement tasks. Participants had to move both arms and leveraged a virtual saber, which extended 1 meter beyond the hand controller, to cut the blocks. Movement tasks were consistently paced ~2 seconds apart. Kinematics were collected at 240Hz using an 8-camera motion capture system (Vicon, Vero). Task performance was extracted using a publicly available modification package, ScoreSaber. All data collections took place in the University of Dayton EMPOWER Lab or the Dayton Children’s Hospital Gait Lab. Kinematic outcomes included peak elbow extension, shoulder and wrist deviation from neutral. Movement task performance outcomes included saber speed, distance to the center of the block (centeredness), and cut angle error (straightness), taken during each movement task. Results/Discussion: For all conditions, SCI participants had lower elbow extension and shoulder deviation but greater wrist deviation compared to control participants (all p

Design & Optimization

Abstract ID: DESS2024-005

Mechanism and Structural Optimization for a Bio-Inspired Concept Aircraft

Jack Studnicka
University of Dayton
Dr. David Myszka
University of Dayton
Dr. Rick Graves
Air Force Research Laboratory
Dr. Andrew Murray
University of Dayton

This presentation will exhibit a method for estimating the weight of a novel concept aircraft designed with a bio-inspired rotating empennage (BIRE). The agile, tailless aircraft is able to tilt its horizontal stabilizers to provide yaw stability. To provide the unusual motion, unique mechanisms and structures are required. Sizing models of the mechanical system for the BIRE were generated using first-order principles and component vendor information. The weight estimator uses aerodynamic and inertial loads and physics-based component reliability constraints. An optimization was implemented to select component alternatives and dimensions that minimize the weight. Traditional weight estimation methods use empirical estimates that are not accurate when applied to novel concepts. Numerical methods, such as finite element analysis, are too complex to include in trade studies. This mid-fidelity weight estimation has been developed specifically for the BIRE platform but consists of sub-models that can be applied to other mechanical components such as shafts, bearings, gears, and actuators.

Abstract ID: DESS2024-008

Structural Index Parameter for Capturing Aerothermal Effects in Conceptual Level Vehicle Design

Samuel Atchison
Air Force Institute of Technology
Jose Camberos
Air Force Institute of Technology

The three phases of vehicle conceptual design include parametric sizing, configuration layout, and configuration evaluation. During the parametric sizing phase, the ability to define and quantify the technology level of an aerospace system allows the assessment of candidate designs based on feasibility given current technology or indicates if one must advance a particular technology. To meet this need, the structural index (Istr) parameter merits exploration to consider structural and aerothermal effects during the parametric sizing phase of conceptual design given materials, structural concepts, and manufacturing capability. This study showcases the utility of this structural/materials technology parameter for high-speed vehicles by modernizing and expanding upon Paul Czysz's original structural index (Istr) versus temperature map. The construction of the modernized and expanded structural index (Istr) map is accomplished by selecting a temperature-through-thickness method for a given thermal protection system (TPS), which simplifies a given temperature and pressure profile into a constant heat pulse. One can then size the TPS to keep the structural temperature within material limits. The newly generated structural index (Istr) maps allow one to observe trends with variations in temperature, cruise time, average atmospheric pressure (Pavg), and TPS materials. Distribution Statement A: Approved for Public Release; Distribution Unlimited. PA# AFRL-2023-6446

Abstract ID: DESS2024-021

High-Fidelity Aeroelastic Shape and Sizing Optimization of a Trimmed Vehicle

Neal Novotny
Air Force Research Laboratory
David Sandler
University of Dayton Research Institute
Nathan Wukie
Air Force Research Laboratory

Physics-based design processes that account for discipline coupling are critical to the design of highly integrated and novel air vehicle configurations. In this work, we implement a framework for gradient-based, high-fidelity, aeroelastic design optimization based on Engi- neering Sketch Pad, FUNtoFEM, Fun3D, TACS, and OpenMDAO. A test-bed wing model was constructed with a parameterized aerodynamic outer mold line and internal structure in order to facilitate verification testing and capability demonstration. Preliminary verification of coupled-discipline sensitivities within the developed processes for geometric, aerodynamic, and structural design variables is presented in this work. Finally, a demonstration of gradient-based, static, aeroelastic design optimization formulated to minimize drag subject to lift and stress constraints is presented. The demonstration results show significantly improved drag while satisfying aerodynamic and structural constraints for a feasible design.

Abstract ID: DESS2024-033

Conformal Triply Periodic Minimal Surface Heat Exchanger Design and Evaluation Heat Exchanger Design and Evaluation

Stephen Richter
Air Force Institute of Technology
Maj. John Brewer
Air Force Institute of Technology
Mr. Thomas Brauer
Air Force Institute of Technology
Dr. Rama Gorla
Air Force Institute of Technology
Dr. Abdeel Roman
Air Force Research Laboratory

Awaiting public release.

Fluid Dynamics / CFD

Abstract ID: DESS2024-014

Development of a Polar Invariant Map for Turbulence Modeling

James Wnek
Wright State University
Mitch Wolff, Ph.D.
Wright State University
Eric Wolf, Ph.D.
Ohio Aerospace Institute
Christopher Schrock, Ph.D.
Air Force Research Laboratory

Invariant maps are a useful tool for turbulence modeling, allowing different turbulent states to be visualized in an interpretable manner and providing a mathematical framework to analyze or enforce realizability. The rapid growth of machine learning-enhanced turbulence modeling research has renewed interest in them, but current invariant maps are limited in their versatility due to the need for potentially costly coordinate transformations or eigendecomposition at each point in the flow field. Here we introduce a new polar turbulence invariant map based on an angle describing the ratio of the principal stresses and a scalar describing the anisotropy magnitude relative to a maximum value. The polar invariant map reframes realizability in terms of a limiting anisotropy magnitude. This leads to a direct link between realizability and general eddy viscosity models via a scaling factor without the need for coordinate transformations or explicit eigendecomposition. The relationships to the Lumley triangle and barycentric map are illustrated through examples of fully developed channel flow and square duct flow. Applications to machine learning-enhanced turbulence modeling are discussed. The polar invariant map provides a foundation for new approaches to enforcing realizability constraints in Reynolds-averaged turbulence modeling. Distribution Statement A: Approved for Public Release; Distribution is Unlimited. PA# AFRL-2024-5686.

Abstract ID: DESS2024-015

Verification of Optimal Flow Control Frequencies from Resolvent Analysis for Eppler 387 Airfoil

Vincent Sheeler
Wright State University
Mitch Wolff
Wright State University
Christopher Marks
Air Force Research Laboratory

A variety of aerodynamic devices operate at low Reynolds number conditions, such as unmanned aerial vehicles and low-pressure turbine blades in aircraft gas turbine engines. At low Reynolds numbers, laminar boundary layer separation can reduce the aerodynamic performance of airfoils, impacting the loading and drag. The effectiveness of active actuation methods can be improved when forcing is at locations and frequencies which excite instabilities in the flow. Gross et al. conducted a resolvent analysis for separation control of a Eppler 387 airfoil. Based on the findings, for wall-normal blowing and suction, maximum forcing response occurred at a frequency of 434Hz located at 46.2% of the chord with a forcing amplitude equal to 5% of the freestream. Verification of this study occurred at the U.S Air Force Research Laboratory’s (AFRL) Low Speed Wind Tunnel Facility (LSWTF). Acoustic speakers were utilized as synthetic jets to generate wall normal blowing and suction on the suction surface. Measurements of the flow velocity, lift, and drag over a range of actuation frequencies showed a 600Hz optimal frequency providing a 12.8% drag reduction. This presentation addresses actuator characterization, experimental techniques, and results of the study. Distribution Statement A: Approved for Public Release; Distribution is Unlimited. PA# AFRL-2024-5432.

Abstract ID: DESS2024-035

Custom Electrical Capacitance Tomography Sensor

Colin Fokine
Wright State University

The desire for a non-proprietary Electrical Capacitance Tomography (ECT) sensor capable of measuring void fraction led to the research, design, construction, and testing of a sensor capable of non-invasively measuring the permittivity in two-phase flow systems. A drop in permittivity across a medium can be used to determine the void fraction. The sensor, built using quartz tubing and copper tape electrodes, used a Silicon Labs C8051F970 microcontroller for data acquisition. The microcontroller was programmed using Simplicity Studio, with results displayed in a terminal via a COM port. Initial calibration used iodized table salt to imitate a “full” and an “empty” reading by tilting the tube. The sensor design was validated through testing, proving its capability to measure changes in capacitance and distinguish between “full” and “empty” readings. This cost-effective, non-propriety sensor provides a solution for measuring permittivity and, eventually, void fraction. Future work will focus on integrating an LCR meter for increased sensitivity, applying the sensor in a two-phase flow system, and integrating it into LabVIEW for real-time measurements of void fractions. Distribution Statement A: Approved for Public Release; Distribution is Unlimited. PA# AFRL-2024-5406

Abstract ID: DESS2024-036

ECT Sensor Generation and L2-Norm Based Flow Regime Analysis

Liam Hackett
Wright State University

An important aspect of two-phase flow research revolves around the ability to accurately measure void fraction within the system. Currently, there is not a satisfactory approach to this in a manner that doesn’t require proprietary components. The work of Kathleen De Kerpel and Caniere showed promising results as they explored the development of their own custom capacitance sensors. These sensors used a non-proprietary, semi-conductor, type setup to determine the permittivity of a given flow. These results will be expanded in this research to optimize the sensor design and calibration. The major contribution of this research will be the use of an industrial Electrical Capacitance Tomography (ECT) sensor to help calibrate the custom sensor being built. The previous researchers did not have such technology to validate their sensor when they conducted their research. Furthermore, additional research utilizing this sensor will expand into two phase flow identification through neural networks. Previously, Dr. Abdeel Roman used statistical analysis of the permittivity probability density function to train a neural network to predict flow regime. This research will expand into the development of a neural network based off image analysis of the tomogram using the L-2 Norm statistic. This approach too seeks to develop an accurate way to determine flow regime based upon the tomogram generated by the ECT sensor. Distribution Statement A: Approved for Public Release; Distribution is Unlimited. PA# AFRL-2024-5419

Abstract ID: DESS2024-043

Evaluation of Event Cameras for 2D2C Velocimetry

Sidaard Gunasekaran
University of Dayton
Keigo Hirakawa
University of Dayton

Unlike well-established Particle Image Velocimetry (PIV) processing algorithms, the algorithms used to extract velocity information from fluid flow using event cameras are still being developed and tested. In this study, we compare three different existing event-based processing algorithms and one in-house written algorithm for error quantification in the spatiotemporal reconstructed data from the experiments. The results from event-based algorithms are also compared with traditional PIV and Particle Tracking Velocimetry (PTV) algorithms. All experiments were conducted in the University of Dayton Water Tunnel (UD-WaT) to capture the flowfield around SD7003 wing at different angles of attack. The surface velocity and the flowfield at different angles of attack determined from different processing algorithms are compared against each other along with Root Mean Square (RMS) velocities.

Abstract ID: DESS2024-044

Wake Characteristics of Non-Elliptical Lift Distribution

Sidaard Gunasekaran
University of Dayton
Charles C.B. Cain
University of Dayton

The aerodynamic performance and wake signatures of wings with non-elliptical distributions were simulated numerically with FlightStream to determine if a nearly two-dimensional wake exists at certain angles of attack conditions. Sensitivity analysis has been conducted to compare the performance and wake signatures between the traditional elliptical lift distribution and the non-elliptical lift distributions, including the Prandtl's bell-shaped lift distribution at different angles of attack. In all the configurations tested, the wingspan and chord length of each rectangular wing were adjusted to maintain the reference area, and the freestream velocity was adjusted to match the Reynolds number across all configurations.

Heat Transfer / Thermal Sciences

Abstract ID: DESS2024-027

Electronic Component Thermal Control via PCM Dynamic Radiators

Anthony Lococo
University of Dayton
Dr. Rydge Mulford
University of Dayton
Abigail Boyer
University of Dayton
Ashley Anderson
University of Dayton
Andrew Gabriel
University of Dayton

This project aims to develop and optimize a dynamic radiator fin to cool any electronic device in a space environment. Historically, these devices have been subject to temperature limits, as they cannot become too hot or too cold within their duty cycle. This duty cycle corresponds to the orbit a given spacecraft takes, as the device needs to either stagnate or perform functions along certain arcs during its orbit. Traditionally, managing this has been done through creating a conduction pathway to space directly from the electronics, or through constructing static radiators which protrude into space. Both of these issues are problematic, as they cannot adapt to the variable heat loads which the electronics induce. The solution is a dynamic radiator, which is able to retreat inside the spacecraft and protrude outward according to the demands of the system. When inside, it will collect the heat of the system, and when outside, it will release it into space. Additionally, having the radiator inside the system will increase the steady state cold temperature of the electronic component. In summary, a dynamic radiator prevents the device from getting too cold when inactive, and too hot when active. This technology can be coupled with the usage of a nitinol phase change material (PCM). This enhancement of thermal management is obtained through the use of nitinol’s solid-solid phase change properties. As nitinol changes phase, it will stay cold for longer when it is heating up, and stay hot for longer when it is cooling down. This allows for more energy to be transferred to and from the nitinol. Our research is as follows: to design and test a prototype of a passively actuated, nitinol, dynamic radiator fin in the shape of a quarter circle which will be used to cool electronic components inside of a CubeSat. The radiator fin will be designed to be used in other spacecraft with requisite retuning. The device will be passively actuated via nitinol, which has shape-memory alloy (SMA) characteristics. As the nitinol undergoes its phase change, it either heats up and stiffens to a trained shape or cools down and subsequently relaxes. This will be implemented by creating nitinol wires, strips, or tubes to bend and twist upon heating up, forcing the radiator fin to extend outwards into space. The design consists of both experimental testing and theoretical modeling of the system in ANSYS and Python. Experimental testing includes identifying an optimal actuation attachment method of the nitinol-PCM actuator to the radiator. This will be done inside our vacuum chamber to maximize an actuation angle. The ANSYS model will be used to tune contact resistances and get a rudimentary design. The Python model will vary properties which are significant to heat transfer to optimize the design. Current results show that proper thermal management can be achieved via ANSYS modeling, and experimental testing has shown an actuation angle of 45 degrees, which provides significant heat transfer into space.

Human Factors

Abstract ID: DESS2024-016

The Influence of Task Difficulty, Color, and Gender on Mental Workload: A Factorial Analysis Using the Multi-Attribute Task Battery

Esther Adeyemi
University of Dayton
Dr. Sharon Bommer, Maura Tierney
University of Dayton

This study investigates the combined effects of Task Difficulty Level (TDL), color, and gender on workload levels using the Multi-Attribute Task Battery (MATB) simulation tool. Task difficulty is a well-known determinant of mental effort, but including gender and color introduces new dimensions to understanding cognitive load. Previous research suggests that color influences workload through emotional, behavioral, and physiological effects, while gender impacts performance and workload due to stereotypes, experiences, and biological differences. A factorial experimental design was employed to assess these factors' independent and interactive effects across four task categories within MATB: Resource Management, Tracking, Monitoring, and Communication. The experiment consisted of 180 runs, with 15 replicates for each combination of the factors: color (3 levels), task difficulty level (TDL: low, high), and gender (male, female), and the results were analyzed using ANOVA. The experiment assigned participants to two groups: Group RBG (Red, Blue, Gray) and Group YGG (Yellow, Green, Gray), with each group including 15 males and 15 females. Findings revealed that TDL consistently increased workload across all tasks, with more challenging tasks significantly elevating cognitive demand. Gender significantly affected tracking performance in both groups, where males outperformed females consistently, and in the communication task for group RBG, males outperformed females. For group YGG, gender yielded a significant effect in resource management, with women doing better and tracking tasks with men doing better. Color did not independently affect workload, except in the YGG group for communication and monitoring tasks. However, it showed significant interactions with gender in the RBG group for Resource Management tasks and TDL in communication tasks in the YGG group. These results highlight the importance of considering demographic and visual factors in workload analysis. The study provides new insights into how color and gender interact with task difficulty to influence mental workload, suggesting further avenues for future research to explore these combined effects. Understanding these dynamics could have practical implications for task design in environments where mental workload management is critical.

Materials

Abstract ID: DESS2024-004

Synthesis and Characterization of Novel Multinary Copper Chalcogenide Nanocrystals for Photovoltaic Applications

Rakesh Tota
University of Dayton
Soubantika Palchoudhury
University of Dayton
Prem Shah
University of Idaho

Awaiting public release.

Abstract ID: DESS2024-039

Liquid Metal: Powering Touch Sensors in Wearables

Ashok Rathanlal
University of Dayton

Smart wearable devices are transforming how we interact with technology, and liquid metals are playing a pivotal role in this shift. Known for their exceptional flexibility, conductivity, and adaptability, liquid metals such as EGaIn (Eutectic Gallium-Indium) and silver-based materials exhibit properties that make them ideal for next-generation wearable technologies. In this project, we focus on utilizing these materials to fabricate a flexible touch screen with highly precise touch detection capabilities, integrated with a strain sensor. This dual functionality showcases the versatility of liquid metals in creating innovative and user-friendly wearables. What sets liquid metals apart is their ability to act as sensors when subjected to mechanical strain. When stretched, these materials can accurately detect changes making them suitable for a wide range of applications in wearable electronics, from health monitoring to interactive user interfaces. By leveraging these properties, our work aims to push the boundaries of wearable design, combining high performance with adaptability. As technology becomes increasingly embedded in our daily lives, the role of liquid metals in wearables is poised to grow. Their integration into flexible electronics represents a major step forward in the development of smart, adaptive devices that not only improve functionality but also enhance user comfort and interaction. This research highlights the potential of liquid metals in transforming the future of digital technology.

Abstract ID: DESS2024-046

Performance and Degradation of Plastic-Free Geotextiles Along the Tidal Exposure Gradient in a Warm-Temperate Salt Marsh Estuary

Robert Lowe
University of Dayton
Evan Smyjunas, Loring Leitzel, Molly Savage, Scott Schneider
University of Dayton
Mariah Livernois, Bruce Pfirrmann, William Strosnider
University of South Carolina

Coastal ecosystems provide numerous benefits to humans, both ecologically and economically. However, the pervasive use synthetic (e.g., polyester, HDPE) geotextiles in coastal environments for erosion control, shoreline stabilization, and water quality protection has led to the unintended release of microplastic pollutants into coastal ecosystems. Consequently, there is significant interest in transitioning away from synthetic geotextiles towards sustainable non-plastic alternatives. However, widespread adoption of non-plastic alternatives has been hindered by a lack of data quantifying their degradation in harsh coastal environments, where materials are subjected to fluctuating temperatures, moisture content, UV exposure, salinity, abrasion, and biofouling. Thus, this project focuses on quantifying the performance and degradation of nine geotextile materials – including six natural-fiber-based materials (jute, coir, and jute-coir composite) with differing weave densities – in coastal environments using a standardized field deployment regime and post-exposure mechanical testing. Ninety-six specimens were placed across three different sites at three different tidal levels in the North Inlet-Winyah Bay Estuary, a warm-temperate salt marsh research reserve in coastal South Carolina. Specimens are being extracted at approximately bi-weekly intervals, dried, and mechanically tested to determine ultimate tensile strength, elongation at break, and toughness. These properties – along with continuous in situ measurements of temperature, salinity, and water level – are being analyzed to determine each material’s rate of degradation and identify the environmental factors driving these changes. This information will be used to suggest appropriate use cases for natural-fiber geotextiles, provide a robust material property database to inform regulatory policies, and encourage the inclusion of sustainable alternatives on qualified products listings published by government agencies (e.g., SCDOT).

Renewable and Clean Energy

Abstract ID: DESS2024-019

Experimental Validation of Temperature and Performance of Optimized Photovoltaic Tilt Angles

Alex Zawacki
University of Dayton
Dr. Rydge Mulford
University of Dayton
Owen Koscho
University of Dayton

Two projects are presented focused on the optimization and ecological impacts of solar energy systems. The first project investigates solar panel tilt optimization to enhance energy production efficiency, while the second examines microclimatic variations in the Curran Place solar prairie ecosystem and its implications for local insect populations. In the solar panel tilt optimization project, the primary goal is to maximize the energy output of solar panels by determining the optimal tilt angle for panels based on location, time of year, and atmospheric conditions. This study involves analyzing the energy production of solar panels at various tilt angles, with a focus on identifying configurations that yield the highest solar irradiance throughout the year. Data collected from a combination of theoretical models and real-world solar panel performance measurements are used to create plots comparing energy output across different tilt angles and azimuth positions. The results of this analysis are expected to provide actionable insights into optimizing solar panel installations for various geographic locations, including areas with fluctuating sunlight conditions. By enhancing the efficiency of solar power generation, this project aims to contribute to more effective and sustainable renewable energy practices. The second project covering microclimatic variations in a solar prairie ecosystem explores the thermal effects of solar infrastructure on local ecosystems. The study is conducted in a solar prairie located at the University of Dayton’s Curran Place, where Thermochron iButtons temperature loggers were deployed across multiple locations within the prairie to monitor temperature variations. The iButtons were positioned in the aisle, directly underneath the solar panels, and in buffer zones to capture comprehensive data on how solar panels impact the microclimate. Temperature data was collected hourly over an extended period to assess the thermal environment’s influence on local insect populations, particularly those living near or beneath the solar panels. Results from the solar prairie project demonstrate that areas underneath the solar panels experience more extreme temperature fluctuations, with higher temperatures during the day and lower temperatures at night, compared to the aisle and buffer zones. This has potential implications for insect habitats, as temperature extremes could affect insect survival and habitat suitability. The study also highlights differences in microclimate behavior during sunny and cloudy days, with more pronounced temperature spikes on sunny days. Together, these projects offer insights into the dual impacts of solar technology on both energy efficiency and ecosystem dynamics. The solar panel tilt optimization project seeks to improve energy production, while the solar prairie project provides valuable data on how renewable energy infrastructure interacts with the local environment. The findings are relevant to both the solar energy industry and ecological conservation efforts, and both attempt to reveal the importance of considering environmental factors when designing and deploying renewable energy systems.

Structures / Solid Mechanics

Abstract ID: DESS2024-002

Micromechanics of soft composites with high surface energy liquid inclusions

Matthew Grasinger
Air Force Research Laboratory
Anh Hoang
University of Alabama
Amanda Koh
University of Alabama

Liquid metal composites are a unique opportunity for materials that are soft, have high thermal conductivity and high electrical permittivity. As a result, they are promising materials for a) wearable sensors, b) advanced prosthetics, c) soft, biologically inspired robotics, d) electromagnetic shielding, and e) interfacing electronics and biology–more broadly. Here a finite deformation, micromechanical model is developed to study the connection between the composite morphology and its emergent elasticity. We show that the apparent elastic modulus of the composite will change (decrease, or even increase!) with the volume fraction of the inclusions to varying degrees depending on inclusion size, deformation mode, and the deformed state. Inclusion size is a readily controllable parameter; hence, the insights gained here can aid in optimizing the performance of liquid metal composites for various DoD applications. We close by outlining future work for (experimentally) controlling liquid inclusion shape (e.g. eccentricity), and associated theory for connecting the statistics of inclusion shapes and orientations to the novel, anisotropic electromechanical behaviors of the bulk material.

Abstract ID: DESS2024-022

Prediction of Composite Bolted Joint Bearing Behavior Using a Mechanistic Modeling Approach

Matthew Fadden
Air Force Institute of Technology
John Brewer
Air Force Institute of Technology
Michael Gran
Air Force Research Laboratory

Awaiting public release.

Abstract ID: DESS2024-023

Johnson-Cook Representation of IN718 Specimens Under High Energy Impact Scenario

Katie Bruggeman
Wright State University
Dr. Anthony Palazotto
Air Force Institute of Technology
Dr. Dan Young
Wright State University

The Johnson-Cook (JC) parameters of wrought Inconel 718 (IN-718) are utilized to characterize the material during high strain rate deformation in a viscoplastic regime. To perform deformation analysis, Taylor Impact tests have been conducted with cylindrical specimen characterized by a diameter of 0.5 inches and a length of 2 inches. During experimentation, the specimens are shot from a pressured system toward a rigid steel anvil. Elastic and plastic waves propagate throughout the specimens during the time of deformation upon impact with an anvil, affecting flow stress of the specimen. An explicit Finite Element Analysis (FEA) is carried out with Abaqus to simulate impact scenarios with the given geometry and JC characteristics. Further Taylor impact experimentation is conducted for Additively Manufactured (AM) IN-718 specimens; the resulting data will be utilized for comparison with the wrought material to find JC parameter representation for the AM material. The JC equation, displayed as Equation 1, contains three parts describing the relationships between flow stress, strain, strain rate, and temperature. The first part contains parameters to describe a strain hardening effect, the second, a strain rate strengthening effect. The third part describes temperature effects. The JC parameters, reference strain, and temperature variables considered for wrought IN-718 are listed in Table 1. The strain rate ratio shown in Equation 2 is a relation of variable strain rates and a reference strain rate. The variable for the temperature effect, T*, is calculated as shown in Equation 3. The variable temperature of the material being considered is displayed as T, Tm is the melting temperature of the material, and Tref is a reference temperature. Figures 1 and 2 display the JC curve characterizing the plastic regime for wrought IN-718 for two scenarios; the first scenario is strain rate and temperature effects not being considered in the analysis. The strain rate is the same as the reference strain rate, .001 s-1. The second scenario considers the effect of strain rate strengthening; as the strain rate increases in comparison to the reference strain rate, the material is subjected to a higher flow stress for a given plastic strain. This study is an evaluation of various strain rates and temperatures that could occur at a specific location (a node) within an impact model. In a specimen shot at a pressure of 700 psi, the experimental and FEA results are comparable. The dynamic deformation yields the various strain rates through an impact model as shown. Thus, the use of the JC equation to represent how stress flows through a given location as a stress wave is propagated at a point in time for impact scenario is efficacious. Further work to find representative JC parameters for AM IN-718 during high strain rate impact is ongoing.

Undergraduate Research Projects

Abstract ID: DESS2024-011

Effect of embedded cavities on fragmentation behaviors of droplets in shock-laden flows

Nhan Truong
University of Cincinnati
Jacob Gamertsfelder
University of Cincinnati
Prashant Khare
University of Cincinnati

Understanding liquid breakup is crucial for optimizing supersonic combustion processed, where effective fuel atomization significantly influences combustion efficiency, emission characteristics, and overall performance of propulsion systems. In supersonic flows, the rapid interaction between droplets and shock wave can lead to complex flow dynamics, impacting the droplet size distribution and combustion characteristics. Consequently, insights into droplet fragmentation behavior are vital for improving the design of fuel injectors and enhancing the efficiency of combustion systems operating under high-speed conditions. This research investigates the effect of cavity on liquid droplets fragmentation when interacting with a normal shock wave using high fidelity computations in Star CCM+. The solver uses an implicit unsteady multiphase framework consisting of two gas phases and one liquid phase, using the volume-of-fluid (VOF) method. The study examines the effects of vapor cavities embedded within droplets by comparing droplets with and without these cavities. The simulation framework is initially validated against the analytical solution of a one-dimensional gas dynamics problem (Sod shock tube test) to confirm the accuracy of the numerical method. Results show good agreement with analytical solutions, validating the reliability of the solver for gas dynamics simulations. Next, the model is validated against experimental data involving the impact of a Mach 2.4 shock wave on a 22mm non-vaporizing water cylindrical column of water. The initial conditions consist of a thin 500-bar region, contrasted with a low-pressure region at 1.01 bars. The simulation results closely match experimental data. The study then focuses on a water droplet with a diameter of 3.04 mm, containing a vapor cavity of 2.71 mm inside, to validate the capturing of flow physics from our framework. To compare the fragmentation and deformation behavior, two simulations are performed on droplets with and without embedded vapor cavities under the same operating conditions: Mach 2.4 shock wave, low-pressure region (P = 1.01 bars, T = 293 K), and high-pressure region (P = 6.553 bars, u = 567.072 m/s, T = 597.8 K). Key parameters include a water viscosity of 0.000889 Pa·s, density of 1000 kg/m³, and surface tension of 0.072 N/m at the gas-liquid interface.

Abstract ID: DESS2024-012

Mechanisms of Turbulent Mixing in the Nearfield of Jets Injected in a Quiescent Environment

Ariana Deluca
University of Cincinnati
Himakar Ganti
University of Cincinnati
Prashant Khare
University of Cincinnati

The entrainment and mixing seen in jet injections has been well studied, and it is known that a self-similarity solution can be obtained for both laminar and turbulent jets sufficiently downstream of the injector location. The nearfield flow physics, however, are more complicated and subject to change depending on the characteristics of the injected jet. In this research effort, an open-source library, SU2, is used to investigate nearfield flow physics and mixing mechanisms. Turbulence closure is achieved using the shear-stress transport (SST) model. First, the solver is validated against experimental and theoretical predictions based on boundary layer theory. Preliminary results of turbulent jet injection show good agreement with experimental measurements, building confidence in the framework to model the phenomenon of interest. After the initial validation of the code, the inlet boundary conditions are modified to investigate mixing in turbulent conditions and nearfield flow physics. More specifically, the radial velocity profiles in the nearfield and spread angle are examined as a function of eddy viscosity and turbulent kinetic energy. The effect of Reynolds number of the jet and variations of temperature are studied. As a next step, we plan to investigate these phenomena for non-ideal gases to assess if self-similarity exists for such fluids, and the mechanisms that dictate turbulent mixing.

Abstract ID: DESS2024-025

Low-Cost Pump Design and Fabrication by Appropriate Technology Engineering Students

Thomas Thompson
Cedarville University
Seth Mitchell, Madelyn Torrans, Jeffrey Jones, Andreas Chaffey, Caleb
Cedarville University
Ethan Thompson, and Thomas Thompson (advisor)
Cedarville University

Rural Bolivia farmers suffer from lack of plentiful sanitary water. To help them supply this need, Cedarville engineering students built on the work of previous years to improve a sanitary water pump using appropriate technology methodology. This pump consists of PVC piping and other easily acquired materials. The focus of the Cedarville team is to design 3D printable seals and intake valves at the ends of the pipes to control the flow rate of water. The Cedarville team created two designs and chose to improve on one of the selected designs. They then practically tested this design for case of use, durability, and practicality; flow rate and volumetric efficiency was tested analytically.

Abstract ID: DESS2024-032

Heat Exchanger Characterization Table

Nathan Lewan
Wright State University

The goal of the Heat Exchanger Characterization Table is to characterize heat exchangers by capturing data at known flow conditions. The testing rig consists of a hot tank, cold tank, heater, chiller, pumps, valves, pressure transducers, flow meters, thermocouples, and Dewesoft’s data acquisition system (DAQ). Everything in the system, besides the heater and chiller, is either controlled or monitored by Dewesoft. The hot and cold tanks are the base of the setup, acting as heat sources/sinks, with the goal of maintaining a constant set temperature throughout testing. To accomplish this, the portable heater and chiller are used to heat and cool the tanks. Fixed-speed pumps are attached to the bottom of the tanks and are used to drive flow within the system. Piping runs from the pumps to the proportional valves, which are used to throttle down and maintain a steady flow during testing. The mass flow rate is then measured by a Coriolis flow meter. For each tank, either a larger or smaller flow meter can be used, with the proportional valves making the switch between the meters depending on the required flow. Temperature and pressure measurements are then taken before and after the test section. The wastewater coming from the outlets of the heat exchanger is then recycled back into the tanks. From the pressure, temperature, and flow rate measurements, heat exchangers of all configurations can be characterized. Distribution Statement A: Approved for Public Release; Distribution is Unlimited. PA# AFRL-2024-5586

Poster Presentations

Additive Manufacturing

Abstract ID: DESS2024-007

Processing and Application of Functional Ink Materials for Inkjet Printed Board Level Heterogeneous Integration

Arashdeep Singh
Wright State University
Ahsan Mian
Wright State University
Daniel Young
Wright State University

Inkjet printing provides a straightforward approach for creating flexible hybrid electronics devices of the next generation. This study focuses on the complex connections between materials, processes, and resulting properties, especially focusing on emerging functional materials such as nanomaterials, polymers, and composites. As such, we processed, printed and characterized the conductive (nanosilver) and dielectric (polyimide and polyimide/Ba2Ti3 nanocomposite) ink-based materials for heterogeneous integration and sensor applications. The ceramic/polymer dielectric composite material was considered for further improvement in dielectric properties of the material. After processing and inkjet printing of the materials, they were characterized by high-resolution imaging and elemental identification using SEM and EDX, respectively. Keywords: Inkjet Printing; Ag Nanoparticle ink; Barium Titanate ink; Polyamide ink; Electronic Packaging; SEM

Abstract ID: DESS2024-010

Effect of Preform Shape on Strain Distribution during Forging using Finite Element Analysis

Vignesh Asam
Wright State University
Showmik Ahsan
Wright State University
Dr. Ahsan Mian
Wright State University
Dr. Daniel Young
Wright State University
Dr. Raghavan Srinivasan
Wright State University

  The overall objective of this project is to investigate the properties, viability, efficiency, cost-effectiveness, lead times, and other potential advantages of employing preforms made by additive manufacturing (AM) to reduce the tooling needed to produce low volume forgings. As part of the project, it is needed to design preforms for AM so that the flow strain can be fairly uniform and is large enough for recrystallization. As such, finite element analysis (FEA) based computation modeling of the forging process in Simufact forging software is ideal to save time. We have performed simulations on multiple preform shapes to study the strain variation, effect of friction and temperature change in the forged part.

Abstract ID: DESS2024-013

Printing of Polyimide/h-BN Nanocomposites Using Direct Ink Write Technology and Exposure to Gamma Rays

Lucas Clark
Wright State University
Ahsan Mian
Wright State University
Fahima Ouchen
Air Force Research Laboratory
Laura Davidson
Air Force Research Laboratory
Carrie Bartsch
Air Force Research Laboratory

Low earth orbit (LEO) electronic systems are a necessity for GPS, weather prediction, and telecommunications. However, space is a harsh environment with radiation capable of causing operation issues and degradation of the entire system. Circuit board scale shielding schemes of electronics against radiation add cost, size, weight, and complexity to these systems. In this study, we propose an alternative approach which focuses on component-based selective radiation shielding also referred to as “spot shielding”. Aerosol jet printing can deposit a wide range of radiation shielding nanocomposite materials on microelectronic components with high precision and resolution. Polyimide (PI) inks containing different loadings of hexagonal boron nitride (h-BN) nanoparticles were formulated and printed using pneumatic atomization aerosol jet printing. The inks consisted of PI, PI-25 wt% h-BN, PI-50 wt% h-BN, PI-75 wt% h-BN, and h-BN. The printed thin films were characterized by Fourier-transform infrared spectroscopy (FTIR) before and after exposure to gamma radiation at a dosage of 80 krad (Si). Keywords: Additive manufacturing, Aerosol jet printing, Gamma radiation, Polyimide, Hexagonal boron nitride, Radiation shielding

Abstract ID: DESS2024-018

ADDITIVE MANUFACTURING OF INCONEL 718 AND 316L ​ PREFORMS FOR FORGING OPERATIONS

Showmik Ahsan
Wright State University
VIgnesh Asam
Wright State University
Ahsan Mian
Wright State University
Henry Young
Wright State University
Raghu Srinivasan
Wright State University

Laser Powder Bed Fusion (LPBF) techniques hold significant potential for manufacturing applications that demand intricate geometries and limited production volumes, making them particularly effective for producing critical metal components in the aerospace sector. Our research explores the use of LPBF to create Inconel 718 and 316L stainless steel preforms that are suitable for subsequent forging and finishing processes. We aim to investigate how compressive strain influences the microstructure and recrystallization behavior of additive manufactured IN718 and 316L materials, considering variables such as temperature, strain, and strain rate. In this report, we present preliminary findings on the variability in microstructure, mechanical properties, and defect density, all of which are crucial for optimizing future forging operations.

Abstract ID: DESS2024-026

Effect of Build Orientation on Mechanical Properties, Dimensional Stability and Surface Characteristics of Polyamide 12 Part Printed Using Powder Bed

Adedamola Adeyemi
Wright State University
Tahseen Alwattar
Wright State University
Ahsan Mian
Wright State University

Polyamide-12 (PA12), a is a synthetic polymer with properties that make it desirable in the automotive industry. There are various ways to manufacture parts with PA12. Injection molding, a traditional manufacturing process, has been the standard manufacturing process for polymers for a long time. Recently, however, additive manufacturing (AM) processes, such as powder bed fusion (PBF) and fused deposition modeling (FDM) have been introduced. PBF is the most effective AM process to fabricate PA12 parts and the most effect manufacturing method to fabricate complex PA12 parts. This process is free of limitations such as the need for support material or proper flow of molten material over a significant distance. These conditions allow parts to be oriented at every angle in the build area, but the build orientation of a part also influences the part’s properties. To determine the best build orientation to optimize mechanical properties, dimensional stability and surface roughness of parts fabricated using PBF, ASTM D638 Type 4 filaments were printed in various build orientations and subjected to testing and inspection.

Aerospace Engineering

Abstract ID: DESS2024-020

Model-Based Systems Engineering Rendezvous and Proximity Operations Mission Planning

Thomas Kelly
Air Force Institute of Technology
David Curtis
Air Force Institute of Technology
Jordan Stern
Air Force Institute of Technology

This study addresses the fragmentation and inefficiencies in current Rendezvous and Proximity Operations (RPO) mission planning by developing an integrated Model-Based Systems Engineering (MBSE) framework. The research explores the design and implemen- tation of RPO missions using System Modeling Language (SysML) and relevant simulation tools to improve mission planning processes. Key objectives include the standardization of mission requirement characterization, the development of evaluation criteria for mission plans, and the implementation of iterative design methods to refine RPO strategies. The study leverages the Hill-Clohessy-Wiltshire (HCW) equations for initial mission planning, incorporating attitude control with constraints generated from the physical description, and the creation of digital twins for real-time validation and monitoring. Simulations validate the SysML model through case studies of various RPO scenarios, ensuring the comprehensive capture of dynamic mission requirements. The findings demonstrate the effectiveness of the MBSE-based approach in enhancing safety, reliability, and efficiency in RPO missions, with implications for broader adoption in space operations and contributions to systems engineering practices.

Design & Optimization

Abstract ID: DESS2024-028

Design and Development of a Telescoping Deployment Mechanism for a Space-Based Origami Mirror

Arturo Luna
Air Force Institute of Technology
Robert Bettinger
Air Force Institute of Technology

The space environment's suboptimal lighting conditions and eclipse periods impede inspection and in-orbit servicing missions. To abate this problem, the Air Force Institute of Technology (AFIT) launched the Mirror Illumination for Reconnaissance and Rendezvous of Orbital Resident Systems (MIRRORS) initiative. This initiative provides augmented illumination to resident space objects (RSOs) by reflecting the sun's rays through a mirror satellite. The use of origami patterns allows for efficient packaging for space missions. As a result, AFIT developed a cubic origami flasher mirror design composed of aluminum panels and incorporated a passive deployment system with Nitinol hinges. This research investigates the necessary deployment and panning mechanisms for a dynamic, space-based origami mirror to advance the MIRRORS initiative. The mission required developing a robust system within a 2U CubeSat volume, accommodating the deployment and panning mechanism with the origami mirror. This research highlights a telescoping design that satisfies mission requirements and NASA General Environmental Verification Standards (GEVS) random vibration test levels using computer-aided design (CAD) and finite element analysis (FEA). An event tree evaluated deployment risk mitigation, identifying potential failures and critical components to ensure the illumination of RSOs. Further research will focus on manufacturing the deployment mechanism and developing a control system that accounts for the mirror's circular motion during passive deployment.

Human Factors

Abstract ID: DESS2024-017

A Systematic Literature Review: Cognitive Workload Assessment in Human Factors Research

Maura Tierney
University of Dayton
Dr. Sharon Bommer, Esther Adeyemi,
University of Dayton

Cognitive workload, the level of how much mental effort it takes to complete a task, is an ever-growing field, especially with increasing developments in technology and systems. This work reviews published articles on cognitive workload, multitasking, the Multi-Attribute Task Battery, physiological measurements of cognitive workload, and the Improved Performance Research Integration Tool and provides an understanding and explanation of these topics as well as examples of their application in the field. The literature review was conducted to gain a deeper understanding of the cognitive workload field and synthesize published literature within the field. Quantitative data is then gathered about the cognitive workload field through a bibliometric analysis. The bibliometric analysis was conducted using Web of Science and four VOSviewer trials to provide quantitative evidence and create predictions about the cognitive workload field. Four trials looking at all key words, author specified key words, authors, and journals were conducted to understand current trends in the field. The reviews conducted in this research lead to the findings that physiological measures of cognitive workload, specifically electroencephalography, virtual reality, and machine learning are rising within the human factors field. This review also found that the Improved Performance Research Integration Tool, a powerful tool used to model and predict workload levels, is less prevalent in recent research. Recommendations for future cognitive workload studies are also highlighted in this review.

Materials

Abstract ID: DESS2024-048

Failure modes of silver versus gallium-alloy conductive traces at flex-to-stretch interfaces

Josafat Jimenez
University of Dayton

This study investigates the fabrication and performance of conductive traces made from two types of inks: liquid metal ink and silver flake composite ink. Conductive traces are blade coated onto a thermoplastic polyurethane (TPU) substrate, and uniaxial strain is applied to assess their electrical performance and failure modes under mechanical deformation. The resistance response of both inks is measured at increasing strain levels, with higher resistance signifying failure. Preliminary testing shows different results for both inks, showing good strain resistance for the liquid metal ink and good initial conductivity for the silver flake ink. Further testing is done combining the two inks in one sample to combine benefits and mitigate downsides. Further research is done on failure modes surging between the stretchable TPU and the flexible non-stretchable Kapton interface. Finally, failure modes appearing due to the TPU substrate properties are also explored by comparing to a styrene-ethylene-butylene-styrene (SEBS) substrate. This new substrate has a lower Young's modulus, which makes it easier to stretch and reduces plastic deformation from high strains.