UMBC Mechanical Engineering Department

Permanent URI for this collectionhttp://hdl.handle.net/11603/52

Mechanical Engineering is one of the oldest and broadest engineering disciplines. In the more traditional sense, Mechanical Engineering employs principles of physics and chemistry and knowledge from the fields of mathematics, mechanics and materials science for the analysis, design, manufacturing and maintenance of mechanical systems. Again, in the traditional sense, a Mechanical Engineer would be the expert in the production and usage of heat and mechanical power which are critical in the design, production and operation of tools, components and machines used in manufacturing durable goods and in using advanced technologies for the benefit of society.

In recent years, the discipline of Mechanical Engineering has taken on an expansive, and critical role in the advancement of transformative new technologies such as the information-, bio-, nano-technologies and MEMS (Micro-Electro-Mechanical Systems). It is clearly a new and exciting world out there. We are delighted to be at the forefront of acquiring new knowledge through cutting edge research in this new and expansive field of Mechanical Engineering. As reflected in their posted profiles and accomplishments, our faculty are recognized nationally and internationally for their research and discoveries. Many of them are Fellows of engineering societies and recipients of prestigious research awards granted for excellence by agencies such as the National Science Foundation and the National Institutes of Health.

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Information is primary and central to meaning-making
(University of Tartu Press, 2024-12-31) Cárdenas-García, Jaime F.
There is the misconception that the concept of information is not applicable to meaning-making in living beings. What is more generally believed is that Peircean semiosis provides a more robust framework to explain meaning-making. This involves the production, exchange, and interpretation of signs as the basis for meaning to an organism. Semiosis establishes a continuous and developing occurrence of triadic relations between a representamen (sign), an object (the other), and an interpretant as the organism engages with its umwelt, resulting in the appearance of meaning as a factor in its life. However, it is not clear that Peircean semiosis is the most fundamental process by which meaning-making may be instantiated in nature. Here we show that information defined by Gregory Bateson as ‘a difference which makes a difference’ can more fundamentally serve as a basis for meaning-making. Both its etymological origins and Bateson’s dictum naturalize the concept of information to identify its cybernetic dynamic motivated by constitutive absence, or the ability of an organism to find in its environment what it teleologically deems missing. This implies an ability to interpret its environmental surroundings. Furthermore, detecting a difference is the most fundamental of acts, revealing that information is the basis for meaning-making for an organism, allowing any level of intricacy in its interpretative capabilities. Indeed, Peircean semiosis is shown to be a special case of informatic meaning-making. In short, information provides a firm foundation for meaning-making for living beings.
Energy density enhancement of scalable thermoelectric devices using a low thermal budget method with film thickness variation
(Elsevier, 2024-04-01) Huang, Jiyuan; Ambade, Rohan B.; Lombardo, Jacob; Brooks, Benjamin; Poosapati, Aswani; Banerjee, Priyanshu; Saeidi-Javash, Mortaza; Zhang, Yanliang; Madan, Deepa
Additive manufacturing has been investigated as a more time, energy, and cost-efficient method for fabricating thermoelectric generators (TEGs) compared to traditional manufacturing techniques. Early results have been promising but are held back by including a high-temperature, long-duration curing process to produce high-performance thermoelectric (TE) films. This work investigates the synergistic effect of four factors – a small amount of chitosan binder (0.05wt%), a combination of micron and nano-sized particles, the application of mechanical pressure (20 MPa), and thickness variation (170, 240, 300 µm) – on the performance of stencil printed p-Bi₀.₅Sb₁.₅Te₃ (p-BST) and n-Bi₂Te₂.₇Se₀.₃ (n-BTS) TE composite films. The combination of these four factors controls the micro and nanostructure of the films to decouple their electrical and thermal conductivity effectively. This resulted in figures of merit (ZTs) of 0.89 and 0.5 for p-BST and n-BTS thinner (170 µm) films, respectively, comparable to other additive manufacturing methods despite eliminating the high-temperature, long-duration curing process. The process was also used to fabricate a 6-couple TEG device, which could generate 357.6 µW with a power density of 5.0 mW/cm² at a ∆T of 40 K. The device demonstrated air stability and flexibility for 1000 cycles of bending. Finally, the device was integrated with a voltage step-up converter to power an LED and charge and discharge capacitor at a ∆T of 17 K, demonstrating its applicability as a self-sufficient power source.
Editorial: Bio-thermal medical devices, methods, and models: new developments and advances
(Frontiers, 2025-03-24) Singh, Manpreet; Bhowmik, Arka; Repaka, Ramjee; Mitra, Kunal
Recent advancements in medical imaging techniques have greatly enhanced the ability to capture anatomically precise and highly detailed vascular structures within biological tissues Singh 2024. This progress is particularly significant for bioheat transfer modeling, where an accurate representation of the vascular network is essential for understanding heat exchange, blood perfusion dynamics, and thermal responses in both healthy and pathological conditions. The integration of high-resolution, three-dimensional geometries extracted from medical imaging data, often at the voxel level, enables more precise simulations, improving the predictive accuracy of thermal treatments and physiological responses Singh 2024. More recently, research efforts have continued to develop anatomically accurate models from medical imaging and develop physics and physiology-based models Singh et al., 2024. On contrary, voxel-based domains generated from medical image are crucial for bioheat transfer modeling; however, a key challenge lies in voxel resolution limitations. Due to the small dimensional scale of blood vessels, not all vessels are captured within a given voxel resolution, resulting in discontinuities in vascular segmentation. Also, pre-capillary vessels such as arterioles, which play a critical role in regulating blood flow resistance, are often modeled within the tissue as a porous domain. Such simplification leads to a loss of critical vascular information, potentially affecting the accuracy of bioheat transfer simulations. Additionally, magnetic particle imaging (MPI) has emerged as a powerful tool for tracking magnetic nanoparticles used in hyperthermia-based cancer treatments. By combining mathematical modeling with MPI, researchers are optimizing nanoparticle induced hyperthermia to improve therapeutic outcomes while minimizing unintended thermal damage to surrounding healthy tissues Singh 2020; Singh et al., 2021; Singh 2023. In this Research Topic, Pawar et al. conducted a sensitivity analysis to assess the impact of the spatial distribution of magnetic iron oxide nanoparticles (MIONs) on tumor temperature. Their study utilized co-registered magnetic resonance (MR)/computed tomography (CT) imaging alongside magnetic particle imaging (MPI) to derive in vivo MION distribution, which was then compared to mathematically generated uniform and Gaussian distributions. Theoretical predictions were based on the Pennes bioheat transfer equation, incorporating the dynamic influence of temperature on blood perfusion. To enhance accuracy, they employed a piecewise function to model the degree of vascular stasis (collapse of vasculature), as previously quantified by Singh 2022 in the context of magnetic hyperthermia. This approach provided valuable insights into optimizing MION distribution for more effective magnetic hyperthermia treatments. In another article of this Research Topic, Amare et al. highlighted the challenges involved in extracting the small blood vessels due to limited resolution of voxels obtained from image data. Their approach clearly provides evidence that mathematical representations of unsegmented blood vessels can approximate the thermal resistance and reduced the need for high-resolution imaging. In addition, their proposed methodology provides a computationally efficient alternative to high-resolution imaging, making it a valuable tool for future applications in biomedical modeling and thermal therapy planning. Besides the above numerical work, Pioletti presented an intriguing and innovative perspective on the role of self-heating in soft tissues, specifically in cartilage, because of mechanical stimulation induced heat effect. The core idea discussed in this work is that temperature changes induced by mechanical activity might be necessary for cartilage maintenance-introduces a potential paradigm shift in how we think about the physiological effects of mechanical loading on musculoskeletal tissues. In addition to the perspective article, Li et al. conducted a bibliometric analysis to assess studies on hypothermia-related injuries, treatment strategies, and underlying mechanisms. This study provides a comprehensive summary of hypothermia's impact on human health and the therapeutic applications of moderate hypothermia. By mapping research trends, frontiers, and key focus areas, the analysis offers valuable insights into the current landscape and future directions of hypothermia research. Additionally, it highlights the distinctions and interconnections between therapeutic and severe hypothermia, offering a clearer understanding of advancements and emerging trends in the field. This Research Topic presents a collection of two research articles, a perspective paper, and a review paper, each showcasing novel discoveries, state-of-the-art advancements, and future directions in the interdisciplinary field of computational modeling in biomedical engineering. These studies emphasize multiscale, multiphysics, and medical imaging-assisted approaches, highlighting their integration and applications. We believe that the insights shared in this collection will pave the way for groundbreaking research in bioheat transfer, accelerating innovations in medical device development.
Delivery of Tempol from Polyurethane Nanocapsules to Address Oxidative Stress Post-Injury
(ACS, 2025-02-08) Ale, Temitope; Ale, Tolulope; Baker, Kimberly J.; Zuniga, Kameel M.; Hutcheson, Jack; Lavik, Erin
Traumatic brain injuries (TBIs) result in significant morbidity and mortality due to the cascade of secondary injuries involving oxidative stress and neuroinflammation. The development of effective therapeutic strategies to mitigate these effects is critical. This study explores the fabrication and characterization of polyurethane nanocapsules for the sustained delivery of Tempol, a potent antioxidant. The nanocapsules were designed to extend the release of Tempol over a 30-day period, addressing the prolonged oxidative stress observed post-TBI. Tempol-loaded polyurethane nanocapsules were synthesized using interfacial polymerization and nanoemulsion techniques. Two generations of nanocapsules were produced, differing in Tempol loading and PEGylation levels. The first generation, with lower Tempol loading, exhibited an average size of 159.8 ± 12.61 nm and a Z-average diameter of 771.9 ± 87.95 nm. The second generation, with higher Tempol loading, showed an average size of 141.4 ± 6.13 nm and a Z-average diameter of 560.7 ± 171.1 nm. The zeta potentials were ?18.9 ± 5.02 mV and ?11.9 ± 3.54 mV for the first and second generations, respectively. Both generations demonstrated the presence of urethane linkages, confirmed by Fourier Transform Infrared Spectroscopy (FTIR). Loading studies revealed Tempol concentrations of 61.94 ± 3.04 ?g/mg for the first generation and 77.61 ± 3.04 ?g/mg for the second generation nanocapsules. Release profiles indicated an initial burst followed by a sustained, nearly linear release over 30 days. The higher PEGylation in the second generation nanocapsules is advantageous for intravenous administration, potentially enhancing their therapeutic efficacy in TBI treatment. This study demonstrates the feasibility of using polyurethane nanocapsules for the prolonged delivery of Tempol, offering a promising approach to manage oxidative stress and improve outcomes in TBI patients. Future work will include testing these nanocapsules in vivo to determine their potential at modulating recovery from TBI.
Tensor-decomposition-based A Priori Surrogate (TAPS) modeling for ultra large-scale simulations
(2025-03-18) Guo, Jiachen; Domel, Gino; Park, Chanwood; Zhang, Hantao; Gumus, Ozgur Can; Lu, Ye; Wagner, Gregory J.; Qian, Dong; Cao, Jian; Hughes, Thomas J. R.; Liu, Wing Kam
A data-free, predictive scientific AI model, Tensor-decomposition-based A Priori Surrogate (TAPS), is proposed for tackling ultra large-scale engineering simulations with significant speedup, memory savings, and storage gain. TAPS can effectively obtain surrogate models for high-dimensional parametric problems with equivalent zetta-scale (10²¹) degrees of freedom (DoFs). TAPS achieves this by directly obtaining reduced-order models through solving governing equations with multiple independent variables such as spatial coordinates, parameters, and time. The paper first introduces an AI-enhanced finite element-type interpolation function called convolution hierarchical deep-learning neural network (C-HiDeNN) with tensor decomposition (TD). Subsequently, the generalized space-parameter-time Galerkin weak form and the corresponding matrix form are derived. Through the choice of TAPS hyperparameters, an arbitrary convergence rate can be achieved. To show the capabilities of this framework, TAPS is then used to simulate a large-scale additive manufacturing process as an example and achieves around 1,370x speedup, 14.8x memory savings, and 955x storage gain compared to the finite difference method with 3.46 billion spatial degrees of freedom (DoFs). As a result, the TAPS framework opens a new avenue for many challenging ultra large-scale engineering problems, such as additive manufacturing and integrated circuit design, among others.
Stencil-Printed Scalable Radial Thermoelectric Device Using Sustainable Manufacturing Methods
(MDPI, 2024-04-24) Jang, Eunhwa; Ambade, Rohan B.; Banerjee, Priyanshu; Topoleski, L. D. Timmie; Madan, Deepa
In this study, we used n-chitosan-Bi₂Te₂.₇Se₀.₃ and p-chitosan-Bi₀.₅Sb₁.₅Te₃ composite inks to print a circular thermoelectric generator (TEG) device using a low-energy-input curing method. Thermoelectric (TE) composite films were fabricated using varying sizes of thermoelectric particles and a small chitosan binder (0.05 wt. %). The particles and binder were hot pressed at an applied pressure of 200 MPa and cured at 200 °C for 30 min. We achieved ZT of 0.35 for the n-type and 0.7 for the p-type TE composite films measured at room temperature. A radial TEG was fabricated using the best-performing n-type and p-type composite inks and achieved a power output of 87 µW and a power density of 727 µW/cm2 at a temperature difference of 35 K; these are among the best-reported values for printed TEG devices. Using a low-energy-input fabrication method, we eliminated the need for high-temperature and long-duration curing processes to fabricate printing devices. Thus, we envisage that the low-energy-input curing process and cost-effective printable strategy presented in this work pave the way for sustainable manufacturing of large-scale energy harvesting TEG devices.
Analysis of Nonlinear Dynamics of a Gear Transmission System Considering Effects of the Extended Tooth Contact
(MDPI, 2025-02-17) Liao, Fulin; Zheng, Xingyuan; Huang, Jianliang; Zhu, Weidong
Considering the elasticity of gear solid bodies, the load applied to gear teeth will force theoretically separated gear teeth to get into engaging state in advance. This phenomenon is named as the extended tooth contact (ETC). Effects of the ETC directly influence the time-varying mesh stiffness of gear pairs and subsequently alter nonlinear dynamic characteristics of gear transmission systems. Time-vary mesh stiffness, considering effects of the ETC, is thus introduced into the dynamic model of the gear transmission system. Periodic motions of a gear transmission system are discussed in detail in this work. The analytical model of time-varying mesh stiffness with effects of the ETC is proposed, and the effectiveness of the analytical model is demonstrated in comparison with finite element (FE) results. The gear transmission system is simplified as a single degree-of-freedom (DOF) model system by employing the lumped mass method. The correctness of the dynamic model is verified in comparison with experimental results. An incremental harmonic balance (IHB) method is modified to obtain periodic responses of the gear transmission system. The improved Floquet theory is employed to determine the stability and bifurcation of the periodic responses of the gear transmission system. Some interesting phenomena exist in the periodic responses consisting of “softening-spring” behaviors, jump phenomena, primary resonances (PRs), and super-harmonic resonances (SP-HRs), and saddle-node bifurcations are observed. Especially, effects of loads on unstable regions, amplitudes, and positions of bifurcation points of frequency response curves are revealed. Analytical results obtained by the IHB method match very well with those from numerical integration.
Polyurethane Nanocapsules Incorporating Epigallocatechin Gallate, A Green Tea Extract
(Wiley, 2025-02-26) Ale, Temitope; Ghunney, Nhyira; Pandala, Narendra; Tucker, Budd; McFadden, Kassandra; Hutcheson, Jack; Lavik, Erin
Explosions cause 79% of combat-related injuries, often leading to traumatic brain injury (TBI) and hemorrhage. Epigallocatechin gallate (EGCG), a green tea polyphenol, aids neuroprotection and wound healing. In this work, we sought to investigate the fabrication and characterization of polyurethane nanocapsules encapsulating EGCG, demonstrating controlled, on-demand release, and highlighting their potential for targeted therapeutic delivery in trauma care.
Delivery of Tempol from Polyurethane Nanocapsules to Address Oxidative Stress Post-Injury
(ACS, 2025-02-19) Ale, Temitope; Ale, Tolulope; Baker, Kimberly J.; Zuniga, Kameel M.; Hutcheson, Jack; Lavik, Erin
Traumatic brain injuries (TBIs) result in significant morbidity and mortality due to the cascade of secondary injuries involving oxidative stress and neuroinflammation. The development of effective therapeutic strategies to mitigate these effects is critical. This study explores the fabrication and characterization of polyurethane nanocapsules for the sustained delivery of Tempol, a potent antioxidant. The nanocapsules were designed to extend the release of Tempol over a 30-day period, addressing the prolonged oxidative stress observed post-TBI. Tempol-loaded polyurethane nanocapsules were synthesized using interfacial polymerization and nanoemulsion techniques. Two generations of nanocapsules were produced, differing in Tempol loading and PEGylation levels. The first generation, with lower Tempol loading, exhibited an average size of 159.8 ± 12.61 nm and a Z-average diameter of 771.9 ± 87.95 nm. The second generation, with higher Tempol loading, showed an average size of 141.4 ± 6.13 nm and a Z-average diameter of 560.7 ± 171.1 nm. The zeta potentials were -18.9 ± 5.02 mV and -11.9 ± 3.54 mV for the first and second generations, respectively. Both generations demonstrated the presence of urethane linkages, confirmed by Fourier Transform Infrared Spectroscopy (FTIR). Loading studies revealed Tempol concentrations of 61.94 ± 3.04 μg/mg for the first generation and 77.61 ± 3.04 μg/mg for the second generation nanocapsules. Release profiles indicated an initial burst followed by a sustained, nearly linear release over 30 days. The higher PEGylation in the second generation nanocapsules is advantageous for intravenous administration, potentially enhancing their therapeutic efficacy in TBI treatment. This study demonstrates the feasibility of using polyurethane nanocapsules for the prolonged delivery of Tempol, offering a promising approach to manage oxidative stress and improve outcomes in TBI patients. Future work will include testing these nanocapsules in vivo to determine their potential at modulating recovery from TBI.
Supporting Campus Activism through Creating DIY-AT in a Social Justice Aligned Makerspace
(ACM, 2025-01-31) Higgins, Erin; Oliver, Zaria; Hamidi, Foad
Utilizing digital fabrication methods (e.g., 3D printing) has exciting implications for the design and production of customized assistive technology (AT). However, utilizing these tools currently requires a high level of technical expertise as well as time and money investments. Furthermore, facilitating collaboration between end users and makers needs effective and inclusive approaches with shared language and support for asynchronous, dispersed communication of design requirements. While these Do-It-Yourself (DIY) approaches are shown to support end-user agency and furthering technology democratization, research has to yet explore how they can further align with social justice values and practices. We explored these possibilities by facilitating DIY-AT design with students with disabilities, activist staff members, and community members within a university makerspace. By explicitly encouraging participants to consider social justice issues important to them as they engaged in DIY-AT design, we studied the considerations and supports needed for facilitating flexible co-design activities and broader conversations about accessibility barriers at the university. Adopting a transdisciplinary approach, we offer lessons learned about the potential of co-designing DIY-ATs as a way to investigate questions of social justice, inclusion, and access in academic contexts. We show how these created DIY-ATs can be leveraged by students and staff as tangible artifacts to encourage more funding and support from university administration for accessibility initiatives.
Polyurethane Nanocapsules Incorporating Epigallocatechin Gallate, A Green Tea Extract
(Wiley, 2025-02-26) Ale, Temitope; Ghunney, Nhyira; Pandala, Narendra; Tucker, Budd; McFadden, Kassandra; Hutcheson, Jack; Lavik, Erin
Explosions cause 79% of combat-related injuries, often leading to traumatic brain injury (TBI) and hemorrhage. Epigallocatechin gallate (EGCG), a green tea polyphenol, aids neuroprotection and wound healing. In this work, we sought to investigate the fabrication and characterization of polyurethane nanocapsules encapsulating EGCG, demonstrating controlled, on-demand release, and highlighting their potential for targeted therapeutic delivery in trauma care.
Neural network-based surrogate model in postprocessing of topology optimized structures
(Springer Nature, 2025-02-28) Persia, Jude Thaddeus; Kyun Sung, Myung; Lee, Soobum; Burns, Devin E.
This paper proposes a general method of creating an accurate neural network-based surrogate model for postprocessing a topologically optimized structure. When topology optimization results are converted into computer-aided design (CAD) files with smooth boundaries for manufacturability, finite element method (FEM) based stresses often do not agree with the topology optimized results due to changes of surface and mesh density. The conversion between topology optimization derived results and CAD files often requires postprocessing, an additional fine tuning of the geometry parameters to reconcile the change of the stress values. In this work, a feedforward, deep artificial neural network (DANN) is presented with varying architecture parameters that are found for each stress output of interest. This network is trained with the data based on a combination of Design of Experiments (DoE) models that have the geometry dimensions as inputs and stress readings under various loads as the outputs. A DANN-based surrogate model is constructed to enable fine tuning of all relevant stress performance metrics. This method of constructing an artificial network-based surrogate model minimizes the number of FEM computations required to generate an optimized, post-processed design. We present a case study of postprocessing a wind tunnel balance, a measurement device that yields the six force and moment components of a test aircraft. It needs to be designed considering multiple stress measures under combinations of the six loading conditions. Excellent performance of a neural network is presented in this paper in terms of accurate prediction of the highly nonlinear stresses under combinations of the six loads. Von Mises stress predictions are within 10% and axial force sensor stress predictions are within 2% for the final post-processed topology. The results support its usefulness for postprocessing of topology optimized structures.
Freestream turbulence effects on low Reynolds number NACA 0012 airfoil laminar separation bubble and lift generation
(2024-06-01) Yu, Meilin ; Hrynuk, John T.; Booth, David T.; Poudel, Naresh
Laminar separation bubbles (LSB's) over the suction surface of a wing at low Reynolds number (O(10⁴) - O(10⁶) based on the airfoil chord length) can significantly affect the aerodynamic performance of the wing, and pose a unique challenge for the predictive capabilities of simulation tools due to their high sensitivity to flow environments and wing surface conditions. In this work a series of two-dimensional (2D) and three-dimensional (3D) low-order, and high-order accurate unstructured-grid-based numerical methods with varying model fidelity levels were used to study LSB physics over a NACA 0012 airfoil both in a clean freestream and in a turbulent freestream at a chord-based Reynolds number of 12,000. Lift production and time-averaged flow fields were compared with available experimental results. A major discovery is that in clean freestream flow a 3D high-order numerical scheme is necessary to capture LSB physics. This is due to the sensitivity of LSB-induced laminar-turbulent transition to flow conditions and boundary geometry at low Reynolds number. In freestream flows with moderate background turbulence (~5%), 2D simulations failed to capture subtle 3D flow physics due to their intrinsic limitation, but can reasonably predict time-averaged airfoil performance. Similarity and distinction between freestream vortex-LSB interaction in 2D and eddy-LSB interaction in 3D were explained. The role of the Kelvin-Helmholtz instability and Klebanoff modes in the transition of 3D airfoils were shown to be critical for understanding laminar-turbulent transition and LSB formation on airfoils in clean and turbulent freestreams.
Supporting Campus Activism through Creating DIY-AT in a Social Justice Aligned Makerspace
(ACM, 2025-01-31) Higgins, Erin; Oliver, Zaria; Hamidi, Foad
Utilizing digital fabrication methods (e.g., 3D printing) has exciting implications for the design and production of customized assistive technology (AT). However, utilizing these tools currently requires a high level of technical expertise as well as time and money investments. Furthermore, facilitating collaboration between end users and makers needs effective and inclusive approaches with shared language and support for asynchronous, dispersed communication of design requirements. While these Do-It-Yourself (DIY) approaches are shown to support end-user agency and furthering technology democratization, research has to yet explore how they can further align with social justice values and practices. We explored these possibilities by facilitating DIY-AT design with students with disabilities, activist staff members, and community members within a university makerspace. By explicitly encouraging participants to consider social justice issues important to them as they engaged in DIY-AT design, we studied the considerations and supports needed for facilitating flexible co-design activities and broader conversations about accessibility barriers at the university. Adopting a transdisciplinary approach, we offer lessons learned about the potential of co-designing DIY-ATs as a way to investigate questions of social justice, inclusion, and access in academic contexts. We show how these created DIY-ATs can be leveraged by students and staff as tangible artifacts to encourage more funding and support from university administration for accessibility initiatives.
Investigation of Solid Fuel Ramjets Using Analytical Theory and Computational Fluid Dynamics
(2025-01-03) Khokhar, Gohar T.; McBeth, Joshua; Hanquist, Kyle M.; Oveissi, Parham; Goel, Ankit
This paper investigates the modeling and analysis of a solid-fuel ramjet (SFRJ) using analytical theory and computational fluid dynamics (CFD). The primary objectives of this study are to first apply analytical theories of an SFRJ in combination with combustion physics from NASA CEA software to establish a foundation for analytically modeling the thrust of an SFRJ with a specified bypass ratio. Secondly, this study aims to model the thrust output of an SFRJ using a simplified backward-facing step computational model consisting of a single inlet and outlet through computational analysis. In the computational model, combustion is modeled as wall heat addition, and thermal choking effects leading to engine unstart conditions are predicted. While there are more complex SFRJ modeling approaches, consideration of computational cost is an important aspect of this work, since these models will be coupled with a control system. This research serves as a foundational step in a broader investigation aimed at coupling SFRJ thrust dynamics with control systems for regulating thrust under uncertain operating conditions.
Learning-Based Thrust Regulation of Solid-Fuel Ramjet in Flight Conditions
(2025-01-03) Oveissi, Parham; Dorsey, Alex; McBeth, Joshua; Hanquist, Kyle M.; Goel, Ankit
This paper investigates the performance of a learning-based control system for regulating the thrust generated by a solid fuel ramjet engine in realistic flight scenarios. An integrated simulation framework is developed that combines a longitudinal missile dynamics model, a missile autopilot, a quasi-static engine dynamics model, and a learning controller for thrust regulation. The missile autopilot is based on the classical three-loop topology. The learning controller is an adaptive PID controller whose gains are recursively optimized using the retrospective cost adaptive control algorithm. First, harmonic acceleration commands are used to simulate variable flight conditions that affect the thrust generated by the engine model. Next, an interception scenario is simulated by integrating a guidance law in the loop. Numerical results indicate that the learning controller can regulate the generated thrust despite wide variations in operating conditions.
Adaptive Numerical Differentiation for Extremum Seeking with Sensor Noise
(2025-01-08) Verma, Shashank; Salazar, Juan Augusto Paredes; Delgado, Jhon Manuel Portella; Goel, Ankit; Bernstein, Dennis S.
Extremum-seeking control (ESC) is widely used to optimize performance when the system dynamics are uncertain. However, sensitivity to sensor noise is an important issue in ESC implementation due to the use of high-pass filters or gradient estimators. To reduce the sensitivity of ESC to noise, this paper investigates the use of adaptive input and state estimation (AISE) for numerical differentiation. In particular, this paper develops extremum-seeking control with adaptive input and state estimation (ESC/AISE), where the high-pass filter of ESC is replaced by AISE to improve performance under sensor noise. The effectiveness of ESC/AISE is illustrated via numerical examples.
Adaptive Combustion Regulation in High-Fidelity Computational Model of Solid Fuel Ramjet
(2025-01-03) Oveissi, Parham; Dorsey, Alex; Khokhar, Gohar T.; Hanquist, Kyle M.; Goel, Ankit
Controlling the combustion process under hypersonic conditions remains a significant challenge. This paper uses a data-driven, learning-based control technique to regulate the combustion process within a solid fuel ramjet, aiming to regulate the generated thrust under uncertain operating conditions. A high-fidelity computational model combining compressible flow theory with equilibrium chemistry is developed to simulate combustion dynamics. This model evaluates the stability of the combustion dynamics and defines the engine’s operational envelope. An online learning controller based on retrospective cost optimization is integrated with the computational model to regulate the thrust. Numerical simulations indicate that the learning control system can regulate the thrust generated by an SFRJ without requiring any modeling information.
Comparison of Several Neural Network-Enhanced Sub-Grid Scale Stress Models for Meso-Scale Hurricane Boundary Layer Flow Simulation
(AIAA, 2025-01-03) Hasan, MD Badrul; Yu, Meilin; Oates, Tim
The complicated energy cascade and backscatter dynamics present a challenge when studying turbulent flows in storms at the meso-scale. When performing standard large-eddy simulations (LES), sub-grid scale (SGS) stress models usually fail to consider energy backscatter. These models assume that kinetic energy only moves continuously from larger to smaller scales. However, coherent energy backscatter structures exist when analyzing hurricane boundary layer flows at the meso-scale. Our recent research has shown that machine-learning SGS models trained with high-resolution data can effectively forecast forward and backward energy transfers in meso-scale hurricane-like vortex flows. Therein, physical and geometrical invariances were introduced to better represent flow physics. This further improved the predictability and generalizability of machine-learning-enhanced SGS models. In this study, we compare the performance of several machine-learning-enhanced SGS models, especially those based on neural networks (NNs), with varying physical and geometrical invariance embedding levels for SGS stress modeling in an a priori sense, which sets the cornerstone for ongoing a posteriori tests of NN models.
FINITE ELEMENT ANALYSIS OF A THREE-STAGE 3D PRINTED TENSEGRITY ICOSAHEDRON STRUCTURE
(2024-01-01) Bharata, Abhinav; Zhu, Weidong; Mechanical Engineering; Engineering, Mechanical
Tensegrity is a crucial factor in the progress of humanity in various domains, including architecture and biology. This work is centered on the tensegrity structure of an icosahedron configuration, which is also referred to as a diamond tensegrity. This structure consists of six bars in each stage, with four strings linked to each end of the bar, forming a diamond shape. A three-stage tensegrity-icosahedron (T-Icosahedron) structure, composed of 3D printed components, was studied. The entire structure contains a total of 72 strings and 18 bars manufactured from Ninja Flex and Polylactic acid material. A novel vibration-based method was developed to measure tensions in the strings in the structure. A Polytec laser vibrometer was utilized to accurately measure the tensions and calibrate the nodal coordinates of the structure. The study subsequently focuses on a finite element analysis technique to examine the natural frequencies of the structure and generate its mode shapes using MATLAB. The obtained results are then compared with those obtained from the commercial finite element program ANSYS. This work provides a thorough examination of the T-Icosahedron structure, as well as potential opportunities for additional investigation involving alterations in geometry and material composition of the structure.

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