Mechanical Engineering

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OPTIMAL PROBING OF BATTERY CYCLES FOR MACHINE LEARNING-BASED MODEL DEVELOPMENT
(2024) Nozarijouybari, Zahra; Fathy, Hosam HF; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This dissertation examines the problems of optimizing the selection of the datasets and experiments used for parameterizing machine learning-based electrochemical battery models. The key idea is that data selection, or “probing” can empower such models to achieve greater fidelity levels. The dissertation is motivated by the potential of battery models to enable theprediction and optimization of battery performance and control strategies. The literature presents multiple battery modeling approaches, including equivalent circuit, physics-based, and machine learning models. Machine learning is particularly attractive in the battery systems domain, thanks to its flexibility and ability to model battery performance and aging dynamics. Moreover, there is a growing interest in the literature in hybrid models that combine the benefits of machine learning with either the simplicity of equivalent circuit models or the predictiveness of physics-based models or both. The focus of this dissertation is on both hybrid and purely data-driven battery models. Moreover, the overarching question guiding the dissertation is: how does the selection of the datasets and experiments used for parameterizing these models affect their fidelity and parameter identifiability? Parameter identifiability is a fundamental concept from information theory that refers to the degree to which one can accurately estimate a given model’s parameters from input-output data. There is substantial existing research in the literature on battery parameter identifiability. An important lesson from this literature is that the design of a battery experiment can affect parameter identifiability significantly. Hence, test trajectory optimization has the potential to substantially improve model parameter identifiability. The literature explores this lesson for equivalent circuit and physics-based battery models. However, there is a noticeable gap in the literature regarding identifiability analysis and optimization for both machine learning-based and hybrid battery models. To address the above gap, the dissertation makes four novel contributions to the literature. The first contribution is an extensive survey of the machine learning-based battery modeling literature, highlighting the critical need for information-rich and clean datasets for parameterizing data-driven battery models. The second contribution is a K-means clustering-based algorithm for detecting outlier patterns in experimental battery cycling data. This algorithm is used for pre-cleaning the experimental cycling datasets for laboratory-fabricated lithium-sulfur (Li-S) batteries, thereby enabling the higher-fidelity fitting of a neural network model to these datasets. The third contribution is a novel algorithm for optimizing the cycling of a lithium iron phosphate (LFP) to maximize the parameter identifiability of a hybrid model of this battery. This algorithm succeeds in improving the resulting model’s Fisher identifiability significantly in simulation. The final contribution focuses on the application of such test trajectory optimization to the experimental cycling of commercial LFP cells. This work shows that test trajectory optimization is s effective not just at improving parameter identifiability, but also at probing and uncovering higher-order battery dynamics not incorporated in the initial baseline model. Collectively, all four of these contributions show the degree to which the selection of battery cycling datasets and experiments for richness and cleanness can enable higher-fidelity data-driven and hybrid modeling, for multiple battery chemistries.
A Framework for Remaining Useful Life Prediction and Optimization for Complex Engineering Systems
(2024) Weiner, Matthew Joesph; Azarm, Shapour; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Remaining useful life (RUL) prediction plays a crucial role in maintaining the operational efficiency, reliability, and performance of complex engineering systems. Recent efforts have primarily focused on individual components or subsystems, neglecting the intricate relationships between components and their impact on system-level RUL (SRUL). The existing gap in predictive methodologies has prompted the need for an integrated approach to address the complex nature of these systems, while optimizing the performance with respect to these predictive indicators. This thesis introduces a novel methodology for predicting and optimizing SRUL, and demonstrates how the predicted SRUL can be used to optimize system operation. The approach incorporates various types of data, including condition monitoring sensor data and component reliability data. The methodology leverages probabilistic deep learning (PDL) techniques to predict component RUL distributions based on sensor data and component reliability data when sensor data is not available. Furthermore, an equation node-based Bayesian network (BN) is employed to capture the complex causal relationships between components and predict the SRUL. Finally, the system operation is optimized using a multi-objective genetic algorithm (MOGA), where SRUL is treated as a constraint and also as an objective function, and the other objective relates to mission completion time. The validation process includes a thorough examination of the component-level methodology using the C-MAPSS data set. The practical application of the proposed methodology in this thesis is through a case study involving an unmanned surface vessel (USV), which incorporates all aspects of the methodology, including system-level validation through qualitative metrics. Evaluation metrics are employed to quantify and qualify both component and system-level results, as well as the results from the optimizer, providing a comprehensive understanding of the proposed approach’s performance. There are several main contributions of this thesis. These include a new deep learning structure for component-level PHM, one that utilizes a hybrid-loss function for a multi-layer long short-term memory (LSTM) regression model to predict RUL with a given confidence interval while also considering the complex interactions among components. Another contribution is the development of a new framework for computing SRUL from these predicted component RULs, in which a Bayesian network is used to perform logic operations and determine the SRUL. These contributions advance the field of PHM, but also provide a practical application in engineering. The ability to accurately predict and manage the RUL of components within a system has profound implications for maintenance scheduling, cost reduction, and overall system reliability. The integration of the proposed method with an optimization algorithm closes the loop, offering a comprehensive solution for offline planning and SRUL prediction and optimization. The results of this research can be used to enhance the efficiency and reliability of engineering systems, leading to more informed decision-making.
Ordering Non-Linear Subspaces for Airfoil Design and Optimization via Rotation Augmented Gradients
(2023) Van Slooten, Alec; Fuge, Mark; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Airfoil optimization is critical to the design of turbine blades and aerial vehicle wings, among other aerodynamic applications. This design process is often constrained by the computational time required to perform CFD simulations on different design options, or the availability of adjoint solvers. A common method to mitigate some of this computational expense in nongradient optimization is to perform dimensionality reduction on the data and optimize the design within this smaller subspace. Although learning these low-dimensional airfoil manifolds often facilitates aerodynamic optimization, these subspaces are often still computationally expensive to explore. Moreover, the complex data organization of many current nonlinear models make it difficult to reduce dimensionality without model retraining. Inducing orderings of latent components restructures the data, reduces dimensionality reduction information loss, and shows promise in providing near-optimal representations in various dimensions while only requiring the model to be trained once. Exploring the response of airfoil manifolds to data and model selection and inducing latent component orderings have potential to expedite airfoil design and optimization processes. This thesis first investigates airfoil manifolds by testing the performance of linear and nonlinear dimensionality reduction models, examining if optimized geometries occupy lower dimensional manifolds than non-optimized geometries, and by testing if the learned representation can be improved by using target optimization conditions as data set features. We find that autoencoders, although often suffering from stability issues, have increased performance over linear methods such as PCA in low dimensional representations of airfoil databases. We also find that the use of optimized geometry and the addition of performance parameters have little effect on the intrinsic dimensionality of the data. This thesis then explores a recently proposed approach for inducing latent space orderings called Rotation Augmented Gradient (RAG) [1]. We extend their algorithm to nonlinear models to evaluate its efficacy at creating easily-navigable latent spaces with reduced training, increased stability, and improved design space preconditioning. Our extension of the RAG algorithm to nonlinear models has potential to expedite dimensional analyses in cases with near-zero gradients and long training times by eliminating the need to retrain the model for different dimensional subspaces
Single- and Multi-Objective Feasibility Robust Optimization under Interval Uncertainty with Surrogate Modeling
(2022) Kania, Randall Joseph; Azarm, Shapour; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This dissertation presents new methods for solving single- and multi-objective optimization problems when there are uncertain parameter values. The uncertainty in these problems is considered to come from sources with no known or assumed probability distribution, bounded only by an interval. The goal is to obtain a single solution (for single-objective optimization problems) or multiple solutions (for multi-objective optimization problems) that are optimal and “feasibly robust”. A feasibly robust solution is one that remains feasible for all values of uncertain parameters within the uncertainty interval. Obtaining such a solution can become computationally costly and require many function calls. To reduce the computational cost, the presented methods use surrogate modeling to approximate the functions in the optimization problem.This dissertation aims at addressing several key research questions. The first Research Question (RQ1) is: How can the computational cost for solving single-objective robust optimization problems be reduced with surrogate modelling when compared to previous work? RQ2 is: How can the computational cost of solving bi-objective robust optimization problems be improved by using surrogates in concert with a Bayesian optimization technique when compared to previous work? And RQ3 is: How can surrogate modeling be leveraged to make multi-objective robust optimization computationally less expensive when compared to previous work? In addressing RQ1, a new single-objective robust optimization method has been developed with improvements over an existing method from the literature. This method uses a deterministic, local solver, paired with a surrogate modelling technique for finding worst-case scenario of parameter configurations. Using this single-objective robust optimization method, improved large-scale performance and robust feasibility were demonstrated. The second method presented solves bi-objective robust optimization problems under interval uncertainty by introducing a relaxation technique to facilitate combining iterative robust optimization and Bayesian optimization techniques. This method showed improved feasibility robustness and performance at larger problem sizes over existing methods. The third method presented in this dissertation extends the current literature by considering multiple (beyond two) competing objectives for surrogate robust optimization. Increasing the number of objectives adds more dimensions and complexity to the search for solutions and can greatly increase the computational costs. In the third method, the robust optimization strategy from the bi-objective second method was combined with a new Monte Carlo approximated method. The key contributions in this dissertation are 1) a new single-objective robust optimization method combining a local optimization solver and surrogate modelling for robustness, 2) a bi-objective robust optimization method that employs iterative Bayesian optimization technique in tandem with iterative robust optimization techniques, and 3) a new acquisition function for robust optimization in problems of more than two objectives.
AUTOMATIC OPTIMIZATION METHODS FOR PATIENT-SPECIFIC TISSUE-ENGINEERED VASCULAR GRAFTS
(2020) Hess, Rachel; Fuge, Mark; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Surgical intervention is sometimes necessary in cases of Coarctation of the Aorta (CoA). The post-repair geometry of the aorta can result in sub-optimal hemodynamics and can have long-term health impacts. Patient-specific designs for tissue-engineered vascular grafts (TEVGs) allow greater control over post-repair geometry. This thesis proposes a method for automatically optimizing patient-specific TEVGs using computational fluid dynamics (CFD) simulations and the ANSYS Fluent adjoint solver. Our method decreases power loss in the graft by 25-60% compared to the native geometry. As patient-specific graft design can be challenging due to incomplete or uncertain flow and geometry data, this thesis also quantifies the robustness of the optimal designs with respect to CFD boundary conditions derived from imaging data. We show that using velocity conditions that deviate by more than 20% of the measured peak systolic velocity, our method produces grafts with deviations on the order of 5% in predicted power loss performance. Lastly, as one way to accelerate the optimization process, we demonstrate and compare how some established machine learning models (K Nearest Neighbors and Kernel Ridge Regression) predict reasonable starting points for an optimizer on a 2D bifurcated pipe dataset.
Optimizing Mass Customization Through Interaction Variability and Manufacturing Trade-offs
(2017) Cage, Kailyn; Vaughn-Cooke, Monifa; Fuge, Mark D; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Design methods that consider the complete physical system (human interfaces and functional capacities of human interfaces) and incremental distinctions in humans are not widely applied. Human beings vary from a cognitive and physical standpoint. Manufacturing approaches have attempted to implement mass customization to provide end users with personalized products. However, these approaches are limited since (1) mass customization is orthogonal to human variability and (2) manufacturing costs are increased, through additional time and parts, required when mass-producing customized products. This research facilitates the integration of traditional engineering performance metrics and biomechanics creating manufacturable innovations in customized design that target population accommodation. The present method captures (1) human and product interface interactions, (2) interaction accommodation, (3) the impact of interaction accommodation on engineering performance metrics (thermal, structural, fluid, reliability, etc.), and (4) number of products required to accommodate the population. Engineering design techniques provide a structured method for reducing product and performance metrics which provide the foundational framework for the optimization model(s) integrating this method. Optimization enables optimal performance metrics constrained by population accommodation, producing the product metrics and the number of products required to accommodate the population. This work is a novel approach for addressing complex questions for interaction variability in mass production targeting population accommodation while maintaining product performance, which facilitate addressing larger problems of mass customization in mass production.
THERMOELECTRIC COOLING OF HIGH FLUX ELECTRONICS
(2017) Yuruker, Sevket Umut; Yang, Bao; Bar-Cohen, Avram; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
On-chip thermoelectric cooling is a promising solution for thermal management of next generation integrated circuits. This thesis focuses on three thermoelectric cooling applications for high flux electronics. A micro contact enhanced thin film thermoelectric cooler was designed for remediation of a 5kW/cm2 hotspot and its integration with manifold microchannel system is numerically demonstrated. In addition, thermoelectric cooling was utilized for thermal de-coupling of electronic chips with different operating temperatures, eliminating the need to over-cool the entire package. Furthermore, effect of decreasing contact resistances in thin film thermoelectrics was numerically investigated to effectively remove 100W (~280W/cm2) of heat dissipation from quantum cascade lasers. Finally, a system-level optimization methodology is established with comprehensive mathematical modeling, verified with numerical simulations. Master curves are generated to understand the effect of system-level parasitics on performance and optimal design variables. In conclusion, the advantages of thermoelectric cooling for high flux electronics is demonstrated in this thesis.
MODELING AND OPTIMIZATION OF MICROGRID ENERGY SYSTEM FOR SHIP APPLICATIONS
(2016) Cao, Tao; Radermacher, Reinhard; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Microgrid energy systems are widely used in remote communities and off-grid sites, where primary energy supplies are dominated by fuels. Limited attentions have been paid to ship applications, which require thorough and in-depth research to address their unique challenges and increasing pressure on reducing fuel consumptions. This dissertation presents comprehensive microgrid system studies for ship applications in four aspects: component modeling and study, dynamic system modeling on novel designs, novel optimization based system design framework development and investigations on two enhancement options: integrating with separate sensible and latent cooling systems, maximizing heat recovery through pinch analysis. Comprehensive component studies consist of new component models addressing unique features of ship applications. Desiccant wheels with new materials were investigated experimentally, especially under high humidity conditions for ship applications. Dynamic system modeling was conducted on several novel solar energy and waste heat powered systems, with a focus on their capabilities to reduce fuel consumptions and CO2 emissions. Results were validated against experimental data. Payload and economic studies were conducted to evaluate feasibilities of applying the designs to ship applications. A novel optimization based design framework was then developed. The framework is capable of conducting both system configuration and control strategy optimization under transient weather and load profiles, differentiating itself with current control strategy focused energy system optimization studies (Jradi and Riffat, 2014). It also extends Buoro et al. (2012)’s study on system configuration optimization to complete design from scratch with comprehensive equipment selections and integrating options. The design framework was demonstrated through a case study on container ships. Optimized systems and control strategies were found from three different scenarios: single-objective optimization, bi-objective optimization and optimization under uncertainty. Finally, two previously listed options were investigated to enhance microgrid system performance regarding thermal comfort and fuel savings. This research fills current research gaps on microgrid energy system for ship applications. It also serves as the basis for advanced microgrid system analysis framework for any applications. The dynamic system modeling platform, optimization based design framework and enhancement methods can help engineers develop and evaluate ultra-high efficiency designs, aiming to reduce energy consumptions and CO2 emissions.
Efficiency Enhancement for Natural Gas Liquefaction with CO₂ Capture and Sequestration through Cycles Innovation and Process Optimization
(2014) Alabdulkarem, Abdullah; Radermacher, Reinhard; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Liquefied natural gas (LNG) plants are energy intensive. As a result, the power plants operating these LNG plants emit high amounts of CO₂. To mitigate global warming that is caused by the increase in atmospheric CO₂, CO₂ capture and sequestration (CCS) using amine absorption is proposed. However, the major challenge of implementing this CCS system is the associated power requirement, increasing power consumption by about 15-25%. Therefore, the main scope of this work is to tackle this challenge by minimizing CCS power consumption as well as that of the entire LNG plant though system integration and rigorous optimization. The power consumption of the LNG plant was reduced through improving the process of liquefaction itself. In this work, a genetic algorithm (GA) was used to optimize a propane pre-cooled mixed-refrigerant (C3-MR) LNG plant modeled using HYSYS software. An optimization platform coupling Matlab with HYSYS was developed. New refrigerant mixtures were found, with savings in power consumption as high as 13%. LNG plants optimization with variable natural gas feed compositions was addressed and the solution was proposed through applying robust optimization techniques, resulting in a robust refrigerant which can liquefy a range of natural gas feeds. The second approach for reducing the power consumption is through process integration and waste heat utilization in the integrated CCS system. Four waste heat sources and six potential uses were uncovered and evaluated using HYSYS software. The developed models were verified against experimental data from the literature with good agreement. Net available power enhancement in one of the proposed CCS configuration is 16% more than the conventional CCS configuration. To reduce the CO₂ pressurization power into a well for enhanced oil recovery (EOR) applications, five CO₂ pressurization methods were explored. New CO₂ liquefaction cycles were developed and modeled using HYSYS software. One of the developed liquefaction cycles using NH₃ as a refrigerant resulted in 5% less power consumption than the conventional multi-stage compression cycle. Finally, a new concept of providing the CO₂ regeneration heat is proposed. The proposed concept is using a heat pump to provide the regeneration heat as well as process heat and CO₂ liquefaction heat. Seven configurations of heat pumps integrated with CCS were developed. One of the heat pumps consumes 24% less power than the conventional system or 59% less total equivalent power demand than the conventional system with steam extraction and CO₂ compression.
NUMERICAL MODELING AND OPTIMIZATION OF SINGLE PHASE MANIFOLD-MICROCHANNEL PLATE HEAT EXCHANGER
(2012) Arie, Martinus Adrian; Ohadi, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In recent years manifold-microchannel technology has received considerable attention from the research community as it has demonstrated clear advantage over state of the art heat exchangers. It has the potential to improve heat transfer performance by an order of magnitude while reducing pressure drop penalty equally impressive, when compared to state of the art heat exchangers for selected applications. However, design of heat exchangers based on this technology requires selection of several critical geometrical and flow parameters. This research focuses on the numerical modeling and an optimization algorithm to determine such design parameters and optimize the performance of manifold-microchannels for a plate heat exchanger geometry. A hybrid method was developed to calculate the total pumping power and heat transfer of this type of heat exchangers. The results from the hybrid method were successfully verified with the results obtained from a full CFD model and experimental work. Based on the hybrid method, a multi-objective optimization of the heat exchanger was conducted utilizing an approximation-based optimization technique. The optimized manifold-microchannel flat plate heat exchanger showed superior performance over a Chevron plate heat exchanger which is a wildly used option for diverse applications.

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