88 research outputs found
An internal state variable mapping approach for Li-Plating diagnosis
No full textClick on the DOI link to access the article (may not be free).Li-ion battery failure becomes one of major challenges for reliable battery applications, as it could cause catastrophic consequences. Compared with capacity fading resulted from calendar effects, Li-plating induced battery failures are more difficult to identify, as they causes sudden capacity loss leaving limited time for failure diagnosis. This paper presents a new internal state variable (ISV) mapping approach to identify values of immeasurable battery ISVs considering changes of inherent parameters of battery system dynamics for Li-plating diagnosis. Employing the developed ISV mapping approach, an explicit functional relationship model between measurable battery signals and immeasurable battery ISVs can be developed. The developed model can then be used to identify ISVs from an online battery system for the occurrence identification of Li-plating. Employing multiphysics based simulation of Li-plating using COMSOL, the proposed Li-plating diagnosis approach is implemented under different conditions in the case studies to demonstrate its efficacy in diagnosis of Li-plating onset timings.National Science Foundation through Faculty Early Career Development (CAREER) award (CMMI-1351414) and the Award (CMMI-1538508), and by the Department of Transportation through University Transportation Center (UTC) Program
Machine-learning-enabled optimization and online monitoring for efficient and high-quality smart drying
No full textDrying is an important process in the food industry that plays a critical role in both food production and preservation. Industrial scale drying processes and systems involve multiple interacting process parameters, conflicting production objectives, and highly uncertain sample characteristics, which make process control extremely challenging. Current industrial practice lacks the necessary decision-making tools to simultaneously achieve high energy efficiency and food quality. To address these challenges, this dissertation develops a suite of machine-learning-based process control tools to enable smart drying with improved process efficiency and product quality. The contributions of this dissertation are summarized as follows.
It is important to devise a drying strategy to optimize drying efficiency, energy consumption, and product quality, especially under intricate input-output relationships with process uncertainties. Chapter 2 develops an uncertainty-aware, machine-learning-based response surface methodology for apple drying. New drying experiments are designed to resemble industrial practice with variable slice thickness. Variable-response relationships are modeled using machine learning models; Monte Carlo simulations are applied to quantify process uncertainties; and a constrained optimization approach identifies feasible design spaces and optimal parameter combinations. The proposed method achieves a 17.9% energy savings and a 19.0% reduction in drying time.
Physical phenomena in drying can be measured by heterogeneous data modalities, with each carrying unique and complementary information. Effectively leveraging multi-modal data is essential for improving the performance of predictive modeling but remains challenging. Chapter 3 develops a multi-modal data fusion framework for accurately predicting final moisture content in apple drying. Tabular data and high-dimensional images are integrated through an encoder-decoder network to capture both process conditions and sample variability. Experimental results demonstrate predictive accuracy improvements of 19.3%, 24.2%, and 15.2% compared to tabular-only, image-only, and standard data fusion models, respectively. It is also shown that the proposed method is robust to varying modality ratios and can effectively capture process variabilities.
Accurate real-time forecasting of the drying readiness (the optimal drying endpoint) is crucial for minimizing energy consumption and ensuring product quality. Chapter 4 presents a multi-modal fusion framework for online cookie drying readiness prediction. The model integrates in-situ video data and tabular process parameters using modality-specific encoders and a transformer-based decoder. The proposed model achieves a 15-second average prediction error, outperforming the state-of-the-art method by 65.7%, while balancing accuracy, model size, and efficiency. The framework is extensible to various other modality fusion tasks for effective online monitoring.
Dynamic changes in food attributes during drying directly reflect product quality, and accurately predicting the trajectories of these attributes provides valuable insights into determining optimal drying conditions. Chapter 5 develops a data-driven approach for zero-shot prediction of surface color trajectories during food drying. The method learns component function parameters to represent color evolution under unseen conditions, with DCT preprocessing and enhanced by multi-modal data fusion and similarity- informed training selection. The method is validated on two case studies: cookie and apple drying, significantly outperforming baseline models by 93.2% and 87.30%, respectively.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo termsThe student, Shichen Li, accepted the attached license on 2025-08-18 at 14:01.The student, Shichen Li, submitted this Dissertation for approval on 2025-08-18 at 14:43.This Dissertation was approved for publication on 2025-08-18 at 16:11.DSpace SAF Submission Ingestion Package generated from Vireo submission #22763 on 2026-02-19 at 18:24:0
Reliability-based co-design and its applications to wind energy and mobile energy storage systems
No full textAutonomous systems, such as autonomous driving vehicles, unmanned aerial vehicles (UAVs), and field robots, received much attentions recently. The performance of autonomous systems relies on both its physical design and the appropriate control strategies, which often takes place at an early stage of design. The plant design and the control design are strongly coupled. Neglecting this coupling effect may cause an imbalance in the feasible design spaces of plant design and control design, such as over-constrained operation conditions, over design, or requirement of skilled operators, which hinders the development of autonomous systems. On the other hand, the products are manufactured goods and usually operate in environments with uncertainty. Reliable operation of such systems ask for balanced physical design and feasible control decisions to address the parametric uncertainty and stochastic environmental disturbances.
While integrated physical and control system co-design has been demonstrated successfully on several engineering system design applications, it has been primarily applied in a deterministic manner without considering uncertainties. An opportunity exists to study non-deterministic co-design strategies, taking into account various uncertainties in an integrated co-design framework. While significant advancements have been made in co-design and RBDO separately, little is known about methods where reliability-based dynamic system design and control design optimization are considered jointly. In this research, we investigate optimal design and control of dynamical systems with model parametric uncertainties, which presumably operate in uncertain environments. Techniques in control co-design (CCD) and reliability-based design optimization (RBDO) are adapted and integrated to solve the proposed problem. Since the proposed method adopts the idea of multi-disciplinary design optimization, it can improve the performance of autonomous systems without leveraging the difficulty in design and control for systems with uncertainties.
First, the problem formulation and strategies to solve the reliability-based control co-design problem is presented. A comparison of accuracy and efficiency is made using numerical and simple engineering case studies. The method is then applied to a horizontal axis wind turbine. The uncertain wind load and model parameters of a wind turbine are compensated through active control or endured by a reliable design regarding its aerodynamics and structural dynamics. Different strategies of reliability assessment are also compared, which provides insights on their advantages and limits under different cases.
In the second application, reliability-based control co-design is applied to Lithium-ion battery. The electrode and charging current are optimized to minimize its charging time while regulating its aging effect for reasonable cycle life. The multi-scale nature of the problem requires first principle model to preserve the coupling effect between electrode design at the micro scale and the charging control at the macro scale. However, it is not feasible to use the first principle model for control optimization. A hybrid physics and machine learning strategy is proposed in this work, which extends the applicability of reliability-based control co-design to multi-scale problems.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-08-01The student, Tonghui Cui, accepted the attached license on 2021-07-14 at 12:23.The student, Tonghui Cui, submitted this Dissertation for approval on 2021-07-14 at 12:46.This Dissertation was approved for publication on 2021-07-16 at 14:17.DSpace SAF Submission Ingestion Package generated from Vireo submission #16820 on 2022-01-12 at 13:04:15Made available in DSpace on 2022-01-12T22:55:01Z (GMT). No. of bitstreams: 2
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Design and development of 2D materials based nanocomposites
No full textSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo termsThe student, Akash Singh, accepted the attached license on 2024-04-09 at 20:32.The student, Akash Singh, submitted this Dissertation for approval on 2024-04-09 at 20:40.This Dissertation was approved for publication on 2024-04-12 at 12:05.DSpace SAF Submission Ingestion Package generated from Vireo submission #19924 on 2024-09-16 at 00:32:46In the forefront of materials science, 2D materials have emerged as a captivating research domain over the past two decades. Among these, graphene stands out as an exceptional 2D material with distinctive mechanical, thermal, and electrical properties, making it a critical component in applications spanning lightweight structural materials, versatile coatings, and flexible electronics. However, the high cost and complexity of experimental investigations have driven the adoption of computational simulations, particularly molecular dynamics (MD), to unveil the underlying microscopic origins of graphene’s unique properties. Yet, such simulations have yielded varying results, owing to the use of diverse empirical interatomic potentials used in these MD simulations. This dissertation aims to create an accurate interatomic potentials for 2D materials like graphene by using an artificial neural network (ANN)-based interatomic potential. These ANN based machine learning interatomic potentials for graphene are trained from the training data developed using first-principle based atomistic simulations. Machine learning potential (MLP) helps us to run high-fidelity Molecular Dynamics (MD) simulations approaching the accuracy of first principle simulations but with a fraction of computational cost. These MLP enables larger-scale simulations and extended timeframes, thereby accelerating the design, development and discovery of novel graphene/graphene based nanomaterials. Also, this dissertation aims to showcase MLP’s capability in estimating critical material properties of graphene, including coefficient of thermal expansion (CTE), lattice parameters, Young’s modulus, yield strength with comparable accuracy of that of experimental and first-principle calculations found from previous literatures. Remarkably, MLP’s capability in capturing dominant mechanisms governing the behaviour of CTE in graphene, including effects of changing lattice parameters and increasing/decreasing out-of-plane rippling with temperature, is a significant highlight of this dissertation. Furthermore, this MLP development method can be extended to other 2D materials, promising to expedite research on novel 2D materials and their unique atomic structures. Moving on to 2D materials-based nanocomposites, in today’s scientific and technological landscape, have assumed a position of significant importance. These hybrid material systems merge organic molecules with inorganic 2D materials, creating a new dimension of functional materials with varied applications i.e. materials for photovoltaics, electronics, nanotribology to aerospace applications. The interfaces between these 2D material and polymers serves as prototype material systems for studying confinement-induced phase transitions in 2D material based nanocomposites. Thorough understanding of dynamic and static behaviour of atoms in these interfaces at small length (nanometers) and time scales (nanoseconds) is critical as it material behaviour at this scale dictates overall material property of the resulting material system. Thus understanding the interfacial behaviour at atomic level will lead in the development of deliberately engineered 2D material and polymer based nanocomposites. But till date, the complexities of experimental testing at these small length and time scales as well as theoretical modeling have hindered a comprehensive understanding of these hetero-interfaces and thereby our ability to use these materials for practical purposes. To address this issues, this dissertation aims to understand the behaviour of 2D material and polymer at these interfaces using molecular dynamics (MD) simulations. By conducting MD simulations we focus on the assembly of polyethylene chains on surface of two dimensional MoSe2 sheet (which serves as a representative material system in this study to analyse the behaviour of 2D material based nanocomposites). All-atom models were created to simulate the dynamic assembly of n-pentacosane chains, which serves as a proxy for polyethylene in this study, on the surface of two dimensional MoSe2 sheet. This study reveals that polyethylene molecules starts crystallizing from 2D MoSe2-polyethylene interface and the crystallization growth front (plane of crystallized polymer chains) moves quickly towards the bulk polyethylene chains starting from the 2D material-polymer interface. At equilibrium, the directional registry of polyethylene chains on the 2D material surface happens through the interplay of free energy of the surface, adhesive interfacial interactions, conformational entropy, and the presence of substrate corrugation. The results suggests the potential of 2D materials, such as MoSe2, as a template for creating 2D material-polymer nanocomposites with specific crystallization orientations creating deliberate anisotropy and thereby resulting in a material system with tunable material properties
Table_4_Network pharmacology analysis and experimental verification reveal the mechanism of the traditional Chinese medicine YU-Pingfeng San alleviating allergic rhinitis inflammatory responses.XLSX
No full textYU-Pingfeng San (YPFS) can regulate inflammatory response to alleviate the symptoms of nasal congestion and runny rose in allergic rhinitis (AR). However, the mechanism of action remains unclear. In this study, 30 active ingredients of three effective herbs included in YPFS and 140 AR/YPFS-related genes were identified by database analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the targets were mainly enriched in immune inflammatory-related biological processes and pathways. Finally, three hub gene targeting epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), and protein kinase B1 (AKT1) related to YPFS and AR were identified by network pharmacology analysis. YPFS treatment decreased the expression of EGFR, MAPK1, and AKT1 in ovalbumin (OVA)-induced AR mice and impaired the production of inflammatory factors interleukin (IL)-4, IL-5, and IL-13, thus alleviating immunoglobulin E (IgE) production and the symptoms of scratching nose in AR. Through molecular docking analysis, we found that the active ingredients decursin, anomalin, and wogonin of YPFS could bind to EGFR, MAPK1, and AKT1 proteins. Moreover, decursin treatment impaired the expression of IL-4 and IL-5 in human PBMCs. These results suggested that YPFS could alleviate the AR inflammatory responses by targeting EGFR, MAPK1, and AKT1, showing the mechanism of action of YPFS in AR treatment.</p
Table_7_Network pharmacology analysis and experimental verification reveal the mechanism of the traditional Chinese medicine YU-Pingfeng San alleviating allergic rhinitis inflammatory responses.XLSX
No full textYU-Pingfeng San (YPFS) can regulate inflammatory response to alleviate the symptoms of nasal congestion and runny rose in allergic rhinitis (AR). However, the mechanism of action remains unclear. In this study, 30 active ingredients of three effective herbs included in YPFS and 140 AR/YPFS-related genes were identified by database analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the targets were mainly enriched in immune inflammatory-related biological processes and pathways. Finally, three hub gene targeting epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), and protein kinase B1 (AKT1) related to YPFS and AR were identified by network pharmacology analysis. YPFS treatment decreased the expression of EGFR, MAPK1, and AKT1 in ovalbumin (OVA)-induced AR mice and impaired the production of inflammatory factors interleukin (IL)-4, IL-5, and IL-13, thus alleviating immunoglobulin E (IgE) production and the symptoms of scratching nose in AR. Through molecular docking analysis, we found that the active ingredients decursin, anomalin, and wogonin of YPFS could bind to EGFR, MAPK1, and AKT1 proteins. Moreover, decursin treatment impaired the expression of IL-4 and IL-5 in human PBMCs. These results suggested that YPFS could alleviate the AR inflammatory responses by targeting EGFR, MAPK1, and AKT1, showing the mechanism of action of YPFS in AR treatment.</p
Multiphysics-informed machine learning platform for interface study
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01The student, Parth Bansal, accepted the attached license on 2024-04-12 at 15:16.The student, Parth Bansal, submitted this Dissertation for approval on 2024-04-12 at 15:21.This Dissertation was approved for publication on 2024-04-19 at 16:43.DSpace SAF Submission Ingestion Package generated from Vireo submission #20385 on 2024-09-16 at 00:43:34With the increasing focus on sustainable technologies both in terms of newer developments and increasing the life of existing ones, there is a need to efficiently and accurately assess these technological systems. This can be achieved through using less expensive and really accurate finite element computational models. However, these Monte-Carlo simulations are still too computationally expensive and require a lot of resources. Hence, this thesis develops finite element models that work together with machine learning techniques to provide a robust framework to perform various studies such as uncertainty quantification, state of health prognostics and design of different physical and electrical systems. The main contribution of this thesis is to demonstrate frameworks that can be used to evaluate the system performance (e.g. corrosion related material loss, capacity loss in batteries) and help in designing better systems by understanding and quantifying the sources of uncertainty in them by the use of physics-informed machine learning. The first step in this process of physics-informed machine learning is to develop the finite element models, whose results are used to inform or train the machine learning algorithms. This thesis focuses on two main systems: galvanic corrosion in dissimilar material joints and the capacity fade in silicon anode based lithium-ion batteries. The finite element models for both these processes include a variety of failure modes that can accurately and reliably predict the system life cycle. Experimental work is also used to partially verify the finite element models. The results from these finite element models are then used with machine learning models such as Gaussian Process Regression models to reduce the overall cost burden. Processes such as probablistic-confidence based adaptive sampling techniques can further reduce the computational costs by thoroughly exploring the design space in an efficient manner. The trained machine learning models can then be used for a variety of applications such as state of health analysis, uncertainty quantification and better system design
Image_1_Network pharmacology analysis and experimental verification reveal the mechanism of the traditional Chinese medicine YU-Pingfeng San alleviating allergic rhinitis inflammatory responses.JPEG
No full textYU-Pingfeng San (YPFS) can regulate inflammatory response to alleviate the symptoms of nasal congestion and runny rose in allergic rhinitis (AR). However, the mechanism of action remains unclear. In this study, 30 active ingredients of three effective herbs included in YPFS and 140 AR/YPFS-related genes were identified by database analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the targets were mainly enriched in immune inflammatory-related biological processes and pathways. Finally, three hub gene targeting epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), and protein kinase B1 (AKT1) related to YPFS and AR were identified by network pharmacology analysis. YPFS treatment decreased the expression of EGFR, MAPK1, and AKT1 in ovalbumin (OVA)-induced AR mice and impaired the production of inflammatory factors interleukin (IL)-4, IL-5, and IL-13, thus alleviating immunoglobulin E (IgE) production and the symptoms of scratching nose in AR. Through molecular docking analysis, we found that the active ingredients decursin, anomalin, and wogonin of YPFS could bind to EGFR, MAPK1, and AKT1 proteins. Moreover, decursin treatment impaired the expression of IL-4 and IL-5 in human PBMCs. These results suggested that YPFS could alleviate the AR inflammatory responses by targeting EGFR, MAPK1, and AKT1, showing the mechanism of action of YPFS in AR treatment.</p
Design and development of 2D materials based nanocomposites
No full textSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo termsThe student, Akash Singh, accepted the attached license on 2024-04-09 at 20:32.The student, Akash Singh, submitted this Dissertation for approval on 2024-04-09 at 20:40.This Dissertation was approved for publication on 2024-04-12 at 12:05.DSpace SAF Submission Ingestion Package generated from Vireo submission #19924 on 2024-09-16 at 00:32:46In the forefront of materials science, 2D materials have emerged as a captivating research domain over the past two decades. Among these, graphene stands out as an exceptional 2D material with distinctive mechanical, thermal, and electrical properties, making it a critical component in applications spanning lightweight structural materials, versatile coatings, and flexible electronics. However, the high cost and complexity of experimental investigations have driven the adoption of computational simulations, particularly molecular dynamics (MD), to unveil the underlying microscopic origins of graphene’s unique properties. Yet, such simulations have yielded varying results, owing to the use of diverse empirical interatomic potentials used in these MD simulations. This dissertation aims to create an accurate interatomic potentials for 2D materials like graphene by using an artificial neural network (ANN)-based interatomic potential. These ANN based machine learning interatomic potentials for graphene are trained from the training data developed using first-principle based atomistic simulations. Machine learning potential (MLP) helps us to run high-fidelity Molecular Dynamics (MD) simulations approaching the accuracy of first principle simulations but with a fraction of computational cost. These MLP enables larger-scale simulations and extended timeframes, thereby accelerating the design, development and discovery of novel graphene/graphene based nanomaterials. Also, this dissertation aims to showcase MLP’s capability in estimating critical material properties of graphene, including coefficient of thermal expansion (CTE), lattice parameters, Young’s modulus, yield strength with comparable accuracy of that of experimental and first-principle calculations found from previous literatures. Remarkably, MLP’s capability in capturing dominant mechanisms governing the behaviour of CTE in graphene, including effects of changing lattice parameters and increasing/decreasing out-of-plane rippling with temperature, is a significant highlight of this dissertation. Furthermore, this MLP development method can be extended to other 2D materials, promising to expedite research on novel 2D materials and their unique atomic structures. Moving on to 2D materials-based nanocomposites, in today’s scientific and technological landscape, have assumed a position of significant importance. These hybrid material systems merge organic molecules with inorganic 2D materials, creating a new dimension of functional materials with varied applications i.e. materials for photovoltaics, electronics, nanotribology to aerospace applications. The interfaces between these 2D material and polymers serves as prototype material systems for studying confinement-induced phase transitions in 2D material based nanocomposites. Thorough understanding of dynamic and static behaviour of atoms in these interfaces at small length (nanometers) and time scales (nanoseconds) is critical as it material behaviour at this scale dictates overall material property of the resulting material system. Thus understanding the interfacial behaviour at atomic level will lead in the development of deliberately engineered 2D material and polymer based nanocomposites. But till date, the complexities of experimental testing at these small length and time scales as well as theoretical modeling have hindered a comprehensive understanding of these hetero-interfaces and thereby our ability to use these materials for practical purposes. To address this issues, this dissertation aims to understand the behaviour of 2D material and polymer at these interfaces using molecular dynamics (MD) simulations. By conducting MD simulations we focus on the assembly of polyethylene chains on surface of two dimensional MoSe2 sheet (which serves as a representative material system in this study to analyse the behaviour of 2D material based nanocomposites). All-atom models were created to simulate the dynamic assembly of n-pentacosane chains, which serves as a proxy for polyethylene in this study, on the surface of two dimensional MoSe2 sheet. This study reveals that polyethylene molecules starts crystallizing from 2D MoSe2-polyethylene interface and the crystallization growth front (plane of crystallized polymer chains) moves quickly towards the bulk polyethylene chains starting from the 2D material-polymer interface. At equilibrium, the directional registry of polyethylene chains on the 2D material surface happens through the interplay of free energy of the surface, adhesive interfacial interactions, conformational entropy, and the presence of substrate corrugation. The results suggests the potential of 2D materials, such as MoSe2, as a template for creating 2D material-polymer nanocomposites with specific crystallization orientations creating deliberate anisotropy and thereby resulting in a material system with tunable material properties
Table_6_Network pharmacology analysis and experimental verification reveal the mechanism of the traditional Chinese medicine YU-Pingfeng San alleviating allergic rhinitis inflammatory responses.XLSX
No full textYU-Pingfeng San (YPFS) can regulate inflammatory response to alleviate the symptoms of nasal congestion and runny rose in allergic rhinitis (AR). However, the mechanism of action remains unclear. In this study, 30 active ingredients of three effective herbs included in YPFS and 140 AR/YPFS-related genes were identified by database analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the targets were mainly enriched in immune inflammatory-related biological processes and pathways. Finally, three hub gene targeting epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), and protein kinase B1 (AKT1) related to YPFS and AR were identified by network pharmacology analysis. YPFS treatment decreased the expression of EGFR, MAPK1, and AKT1 in ovalbumin (OVA)-induced AR mice and impaired the production of inflammatory factors interleukin (IL)-4, IL-5, and IL-13, thus alleviating immunoglobulin E (IgE) production and the symptoms of scratching nose in AR. Through molecular docking analysis, we found that the active ingredients decursin, anomalin, and wogonin of YPFS could bind to EGFR, MAPK1, and AKT1 proteins. Moreover, decursin treatment impaired the expression of IL-4 and IL-5 in human PBMCs. These results suggested that YPFS could alleviate the AR inflammatory responses by targeting EGFR, MAPK1, and AKT1, showing the mechanism of action of YPFS in AR treatment.</p
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