Mathematical Modeling of Lithium-ion Batteries and Improving Mathematics Learning Experience for Engineering Students

Increase in the world’s energy consumption along with the environmental impacts of conventional sources of energy (gas, petroleum, and coal) makes the shift to clean energy sources unavoidable. To address the energy needs of the world, using clean energy sources would not provide the sufficient answer to the world’s energy issues if it is not accompanied by developing energy storage systems that are capable of storing energy efficiently. Lithium-ion batteries are the main energy storage devices that are developed to satisfy the ever-growing energy needs of the modern world. However, there are still important features of Li-ion battery systems (such as the battery microstructural effects) that need to be studied to a broader extent. In this regard, some of the battery microstructural phenomena, such as the formation of solid electrolyte interface, is believed to be the main reason behind battery degradation and drop in performance. Previous studies have focused on the experimental and computational investigation of micro- and macro- structural features of the Li-ion battery; however, further study is needed to focus on incorporating the effects of microscale features of the Li-ion batteries into the total response of the battery system. In the present work, the details of developing a multiscale mathematical model for a Li-ion battery system is explained, and a multiscale model for the battery system is developed by employing variational multiscale modeling method. The developed model is capable of considering the effects of the battery microstructural features (e.g., the random shape of the active material particles) on the total battery performance. In the developed multiscale framework, the microstructural effects are accounted for in the governing equations of the battery macroscale with the help of Green’s function and variational formulation. This part of the present work provides a clear framework for understanding the details and process of developing a multiscale mathematical model for a Li-ion battery system. Learning mathematics is essential in engineering education and practice. With increasing number of students and emergence of online/distance learning programs, it is critical to look for new approaches in teaching mathematics that different in content development and design. Special consideration should be in place in designing an online program for teaching mathematics to ensure students’ success and satisfaction in the engineering curriculum. Previous investigations studied the effects of enrolling in online programs on students’ achievement. However, more implementations of such educational frameworks are needed to recognize their shortcomings and enhance the quality of online learning programs. In addition, the idea of the blended classroom should be put into practice to a further extent to ensure the high-quality development of online instructional content. In this work, an online learning program was provided for engineering students enrolled in an introductory engineering mechanics course. Online interactive instructional modules were developed and implemented in the targeted engineering course to cover prerequisite mathematical concepts of the course. Students with access to the developed online learning modules demonstrated improvement in their learning and recommended employing such modules to teach fundamental concepts in other courses. This part of the work improves the understanding of the development process of the online learning modules and their implementation in lecture-based classrooms

Modern applications of machine learning in quantum sciences

In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning

Modern applications of machine learning in quantum sciences

In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.Comment: 268 pages, 87 figures. Comments and feedback are very welcome. Figures and tex files are available at https://github.com/Shmoo137/Lecture-Note

NASA multidisciplinary research grant

Research is discussed in the multidisciplinary areas of space and planetary science; materials and radiation; systems, instrumentation, and structures; and technology and man. Highlights are identified as an alpha-recoil track method of archeological dating; infrared astronomical telescope; reaction rates data, semiconductor radiation detectors, and analysis of time-dependent systems; Gunn effect devices for microwave generation and detection, mode-locked lasers, and radiation theory; and the application of a satellite communication system to educational development. Detectors to be flown on Apollo 16 to measure heavy particle flux in the solar wind and to be part of the HEAO-A experiment on extremely heavy nuclei in cosmic rays were developed. The impact of the multidisciplinary research on university activities is described, and individual departmental reports are included

The 2nd International Conference on Mathematical Modelling in Applied Sciences, ICMMAS’19, Belgorod, Russia, August 20-24, 2019 : book of abstracts

The proposed Scientific Program of the conference is including plenary lectures, contributed oral talks, poster sessions and listeners. Five suggested special sessions / mini-symposium are also considered by the scientific committe

Graduate Course Descriptions, 2006 Winter

Wright State University graduate course descriptions from Winter 2006

Graduate Course Descriptions, 2005 Fall

Wright State University graduate course descriptions from Fall 2005

Semester Courses and Course Equivalents: Graduate Courses Summary

A list comprised of summaries of all graduate courses and course equivalents at Wright State University

Undergraduate and Graduate Course Descriptions, 2023 Spring

Wright State University undergraduate and graduate course descriptions from Spring 2023

Undergraduate and Graduate Course Descriptions, 2022 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2022