SCIENTIFIC MACHINE LEARNING METHODS FOR REACTIVE-TRANSPORT AND THERMAL-TRANSPORT PROBLEMS

Scientific machine learning (SciML) involves development of machine learning models trained using scientific data. SciML involves confluence of machine learning and scientific computing tools and has accelerated research in a gamut of scientific disciplines. This dissertation presents novel SciML frameworks for reactive-transport and thermal-transport problems. The traditional numerical methods use initial and boundary conditions for tran- sient problems and cannot use the limited time history of solutions that might be available. The framework developed for reactive-transport problems overcomes this shortcoming to improve prediction accuracy using available time-history data. The framework uses convolutional neural networks for capturing spatial patterns and long short-term memory networks for forecasting temporal variations in mixing. The framework was carefully built to ensure non-negativity of the chemical species at all space-time points. The framework was validated for 2D problems and is easily extensible to 3D problems. The time needed to obtain a forecast using the model is a fraction of the time needed to obtain the results using a high-fidelity simulation. Therefore, the proposed framework will be a valuable tool for modeling reactive- transport in a wide range of applications. The framework for thermal-transport utilizes physics-informed neural networks (PINNs) to solve forward and inverse problems for active cooling due to fluid circula- tion through the microvasculatures embedded in thin components. Such components are used in emerging technologies like hypersonic aircraft, space exploration vehicles and batteries for efficient thermal regulation. Modeling is vital during the design and operational phases of such systems. However, what is lacking is an accurate frame- work that (i) captures sharp discontinuity in thermal flux across complex vasculature layouts, (ii) accommodates oblique derivatives of the temperature gradients, (iii) han- dles nonlinearity because of radiative heat transfer, (iv) provides a high-speed forecast for real-time monitoring, and (v) solves inverse problems with a noisy data. A fast, reliable, and accurate framework for vascular thermal regulation is presented that is mesh-less and elegantly overcomes all aforementioned challenges. The framework is valuable for real-time monitoring of thermal regulatory systems because of rapid forecasting. It facilitates systematic inverse modeling studies, e.g., for parameter identification, which is the most significant utility of the framework.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Houston

Year dc:date.issued

2022

Author dc:creator

• Jagtap, Nimish Vijay

Advisor dc:contributor.advisor

• Nakshatrala, Kalyana Babu

Committee members dc:contributor.committeemember

• Liu, Dong
• Mo, Yi-Lung
• Rao, Jagannatha R.
• Kulkarni, Yashashree
• Mudunuru, Maruti K.

• Scientific Machine Learning
• Deep learning
• Reactive-transport
• Non-negative solutions
• Spatial-temporal forecasting
• Pattern recognition
• Convolutional neural networks (CNN)
• Long short-term memory (LSTM) networks
• Thermal-transport
• Physics-informed neural networks (PINNs)
• Thermal regulation
• Microvasculatures
• Active cooling
• Verification
• Inverse problems
• Informed decision making

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10657/14321

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/14321