Parametrising temperature dependent properties in thermal-mechanical analysis of power electronics modules using parametric Model Order Reduction

In this paper, a direct-coupled thermal-mechanical analysis of a Power Electronics Modules (PEM) using ANSYS-FEM (Finite Element Method) is integrated with a Parametric Model Order Reduction (pMOR) technique. Unlike most present studies on model order reduction, which perform the coupled thermal-mechanical analysis by sequential-coupled thermal-mechanical models, the direct-coupled thermal-mechanical approach deployed in this study solves the thermal and structural models simultaneously. Commonly, pMOR mainly focuses on parametrising model parameters (e.g., material properties, loads.) that are constants. In this investigation, a new approach to parametrise temperature-dependent properties using pMOR, such as the coefficient of thermal expansion (CTE) of the materials in PEM structures, has been demonstrated in the context of the reliability assessment of electronic modules. A two-dimensional finite element model of a PEM is developed and used to study the temperature-dependent CTE effects of the Aluminium (Al) alloy on the thermal-mechanical response of the system under thermal load. A Krylov subspace-based technique, PRIMA, has been used for the model order reduction and a linear approach of matrix interpolation for the parametrisation in the pMOR. The full-order state-space model has 30,612 degrees of freedom (DOFs), and the reduced model achieved by pMOR has just 8 DOFs. The simulation runs show that with this approach, a substantial reduction in computational time can be achieved, for this problem, by 81% between the full and the reduced order models. In modelling predictions, the pMOR-based solution has retained the accuracy of results. In this instance, the average difference in stress result, compared to the ANSYS-FEM model (FOM) solution, is only 0.43%

Reduced-Order Equivalent-Circuit Models Of Thermal Systems Including Thermal Radiation

We established a general, automatic, and versatile procedure to derive an equivalent circuit for a thermal system using temperature data obtained from FE simulations. The EC topology was deduced from the FE mesh using a robust and general graph-partitioning algorithm. The method was shown to yield models that are independent of the boundary conditions for complicated 3D thermal systems such as an electronic chip. The results are strongly correlated with the geometry, and the EC can be extended to yield variable medium-order models. Moreover, a variety of heat sources and boundary conditions can be accommodated, and the EC models are inherently modular. A reliable method to compute thermal resistors connecting different regions was developed. It appropriately averages several estimates of a thermal resistance where each estimate is obtained using data obtained under different boundary or heating conditions. The concept of fictitious heat sources was used to increase the number of simulation datasets. The method was shown to yield models that are independent of the BCs for complicated 2-D thermal systems such as a 2D cavity. A reliable method to compute thermal resistors connecting different regions was developed. In general, the number of regions required for getting an accurate reduced-order model depends on the complexity of the system to be modeled. We have extended the reduced-order modeling procedure to include a view-factor based thermal radiation heat transfer model by including voltage controlled current sources in the equivalent circuit

Thermal-mechanical analysis of a power module with parametric model order reduction

This paper presents parametric model order reduction (pMOR) by the Lagrange approach of matrix interpolation for the thermal-mechanical and reliability study of a power electronics module (PEM) with nonlinear behaviours. Most pre-vious research in model order reduction (MOR) studies reports thermal-mechanical simulations using a sequentially coupled method. In this research, a direct-coupled thermal-mechanical analysis, which simultaneously solves the thermal and structural governing equations, has been used to obtain thermal and defor-mation results. Furthermore, for pMOR, the linear approach of matrix interpolation is limited to linear changes between sampled-parametric points. Hence, a new way of interpolating system matrices using the Lagrange interpolation method has been adopted to implement the matrix interpolation efficiently. The parametric reduced-order model (pROM) solution by the Lagrange approach of matrix interpolation agrees well with the full-order model (FOM) and takes similar computational time as the linear (bi-linear) approach of matrix interpolation. pROM simulations offer up to 85.5 % reduction in computational time

小型衛星搭載の合成開口レーダー用の集中型送受信システムを有する２偏波対応進行波型アンテナ

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 齋藤 宏文, 東京大学教授 橋本 樹明, 東京大学教授 保立 和夫, 東京電機大学教授 小林 岳彦, 東京工業大学教授 廣川 二郎University of Tokyo(東京大学

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system