Application of Genetic Algorithms in Nonlinear Heat Conduction Problems

Genetic algorithms are employed to optimize dimensionless temperature in nonlinear heat conduction problems. Three common geometries are selected for the analysis and the concept of minimum entropy generation is used to determine the optimum temperatures under the same constraints. The thermal conductivity is assumed to vary linearly with temperature while internal heat generation is assumed to be uniform. The dimensionless governing equations are obtained for each selected geometry and the dimensionless temperature distributions are obtained using MATLAB. It is observed that GA gives the minimum dimensionless temperature in each selected geometry

Constraint-Aware, Scalable, and Efficient Algorithms for Multi-Chip Power Module Layout Optimization

Moving towards an electrified world requires ultra high-density power converters. Electric vehicles, electrified aerospace, data centers, etc. are just a few fields among wide application areas of power electronic systems, where high-density power converters are essential. As a critical part of these power converters, power semiconductor modules and their layout optimization has been identified as a crucial step in achieving the maximum performance and density for wide bandgap technologies (i.e., GaN and SiC). New packaging technologies are also introduced to produce reliable and efficient multichip power module (MCPM) designs to push the current limits. The complexity of the emerging MCPM layouts is surpassing the capability of a manual, iterative design process to produce an optimum design with agile development requirements. An electronic design automation tool called PowerSynth has been introduced with ongoing research toward enhanced capabilities to speed up the optimized MCPM layout design process. This dissertation presents the PowerSynth progression timeline with the methodology updates and corresponding critical results compared to v1.1. The first released version (v1.1) of PowerSynth demonstrated the benefits of layout abstraction, and reduced-order modeling techniques to perform rapid optimization of the MCPM module compared to the traditional, manual, and iterative design approach. However, that version is limited by several key factors: layout representation technique, layout generation algorithms, iterative design-rule-checking (DRC), optimization algorithm candidates, etc. To address these limitations, and enhance PowerSynth’s capabilities, constraint-aware, scalable, and efficient algorithms have been developed and implemented. PowerSynth layout engine has evolved from v1.3 to v2.0 throughout the last five years to incorporate the algorithm updates and generate all 2D/2.5D/3D Manhattan layout solutions. These fundamental changes in the layout generation methodology have also called for updates in the performance modeling techniques and enabled exploring different optimization algorithms. The latest PowerSynth 2 architecture has been implemented to enable electro-thermo-mechanical and reliability optimization on 2D/2.5D/3D MCPM layouts, and set up a path toward cabinet-level optimization. PowerSynth v2.0 computer-aided design (CAD) flow has been hardware-validated through manufacturing and testing of an optimized novel 3D MCPM layout. The flow has shown significant speedup compared to the manual design flow with a comparable optimization result

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Software for evaluating probability-based integrity of reinforced concrete structures

In recent years, much research work has been carried out in order to obtain a more controlled durability and long-term performance of concrete structures in chloride containing environment. In particular, the development of new procedures for probability-based durability design has proved to give a more realistic basis for the analysis. Although there is still a lack of relevant data, this approach has been successfully applied to several new concrete structures, where requirements to a more controlled durability and service life have been specified. A probability-based durability analysis has also become an important and integral part of condition assessment of existing concrete structures in chloride containing environment. In order to facilitate the probability-based durability analysis, a software named DURACON has been developed, where the probabilistic approach is based on a Monte Carlo simulation. In the present paper, the software for the probability-based durability analysis is briefly described and used in order to demonstrate the importance of the various durability parameters affecting the durability of concrete structures in chloride containing environment

Multivariable optimization of pyramidal compound substrates for cooling of power-electronics in modern hybrid and electric propulsion systems

We present a method for the optimization of the thermal cooling of heat sources mounted on top of layered composites and pyramidal substrates, that are widely used in the power electronics of hybrid-electric propulsion systems. The analytical solution of the Laplace's heat equation is approximated via Fourier expansion series and it is coupled to the Influence Coefficient Method (ICM) to provide a functional of the overall thermal stress to minimize. A multivariable optimization method is derived by coupling the equations for the heat transfer with the Sequential Least-Square Quadratic Programming (SLSQP), or the Bounded Limited-Memory BFGS (L-BFGS-B) algorithm. Code validation is performed against three-dimensional simulations and experimental data available from the literature. It is shown that an optimal component relocation and apportionment of the overall thickness of the multilayer substrate promotes a sensible reduction of the thermal stress

In-Mold Assembly of Multi-Functional Structures

Combining the recent advances in injection moldable polymer composites with the multi-material molding techniques enable fabrication of multi-functional structures to serve multiple functions (e.g., carry load, support motion, dissipate heat, store energy). Current in-mold assembly methods, however, cannot be simply scaled to create structures with miniature features, as the process conditions and the assembly failure modes change with the feature size. This dissertation identifies and addresses the issues associated with the in-mold assembly of multi-functional structures with miniature components. First, the functional capability of embedding actuators is developed. As a part of this effort, computational modeling methods are developed to assess the functionality of the structure with respect to the material properties, process parameters and the heat source. Using these models, the effective material thermal conductivity required to dissipate the heat generated by the embedded small scale actuator is identified. Also, the influence of the fiber orientation on the heat dissipation performance is characterized. Finally, models for integrated product and process design are presented to ensure the miniature actuator survivability during embedding process. The second functional capability developed as a part of this dissertation is the in-mold assembly of multi-material structures capable of motion and load transfer, such as mechanisms with compliant hinges. The necessary hinge and link design features are identified. The shapes and orientations of these features are analyzed with respect to their functionality, mutual dependencies, and the process cost. The parametric model of the interface design is developed. This model is used to minimize both the final assembly weight and the mold complexity as the process cost measure. Also, to minimize the manufacturing waste and the risk of assembly failure due to unbalanced mold filling, the design optimization of runner systems used in multi-cavity molds for in-mold assembly is developed. The complete optimization model is characterized and formulated. The best method to solve the runner optimization problem is identified. To demonstrate the applicability of the tools developed in this dissertation towards the miniaturization of robotic devices, a case study of a novel miniature air vehicle drive mechanism is presented

NASA Tech Briefs, July 2007

Topics covered include: Miniature Intelligent Sensor Module; "Smart" Sensor Module; Portable Apparatus for Electrochemical Sensing of Ethylene; Increasing Linear Dynamic Range of a CMOS Image Sensor; Flight Qualified Micro Sun Sensor; Norbornene-Based Polymer Electrolytes for Lithium Cells; Making Single-Source Precursors of Ternary Semiconductors; Water-Free Proton-Conducting Membranes for Fuel Cells; Mo/Ti Diffusion Bonding for Making Thermoelectric Devices; Photodetectors on Coronagraph Mask for Pointing Control; High-Energy-Density, Low-Temperature Li/CFx Primary Cells; G4-FETs as Universal and Programmable Logic Gates; Fabrication of Buried Nanochannels From Nanowire Patterns; Diamond Smoothing Tools; Infrared Imaging System for Studying Brain Function; Rarefying Spectra of Whispering-Gallery-Mode Resonators; Large-Area Permanent-Magnet ECR Plasma Source; Slot-Antenna/Permanent-Magnet Device for Generating Plasma; Fiber-Optic Strain Gauge With High Resolution And Update Rate; Broadband Achromatic Telecentric Lens; Temperature-Corrected Model of Turbulence in Hot Jet Flows; Enhanced Elliptic Grid Generation; Automated Knowledge Discovery From Simulators; Electro-Optical Modulator Bias Control Using Bipolar Pulses; Generative Representations for Automated Design of Robots; Mars-Approach Navigation Using In Situ Orbiters; Efficient Optimization of Low-Thrust Spacecraft Trajectories; Cylindrical Asymmetrical Capacitors for Use in Outer Space; Protecting Against Faults in JPL Spacecraft; Algorithm Optimally Allocates Actuation of a Spacecraft; and Radar Interferometer for Topographic Mapping of Glaciers and Ice Sheets

A study of III-V/HfO2 interfaces and silicon structure optimization

Computational materials physics is a field where we try to understand the phenomena related to materials all around us. However we can only model a small sections portion of these materials in our computations, quantum mechanical or classical. In quantum mechanical calculations we are often limited to systems that are sub thousand atoms and classical simulations can reach millions atoms, which is still no where near the ballpark of 1020 atoms that will be present in a gram of any material. The III-V semiconductors are interesting topic due to their potential in device applications [1; 2]. In this research the main issue we have studied is the interface of III-Vs and HfO2. We have been interested in the structure, electrical properties and characterization of this system. The sub issue of this is a more technical issue that comes with studying these systems: finding the energy minimum structure of any atomic system. We have approached the main issue through quantum mechanical inspection of the system done through the VASP software on different constructed models. The structure optimization was worked on classically with an algorithm built on Genetic Hybrid Algorithm(GHA) and LAMMPS. For the first issue, we started with the few existing models and studied them. We then created our models with several different crystal forms of HfO2 and created multiple interface models. Then we studied the effects of different configurations and defects in these models. We found out that the dangling bonds were one of causes for the unwanted defect states in many of the models. We also explored the relative stability of the different models we presented by comparing to each others. In the second paper we continue working on these models and creating new models with native oxides involved. However this time the focus is on providing reference values for interpreting the X-ray photoelectron spectroscopy data from the experiments. The optimization branch is a more exploratory research line about our approach on optimizing the structure with an algorithm built on the GHA-platform. The first silicon bulk research especially is more of a test case for the used GHA platform and its compatibility with the task. With the silicon dioxide research we dwell more into the different ways we can try to aid the algorithm and what are the pitfalls during the optimization

Selective Resistive Sintering: A Novel Additive Manufacturing Process

Selective laser sintering (SLS) is one of the most popular 3D printing methods that uses a laser to pattern energy and selectively sinter powder particles to build 3D geometries. However, this printing method is plagued by slow printing speeds, high power consumption, difficulty to scale, and high overhead expense. In this research, a new 3D printing method is proposed to overcome these limitations of SLS. Instead of using a laser to pattern energy, this new method, termed selective resistive sintering (SRS), uses an array of microheaters to pattern heat for selectively sintering materials. Using microheaters offers significant power savings, significantly reduced overhead cost, and increased printing speed scalability. The objective of this thesis is to obtain a proof of concept of this new method. To achieve this objective, we first designed a microheater to operate at temperatures of 600⁰C, with a thermal response time of ~1 ms, and even heat distribution. A packaging device with electrical interconnects was also designed, fabricated, and assembled with necessary electrical components. Finally, a z-stage was designed to control the airgap between the printhead and the powder particles. The whole system was tested using two different scenarios. Simulations were also conducted to determine the feasibility of the printing method. We were able to successfully operate the fabricated microheater array at a power consumption of 1.1W providing significant power savings over lasers. Experimental proof of concept was unsuccessful due to the lack of precise control of the experimental conditions, but simulation results suggested that selectivity sintering nanoparticles with the microheater array was a viable process. Based on our current results that the microheater can be operated at ~1ms timescale to sinter powder particles, it is believed this new process can potentially be significantly quicker than selective laser sintering by increasing the number of microheater elements in the array. The low cost of a microheater array printhead will also make this new process affordable. This thesis presented a pioneering study on the feasibility of the proposed SRS process, which could potentially enable the development of a much more affordable and efficient alternative to SLS