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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,171 records · Page 65

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DOE OSTI2020

Parametric Design Study of a Power Electronics Package for Improving Solder Joint Reliability

The lifetime of a power electronics package is, to a large extent, determined by the reliability of its bonded interfaces under major loading conditions in an operational environment. Based on the application-level requirements of the package, bonded interface material is selected based on the results obtained from accelerated tests such as thermal cycling and power cycling. In addition to evaluating the reliability through accelerated tests, it is important to consider the impact of other component layers on the thermomechanical performance of the interface material, both from a material and geometric perspective. The co-efficient of thermal expansion (CTE) mismatch introduced by the use of different materials within a package and its structural design plays a critical role in determining the interface material reliability. In this paper, we present the results of a parametric modeling study of a power electronics package under thermal cycling in which the materials and geometric design of the different component layers were varied with respect to a baseline design to understand their impact on the reliability of the interface material. We chose the volume-averaged strain energy density per cycle computed at the corner region of the interface material as the metric for the reliability comparisons. Our results indicate that in addition to the CTE mismatch, the stiffness of individual component layers has a major impact on reliability. Among the different baseplates that we studied, aluminum silicon-carbide baseplates offered superior reliability over their copper and aluminum counterparts. We also found that the magnitude of the impact of stiffness variation—amongst the adjacent layers—on the reliability of the solder joint is dependent on the inherent CTE mismatch between the layers.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

STEPS: A Portable Numerical Simulation Toolkit for Electrical Power System Dynamic Studies

Numerical simulation is the key technique for large scale power system analysis. Redistribution of global renewable power via international interconnections requires new simulation tools to study the interconnected systems with different nominal frequencies as a whole. In this paper we introduce an open source simulation toolkit for electrical power systems (STEPS) which is hosted at Github. Its kernel is coded in C++ with major functions of power flow and electro-mechanical dynamic simulation. Flexible options are provided and configurable to improve power flow solution and dynamic simulation. Common devices and models are supported in STEPS for AC/DC hybrid system studies. Studies of interconnected systems with different nominal frequencies is supported in STEPS for research of international interconnection. Application program interfaces are provided and wrapped with Python to enable high-level interfaces for general applications. STEPS is thread safe and parallel computation is supported in both kernel and script levels to accelerate simulation. It is portable and works on Windows and GNU/Linux platforms. Cases from small to large scale systems are thoroughly tested to validate the toolkit with commercial packages as benchmarks.

42 ENGINEERING↗

DOE OSTI2022

ASAP: Automatic Synthesis of Area-Efficient and Precision-Aware CGRAs

Coarse-grained reconfigurable accelerators (CGRAs) are a promising accelerator design choice that strikes a balance between performance and adaptability to different computing patterns across various applications domains. Designing a CGRA for a specific application domain involves enormous software/hardware engineering effort. Recent research works explore loop transformations, functional unit types, network topology, and memory size to identify optimal CGRA designs given a set of kernels from a specific application do- main. Unfortunately, the impact of functional units with different precision support has rarely been investigated. To address this gap, we propose ASAP – a hardware/software co-design framework that automatically identifies and synthesizes optimal precision-aware CGRA for a set of applications of interest. Our evaluation shows that ASAP generates specialized designs 3.2×, 4.21×, and 5.8× more efficient (in terms of performance per unit of energy or area) than non-specialized homogeneous CGRAs, for the scientific computing, embedded, and edge machine learning domains, respectively, with limited accuracy loss. Moreover, ASAP provides more efficient designs than other state-of-the-art synthesis frameworks for specialized CGRAs.

artificial intelligence↗

DOE OSTI2023

Revisiting Temporal Blocking Stencil Optimizations

Iterative stencils are used widely across the spectrum of High Performance Computing (HPC) applications. Many efforts have been put into optimizing stencil GPU kernels, given the prevalence of GPU-accelerated supercomputers. To improve the data locality, temporal blocking is an optimization that combines a batch of time steps to process them together. Under the observation that GPUs are evolving to resemble CPUs in some aspects, we revisit temporal blocking optimizations for GPUs. We explore how temporal blocking schemes can be adapted to the new features in the recent Nvidia GPUs, including large scratchpad memory, hardware prefetching, and device-wide synchronization. We propose a novel temporal blocking method, EBISU, which champions low device occupancy to drive aggressive deep temporal blocking on large tiles that are executed tile-by-tile. We compare EBISU with state-of-the-art temporal blocking libraries: STENCILGEN and AN5D. We also compare with state-of-the-art stencil auto-tuning tools that are equipped with temporal blocking optimizations: ARTEMIS and DRSTENCIL. Over a wide range of stencil benchmarks, EBISU achieves speedups up to 2.53x and a geometric mean speedup of 1.49x over the best state-of-the-art performance in each stencil benchmark.

Zhang, Lingqi↗

DOE OSTI2023

Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput

Undoubtedly, quantum computing offers valuable acceleration for solving intricate problems. One of the primary hurdles lies in executing large-scale quantum applications on backend machines. Qubit noise, among other factors, dramatically influences the execution process. Implementing effective scheduling techniques for quantum circuits is crucial for practical quantum computing and preventing excessive waiting times. The quantum realm is distinct from classical computing in terms of optimization, performance, utilization, and waiting periods. Consequently, the parameters and components of quantum circuit scheduling diverge from those of classical computing. This paper presents Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput, a straightforward yet effective scheduling framework and policy that enhances noise resilience, throughput, and the fidelity of quantum circuits. Drawing inspiration from classical methods, our scheduling approach incorporates additional constraints tailored for quantum logic. The outcome demonstrates a substantial improvement in fidelity and resource management, which is vital for real-world quantum applications.

Baheri, Betis↗

DOE OSTI2020

HERON

HERON is a modeling toolset and plugin for RAVEN to accelerate stochastic technoeconomic assessment of the economic viability of various grid-energy system configurations, especially with application to electrical grids and integrated energy systems (IES). The frameworks enabling this toolset are INL's RAVEN and its TEAL plugin. The toolset includes automated RAVEN workflow generation tools for performing stochastic analysis and optimization driven by technoeconomic factors. The workflows generated employ RAVEN for parametric or optimizing study of grid component characteristics, and again for stochastic sampling of regulated and deregulated markets, load, and weather histories. The toolset further provides methods for economically-optimal dispatch of system components given the stochastic samples of market, load, and weather histories. The toolset allows assembling of existing and new models through the RAVEN interface to the Modelica process modeling language.

Talbot, PaulW↗

DOE OSTI2024

Integrating ytopt and libEnsemble to autotune OpenMC

Ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that consists of sampling a small number of input parameter configurations and progressively fitting a surrogate model over the input-output space until exhausting the user-defined maximum number of evaluations or the wall-clock time. libEnsemble is a Python toolkit for coordinating workflows of asynchronous and dynamic ensembles of calculations across massively parallel resources developed within the ECP PETSc/TAO project. libEnsemble helps users take advantage of massively parallel resources to solve design, decision, and inference problems and expands the class of problems that can benefit from increased parallelism. In this paper we present our methodology and framework to integrate ytopt and libEnsemble to take advantage of massively parallel resources to accelerate the autotuning process. Specifically, we focus on using the proposed framework to autotune the ECP ExaSMR application OpenMC, an open source Monte Carlo particle transport code. OpenMC has seven tunable parameters some of which have large ranges such as the number of particles in-flight, which is in the range of 100,000 to 8 million, with its default setting of 1 million. Setting the proper combination of these parameter values to achieve the best performance is extremely time-consuming. Therefore, we apply the proposed framework to autotune the MPI/OpenMP offload version of OpenMC based on a user-defined metric such as the figure of merit (FoM) (particles/s) or energy efficiency energy-delay product (EDP) on Crusher at Oak Ridge Leadership Computing Facility. In conclusion, the experimental results show that we achieve the improvement up to 29.49% in FoM and up to 30.44% in EDP.

Autotuning↗

DOE OSTI2020

Integration of Kokkos into MonteRay [Slides]

Monte Carlo Neutron Transport codes are C++-based and simulate interaction of nuclear particles with materials. MonteRay is a library for accelerating Monte Carlo ray-casting tallies with GPUs. Kokkos is commonly used to write performance portable applications for HPC platforms. A goal is to implement Kokkos into the Expected Path Length file that uses Cuda as a backend. The methodology and results are summarized.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Advanced Module Architecture for Reduced Costs, High Durability and Significantly Improved Manufacturability (Final Report)

This project demonstrated a new module architecture (referred to herein as Glass/Glass) using silicone edge seals and an interlayer polymer that reduces manufacturing costs, significantly streamlines manufacturing processes, and reduces cap-ex costs while improving module reliability. A prototype manufacturing process to fabricate this new module architecture was demonstrated with a significantly improved process cycle time for the lamination step from the current industry standard of 13.5 minutes to approximately 30 seconds for each of the individual edge seal and internal polymer application steps. Samples are fabricated, strenuously stressed, and characterized to quantify the cost benefits, hardware capacities and accelerated stress performance of the new architecture. These results are being compared to a standard, laminated baseline architecture using a Glass/EVA/Glass or Glass/Thermoplastic/Glass design that traditionally has high moisture vapor transmission rates (MVTR).

14 SOLAR ENERGY↗

DOE OSTI2022

White Paper on Leading-Edge technology And Feasibility-directed (LEAF) Program aimed at readiness demonstration for Energy Frontier Circular Colliders (pp, μμ) by the next decade

In this White Paper for the Snowmass 2021 Process, we propose the establishment of a magnet Leading-Edge technology And Feasibility-directed Program (LEAF Program) to achieve readiness for a future collider decision on the timescale of the next decade. The LEAF Program would rely on, and be synergetic with, generic R&D efforts presently covered - in the US - by the Magnet Development Program (MDP), the Conductor Procurement and R&D (CPRD) Program and other activities in the Office of HEP supported by Early Career Awards (ECA) or Lab Directed R&D (LDRD) funds. Where possible, ties to synergetic efforts in other Offices of DOE or NSF are highlighted and suggested as wider Collaborative efforts on the National scale. International efforts are also mentioned as potential partners in the LEAF Program. We envision the LEAF Program to concentrate on demonstrating the feasibility of magnets for muon colliders as well as next generation high energy hadron colliders, pursuing, where necessary and warranted by the nature of the application, the transition from R&D models to long models/prototypes. The LEAF Program will naturally drive accelerator-quality and experiment-interface design considerations. LEAF will also concentrate, where necessary, on cost reduction and/or industrialization steps.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Evaluation of the Reliability of Passive Infrared (PIR) Occupancy Sensors for Residential Indoor Lighting

Solid-state lighting (SSL) technologies have penetrated the general illumination market in recent years, largely replacing conventional technologies such as incandescent and fluorescent lighting. Most of the initial excitement about light-emitting diode (LED) sources for SSL devices focused on their energy savings potential resulting from vast improvements in source efficiency and luminous efficacy compared with conventional illumination products. More recently, the focus has shifted toward other aspects of lighting application efficiency, namely intensity effectiveness and spectral efficiency, because of the ease of controlling both the light intensity of LEDs with drive voltage and the color properties of the LED-based illuminators. To capitalize on the energy savings of increased intensity effectiveness and spectral efficiency, a lighting control system (LCS) is often used. While the current penetration of LCSs is relatively modest, it is anticipated that lighting controls (i.e., connected lighting, controls and LED and conventional lighting) could have an installed penetration as higher as 46% by the year 2035, saving an additional 1.3 quads of energy. Despite the large energy savings that can be gained from using LCSs, standard test methods for evaluating sensors employed in LCSs and reliability data of the LCSs and their components are generally lacking. The National Electrical Manufacturers Association (NEMA) developed the only standard, NEMA WD 7-2011 (R2016), to test occupancy and motion sensor performance (herein referred to as “the NEMA protocol”). In a previous U.S. Department of Energy (DOE) effort, some concerns of the NEMA protocol were identified (e.g., strict height and weight limits on test subjects, large amounts of manual effort, sometimes inconsistent repeatability). During the current work, a consistent detection length test (DLT) and a Robotic Sensor Evaluation System (RoboSES) were developed to alleviate some of these concerns. RoboSES acts as a human surrogate by using thermal pads on a mannequin and a remote-controlled mobile base to test a sensor’s field of view (FOV). This report builds on the earlier DOE efforts to understand sensor technologies used in LCSs for general illumination. Specifically, this report describes the optimization of RoboSES, characterizes and establishes test methods to assess the reliability of multiple passive infrared (PIR) sensors, and reports the findings of robustness and reliability testing on two commercial PIR sensors intended for residential applications. The information presented in this report is gained from up to 4,000 hours (hrs) of accelerated stress tests (ASTs).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Comparative study of machine learning techniques for post-combustion carbon capture systems

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Parametric Design Study of a Power Electronics Package

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Dual Channel Dual Staging: Hierarchical and Portable Staging for GPU-Based In-Situ Workflow

In-situ workflows have emerged as an attractive approach for addressing data movement challenges at very large scales. Since GPU-based architectures dominate the HPC landscapes, porting these in-situ workflows, and, specifically, the inter-application data exchange, to GPU-based systems can be challenging. Technologies such as GPUDirect RDMA (GDR), which is typically used for I/O in GPU applications as an optimization that circumvents the CPU overhead, can be leveraged to support bulk data exchanges between GPU applications. However, current GDR design often lacks performance portability across HPC clusters built with different hardware configurations. Furthermore, the local CPU may also be effectively used as an auxiliary communication mechanism to offload data exchanges. In this paper, we present a dual channel dual staging approach for efficient, scalable, and performance-portable inter-application data exchange for in-situ workflows. This approach exploits the data access pattern within in-situ workflows along with the inherent execution asynchrony to accelerate data exchanges and, at the same time, improve performance portability. Specifically, the dual channel dual staging method leverages both the local CPU and the remote data staging server to build a hierarchical joint staging area and uses this staging area to transform blocking inter-application bulk data exchanges into best-effort local data movements between GPU and CPU. The dual channel dual staging is implemented as a portability extension of the Dataspaces-GPU staging framework. We present an experimental evaluation of its performance, portability, and scalability using this implementation on three leadership GPU clusters. The evaluation results demonstrate that the dual channel dual staging method saves up to 75% in data-exchange time compared to host-based, GDR, and alternate portable designs, while maintaining scalability (up to 512 GPUs) and performance portability across the three platforms.

Zhang, Bo [University of Utah]↗

DOE OSTI2024

State of the art in low-temperature and high-temperature electrolysis

Water electrolysis is gaining traction in large-scale applications, with production of multiple technologies scaling to hundreds and thousands of megawatts of new electrolyzer capacity annually. Low-temperature electrolysis has dominated the electrolyzer market for decades, but still only represents a small amount of the overall hydrogen market, due to the higher production costs versus hydrogen derived from fossil fuels. Advances are needed in capital cost and efficiency to close the cost gap, especially for energy applications. Similarly, while high-temperature electrolyzers can operate more efficiently, reducing the operating cost, they still need further scale-up and cost reduction to compete in these markets. Understanding the recent advances in each and the priority research directions is important to focus and accelerate innovation, and will be discussed in this article. The different advantages and disadvantages of each of these technologies will also be reviewed; there will likely be applications for each in the overall deployment of renewable hydrogen.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Investigation of boosted proton energies through proton radiography of target normal sheath acceleration fields in the multi-ps regime

Multi-kilojoule, multi-picosecond short-pulse lasers, such as the National Ignition Facility-Advanced Radiographic Capability laser and the OMEGA-Extended Performance laser, which have been constructed over the last two decades, enable exciting opportunities to produce high-brightness, high-energy laser-driven proton sources for applications in high-energy-density science like proton fast ignition for inertial fusion energy, particle radiography, and materials science studies. Results on these platforms have demonstrated enhanced accelerated proton energies and electron temperatures when compared to established scaling laws. Recent work has developed a new scaling for proton TNSA in the multi-ps regime. However, this new physics in the multi-ps regime motivates the need to understand the origin of the enhancement in proton energies. Toward this goal, here this work presents the first measurements of the TNSA accelerating sheath field in the multi-ps regime for pulse durations of 0.6, 5, and 10 ps. This measurement was achieved by using a separate TNSA proton source to radiograph the spatiotemporal profile of the accelerating sheath that is responsible for proton acceleration. The use of stacked radiochromic film detectors allows for a discrete time profile of the radiographs, thus enabling the measurement of the temporal and spatial evolution of the accelerating field. In performing this measurement, we extract quantities such as the sheath strength as a function of time and pulse duration, which shows that longer pulse durations sustain a stronger electric field for a longer duration when compared to sub-ps laser pulses, which may enable the observed boosted proton energies and proton conversion efficiencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Investigation of resistive magnetic field generation by intense proton beams in dense plasmas

Current and future applications of intense proton sources abound, including radiography, cancer therapy, warm dense matter generation, and inertial confinement fusion. With increasingly efficient acceleration and focusing mechanisms, proton current densities may soon approach and exceed 10 10 A/cm 2 , e.g., via intense laser drivers. Simulations have previously shown that in this current density regime, beam-induced field generation plays a significant role in beam transport through dense plasmas. Here, we present a theoretical model for the generation of resistive magnetic fields by intense proton beam transport through solid density plasmas. The theoretical evolution of the magnetic field profile is calculated using an analytic model for aluminum resistivity, heat capacity, and stopping power, applicable from cold matter to hot plasma. The effects of various beam and material parameters on the field are investigated and explained for both monoenergetic and Maxwellian proton beams. For a proton beam with Maxwellian temperature 5 MeV and total energy 10 J, the model calculates resistive magnetic fields up to 150 T in aluminum. In conclusion, the calculated field profiles from several beam cases are compared with 2D hybrid particle-in-cell simulations, with good agreement found in magnitude and time scale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Language model-accelerated deep symbolic optimization

Symbolic optimization methods have been used to solve varied challenging and relevant problems such as symbolic regression and neural architecture search. However, the current state of the art typically learns each problem from scratch and is unable to leverage pre-existing knowledge and datasets that are available for many applications. Here, inspired by the similarity between sequence representations learned in natural language processing and the formulation of symbolic optimization as a discrete sequence optimization problem, we propose language model-accelerated deep symbolic optimization (LA-DSO), a method that leverages language models to learn symbolic optimization solutions more efficiently. We demonstrate LA-DSO in two tasks: symbolic regression, which allows us to perform extensive experimentation due to its low computation requirements, and computational antibody optimization, which shows that our proposal accelerates learning in challenging real-world problems.

97 MATHEMATICS AND COMPUTING↗