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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

In-Depth Optimization with the OpenACC-to-FPGA Framework on an Arria 10 FPGA

The reconfigurable computing paradigm that uses field programmable gate arrays (FPGAs) has received renewed interest in the high-performance computing field due to FPGAs' unique combination of performance and energy efficiency. However, difficulties in programming and optimizing FPGAs have prevented them from being widely accepted as general-purpose computing devices. In accelerator-based heterogeneous computing, portability across diverse heterogeneous devices is also an important issue, but the unique architectural features in FPGAs make this difficult to achieve. To address these issues, a directive-based, high-level FPGA programming and optimization framework was previously developed. In this work, developed optimizations were combined holistically using the directive-based approach to show that each individual benchmark requires a unique set of optimizations to maximize performance. The relationships between FPGA resource usages and runtime performance were also explored.

Lambert, Jacob↗
DOE OSTI2022

E-transit-bench: simulation platform for analyzing electric public transit bus fleet operations

When electrified transit systems make grid aware choices, improved social welfare is achieved by reducing grid stress, reducing system loss, and minimizing power quality issues. Electrifying transit fleet has numerous challenges like non availability of buses during charging, varying charging costs and so on, that are related the electric grid behavior. However, transit systems do not have access to the information about the co-evolution of the grid's power flow and therefore cannot account for the power grid's needs in its day-to-day operation. In this paper we propose a framework of transportation-grid co-simulation, analyzing the spatio-temporal interaction between the transit operations with electric buses and the power distribution grid. Real-world data for a day's traffic from Chattanooga city's transit system is simulated in SUMO and integrated with a realistic distribution grid simulation (using GridLAB-D) to understand the grid impact due to transit electrification. Charging information is obtained from the transportation simulation to feed into grid simulation to assess the impact of charging. We also discuss the impact to the grid with higher degree of transit electrification that further necessitates such an integrated transportation-grid co-simulation to operate the integrated system optimally. Our future work includes extending the platform for optimizing the charging and trip assignment operations.

Sen, Rishav↗
DOE OSTI2024

Optimization of Fairing Geometry for ORPC Modular RivGen Power System: Cooperative Research and Development (Final Report)

The work will optimize the hydrodynamic performance and flow augmentation of Ocean Renewable Power Company's (ORPC) modular fairing for their cross-flow marine hydrokinetic (MHK) turbine, using a computational fluid dynamics study. The influence of the fairing cross-sectional shape and rotor-fairing spacing will be assessed, to maximize power production and minimize structural loads.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗
DOE OSTI2022

Vertex finding in neutrino-nucleus interaction: a model architecture comparison

We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

DeepOpt

DeepOpt is a simple and easy-to-use library for performing Bayesian optimization, leveraging the powerful capabilities of BoTorch. Its key feature is the ability to use neural networks as surrogate functions during the optimization process, allowing Bayesian optimization to work smoothly even on large datasets and in many dimensions. DeepOpt also provides simplified wrappers for BoTorch fitting and optimization routines.

Kur, Eugene↗
DOE OSTI2020

Design Paradigm for Modular Multilevel Converter-Based Generator Rectifier Systems

Modular Multilevel Converters (MMC) are being widely considered for medium to high voltage DC applications. Designing such converters through multi-objective optimization is of interest because such an approach allows the trade-off between competing objectives (for example mass and loss) to be explicitly and quantitatively identified. In this work, an optimization based design paradigm for MMC based generator rectifier systems is proposed. Such development typically requires detailed component design and simulation models for the electric machine and converter which are computationally expensive. As an alternative, the proposed work utilizes an electric machine metamodel, inductor metamodel, and high-speed steady-state simulation model for the MMC to facilitate multi-objective optimization minimizing system metrics of interest while satisfying system constraints. A case study is undertaken to demonstrate the functionality of the proposed design paradigm.

42 ENGINEERING↗
DOE OSTI2021

Multi-objective optimization of low-GWP mixture composition and heat exchanger circuitry configuration for improved system performance and reduced refrigerant flammability

The recently introduced hydrofluoroolefin (HFO) refrigerants, including R1234yf and R1234ze(E), have significantly lower global warming potentials (GWPs) than traditional hydrofluorocarbon (HFC) refrigerants like R410A. However, prior tests show that direct drop-in of pure R1234yf or R1234ze(E) into equipment designed for R410A results in a decrease in heat exchanger capacity and the system coefficient of performance. The primary reason is the lower in-tube heat transfer performance of R1234yf and R1234ze(E) compared with that of R410A. To address this issue, previous studies have mixed the mildly flammable HFC R32 with HFOs to improve system performance, with HFC R125 also added to suppress flammability. Previous studies selected compositions based on simple cycle analyses and did not consider modifications of the heat exchanger circuitry configuration to adapt to the new refrigerants. This study presents a novel multi-objective optimization approach to design a refrigerant composition that maximizes energy efficiency within flammability and GWP limits. The approach in this work simultaneously optimizes mixture composition and heat exchanger circuitry configuration. A case study on a rooftop unit indicates that, compared with mixture-only optimization, simultaneous optimization of mixture and heat exchanger circuitry yields a 5.9% improvement in cycle efficiency and a 48.6% reduction in refrigerant flammability with a GWP of 268. Circuitry optimization using refrigerants with different temperature glides shows that the larger the temperature glide is, the larger EER improvement is obtained. The results show that zeotropic blends with a large temperature glide are more sensitive to the refrigerant circuitry than pure refrigerants and may suffer significant performance degradation with subpar heat exchanger circuitry design. The proposed optimization approach is generally applicable to mixtures with any number of components. Using this approach to design a HVAC system can yield higher system efficiency within flammability and GWP constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Optimization of Refrigerant Compositions for Low-GWP Refrigerant Mixtures Using Segment-by-segment Heat Exchanger and Detailed System Models

The recently introduced hydrofluoroolefin (HFO) refrigerants, including R1234yf and R1234ze(E), have significantly lower global warming potentials (GWPs) than traditional hydrofluorocarbon (HFC) refrigerants like R410A. However, prior tests show that direct drop-in of pure R1234yf or R1234ze(E) into equipment designed for R410A results in a decrease in heat exchanger capacity and the system coefficient of performance. The primary reason is the lower in-tube heat transfer performance of R1234yf and R1234ze(E) compared with that of R410A. To address this issue, previous studies have mixed the mildly flammable HFC R32 with HFOs to improve system performance, with HFC R125 also added to suppress flammability. Previous studies selected compositions based on simple cycle analyses and did not consider modifications of the heat exchanger circuitry configuration to adapt to the new refrigerants. This study presents a novel multi-objective optimization approach to design a refrigerant composition that maximizes energy efficiency within flammability and GWP limits. The approach in this work simultaneously optimizes mixture composition and heat exchanger circuitry configuration. A case study on a rooftop unit indicates that, compared with mixture-only optimization, simultaneous optimization of mixture and heat exchanger circuitry yields a 5.9% improvement in cycle efficiency and a 48.6% reduction in refrigerant flammability with a GWP of 268. Circuitry optimization using refrigerants with different temperature glides shows that the larger the temperature glide is, the larger EER improvement is obtained. The results show that zeotropic blends with a large temperature glide are more sensitive to the refrigerant circuitry than pure refrigerants and may suffer significant performance degradation with subpar heat exchanger circuitry design. The proposed optimization approach is generally applicable to mixtures with any number of components. Using this approach to design a HVAC system can yield higher system efficiency within flammability and GWP constraints.

Li, Zhenning↗
DOE OSTI2020

Accelerator physics at NSLS-II: research accomplishments in 2020

NSLS-II accelerator physicists provide scientific support for user operations, as well as for improvements and developments of the accelerator systems. This work includes optimization of the NSLS-II performance with high beam current and low vertical emittance, improvement of the beam stability, development of new software tools for operations and beam studies. The accelerator physicists productively worked on a number of research activities such as the conceptual design of NSLS-II accelerator upgrade; design and optimization of advanced low-emittance lattices; development of novel methods to study nonlinear beam dynamics; ultrafast electron diffraction and microscopy. The physicists also contributed to the BNL electron-ion collider project, to the APS and ALS upgrade projects. Research accomplishments achieved in 2020, are summarized in this report.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Port and optimize the CEED software stack to Aurora/Frontier EA (ECP Milestone Report)

The goal of this milestone was to port the CEED software stack, including Nek, MFEM and libCEED to the Frontier and Aurora early access hardware, work on optimizing the performance on AMD and Intel GPUs, and demonstrate impact in CEED-enabled ECP applications. As part of this milestone, we also performed a number of other activities, including continued optimizations for CEED applications at scale on Summit, performance evaluation on non-ECP hardware, including the Fugaku’s A64FX chip and the NVIDIA A100 GPU architecture, exploring the use of just-in-time compilations in applications at scale and potential of mixed precision optimizations in the CEED discretization and solver algorithms, and more. During the milestone period, the CEED team released new versions of 6 of its packages, including major releases of MFEM, NekRS and OCCA. We also organized the fifth CEED Annual meeting (CEED5AM) which included nearly 100 researchers from national labs, universities and industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Data-driven analysis to understand GPU hardware resource usage of optimizations

With heterogeneous systems, the number of GPUs per chip increases to provide computational capabilities for solving science at a nanoscopic scale. However, low utilization for single GPUs defies the need to invest more money in expensive accelerators. Although related work develops optimizations to improve application performance, none studies how these optimizations impact hardware resource usage or average GPU utilization. Here, this paper takes a data-driven analysis approach in addressing this gap by (1) characterizing how hardware resource usage affects device utilization, execution time, or both, (2) presenting a multiobjective metric to identify important application-device interactions that can be optimized to improve device utilization and application performance jointly, (3) studying hardware resource usage behaviors of several optimizations for a benchmark application, and finally (4) identifying optimization opportunities for several scientific proxy applications based on their hardware resource usage behaviors. Furthermore, we demonstrate the applicability of our methodology by applying the identified optimizations to a proxy application, which improves the execution time, device utilization, and power consumption by up to 29.6%, 5.3% and 26.5% respectively.

Computer science↗
DOE OSTI2023

Optimization of Marine Energy Conversion Systems Through Modeling, Optimization, and CHIL Validation

The work aims to achieve optimal tidal energy conversion through a comprehensive approach of modeling, optimization, and control hardware-in-the-loop (CHIL) validation. By developing accurate models and employing optimization techniques, it seeks to identify efficient system configurations and control strategies. HIL validation will ensure the performance and reliability of the optimized tidal energy conversion system. The preparation of the present manual has been supported by the U.S. Department of Energy.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Convex Optimization with Smart Grid Examples

In this talk, we give an overview of the field of convex optimization and work through four canonical problems that relate to electrical power systems and smart grids. The purpose of these examples is to demonstrate the breadth of applications of convex optimization in energy research and to show that toy versions of these problems can be solved in just a few lines of code, indicating the scale and complexity of problems that can be tackled with a more detailed treatment. We emphasize the cvxpy modeling language as a foundational technology that enables rapid development and prototyping of convex optimization problems, allowing researchers to focus on model development rather than get caught in the weeds of numerical and code implementation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S.

Weather data plays a critical role in renewable energy analysis. Compared to using multiple Actual Meteorological Years, simulations using a single typical year require significantly fewer computational resources. Previous efforts to create typical weather datasets for renewable energy analysis either lack justified or optimized strategies for selecting and weighting different weather parameters or are limited to a few specific locations. Here, in this study, we developed a dataset comprising Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for over 2000 locations across the U.S., based on data from NASA's POWER project. The strategies for creating TSYs and TWYs were optimized based on the simulated outputs of various PV systems and wind turbines in 16 representative cities. This dataset provides an efficient means for the rapid evaluation and optimization of renewable energy systems throughout the entire U.S. Additionally, the optimal strategies identified in this study can be directly applied to create near-optimal TSYs and TWYs for most locations worldwide. However, readers can also employ the optimization approach presented in this work to develop optimal strategies tailored for particular regions.

NASA POWER↗
DOE OSTI2020

Isotopic analysis of plutonium measured with very thick absorbers and high-granularity spectra

FRAM is LANL-designed software designed to extract plutonium and uranium isotopic information from gamma ray spectra. This is an important capability for NEST and STAB; however, FRAM performance has been poor when applied to field data collected in these programs. FRAM Version 5 (commercially available) was fine-tuned and validated for laboratory quality measurements but not for measurements of the high-mass and heavily shielded SNM items encountered in NEST. FRAM is untested for spectra collected with high-granularity lower resolution instruments such as the new 8-MeV Detective-X favored by STAB or the new custom electrocooled systems coming into use for NEST. FRAM Version 6.1 (unreleased) has improvements in physics capabilities, and parameter sets may be adapted that are more specific to NEST and STAB, which is what we have attempted for this assignment. Thick shielding attenuates gamma rays profoundly, especially those at low energy, making them too weak to be measured. The missing low-energy peaks force the analysis to employ only the highest-energy peaks. We adapted FRAM 6.1 for analysis of plutonium spectra obtained with very thick Pb filters (a surrogate for NEST-type configurations). New parameter sets employing only very high energy peaks were created for the analysis. High granularity and low resolution make deconvolution of the 600-keV region particularly challenging, and we have worked to optimally extract information from this region. The results shown in this paper give high confidence that we are on the right track. We used a combination of real and synthetically-modified data. Remaining work includes testing the new software and parameters against genuine data collected with Detective-X and new NEST systems, but such datasets at the moment are unavailable. It also remains to develop and test parameter sets specific to uranium and the new detectors.

07 ISOTOPE AND RADIATION SOURCES↗