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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Algal Biomass Conversion to Fuels via Combined Algae Processing (CAP): 2022 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office (BETO). It allows for the impact of research progress (both directly achieved in-house at NREL and furnished by partner organizations) to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked toward out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae SOT benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) consortium efforts and driven by data furnished by Arizona State University's (ASU's) Arizona Center for Algae Technology and Innovation (AzCATI) test bed site. The CAP model is primarily based on experimental efforts conducted under NREL research and development projects, with some process parameters provided by partner organizations. Assumptions regarding the wet storage of algae use data provided by Idaho National Laboratory (INL), while parts of the polyurethane production process leverage BETO-funded research from collaborators at Algenesis and the University of California, San Diego (UCSD). This report focuses on back-end conversion of algal biomass through the CAP pathway, highlighting the 2022 updates to minimum fuel selling price (MFSP). This update incorporates improvements to fermentation performance for two biological pathways through carboxylic acid and 2,3-butanediol (BDO) intermediates, as demonstrated through parallel research on the biochemical conversion of corn stover. Improvements are applied to the glucose fraction of the biomass only, while parameters regarding the conversion of the mannose fraction (not a significant component in corn stover) are maintained consistently with prior CAP SOTs. Additional parameters are also updated to reflect the most current understanding of each pathway, including an increase in the catalyst loading requirement in the ketonization step of the acids pathway and a decrease in the fermentation productivity in the BDO pathway. Additionally, the biomass feedstock costs (minimum biomass selling price [MBSP]), yields, and seasonal variability from the upstream cultivation SOT model were also incorporated into downstream Aspen Plus CAP models.

09 BIOMASS FUELS↗
NASA NTRSDate not supplied

Benchmark Models for Classification of Radiation Type Induced in Immune Cells

NASA Biological and Physical Sciences and the Science Mission Directorate have published a benchmark dataset of mouse immune cells subjected to radiation-induced DNA damage. The dataset comprises ML-ready microscopic imagery of said cells, including labels indicating radiation type and dose. The machine learning team at NASA Interagency Implementation and Advanced Concept Team (IMPACT) created multiple benchmark models. Initially, we conducted a preliminary analysis using thresholding. The algorithm used thresholds on average brightness of the available images to classify them into their respective radiation type. We also tested machine learning approaches. Convolutional Neural Networks (CNN) emerged as the best-performing model. This poster presents the benchmark scores obtained by the models.

Vishal Perekadan↗
DOE OSTI2020

The Rodinia Benchmark Suite in SYCL

As opposed to the Open Computing Language (OpenCL) programming model in which host and device codes are generally written in different programming languages, SYCL can combine host and device codes for an application in a type-safe way to improve development productivity and performance portability. Rodinia is a widely used benchmark suite for heterogeneous computing. Hence, we port the OpenCL implementations of the benchmark suite to SYCL manually. The SYCL benchmark suite is an opensource project for tracking the development of the mainstream SYCL compilers, and for developers and researchers interested in programming productivity, performance analysis, and portability across different computing platforms. We organize the remainder of the report as follows. Section II introduces the SYCL programming model, shows the major differences between an OpenCL program and a SYCL program, and gives a summary of the benchmark suite. In Section III, we describe our SYCL implementations in more details. We evaluate the SYCL benchmarks on the Intel® central processing units (CPUs) and graphics processing units (GPUs) in Section IV. Section V concludes the report.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

US-DOE and Republic of South Africa Uranium Benchmarking: Results From Los Alamos National Laboratory (Study Report)

This report details work performed at Los Alamos National Laboratory (LANL) in support of an interlaboratory uranium (U) benchmarking collaboration between LANL, Lawrence Livermore National Laboratory (LLNL), and South African Nuclear Energy Corporation (Necsa). The goal of this interlaboratory benchmarking exercise is to compare analytical methods and data obtained on uranium reference materials CUP-2 and New Brunswick Laboratory (NBL) certified reference material (CRM) 124-2. The LANL commitments for this project include performing microwave digestion of fresh CUP-2 and CRM 124-2 powders, measuring the U isotope composition of CUP-2 and CRM 124-2 by both single-collector and multi-collector inductively coupled plasma mass spectrometry (SC-ICP-MS and MC-ICP-MS, respectively), and determining trace-element abundances in CUP-2 and CRM 124-2 by inductively coupled plasma mass spectrometry (ICP-MS). This report details the methods employed by LANL in support of these objectives and presents the data obtained during this collaborative exercise.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS1993

Developing scheduling benchmark tests for the Space Network

A set of benchmark tests were developed to analyze and measure Space Network scheduling characteristics and to assess the potential benefits of a proposed flexible scheduling concept. This paper discusses the role of the benchmark tests in evaluating alternative flexible scheduling approaches and defines a set of performance measurements. The paper describes the rationale for the benchmark tests as well as the benchmark components, which include models of the Tracking and Data Relay Satellite (TDRS), mission spacecraft, their orbital data, and flexible requests for communication services. Parameters which vary in the tests address the degree of request flexibility, the request resource load, and the number of events to schedule. Test results are evaluated based on time to process and schedule quality. Preliminary results and lessons learned are addressed.

Moe, Karen L.↗
DOE OSTI2021

Updates to Improve the Evaluated Nuclear Data of Copper Isotopes in the Resolved and Unresolved Resonance Regions

The evaluated nuclear data of the 63,65 Cu isotopes have been updated in the resolved and unresolved resonance energy regions with the goal of improving the fidelity between calculated and measured critical benchmarks. Particular focus was devoted to the 63 Cu(n, γ) cross sections because of their large sensitivity to the reactivity in the benchmark calculations. By focusing on these cross sections, the set of parameters in both resolved and unresolved resonance regions were fitted to available experimental data. The newly updated evaluations were compared to the currently available ENDF/B-VIII.0 evaluations in reactivity calculations for copper-sensitive integral benchmarks. An initial study of the sensitivity of the benchmarks to the angular distributions was performed, finding it necessary to describe the angular distributions more accurately above 100 keV. This investigation uncovered the need for more accurate data for the 63 Cu(n, γ) cross section above 100 keV. An increase in the capture cross section was found to improve agreement for the considered benchmarks.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Algal Biomass Production via Open Pond Algae Farm Cultivation: 2021 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office. It allows for the impact of research progress to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked towards out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae state of technology benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) consortium efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted under NREL research and development projects. This report focuses on front-end algal biomass production, documenting the pertinent algal biomass cultivation parameters that were input to the NREL open pond algae farm model based on the latest DISCOVR cultivation performance data. Relative to the fiscal year (FY) 2020 SOT at $683/ton or $603/ton for ASU and FA evaporation scenarios, respectively (unlined pond basis), the FY 2021 SOT represents a slight increase in MBSP of 1%-2%. This is primarily attributed to a slight 4% reduction in annual cultivation productivity achieved at the AzCATI site (supported by the efforts under the DISCOVR consortium noted above) observed during FY 2021 cultivation campaigns.

09 BIOMASS FUELS↗
DOE OSTI2020

The ESIF-HPC-2 Benchmark Suite

We describe the development of the ESIF-HPC-2 benchmark suite, a collection of kernel and application benchmark codes for measuring computational and I/O performance from single nodes to full HPC systems that was used for acceptance testing in our recent HPC procurement. The configurations of the benchmarks used for our system is presented. We also describe a set of "dimensions" that can be used to classify benchmarks and assess coverage of a suite systematically. The collection is offered cost-free as a GitHub repository for general usage and further development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2018

Structural Analysis Methodology for Space Deployable Structures Using Multi-Body Dynamic Solver

The purpose of this paper is to provide detailed structural analysis implementation for space deployable structures using a multi-body dynamic solver. The selected commercial code is MSC/ADAMS. Four benchmark problems are used to evaluate mechanism motion, frictional contacts, and geometrically non-linear large deformation. This paper examines the software functionality, performance, accuracy and ability to solve the benchmark problems. The discussions covered are related to solver convergence, tracking internal force, velocity and acceleration of different parts in each model and comparing results to closed form solutions if available.

Montazersadgh, Farzin↗
DOE OSTI2020

The ESIF-HPC-2 Benchmark Suite: Preprint

We describe the development of the ESIF-HPC-2 benchmark suite, a collection of kernel and application benchmark codes for measuring computational and I/O performance from single nodes to full HPC systems that was used for acceptance testing in our recent HPC procurement. The configurations of the benchmarks used for our system is presented. We also describe a set of 'dimensions' that can be used to classify benchmarks and assess coverage of a suite systematically. The collection is offered cost-free as a GitHub repository for general usage and further development.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Reactor physics benchmark of Westinghouse PWR core design suite for high burnup/high enrichment fuel - Part 1: Pin and lattice

Westinghouse has performed a comprehensive set of code-to-code benchmark comparisons to corroborate application of its state-of-the-art NEXUS-based code suite to high burnup and high enrichment (HB/HE) fueled PWR core design and reload analysis. This paper focuses on analysis of the predictions for pin and lattice designs representative of HB/HE core reloads from the Westinghouse advanced lattice code PARAGON2, the Monte-Carlo code SERPENT2 and MPACT, the deterministic neutronic component of VERA. A companion paper presented at this conference analyzes results obtained with the Westinghouse ANC9 core simulator, using lattice data provided by PARAGON2 with the NEXUS cross-section methodology, and MPACT for 2D and 3D core configurations incorporating HB/HE fuel reloads to achieve cycle length extension to 24-month in high-power density PWRs. The results of the pin and lattice benchmark presented in this paper show that the neutronic predictions from PARAGON2 for HB/HE fueled PWRs are in remarkable agreement with predictions from both SERPENT2 and MPACT including for depletion to high fuel burnup values, and employing a set of challenging lattices which feature combined use of burnable absorbers to obtain the required reactivity hold-down to support PWR transition to HB/HE fuel and 24-month operational cycles. As corroborated by this analysis and the analysis contained in the companion paper, the Westinghouse NEXUS-based PWR core analysis package can be confidently applied by industry for design analysis of PWR core reloads adopting HB/HE fuel and properly support the industry commercialization plans for in-reactor deployment. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Detection of Defects in Additively Manufactured Metallic Materials with Machine Learning of Pulsed Thermography Images

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Quality control (QC) requires nondestructive evaluation (NDE) of actual AM structures. Pulsed thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, detection sensitivity of this method is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. The ML methods with higher accuracy require longer run time. However, this runtime is sufficiently short to perform QC within a realistic time frame.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Quality Control of Additively Manufactured Metallic Structures with Machine Learning of Thermography Images

Additive manufacturing (AM) of high-strength metals is currently based on the laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Quality control (QC) requires nondestructive evaluation of actual AM structures. Flash thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, the detection sensitivity of this method is limited by noise. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatial–temporal blind source separation and spatial–temporal sparse dictionary learning methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced by the LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, the ML methods with higher accuracy require a longer runtime. However, this runtime is sufficiently short to perform QC within a realistic time frame.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Algal Biomass Conversion to Fuels via Combined Algae Processing (CAP): 2020 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office. It allows for the impact of research progress to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked towards out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae state of technology benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under the Development of Integrated Screening, Cultivar Optimization, and Verification Research consortium (DISCOVR) efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted under NREL research and development projects. This report focuses on back-end conversion of algal biomass through the CAP pathway, highlighting the 2020 updates to minimum fuel selling price (MFSP). Notably, this analysis marks the first time that polyurethane (PU) is included as a value-added coproduct in the SOT case. Relative to prior 2019 benchmarks, the 2020 CAP SOT achieved further MFSP reductions of over $4/gallon gasoline equivalent, reflective of (a) the inclusion of PU co-production, (b) reduced SOT biomass production costs, and (c) higher pretreatment sugar yield data.

09 BIOMASS FUELS↗
DOE OSTI2022

Algal Biomass Conversion to Fuels via Combined Algae Processing (CAP) (2021 State of Technology and Future Research)

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office. It allows for the impact of research progress to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked towards out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae state of technology benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under the Development of Integrated Screening, Cultivar Optimization, and Verification Research consortium (DISCOVR) efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted under NREL research and development projects. This report focuses on back-end conversion of algal biomass through the CAP pathway, highlighting the 2021 updates to minimum fuel selling price (MFSP). This update maintains an important recent inclusion of polyurethane (PU) previously incorporated in the 2020 SOT as a value-added coproduct. Relative to the 2020 SOT case, this indicates a minimal increase of $0.10-$0.14/GGE (roughly 2%) for both the acids and BDO pathways, attributed to minimal increases in upstream algal biomass costs from slightly lower demonstrated cultivation productivities in the 2021 SOT.

09 BIOMASS FUELS↗
DOE OSTI2022

Biochemical Conversion of Lignocellulosic Biomass to Hydrocarbon Fuels and Products (2021 State of Technology and Future Research)

The annual State of Technology (SOT) assessment is an essential activity for biochemical platform research. It allows the impact of research progress to be quantified in terms of economic improvements in the overall cellulosic biofuel production process for a particular conversion pathway. As such, initial benchmarks can be established for currently demonstrated performance and progress can be tracked towards out-year goals to ultimately demonstrate cost-competitive cellulosic biofuel technology. The purpose of this report is to benchmark the latest experimental developments across a number of potential bioconversion pathways as quantified by modeled minimum fuel selling prices (MFSPs), as a measure of current status relative to those final targets. For this state of technology, TEA models were run for two separate biological conversion pathways to fuels, based on available data for integrated biomass deconstruction and hydrolysate processing; namely carboxylic acids (primarily butyric acid) and diols (2,3-butanediol [BDO]), reflecting NREL's recently-published 2018 biochemical design report focused on those two pathways. The models were run across three scenarios for lignin utilization, namely combustion, conversion to coproducts based on "base case" performance with biomass hydrolysate, and conversion to coproducts based on "high" performance demonstrated with model lignin monomer components. A key improvement reflected in the 2021 SOT is centered around making use of the latest lignin conversion data, which over the past year focused primarily on production of ß-ketoadipate (BKA) as a more optimal molecule compared to the closely-related adipic acid coproduct of prior recent focus, both in terms of superior product properties and biology, as well as reduced processing complexity (reducing two steps for sequential production of muconate followed by hydrogenation to adipic acid down to a single step for direct production of BKA). This update translated to a roughly 17% increase in mass yield of final coproduct output at a nearly four-fold increase in fermentation productivity on lignin monomers relative to prior 2020 SOT benchmarks for muconic/adipic acid production.

09 BIOMASS FUELS↗
DOE OSTI2023

Defect Quantification in Metal Halide Perovskites Anticipates Photoluminescence and Photovoltaic Performance

Semiconductor material optimization requires quantification of performance and stability-dependent near-valence maximum and near-conduction minimum defects with sufficient energy resolution and sensitivity. In this work, we utilize a spectroscopy-electrochemistry approach to resolve the energy-distinct donor and acceptor defect concentrations in wide-gap (Cs .05 FA .79 MA .16 )Pb(I .87 Br .13 ) 3 perovskites, benchmarked against photoluminescence and photovoltaic device performance. Monitoring charge transfer events to electron acceptor and donor molecules within solid electrolyte top contacts enables defect quantification below 10 15 cm –3 at an energy resolution of 10 meV under device-relevant bias, well below levels reported by other methods. Further method sensitivity is demonstrated for defects arising from <2% formamidinium concentration modifications, mimicking compositional imperfections resulting from nonoptimized processing. This method provides the first complete perovskite energetic diagrams with small changes in composition, is nondestructive, compatible with in-line processing characterization, and will enable the semiconductor community to link molecular origins of defects with limitations in device performance across a wide array of optoelectronic platforms.

14 SOLAR ENERGY↗
DOE OSTI2025

Toward a persistent event-streaming system for high-performance computing applications

High-performance computing (HPC) applications have traditionally relied on parallel file systems and file transfer services to manage data movement and storage. Alternative approaches have been proposed that use direct communications between application components, trading persistence and fault tolerance for speed. Event-driven architectures, as popularized in enterprise contexts, present a compelling middle ground, avoiding the performance cost and API constraints of parallel file systems while retaining persistence and offering impedance matching between application components. However, adapting streaming frameworks to HPC workloads requires addressing challenges unique to HPC systems. This paper investigates the potential for a streaming framework designed for HPC infrastructures and use cases. We introduce Mofka, a persistent event-streaming framework designed specifically for HPC environments. Mofka combines the capabilities of a traditional streaming service with optimizations tailored to the HPC context, such as support for massively multicore nodes, efficient scaling for large producer-consumer workflows, RDMA-enabled high-performance network communications, specialized network fabrics with multiple links per node, and efficient handling of large scientific data payloads. Built using the Mochi suite of HPC data service components, Mofka provides a lightweight, modular, and high-performance solution for persistent streaming in HPC systems. We present the architecture of Mofka and evaluate its performance against Kafka and Redpanda using benchmarks on diverse platforms, including Argonne's Polaris and Oak Ridge's Frontier supercomputers, showing up to 8× improvement in throughput in some scenarios. We then demonstrate its utility in several real-world applications: a tomographic reconstruction pipeline, a workflow for the discovery of metal-organic frameworks for carbon capture, and the instrumentation of Dask workflows for provenance tracking and performance analysis.

HPC↗