Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Performance Modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Modifications to MiniFuel Vehicle Needed to Enable Higher Temperature UO 2 Irradiation Capabilities (Summarizing Issue Report)

The Advanced Fuels Campaign (AFC) within the US Department of Energy Office of Nuclear Energy (DOE-NE) supported the conception and initial demonstration of the miniature fuel (MiniFuel) irradiation capability in the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. These MiniFuel experiments are a key component of a larger effort to accelerate the qualification of novel fuel concepts and to reduce uncertainty in fuel performance models. MiniFuel experiments to date have been conducted in HFIR’s outer vertical experimental facility (VXF) positions. However, there is motivation to achieve irradiation temperatures and fuel burnup accumulation rates greater than what is achievable in the VXF positions to explore high-temperature phenomena and further accelerate fuel qualification. Therefore, MiniFuel irradiation experiments are being considered in HFIR’s removable beryllium region, which is closer to the fueled core than the VXF region. This report summarizes the design modifications to the MiniFuel irradiation vehicle needed to enable high-temperature (i.e., >1,000°C) irradiation tests with rapid burnup accumulation.

10 SYNTHETIC FUELS↗
DOE OSTI2022

Electric Grid Blackstart: Trends, Challenges, and Opportunities

This report studies the expected future state of the grid and recommends actions that can be taken to increase grid resiliency, improve system modeling, perform more extensive studies, enhance training activities, and perform industry outreach for the purpose of the blackstart capabilities of power systems. The report is a revised version of PNNL-29118.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Agilent AgileBioFoundry CRADA (Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOF-MS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Therefore, our accomplishment in this CRADA improved the efficiency and accuracy of strain testing by developing and implementing fast analytical methods, robust processing tools, and software for predicting the best methods for UHPLC analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Understanding and Improving Energy Efficiency of Regional Mobility Systems Leveraging System-Level Data

Increased congestion required urban Americans to travel 6.8 billion hours more and purchase 3.1 billion gallons of fuel for a congestion cost of $\$$153 billion, according to the 2019 Urban Mobility Report. How to effectively manage the regional mobility system and improve the energy efficiency presents a big challenge to public agencies. Recent years have witnessed massive multi-jurisdictional multi-modal system-level data from various sources, which provides an unprecedented opportunity to improve the mobility system and its energy efficiency. However, implications of system-level data for mobility and energy efficiency are unclear. Those system-level data sets are siloed, spatially and temporally sparse, biased, not unified, and lacking of insights for system management. Consequently, there is a real need to acquire, fuse, mine and learn from multi-source system-level data to prepare public agencies to deal more effectively with large-scale energy efficiency modeling, management and planning. This project proposes to intensively review inexpensive, replicable and openly-accessible data from multi-modal systems, develop a data-driven system-level modeling framework enabled and validated by data, identify the energy inefficiencies of mobility systems from infrastructure, vehicles, passenger systems, and quantify the benefits of system-level strategies to improve mobility/energy efficiency. In addition, this research develops models to effectively estimate energy consumption and emissions from various types of vehicles on the roadway networks, with high granularity and high fidelity. Traditional models often heavily rely on aggregated infrastructure or vehicle/passenger data, for example, the census survey, land-use, and traffic counts of one or several classes, which may lead to research gaps considering the emerging vehicle technologies. Those models do not contain individual vehicular information. We propose an integrated data-driven method that combines multiple network modeling components, featuring the utilization of state-wide vehicle registration data. The additional vehicle registration data improve the model performance, and produce high-resolution vehicle-specific estimates of emissions and network performance metrics. Two case studies on the Pittsburgh and Philadelphia regional network show that the proposed method can efficiently and effectively estimate the emissions of a large-scale network, and provide valuable information for evaluating common management strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Accelerating Additive Manufacturing Process Design for Energy Conversion Materials using In-situ Sensing and Machine Learning

One promising candidate for manufacturing of the bismuth Telluride thermoelectric legs is laser powder bed fusion (LPBF) additive Manufacturing (AM). AM processing parameters highly influence the material properties, however current processing parameter development methods in AM are costly and time consuming. In-situ sensors allow for the capture of physically relevant process information on a layer-by-layer basis and will be used to aide process development. To optimize the AM process for the best thermoelectric performance, process variables, in-situ process sensor data and ex-situ material characterization data are collected. Several different interpretable machine learning (ML) approaches are used, and the performance of each method are assessed. Significant input process variables include laser focus, hatch spacing and laser power. The best performing models are used to determine the manufacturing parameters that maximize the power factor. AM of bismuth telluride material provides the ability to create complex geometries enabling more efficient energy conversion.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Mass Production Cost Estimation of Direct H 2 PEM Fuel Cell Systems for Transportation Applications (2017 - 2021) (Final Report)

This report summarizes project activities for an assessment of transportation fuel cell system cost from 2017 to 2021. The project defined and projected the mass production costs of direct hydrogen PEM fuel cell power systems for LDVs (automobiles), MDVs, and HDVs for current and future technologies. In each year of the project, the fuel cell power system designs and cost projections were updated to reflect technological advances. Systems were defined corresponding to direct H2 PEM FC power systems for ~80 kWnet LDVs, 70-170 kWnet MDVs, and 275-330 kWnet HDVs. A selection of systems was analyzed in each year. Major components, their functionality, and relevant parameters were defined in system diagrams. The system definitions were supported by system performance modeling calculations. A BOM for each system analyzed was created that tabulated all system components and subsystems contained in the power systems. Several annual manufacturing rates were considered for each system analyzed.

08 HYDROGEN↗
DOE OSTI2024

Generative network-based approaches to generate stochastic realizations

Generative Adversarial Network (GAN) – based models have been successfully applied in generating different geological models in the literature. However, it is still challenging to use GAN to generate geological realizations with extremely sparse conditioning data (e.g. several well data), which may be regarded as local noise by GAN during the training process. In this work, we propose a novel conditional Generative Adversarial Neural Operator (cGANO) to tackle this challenge. In cGANO, the mapping between conditioning data and output is established through the U-shaped neural operators (UNO), which better preserves local information. Another advantage of using UNO comes from its grid-independent property, which makes the generation of downscaling stochastic geologic realizations possible. We tested the model performance on the IBDP geostatistical dataset with 100 realizations.

58 GEOSCIENCES↗
DOE OSTI2021

Multiscale thermal properties prediction in the Multiphysics Object Oriented Simulation Environment (MOOSE) via a general Boltzmann solver [Poster]

The project objectives were: (1) Engineering-scale fuel performance modeling relies on accurate thermal properties; (2) Thermal properties are inherently multi-scale, arising from atomistic processes and interactions with a material’s microstructure; (3) Heat transport in solids via conduction occurs through transport and scattering of electrons and phonons; (4) Use the Boltzmann transport equation (BTE) to predict the macroscopic behavior of a materials system in terms of the microscopic dynamics of its heat carriers; and (5) This project establishes a new MOOSE (Multiphysics Object Oriented Simulation Environment) module, Boltzmann, dedicated to phonon and thermal electron transport.

36 MATERIALS SCIENCE↗
DOE OSTI2024

User Stories for PV Operations and Maintenance: Findings from the 2024 PVPMC Workshop

At the 2024 Photovoltaic (PV) Performance Modeling Workshop we collected user stories related to PV system monitoring, analytics, and operations and maintenance (O&M). In this context, user stories describe challenges with current practices and tools or imagining opportunities for improvements. From these user stories, we identified several near-term opportunities to improve photovoltaic system monitoring, analytics and O&M.

14 SOLAR ENERGY↗
DOE OSTI2024

ROADRUNNER MiniFuel Experiment: Irradiation Target Design and Sample Characterization

High-density uranium nitride (UN) is a fuel candidate for several advanced nuclear reactor designs currently under development. Because there are limited UN performance data relative to fuel fabrication impurity and density variation, an irradiation campaign has been developed as part of a collaborative effort among the University of Texas at San Antonio (UTSA), Westinghouse Electric Company, Oak Ridge National Laboratory (ORNL), and Los Alamos National Laboratory (LANL) under the Nuclear Science User Facilities program. This project, entitled ROADRUNNER, or Research On ADvancing the peRformance of UraNium Nitrides in Extreme enviRonments, aimsto support UN fuel qualification for advanced reactors by investigating the impact of density and impurity variations on UN performance as a function of irradiation temperature and burnup. The MiniFuel experiment vehicle developed by ORNL, which leverages the High Flux Isotope Reactor, was selected to perform this accelerated separate-effects irradiation testing. The experiment test matrix consists of six MiniFuel targets containing miniature UN fuel disks, and targets three distinct burnup levels (37.5, 60, and 75 MWd/kg U) and three distinct temperatures (600, 900, and 1200°C). Neutronics and thermal analyses were performed to determine the experimental parameters needed to meet the desired irradiation conditions and to predict the experiment components temperatures. UN pellets were fabricated at LANL with tightly controlled parameters to produce specimens with three distinct densities and three levels of carbon content. The pellets were then thinned down by UTSA to the experiment-required thickness. The pre-characterization of the specimens includes density measurements, carbon and oxygen contents, microstructure analysis, and x-ray computed tomography. The selected specimens will be assembled into the MiniFuel experiment, and the first ROADRUNNER MiniFuel targets are intended for HFIR insertion during the Fall of 2024. After irradiation, the targets will be shipped to ORNL’s hot cell facility for disassembly. The post-irradiation examination on the fuel specimens includes fission gas release measurements, visual inspection, fuel swelling measurements, gamma spectroscopy, and microstructure analysis. The data collected post-irradiation will be used to develop fuel performance models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Recent High Burnup LOCA Testing at Oak Ridge National Laboratory

To improve fuel cycle economics, increasing the fuel burnup limit in light-water reactors requires a solid technical foundation. Observations from experiments at Halden and Studsvik highlighted severe fuel fragmentation during loss-of-coolant-accident (LOCA) conditions, indicating a need for further technical considerations. These experiments suggest that the fragmentation threshold for high-burnup fuel might be influenced by pre-transient power levels. Consequently, additional LOCA test data are essential to complement existing findings and to deepen our understanding. Oak Ridge National Laboratory’s Severe Accident Test Station has been instrumental in advancing knowledge about high-burnup fuel fragmentation, relocation, and dispersal. This milestone report details two high-burnup LOCA tests designed to evaluate the effects of terminal temperature and grid spacers (or cladding restraints) on fuel fragmentation and relocation susceptibility. In addition, BISON fuel performance modeling and out-of-cell benchmark testing were conducted to better interpret the in-cell test results. These tests were developed in collaboration with fuel vendors to ensure that the results provide valuable data for topical reports and support the Nuclear Regulatory Commission’s review.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Techno-Economic Optimization of a Solvent Absorption Process for CO2 Capture with 3D-Printed Intensified Packing

Presentation given at the 2024 annual AICHE meeting held October 27-31, 2024. The presentation focuses on modeling performance and economics of a solvent absorption system for CO2 capture with towers utilizing intensified packing. The packing is an alternative to traditional structured packing by incorporating cooling channel for simultaneous mass and heat transfer.

Summits, Stephen↗
DOE OSTI2024

Machine Learning meets Algebraic Combinatorics: A Suite of Benchmark Datasets to Accelerate AI for Mathematics Research

The use of benchmark datasets has become an important engine of progress in machine learning (ML) over the past 15 years. Recently there has been growing interest in utilizing machine learning to drive advances in research-level mathematics. However, off-the-shelf solutions often fail to deliver the types of insights required by mathematicians. This suggests the need for new ML methods specifically designed with mathematics in mind. The question then is: what benchmarks should the community use to evaluate these? On the one hand, toy problems such as learning the multiplicative structure of small finite groups have become popular in the mechanistic interpretability community whose perspective on explainability aligns well with the needs of mathematicians. While toy datasets are a useful benchmark for initial work, they lack the scale, complexity, and sophistication of many of the principal objects of study in modern mathematics. To address this, we introduce a new collection of benchmark datasets, Algebraic Combinatorics Benchmarks (ACBench), representing either classic or open problems in algebraic combinatorics, a subfield of mathematics that studies discrete structures arising from abstract algebra. After describing the datasets, we discuss the challenges involved in constructing “good” mathematics benchmarks, describe baseline model performance, and discuss some of the insights these datasets can provide that may be of interest even to those who are not interested in mathematics research itself.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Post-Irradiation Examination to Quantify Irradiation-Induced Bowing of SiGA® Silicon Carbide Composite Structures (Final CRADA Report – NFE-23-09937)

As part of the SiC-based material development at General Atomics Electromagnetic Systems (GA-EMS), this project involved the first-of-a-kind experimental post-irradiation examination of the irradiation-induced bowing response in SiC composite structures under a neutron flux gradient. The SiC composite miniature channel specimen was provided by GA-EMS for the irradiation experiment. To quantify the irradiation-induced bowing of the channel specimen, a series of visual and dimensional inspections were conducted using the unique capabilities at Oak Ridge National Laboratory (ORNL), including a custom profilometry rig. The post-irradiation examination results of this project will assist GA-EMS in validating their fuel performance model for SiC-based core structures in neutron irradiation environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solar and Storage Integration in the U.S. Southeast: Implications for Resource Adequacy

Resource adequacy concerns may be very different in electricity systems that have higher levels of solar and storage, requiring changes to existing planning models. This study explores a novel approach to evaluating resource adequacy under future scenarios with higher solar and storage in the Southeast U.S. It uses NREL’s Probabilistic Resource Adequacy Suite (PRAS), a collection of probabilistic resource adequacy modeling tools, and compares results when interacting PRAS and a portfolio planning tool with a more traditional modeling approach. The results suggest that traditional models perform reasonably well with lower levels of solar PV, but at higher levels of solar probabilistic tools better capture the changes in resource adequacy concerns—such as winter energy availability—associated with higher solar systems. This is the final study in the Preparing Southeast Markets for Reliable and Affordable Integration of Solar into Operations and Planning project. Two prior reports can be found at: Solar and Storage Integration in the Southeastern United States: Economics, Reliability, and Operations. https://emp.lbl.gov/publications/solar-and-storage-integration Solar and Wind Forecast Error Reserve Sharing in a Multi-Utility Region. https://eta-publications.lbl.gov/sites/default/files/2024-11/multiutility_fe_reserve_sharing_final.pdf

14 SOLAR ENERGY↗
DOE OSTI2025

Rapid SACR Observations of Convection at Bankhead National Forest (RAPID) Field Campaign Report

Improving our representation of convective cell processes requires better quantification of convective clouds throughout their entire life cycle. This includes gaining a clearer understanding of the controls on key convective cloud properties, such as updraft intensity, particle size distributions, rainfall rates, and hydrometeor species. Our inability to improve convective cloud process modeling stems, in part, from a limited understanding of convective cell properties, particularly given how rapidly these storms evolve. This lack of detailed observations in the most intense and organized convective storms is especially significant, as large errors remain in representing these clouds, which are critical for severe weather prediction and Earth system model performance. Cloud and precipitation radars are essential tools for studying cloud microphysics and dynamics, particularly in deeper convective clouds. The recent U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s third Mobile Facility (AMF3) Bankhead National Forest (BNF) deployment provides a unique opportunity to investigate important land–atmosphere interactions, as well as the environmental controls on deep convective cloud processes, in a location favorable for frequent convection. We operate the X/Ka-band Scanning ARM Cloud Radar (X/Ka SACR) using a scan strategy optimized to capture these rapidly evolving clouds and their properties, thereby improving studies of deep convective cloud processes. This effort is strengthened by a complementary and coordinated partnership with ongoing university and multi-agency radar activities collocated in north Alabama—a unique opportunity to examine clouds and precipitation from a lifetime-centric perspective.

54 ENVIRONMENTAL SCIENCES↗