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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-hour time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

End-Use Load Profiles for the U.S. Building Stock: Methodology and Results of Model Calibration, Validation, and Uncertainty Quantification

The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High- quality end-use load profiles (EULPs) provide this information, and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation (Frick, Eckman, and Goldman 2017; Frick 2019). To help fill this gap, the U.S. Department of Energy (DOE) funded a three-year project - End-Use Load Profiles for the U.S. Building Stock - that culminated in the release of a publicly available dataset1 of simulated EULPs representing residential and commercial buildings across the contiguous United States. The motivation for this work is further detailed in a November 2019 report: Market Needs, Use Cases, and Data Gaps (Mims Frick et al. 2019). This Methodology and Results report provides detailed descriptions of how the dataset was developed, intended for an audience of dataset and model users interested in the technical details. These details include descriptions of all of the model improvements made for calibration and the final comparisons to empirical data sources. A companion report, End-Use Load Profiles for the U.S. Building Stock: Applications and Opportunities, will be published subsequently and will describe example applications and considerations for using the dataset, intended for an audience of general dataset users.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Validation of modelled imaging plates sensitivity to 1-100 keV x-rays and spatial resolution characterisation for diagnostics for the “PETawatt Aquitaine Laser”

Thanks to their high dynamic range and ability to withstand electromagnetic pulse, imaging plates (IPs) are commonly used as passive detectors in laser-plasma experiments. In the framework of the development of the diagnostics for the Petawatt Aquitaine Laser facility, we present an absolute calibration and spatial resolution study of five different available types of IP (namely, MS-SR-TR-MP-ND) performed by using laser-induced K-shell X-rays emitted by a solid silver target irradiated by the laser ECLIPSE at CEntre Lasers Intenses et Applications. In addition, IP sensitivity measurements were performed with a 160 kV X-ray generator at CEA DAM DIF, where the absolute response of IP SR and TR has been calibrated to X-rays in the energy range 8-75 keV with uncertainties of about 15%. Finally, the response functions have been modeled in Monte Carlo GEANT4 simulations in order to reproduce experimental data. Simulations enable extrapolation of the IP response functions to photon energies from 1 keV to 1 GeV, of interest, e.g., for laser-driven radiography.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Neutron based evaluation in support of NEAMS

The primary objective of the Advanced Non-Destructive fuel Examination (ANDE) work package is to develop capability that has the potential to accelerate insight and development of ceramic and metallic fuels. Establishing unique validation opportunities for new models is a key component of this effort. To explore opportunities a series of interactions were held with NEAMS modelers at LANL. The focus was to identify experiments that draw on the unique capabilities of neutron scattering and imaging for studies of nuclear fuel particularly in areas where experimental data can be valuable for of models validation. The neutron characterization techniques applied in the ANDE program span length scales from millimeter to micrometer to angstroms. Spatial heterogeneities of interest include cracks, pores and inclusions, crystal structure, phase composition, stoichiometry texture, chemistry and atomic thermal motion. Neutrons offer characterization opportunities that are distinct from other probes such as X-rays, electrons or protons. This report describes a variety of opportunities whereby neutron data can be related to models and lists some opportunities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Inclusive cross section measurements in final states with and without protons for charged-current ν μ -Ar scattering in MicroBooNE

A detailed understanding of inclusive muon neutrino charged-current interactions on argon is crucial to the study of neutrino oscillations in current and future experiments using liquid argon time projection chambers. To that end, we report a comprehensive set of differential cross section measurements for this channel that simultaneously probe the leptonic and hadronic systems by dividing the channel into final states with and without protons. Measurements of the proton kinematics and proton multiplicity of the final state are also presented. For these measurements, we utilize data collected with the MicroBooNE detector from 6.4 × 10 20 protons on target from the Fermilab booster neutrino beam at a mean neutrino energy of approximately 0.8 GeV. We present in detail the cross section extraction procedure, including the unfolding, and model validation that uses data to model comparisons and the conditional constraint formalism to detect mismodeling that may introduce biases to extracted cross sections that are larger than their uncertainties. The validation exposes insufficiencies in the overall model, motivating the inclusion of an additional data-driven reweighting systematic to ensure the accuracy of the unfolding. The extracted results are compared to a number of event generators and their performance is discussed with a focus on the regions of phase space that indicate the greatest need for modeling improvements. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Application of machine learning to evaluating and remediating models for energy and environmental engineering

Machine learning (ML) algorithms have been increasingly successful in their applications to solve energy and environmental engineering problems. ML algorithms have the advantage of being able to solve highly nonlinear issues effectively. Furthermore, considering the limited sample size of data collected in energy and environmental engineering, obtaining a ML model with reasonable accuracy is simple. Unfortunately, the vast majority of the current applications of ML algorithms lack effective screening of dominant factors and comprehensive model validation, which weakens the predictive ability of the models. The present study takes the minimum miscible pressure (MMP) of CO 2 - oil systems as an example. It establishes a systematic and robust predictive model to address this issue. Based on 147 sets of slim tube tests, the predictive models of the MMPs are investigated by application of eight ML algorithms. The paper concludes that most of the published ML models in the field of energy and environmental engineering prediction are not reliable. Furthermore, it addresses the main reasons for the poor performance of some predictive models built by ML and provides guidelines on how to make such models robust. Further, to the best of our knowledge, this is the first study to point out the defects of current ML modeling methods and propose countermeasures for their application in energy and environmental engineering problems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Intensified biogas to liquid (IBGTL) Process: Experimental validation and modeling analysis

The Intensified Biogas to Liquid (IBGTL) process aims to overcome traditional economy-of-scale barriers in biogas-to-liquid fuel production by integrating bi-reforming and Fischer-Tropsch synthesis (FTS) in a single IBGTL reactor. This reactor operates at uniform pressure with different optimized temperatures across two zones for efficient conversion, utilizing multifunctional bi-reforming catalysts and high-temperature FTS catalysts. Bench scale experiments were carried out using landfill gas (LFG) in a single pass process, and the yield data from these experiments were fed into process scale-up design and techno-economic analysis (TEA) across four scenarios: (1) a single pass process, (2) a process with material recycling, (3) a process with liquefied petroleum gas (LPG) co-product recovery, and (4) a process with electricity generation from the fuel gas produced. TEA identified Scenario 2 as the most cost-effective, achieving a Minimum Fuel Selling Price (MFSP) of $\$4.59$ per gallon, competitive with the current national diesel price of $\$4.7$ per gallon. However, comparison with the conventional two-reactor system highlights the need for catalyst performance improvements. Sensitivity analysis emphasized the importance of manufacturing cost, liquid fuel yield, and biogas flow rate. Further analysis determined that the IBGTL process must achieve a diesel mass yield beyond 11.7% to surpass the economic viability of the conventional TriFTS (Tri-reforming followed by Fischer-Tropsch Synthesis) process. If the IBGTL process attains the TriFTS yield of up to 17%, the resulting MFSP could be approximately 31% lower than the current TriFTS MFSP. Furthermore, renewable energy credits and carbon credits can further enhance the economic viability of BGTL processes.

09 BIOMASS FUELS↗
DOE OSTI2019

Determining the impact of a severe dry to wet transition on watershed hydrodynamics in California, USA with an integrated hydrologic model

With the onset of climate change, regions relied upon for water supply are increasingly subject to end-member fluctuations between periods of severe drought followed by extreme precipitation. The impacts of these extreme conditions on watershed hydrodynamics in water-resource sensitive regions such as California are unknown despite their great importance for resilience and water management purposes. Understanding these impacts requires high-resolution physically based models to capture sharp variations of topography, land use, wetting fronts, etc. Here, an integrated hydrologic model was used in a high-performance computing framework to study the complex nonlinear dynamics occurring at a representative Californian watershed. The Cosumnes Watershed, one of the last major rivers in California without a dam, offers a rare opportunity to isolate the effects of water management from climate extremes. Here, we show model validation with comparisons between model outputs and local measurements in addition to various satellite-based products including (1) Snow Water Equivalent (SWE) with Snow Data Assimilation System (SNODAS) and a reconstruction method by Bair and co-authors, (2) soil moisture with Soil Moisture Active Passive (SMAP), and (3) evapotranspiration (ET) with Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC). To assess changes in hydrodynamic behavior during climate extremes and their transitions, a simulation spanning a recent drought followed by the highest precipitation year on record (2015–2017) is discussed. From these simulations, we are able to highlight regions that are the most sensitive to climate extremes, which depend on many factors including hydrologic connectivity, geology and topography. These analyses provide a better understanding of the physical phenomena occurring in the watershed, strengthening our knowledge of how the system may respond to extreme conditions which might become the “new normal.”

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Experimentally validated multiphysics modeling of fracture induced by thermal shocks in sintered UO 2 pellets

Uranium Dioxide (UO 2 ) fuel powers almost all commercial Nuclear Power Plants (NPPs) worldwide, generating carbon-free energy and contributing to the fight against climate change. UO 2 fuel incurs damage and fractures due to large thermal gradients that develop across the fuel pellet during normal and transient operating conditions. A comprehensive understanding of the underlying mechanisms by which these processes take place is still lacking. A combined experimental and computational approach is utilized here to quantify the behavior of UO 2 fuel fracture induced by thermal shock. Here, this work introduces both (1) an experimental study to understand the fuel fracturing behavior of sintered UO 2 pellets when exposed to thermal shock, and (2) a Multiphysics phase-field fracture model capable of simulating this process. Parametric studies were conducted to evaluate the effects of uncertainties in fracture properties on the fracture behavior of UO 2 due to thermal shocking. A set of energy release rate (or equivalently fracture toughness) and contract area (the part of the fuel pellet in direct contact with the cold bath) were able to capture the overall fracture trends of the corresponding experimental data. Our combined approach presents a new method for accounting for the effects of microstructure and sample size on the energy release rate/fracture toughness. The experimental data were collected from multiple experiments that exposed UO 2 pellets to high-temperature conditions (589–676 °C) followed by a quench in sub-zero water. This work demonstrates that joint experimental and computational efforts are able to advance the understanding of thermal fracture in the primary fuel source for existing and future NPPs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Experimental validation of model predictive control for solid oxide fuel cells

Here, this paper presents implementation of a model predictive controller (MPC) for an experimental solid oxide fuel cell (SOFC) system. The MPC controller is based on a gain-scheduled predictor with block-oriented structure that can capture important non-linear effects while still keeping the computational complexity low enough to meet real time control requirements. Experimental results show the MPC is able to regulate the SOFC cathode outlet temperature in the face of startup transients and input perturbations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

CORAL: A framework for rigorous self-validated data modeling and integrative, reproducible data analysis

Abstract Background Many organizations face challenges in managing and analyzing data, especially when relevant datasets arise from multiple sources and methods. Analyzing heterogeneous datasets and additional derived data requires rigorous tracking of their interrelationships and provenance. This task has long been a Grand Challenge of data science and has more recently been formalized in the FAIR principles: that all data objects be Findable, Accessible, Interoperable, and Reusable, both for machines and for people. Adherence to these principles is necessary for proper stewardship of information, for testing regulatory compliance, for measuring the efficiency of processes, and for facilitating reuse of data-analytical frameworks. Findings We present the Contextual Ontology-based Repository Analysis Library (CORAL), a platform that greatly facilitates adherence to all 4 of the FAIR principles, including the especially difficult challenge of making heterogeneous datasets Interoperable and Reusable across all parts of a large, long-lasting organization. To achieve this, CORAL's data model requires that data generators extensively document the context for all data, and our tools maintain that context throughout the entire analysis pipeline. CORAL also features a web interface for data generators to upload and explore data, as well as a Jupyter notebook interface for data analysts, both backed by a common API. Conclusions CORAL enables organizations to build FAIR data types on the fly as they are needed, avoiding the expense of bespoke data modeling. CORAL provides a uniquely powerful platform to enable integrative cross-dataset analyses, generating deeper insights than are possible using traditional analysis tools.

97 MATHEMATICS AND COMPUTING↗