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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Developing Non-Food Grade Brassica Biofuel Feedstock Cultivars with High Yield, Oil Content, and Oil Quality that are Suitable for Low Input Production Dryland Systems (Final Report)

The U.S. uses a substantial amount of fossil fuel as an energy source for a wide range for functions including home heating, agriculture and transportation. In the transportation sector, diesel and jet fuel are consumed at a rapid rate, and alternative liquid energy is being investigated globally and nationally to reduce our dependence on fossil fuel and reduce the impact of our carbon footprint on global climate change. Non-food Brassica crops have the potential of producing high oil yield (over 250 gal acre-1) and have oil quality highly desirable for use as biodiesel or bio jet fuel. Developing oilseed feedstock Brassica cultivars with higher seed and oil yield, with high oil quality and with resistance to pathogens, that can be grown with few chemical inputs will helping break our dependence on fossil fuels and reduce importation of fossil fuels. While some oilseed Brassicas have been grown on a small scale for many years in the Pacific Northwest (PNW), adoption has been limited, and the potential of the crops have not been realized or even fully investigated. This report summarizes the results of a study to develop superior non-food grade winter (B. napus) and spring (B. napus and B. juncea) oilseed cultivars suitable for a range of PNW, and other US environments with high resistance to the biotic and abiotic stresses suitable for high-quality biofuel feedstocks. In conducting this work, genome-wide association selection was used to dissect the genetic architecture of industrial Brassica oilseed germplasm for yield, quality, and resistance to biotic and abiotic stresses. A genome-wide bioinformatics approach was used to identify putative PRR (pattern recognition receptor) - type resistance genes that confer durable resistance to blackleg. A novel transgenic approach was developed to generate resistant non-food oilseed lines using PPR genes Br1033 and Br8486. These genes were inserted into regionally adapted oilseed cultivars.

09 BIOMASS FUELS↗
DOE OSTI2020

First Principle based Thermal Conductivity Modeling of Metal Alloys with Applications on Uranium Alloys

Thermal conductivity is an important materials property related to heat transport, which is essential to many applications, ranging from thermoelectrics to nuclear reactor materials. High- quality thermal conductivity data is critical to these materials and their associated technologies, including the nuclear fuel materials like traditional oxide fuels and metallic uranium (U) fuels, e.g. U-Zr and U-Mo alloys. And thermal conductivity modeling is widely used to interpolate or extrapolate experimental data, to obtain high-quality thermal conductivity data over wide temperature and composition range, and to understand the impacts of different factors including defects and microstructures. However, a general thermal conductivity model for metal alloys which can work regrading different phase components is still missing. Also, for U alloys, a thermal conductivity model working with different phases is needed. Therefore, in this work, we developed thermal conductivity models for metal alloys, based on ab-initio calculations, semi-classical physics rules, and limited experimental data. The goals of these models are to help obtain high- quality thermal conductivity data within a little experimental input as possible, have models that are extendable to varying microstructures, including different types of irradiation effects, and provide mechanistic understanding of heat transfer in the modeled alloys. In this work, our model solves several challenges in the development. The DFT-BTE approach is applied to decrease the reliance on experimental data and make the model extendable to composition changes and defects. A practical and efficient way to combine the DFT inputs with physics rules is pointed out in this work too. A staged approach, which starts from simple cases of elemental metals, then extends to solid solutions, different compound phases, and multi-phase mixtures, is presented to work with the metal alloys in different phase components. Our models are demonstrated respectively on aU for elemental metal model, on U-Zr and U-Mo alloys in aU temperature range for multi-phase mixture model, on high-temperature U-Zr, U-Nb, and U-Mo alloys in the body-centered cubic phase for concentrated solid solution model, and on irradiated U-Mo alloys for irradiated metal alloy model. All models show great agreement with experimental data. In these demonstrations, our model shows its advantages compared to previous empirical fitting model. Our model requires fewer experimental data, due to the inputs from DFT. The quantitative insights into the different physical factors are provided in our model, as it incorporates the electron and phonon scattering mechanisms. Our model also can be extended to incorporate the contributions of point defects, grain boundaries, and noble gas bubbles, to integrate their effects on heat transfer. This model can serve as both a foundation for understanding the more complex thermal conductivity of realistic U alloy fuels and a useful tool to guide further modeling of thermal conductivity to aid materials and device design.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

An Integrated Simulation of Multiple-Pass U-10Mo Alloy Hot Rolling and Static Recrystallization

To achieve a desired microstructure and minimize the thickness variation in rolled foils, researchers must understand the effects of foil fabrication process variables on microstructure evolution. We developed an integrated simulation of deformation and recrystallization that employs the finite element method (FEM) and the kinetic Monte Carlo (KMC) Potts model, respectively, to investigate microstructure evolution during multiple-pass hot rolling and heat treatment in polycrystalline U-10Mo fuel. Scanning electron microscopy and electron backscatter diffraction images of microstructures were directly used as input in FEM calculation of deformation, and the calculated strains were used to determine the driving force of nucleation and growth of recrystallized grains in the Potts model. Grain structures predicted by the Potts model were used to update the grain structure and material properties for FEM. Simulation alternated between FEM and the Potts model to simulate grain structure evolution during multiple rolling and heat treatments. The initial model parameters were determined by benchmarking the recrystallization kinetics against experimental data. Then, the model was applied to predict the grain structure evolution. Results showed that our model can capture the coupling between deformation and recrystallization and can quantitatively reproduce the observed U-10Mo recrystallization and grain growth kinetics. The simulation results demonstrated that the developed model can predict U-10Mo grain structures as a function of initial microstructure and foil fabrication parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content

Abstract. The international Thermodynamic Equation of Seawater 2010 (TEOS-10) defined the enthalpy and entropy of seawater, thus enabling the global ocean heat content to be calculated as the volume integral of the product of in situ density, ρ, and potential enthalpy, h0 (with reference sea pressure of 0 dbar). In terms of Conservative Temperature, Θ, ocean heat content is the volume integral of ρcp0Θ, where cp0 is a constant “isobaric heat capacity”. However, many ocean models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) as well as all models that contributed to earlier phases, such as CMIP5, CMIP3, CMIP2, and CMIP1, used EOS-80 (Equation of State – 1980) rather than the updated TEOS-10, so the question arises of how the salinity and temperature variables in these models should be physically interpreted, with a particular focus on comparison to TEOS-10-compliant observations. In this article we address how heat content, surface heat fluxes, and the meridional heat transport are best calculated using output from these models and how these quantities should be compared with those calculated from corresponding observations. We conclude that even though a model uses the EOS-80, which expects potential temperature as its input temperature, the most appropriate interpretation of the model's temperature variable is actually Conservative Temperature. This perhaps unexpected interpretation is needed to ensure that the air–sea heat flux that leaves and arrives in atmosphere and sea ice models is the same as that which arrives in and leaves the ocean model. We also show that the salinity variable carried by present TEOS-10-based models is Preformed Salinity, while the salinity variable of EOS-80-based models is also proportional to Preformed Salinity. These interpretations of the salinity and temperature variables in ocean models are an update on the comprehensive Griffies et al. (2016) paper that discusses the interpretation of many aspects of coupled Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Synergizing Electron and Heat Flows in Photocatalyst for Direct Conversion of Captured CO 2

We report a ternary hybrid photocatalyst architecture with tailored interfaces that boost the utilization of solar energy for photochemical CO 2 reduction by synergizing electron and heat flows in the photocatalyst. The photocatalyst comprises cobalt phthalocyanine (CoPc) molecules assembled on multiwalled carbon nanotubes (CNTs) that are decorated with nearly monodispersed cadmium sulfide quantum dots (CdS QDs). The CdS QDs absorb visible light and generate electron-hole pairs. The CNTs rapidly transfer the photogenerated electrons from CdS to CoPc. The CoPc molecules then selectively reduce CO 2 to CO. Here, the interfacial dynamics and catalytic behavior are clearly revealed by time-resolved and in situ vibrational spectroscopies. In addition to serving as electron highways, the black body property of the CNT component can create local photothermal heating to activate amine-captured CO 2 , namely carbamates, for direct photochemical conversion without additional energy input.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Preliminary Modeling of Chloride Deposition on Spent Nuclear Fuel Canisters in Dry Storage Relevant to Stress Corrosion Cracking

This study presents a method that can be used to gain information relevant to determining the corrosion risk for spent nuclear fuel (SNF) canisters during extended dry storage. Currently, it is known that stainless steel canisters are susceptible to chloride-induced stress corrosion cracking (CISCC). However, the rate of CISCC degradation and the likelihood that it could lead to a through-wall crack is unknown. This study uses well-developed computational fluid dynamics and particle-tracking tools and applies them to SNF storage to determine the rate of deposition on canisters. The deposition rate is determined for a vertical canister system and a horizontal canister system, at various decay heat rates with a uniform particle size distribution, ranging from 0.25 to 25 µm, used as an input. In all cases, most of the dust entering the overpack passed through without depositing. Most of what was retained in the overpack was deposited on overpack surfaces (e.g., inlet and outlet vents); only a small fraction was deposited on the canister itself. These results are provided for generalized canister systems with a generalized input; as such, this paper is intended to demonstrate the technique. This study is a part of an ongoing effort funded by the U.S. Department of Energy, Nuclear Energy Office of Spent Fuel Waste Science and Technology, which is tasked with doing research relevant to developing a sound technical basis for ensuring the safe extended storage and subsequent transport of SNF. This work is being presented to demonstrate a potentially useful technique for SNF canister vendors, utilities, regulators, and stakeholders to utilize and further develop for their own designs and site-specific studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Beam modulation and bump-on-tail effects on Alfvén eigenmode stability in DIII-D

Beam modulation effects on Alfvén eigenmode stability have been investigated in a recent DIII-D experiment and show that variations in neutral beam modulation period can have an impact on the beam driven Alfvén eigenmode spectrum and resultant fast ion transport despite similar time-averaged input power. The experiment was carried out during the current ramp phase of L-mode discharges heated with sub-Alfvénic 50–80 kV deuterium neutral beams that drive a variety of Alfvén eigenmodes unstable. The modulation period of two interleaved beams with different tangency radii was varied from shot to shot in order to modify the relative time dependent mix of the beam pitch angle distribution as well as the persistence of a bump-on-tail feature near the injection energy (a feature confirmed by imaging neutral particle analyzer measurements). As the beam modulation period is varied from 7 ms to 30 ms on/off (typical full energy slowing down time of τ slow ≈ 50 ms at mid-radius), toroidicity-induced Alfvén eigenmodes (TAEs) located in the outer periphery of the plasma become intermittent and coincident with the more tangential beam. Core mode activity changes from reversed shear Alfvén eigenmodes (RSAEs) to a mix of RSAE and beta-induced Alfvén eigenmodes. Discharges with 30 ms on/off period do not have a persistent bump-on-tail feature, have the lowest average mode amplitude and least fast ion transport. Furthermore, detailed analysis of an individual TAE using TRANSP kick modeling (Monte Carlo evolution of the distribution function with probabilistic 'kicks' by the AEs) and the resistive MHD code with kinetic fast ions, MEGA, find no strong role of energy gradient drive due to bump-on-tail features. Instead, the observed TAE modulation with interleaved beams is likely a pitch angle dependent result combined with slowing down of the tangential beam between pulses. For the conditions investigated, bump-on-tail contributions to TAE drive were found to be 5% or less of the total drive at any given time.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

Additive manufacturing↗
DOE OSTI2020

Environmental Impact Calculations for Geothermal DDU system on WVU Campus

This dataset contains all the inputs used and output produced from Matlab for the environmental analysis of an improved hybrid geothermal district heating and cooling (GDHC) system with a heat pump, without a heat pump, and for a hot water GDHC. Detailed descriptions of the contents of this repository are provided below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Wood Heater Design Challenge Workshop Report

To support the United States Department of Energy (DOE) Bioenergy Technologies Office’s (BETO) goal of advancing residential wood heating technologies, Brookhaven National Laboratory (BNL), Lawrence Berkeley National Laboratory (LBNL), and the Alliance for Green Heat (AGH) hosted the fifth Wood Heater Design Challenge (WHDC). The Challenge consists of three virtual workshops and a Technology Slam aimed at inspiring and advancing the next generation of residential wood heaters. With an estimated 12.5 million homes in the United States using wood or pellets for space heating, this challenge stands to create a significant impact on the environmental footprint of domestic heating. This report provides a summary of the activities and outcomes from the three virtual workshops held in January, March, and April of 2022. The goal of the workshops was to bring together stakeholders from across the world to discuss research and development (R&D) challenges and opportunities for advancing residential wood heaters in the United States. The workshops focused on three topics: 1. Advances in wood heater design and technology; 2. Advances in instrumentation used for wood heater testing and field data collection; and 3. Adoption of new wood heater technology and integration with other renewables. Each workshop had more than 100 participants each day which included wood heater manufacturers, researchers, academics, government agencies, and policy makers. Overall, a clear message was presented that collaborative input from experts at air quality agencies, industry, academic institutions, and non-profits is necessary to ensure that wood heating technology continues as a mainstream renewable energy option in our changing energy landscape and to facilitate innovative technology solutions. Participants also provided insights and recommendations that may help guide future R&D investments.

09 BIOMASS FUELS↗
DOE OSTI2026

HFIR Steady State Heat Transfer Code (HSSHTC) Statistical Uncertainty Analysis

HSSHTC, the safety basis steady state TH code for HFIR, uses a highly conservative approach in which all input and calculation uncertainties are resolved simultaneously at their most limiting setting. This results in excessive conservatism which does not account for the high unlikelihood of such simultaneous worst-case conditions. The present study explores an alternative approach, BEPU, in which reasonable working assumptions for the probability distribution of each input uncertainty are used to determine a relationship between burnout power margin and core fuel failure probability. This was performed under a philosophy of perturbing uncertainty parameters already defined within the HSSHTC methodology while preserving the HSSHTC calculation approach and solution methodology itself. Based on the assumptions employed in this study, the BEPU approach resulted in a 0.29 increase in burnout power ratio (25 MW increase in burnout power) compared to the latest HSSHTC calculations of C-HFIR-2026-004. The study can be refined in the future by employing fuel fabrication data to provide more realistic input distributions. Future changes to the HSSHTC methodology would potentially allow a more comprehensive treatment of uncertainties which may further increase the burnout power ratio.

Wysocki, Aaron [ORNL] (ORCID:0000000222043779)↗
DOE OSTI2026

MODEL DEVELOPMENT AND VERIFICATION OF A 1,350°C AIR RECEIVER TEST SYSTEM

Open-loop volumetric receivers work with air at atmospheric pressure and are suitable for single-cycle or multicycle power plants. A novel additively manufactured (AM) silicon carbide (SiC), ceramic matrix composite (CMC) volumetric receiver was developed that consisted of a lattice structure, which absorbs solar radiation and converts it into heat energy. Heat energy from the porous receiver was transferred to heated ambient air. The receiver acted as a convective heat exchanger, transferring heat to the fluid through convection. Engineering was facilitated to develop a high-temperature air receiver test bed at Sandia national Laboratories (SNL) capable of demonstrating a 50kWth open-loop volumetric SiC air receiver module developed by General Electric Aerospace Research (GE Aerospace). This paper presents the development of the high temperature AM SiC-CMC air receiver, the test bed, and the first testing operations of this device, which was experimentally demonstrated to achieve 1,350°C for over 3.5 hours of operation, based on input flux levels of 67–91 W/cm2 0.5-2.5 kg/s flow rates. From the test campaign results, based on the measured convective heat transfer to the airflow and bounded radiative-loss assumptions, the solar-thermal efficiency of HOTSSTAR test module was estimated to be approximately as high as 74% for representative test conditions.

14 SOLAR ENERGY↗
DOE OSTI2024

Component innovations for lower cost mechanical vapor compression

Despite significant capital and operating costs, mechanical vapor compression (MVC) remains the preferred technology for challenging brine concentration applications. Here, this work seeks to assess the dependence of MVC costs on feedwater salinity and desired water recovery and to quantify the value of improved component performance or reduced component costs for reducing the levelized cost of water (LCOW) of MVC. We built a cost optimization model coupling thermophysical, heat and mass transfer, and technoeconomic models to optimize and identify low cost MVC system designs as a function of feedwater salinity and water recovery. The LCOW ranges over 3.6 to 6.1 $\$$/m 3 for seawater feed salinities of 25–150 g/kg and water recoveries of 40–80 %. We then perform sensitivity analysis on parameter inputs to isolate irreducible costs and determine high value component innovation targets. The LCOW was most sensitive to evaporator material costs and performance, including the overall heat transfer coefficient in the evaporator. Process and material innovations such as polymer-composite evaporator tubes that reduce evaporator costs by 25 % without reducing heat transfer performance by more than 10 % would result in MVC cost reductions of 8 %.

42 ENGINEERING↗
DOE OSTI2023

Virtual Volumetric Additive Manufacturing (VirtualVAM)

Abstract Tomographic volumetric additive manufacturing (VAM) produces arbitrary 3D geometries by exposure of a rotating volume of photopolymer resin to tomographically‐patterned illumination. This enables high speed, layer‐less printing of parts from a wide range of photopolymers not amenable to layer‐by‐layer processes. Since the entire geometry is produced at once over the course of a few seconds to minutes, molecular diffusion length scales become significant to the printing process. Understanding these molecular reaction and diffusion processes is imperative for advancing VAM to a usable technology. These processes are experimentally very difficult to monitor and measure. Herein, VirtualVAM ‐ a simulation framework for modeling the tomographic VAM process, is developed and experimentally validated. VirtualVAM simulates reaction, diffusion, and heat generation processes over the course of a print with single‐voxel resolution. From a few experimentally‐determined input parameters and a set of images for projection, VirtualVAM is able to generate a large spatio‐temporal data set for any given tomographic VAM print. Using VirtualVAM, a number of experimentally‐unattainable aspects of the VAM process are investigated such as single‐voxel conversion profiles, effect of molecular oxygen, and stopping time determination. VirtualVAM also enables the optimization of exposure patterns to further improve contrast between in‐part and out‐of‐part delivered dose.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Modeling of hydrogen liquefaction using magnetocaloric cycles with permanent magnets

Hydrogen (H 2 ) is promising alternative to replace fossil fuels, but its transport and storage has been challenging. As H 2 fuel cell vehicles are gaining traction, the infrastructure for storing large amounts of liquid H 2 is needed. However, liquid H 2 would suffer from boil-off loss, and traditional vapor compression refrigeration systems would not be able to economically recover the lost H 2 due to the low efficiencies at cryogenic temperature. Magnetocaloric (MC) refrigeration systems could possess much higher coefficient of performance (COP) at cryogenic temperature compared to the vapor compression ones. Previous work on cryogenic MC systems, however, have only focused on large scale applications which use superconducting magnets to provide a large magnetic field but are prohibitively expensive to operate for small scale applications, such as that of a H 2 refilling station. In this work, we model the performance of a MC refrigeration cycle using 1-Tesla permanent magnets for H 2 liquefaction, with the objective of cooling H 2 from 80 K (using liquid nitrogen as the heat sink) to 20 K (boiling point of hydrogen). We evaluate main performance metrics including the total work input to the refrigeration system, COP, total MCM mass in the system, and total volume of the permanent magnets, etc. Our modeling results indicate that such a permanent magnet-based MC cooling system is feasible for small-scale H 2 liquefaction, with projected COP values significantly higher than those of vapor compression systems. In conclusion, this work provides design guidelines for future experimental efforts on permanent magnet MC cooling systems for cryogenic cooling.

08 HYDROGEN↗