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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

The Research Plan: Closing the ExMC Med02 "Pharmacy" Gap

HRP Human Research Roadmap: Risk and Gap Risk of Adverse Health Outcomes and Decrements in Performance due to Inflight Medical Conditions. Med02 "Pharmacy" Gap: We do not have the capability to provide a safe and effective medication formulary for exploration missions delivering a recommendation for a chemically stable, safe, and effective medication formulary that will support the operational needs of exploration space missions research strategy evidence-based formulary and models innovative analytical tools and methodologies novel treatments and preventive measures Planned review by a panel of experts from the pharmaceutical industry, regulatory, and academic scientific communities Formulary Selection Formulary Potency and Shelf life Formulary Safety and Toxicity Novel Technology Proof-of-Concept Portable real-time chemical analysis Innovative drug development / design

Daniels, Vernie↗
DOE OSTI2025

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm 2 aqueous organic redox flow battery

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗
DOE OSTI2024

Evaluating large scale aqueous organic redox flow battery performance with a hybrid numerical and machine learning framework

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low-capacity degradation in 10 cm$^2$ cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier to commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm$^2$ DHP-based AORFB by combining a physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. Such combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗
DOE OSTI2019

DiagSoftfailure: Automated Soft-Failure Diagnostic Tool Using Machine Learning for Network Users

Network performance problems resulting in slow data communication speed become a significant obstacle for a satisfactory user experience. Currently, there is a lack of an automated diagnostic tool that can help network users to find the complicated network problems that degrade network performance. To fill up this gap, InfoBeyond proposes DiagSoftfailure to provide an automated diagnosis tool. DiagSoftfailure is a machine learning-based approach to infer the location and root cause of network failures that result in performance degradation. As a user-focused tool, it allows a network user to request a diagnosis. DiagSoftfailure can automatically collect the raw packet traces in the network. Base on the network signature, automated classifiers trained by combining supervised and semi-supervised machine learning is used to identify both known and unknown soft-failures in the network. A report allows novice and expert users to understand network problems for troubleshooting. In this document, we describe the DiagSoftfailure diagnosis algorithms and our experiments in a live high-speed network. DiagSoftfailure achieves 96.39% accuracy for known soft-failure diagnosis and 99.82% for unknown soft-failure diagnosis, respectively. Such results demonstrate that DiagSoftfailure is a viable technology. InfoBeyond would develop it as a marketable product that can be used by universities, Labs, DOE, government agencies, organizations, enterprises, and scientific communities to improve their network diagnosis capabilities.

42 ENGINEERING↗
DOE OSTI2024

Challenges in Tracking Waste Reduction Performance Improvement in Manufacturing Plants

The recently released Circularity Gap Report 2023 by Circle Economy Foundation found that the circularity score for the global economy is declining. This indicates that material extraction from virgin sources is climbing over earlier years. As per data released by US Geological Survey, material use in the US economy has increased exponentially with the expanding US economy over last century. EPA tracked municipal solid waste from 1960 to 2018 and found that 50% of the waste is destined for landfills. EPA estimates that US industry is responsible for 2.7 billion ton of solid non-hazardous waste annually in our mostly linear economy model. The circular economy framework aims at decoupling economic value generation from virgin materials extraction from nature. The linear model of material extraction and disposal at end of life is highly unsustainable. Manufacturing companies have realized this and in their commitments to sustainability are adopting ambitious waste reduction targets. Through Better Plants program US Department of Energy has established Waste Reduction Network where it is offering technical assistance to partners to achieve these ambitious waste reduction goals. Basic requirements of establishing a target include identifying a baseline, quantifying waste performance, and measuring progress over time. One problem faced by industry is the unstandardized metrics to quantify waste performance that may not be well suited to demonstrate progress. In this paper, we research methods traditionally used to measure waste performance and highlight advantages, gaps and limitations of each method. Suitability of the methods applicable to different manufacturing circumstances are also examined. Finally, we present a case study of a large manufacturer that faced inconsistencies in their tracked measurement metric. A solution was proposed to alter the methodology to enable more accurate waste performance tracking against a baseline.

Chaudhari, Subodh↗
DOE OSTI2022

Tuning Intermediate Bands of Protective Coatings to Reach the Bulk-Recombination Limit of Stable Water-Oxidation GaP Photoanodes

Stable photoelectrochemical solar fuel production requires protective coatings to achieve effective charge separation, transport, and injection at the semiconductor–liquid interfaces, implying that the coating should energetically align its intermediate band (IB) with both the photoabsorber's band edge and co-catalyst's potentials. Yet approaches to adjust coating IB positions to accommodate various semiconductor light absorbers for constructing efficient and stable photoelectrodes have not been developed. In this report, three types of transition metal (M = Mn 2+ , Mn 3+ , and Cr 3+ ions) alloyed TiO2 coatings are discovered using atomic layer deposition (ALD). The IB energetics of these coatings are characterized by X-ray photoelectron spectroscopy and are found to be tunable inside the TiO 2 bandgap, through varying ALD growth conditions. By applying these coatings to n-type GaP and integrating with IrO x co-catalysts, the water-oxidation J–E performance is comparable to an uncoated corroding GaP photoanode. It reaches the bulk recombination limit of the GaP and achieves ≈28% absorbed photon to current efficiency under 475-nm light excitation (6.48 mW cm -2 ) and 100-h stable water oxidation. The outstanding performance and stability are attributed to the efficient charge separation and hole transport, as allowed by the energy alignment of the coating IB and the GaP valence band edge.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Green Propulsion : A NASA GSFC Assessment

In the ever-changing paradigm of efficient and capable spacecraft design, scientific missions continue pushing spacecraft subsystems to deliver effective solutions to meet challenging new mission/spacecraft applications. From an in-space storable liquid chemical propulsion perspective, monopropellant hydrazine is a dependable propellant. Bi-propellant architectures offer even superior performance, but add the complexity of a hypergolic fuel (hydrazine/ mono-methyl hydrazine) and oxidizer (Nitrogen Tetroxide/ mixed oxides of nitrogen) dual tank combination. These propulsion system designs (mono-propellant and bi-propellant) have high heritage, high propellant throughput qualified engines, widely tested material compatibility, qualified fluid delivery commercial-off-the-shelf components, known handling practices, and repeatable performance in successfully delivering on mission requirements. NASA and the broader propulsion community have historically selected hypergolic propellants for most mission applications. The space propulsion community has learned to successfully handle these highly toxic and hazardous materials, navigate the regulated use and the associated safety protocols, personnel protective equipment, and unique training standards – all requisite for loading spacecraft propulsion systems with hypergolic propellants. The question now arises as to what is next for in-space chemical propulsion? Is there an alternative, or even replacement, to the reliable hypergolic fluids, or propellant alternatives that promise increased mission benefits? With the evolution and proven advancements in innovative in-space green propellant technologies capable of delivering benefits to scientific missions, concern over the reliability and infusibility of this higher performing and safer to handle class of propellants is waning. As NASA science missions move forward with the potential flight infusion of green propulsion, NASA and the broader propulsion community are working to address remaining gaps in hardware development, reliability, performance, unique operational considerations, and risk mitigations for high value scientific assets.

Henry W. Mulkey↗
DOE OSTI2015

TiN coated aluminum electrodes for DC high voltage electron guns

Preparing electrodes made of metals like stainless steel, for use inside DC high voltage electron guns, is a labor-intensive and time-consuming process. In this paper, the authors report the exceptional high voltage performance of aluminum electrodes coated with hard titanium nitride (TiN). The aluminum electrodes were comparatively easy to manufacture and required only hours of mechanical polishing using silicon carbide paper, prior to coating with TiN by a commercial vendor. The high voltage performance of three TiN-coated aluminum electrodes, before and after gas conditioning with helium, was compared to that of bare aluminum electrodes, and electrodes manufactured from titanium alloy (Ti-6Al-4V). Following gas conditioning, each TiN-coated aluminum electrode reached −225 kV bias voltage while generating less than 100 pA of field emission (<10 pA) using a 40 mm cathode/anode gap, corresponding to field strength of 13.7 MV/m. Smaller gaps were studied to evaluate electrode performance at higher field strength with the best performing TiN-coated aluminum electrode reaching ∼22.5 MV/m with field emission less than 100 pA. These results were comparable to those obtained from our best-performing electrodes manufactured from stainless steel, titanium alloy and niobium, as reported in references cited below. The TiN coating provided a very smooth surface and with mechanical properties of the coating (hardness and modulus) superior to those of stainless steel, titanium-alloy, and niobium electrodes. These features likely contributed to the improved high voltage performance of the TiN-coated aluminum electrodes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

I/O Bottleneck Detection and Tuning: Connecting the Dots using Interactive Log Analysis

Using parallel file systems efficiently is a tricky problem due to inter-dependencies among multiple layers of I/O software, including high-level I/O libraries (HDF5, netCDF, etc.), MPI-IO, POSIX, and file systems (GPFS, Lustre, etc.). Profiling tools such as Darshan collect traces to help understand the I/O performance behavior. However, there are significant gaps in analyzing the collected traces and then applying tuning options offered by various layers of I/O software. Seeking to connect the dots between I/O bottleneck detection and tuning, we propose DXT Explorer, an interactive log analysis tool. In this paper, we present a case study using our interactive log analysis tool to identify and apply various I/O optimizations. We report an evaluation of performance improvement achieved for four I/O kernels extracted from science applications.

Bez, Jean Luca↗
DOE OSTI2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of methane fluxes measured by eddy-covariance require gap-filling to estimate annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for methane, with regards both to the best model algorithms and predictors. In this study, we address the need for standardization by synthesizing results of gap-filling methods applied at 17 wetland sites spanning boreal to tropical regions including all major wetlands classes and two rice paddies. We introduce new procedures for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with robust uncertainty estimates. We tested a conventional method (marginal distribution sampling) and four machine learning algorithms - penalized linear regression, artificial neural networks, random forests, and boosted decision trees - and four predictor sets, including temporal, meteorological, ecosystem carbon and energy flux, and soil predictors. We find that the conventional method can achieve similar median performance to the machine learning models but is worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. Finally, we gap-fill and provide summary evaluation metrics for all 81 sites in the FLUXNET-CH4 community dataset and publicly release the python code for model development, evaluation, and uncertainty estimation.

42 ENGINEERING↗
DOE OSTI2020

Why Wideband Gap Needs Techno-Economics

The wideband gap community when discussing projects antidotally refers to the cost of the technology, frequently noting the higher wideband gap upfront cost in comparison to the incumbent state of art. Other technologies such as photovoltaics have embraced techno-economics analysis (TEA) as a method of analytically articulating the impacts of research advancements on system performance and lifetime costs. Our TEA shows the potential impact of research advancements on Gallium Oxide (Ga2O3) and Silicon Carbide (SiC) wafer manufacturing cost, the impact on the levelized cost of electricity (LCOE) when using a SiC based solar inverter, and lastly how the high cost of devices (SiC in this case) can be offset by other system cuts, such as the footprint and cooling reductions in a 1MW variable frequency motor drive. The specific goal is to show analytically the lifetime cost and performance benefits of adopting wideband gap technology whether it is at the device level or it is cumulative benefit though a simplified balance of system.

14 SOLAR ENERGY↗
DOE OSTI2022

A Comparison of Time-Series Gap-Filling Methods to Impute Solar Radiation Data: Preprint

Complete solar resource data sets play a critical role at every stage of solar energy projects; however, measured or modeled solar resource data come with significant uncertainties and usually suffer from several issues, including, but not limited to, data gaps and data quality issues. To mitigate these issues, an appropriate data imputation method should be implemented to build a complete and reliable temporal (and spatial) database. Motivated by this, in this study, we extensively compare the performance of eight different gap-filling methods by creating random and artificial data gaps in (i) hourly irradiance data for 1 year using a few locations of the National Solar Radiation Database (NSRDB) and (ii) 1-minute ground measurement data sets from the Surface Radiation Budget Network (SURFRAD) and the National Renewable Energy Laboratory (NREL) stations.

clearness index↗
NASA NTRSDate not supplied

Developments on Reusable TPS Materials Based Upon Shuttle Tile

The insulating tiles used on the Space Shuttler Orbiter, principally AETB (Alumina Enhanced Thermal Barrier), have been seen by new vehicle developers in the aerospace industry as a fast-track approach for fielding a reusable thermal protection system (TPS) given the extensive use by both the Orbiter program and the current Orion vehicle. NASA has, and is, supporting the industry through technology transfer and commercial crew and payloads programs. However, the reusable TPS community is rapidly encountering challenges rooted in Orbiter’s and Orion’s dependence on legacy material and processes. With the aim of alleviating these issues, improving availability of reusable TPS, and strengthening the nation’s space economy, ongoing efforts at NASA Ames have focused on understanding process-property relationships in AETB as well as novel materials and technologies for reusable TPS. The first portion of this talk will address the effects of raw materials and processing conditions on the properties of AETB-8, including the raw materials used, mixing and casting parameters, and thermal history. Understanding these relationships, and how they extend to aerothermal performance, is instrumental in alleviating supply chain constraints and improving manufacturability. The implications of these finding on development of novel TPS materials and systems will be discussed. AETB will likely continue to fill a niche of relatively high performance with associated costs. There exists, therefore, a gap in developed reusable TPS for a lower cost, moderate performing material system. The later portion of this talk will touch on current development efforts at Ames focusing on reduced cost and ease of integration, which are being pursued in addition to AETB and higher performing systems. In addition to summarizing these efforts, an outlook for these projects and collaborations will be given.

Peter Edward Marshall↗
NASA NTRSDate not supplied

Developments on Reusable TPS Materials Based Upon Shuttle Tile

The insulating tiles used on the Space Shuttler Orbiter, principally AETB (Alumina Enhanced Thermal Barrier), have been seen by new vehicle developers in the aerospace industry as a fast-track approach for fielding a reusable thermal protection system (TPS) given the extensive use by both the Orbiter program and the current Orion vehicle. NASA has, and is, supporting the industry through technology transfer and commercial crew and payloads programs. However, the reusable TPS community is rapidly encountering challenges rooted in Orbiter’s and Orion’s dependence on legacy material and processes. With the aim of alleviating these issues, improving availability of reusable TPS, and strengthening the nation’s space economy, ongoing efforts at NASA Ames have focused on understanding process-property relationships in AETB as well as novel materials and technologies for reusable TPS. The first portion of this talk will address the effects of raw materials and processing conditions on the properties of AETB-8, including the raw materials used, mixing and casting parameters, and thermal history. Understanding these relationships, and how they extend to aerothermal performance, is instrumental in alleviating supply chain constraints and improving manufacturability. The implications of these finding on development of novel TPS materials and systems will be discussed. AETB will likely continue to fill a niche of relatively high performance with associated costs. There exists, therefore, a gap in developed reusable TPS for a lower cost, moderate performing material system. The later portion of this talk will touch on current development efforts at Ames focusing on reduced cost and ease of integration, which are being pursued in addition to AETB and higher performing systems. In addition to summarizing these efforts, an outlook for these projects and collaborations will be given.

Thermal Protection Systems↗
DOE OSTI2024

Characterization of Fuel and Cladding In and Near the Pellet-Pellet Gap of a High-Burnup Pressurized Water Reactor Fuel Rod

At TopFuel 2021, we reported on metallographic examinations of a high burnup ZIRLO-clad spent fuel rod operated at the North Anna Power Station having a 3-mm gap between pellets at an elevation of ~1,403 mm. The rod was sectioned axially at that elevation to reveal the pellet-pellet interfaces and the pellet-pellet gap. Sections were mounted and polished to reveal the distribution of cladding inner and outer oxide layer thicknesses above, below, and within the gap. Since then, total cladding hydrogen measurements have been performed to quantify the cladding hydrogen content through the gap and above and below the pellet-pellet gap region. This paper provides the cladding hydrogen concentrations and discusses the overall findings regarding cladding waterside oxidation and cladding hydrogen diffusion to a pellet-pellet gap.

Montgomery, Rose↗
DOE OSTI2023

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

Eisenmann, Matthias↗
DOE OSTI2019

A New Design Approach to Mitigating the Effect of Parasitics in Capacitive Wireless Power Transfer Systems for Electric Vehicle Charging

This paper introduces a new design approach to mitigate the effect of parasitic capacitances and achieve high performance in large air-gap capacitive wireless power transfer (WPT) systems for electric vehicle (EV) charging. In a capacitive WPT system for EVs, the vehicle chassis and roadway introduce multiple parasitic capacitances that can overwhelm the coupling capacitance and severely degrade power transfer and efficiency. The proposed approach addresses this challenge by employing split-inductor matching networks, which allow the complex network of parasitic capacitances to be simplified into an equivalent four-capacitance model. The shunt capacitances of this model are directly utilized as the matching network capacitors, hence, absorbing the parasitic capacitances and eliminating the need for discrete high-voltage capacitors. A systematic procedure is developed to accurately measure the equivalent capacitances of the model, enabling the system's performance to be reliably predicted. The proposed approach is used to design two 6.78-MHz 12-cm air-gap prototype capacitive WPT systems with capacitor-free matching networks. The first system transfers up to 590 W using 150-cm 2 square coupling plates and achieves an efficiency of 88.4%. The second prototype system transfers up to 1217 W using 118-cm 2 circular coupling plates, achieving a power transfer density of 51.6 kW/m 2 . The measured output power profiles of the two systems match well with their predicted counterparts, validating the proposed design approach.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Extended two-temperature model for ultrafast thermal response of band gap materials upon impulsive optical excitation

Thermal modeling and numerical simulations have been performed to describe the ultrafast thermal response of band gap materials upon optical excitation. A model was established by extending the conventional two-temperature model that is adequate for metals, but not for semiconductors. It considers the time- and space-dependent density of electrons photoexcited to the conduction band and accordingly allows a more accurate description of the transient thermal equilibration between the hot electrons and lattice. Ultrafast thermal behaviors of bismuth, as a model system, were demonstrated using the extended two-temperature model with a view to elucidating the thermal effects of excitation laser pulse fluence, electron diffusivity, electron-hole recombination kinetics, and electron-phonon interactions, focusing on high-density excitation.

36 MATERIALS SCIENCE↗