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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Probabilistic Groundwater Modeling of the RDX Plume at Los Alamos National Laboratory to Support Risk Assessment - 20359

A plume of the contaminant hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) with concentrations greater than the New Mexico tap water drinking standard (9.66 ppb) is present in the regional aquifer near the southwestern boundary of Los Alamos National Laboratory (LANL). A risk assessment is performed for exposure to regional aquifer groundwater, with long-term predictions of RDX concentrations provided by a calibrated, probabilistic, numerical fate and transport model. The structure of the model is hierarchical, with the RDX Regional Aquifer (RA) groundwater model acting as the primary tool for analysis of downgradient RDX concentrations. The RA model is deeply informed by the conceptual site model (CSM) and is calibrated using site RDX concentration data and hydraulic head measurements, along with other analyses. The model is calibrated using data through December 2019. Where data are scarce other lines of evidence are used to inform inputs, including the multiphase RDX Vadose Zone (VZ) model and Pipe and Disk analytical screening tool. Model inputs are described with informative prior distributions using a robust approach to development that incorporates all available lines of evidence for every parameter used as an input to the RA model. Calibration is performed using a classical nonlinear optimization routine, which is then used to initialize a Bayesian calibration. The Bayesian calibration constrains the uncertainty in the classical calibration, ultimately providing posterior distributions for all model parameters. The challenges of the calibration include high-dimensional parameter space, including spatially heterogeneous hydraulic conductivities, comparatively sparse data, and low RDX concentrations. Posterior parameter distributions developed in the calibration process are then used for stochastic predictive model runs into the future. The result of the forward runs is spatially and temporally explicit estimates of head and concentration with uncertainty at all points in space and time. The probabilistic modeling approach presented here includes innovative computational and statistical methods that leverage high-performance computing (HPC) resources. It makes use of prior modeling work performed at the chromium plume site in the central LANL area, with extensive updates. The risk assessment will ultimately be used to support decision-making at the site, using multiple appropriately weighted sources of information, as well as uncertainty, sensitivity, and value-of-information analyses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Improved thermoelectric performance of α- and β- Cu 2 Se through suppression of hole density using extrinsic copper vacancies

Modulating Cu + ion disorder in Cu 2 Se can enable control over the polymorphism and the carrier density leading to enhanced thermoelectric properties for both α- and β-Cu 2 Se. Here we report that the incorporation of Cr 3+ into the Cu 2 Se crystal lattice facilitates the stabilization of α-Cu 2 Se at 300 K leading to a large (~140%) reduction in the carrier density both below and above the phase transition. This is attributed to the reduction in the density of intrinsic copper interstitials (Cu$^{•}_{i}$) within the Cu (2-δ-λ) (Cr$^{··}_{Cu}$)λ(V$^{'}_{Cu}$)δ(Cu$^{π}_{i}$) δ-2λ (h • ) δ-2λ Se crystal lattice. Such optimization of the carrier density led to a large (63%) increase in the thermopower and a drastic (46%) reduction in the total thermal conductivity for both α- and β-Cu 2 Se matrices. Consequently, a significant enhancement of the thermoelectric performance is observed in the entire temperature range from 300 K to 773 K. This results in high average ZT values for both α-Cu 2 Se (ZT ave = 0.60) and β-Cu 2 Se (ZT ave = 0.97), which paves the way for both near room temperature and high temperatures applications. Furthermore, this work provides a new approach to optimize the thermoelectric performance of Cu 2 Se-based materials by leveraging the interaction between mobile intrinsic Cu$^{·}_{i}$ and extrinsic V$^{'}_{Cu}$ to suppress the hole density.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

CFD Simulation of Aerobic Gas Fermentation to Enable Commercial Conversion of CO 2 into Aquaculture and Animal Feed: Cooperative Research and Development (Final Report)

NovoNutrients’ fermentation technology uses energy from hydrogen to transform industrial CO2 emissions into premium animal feed ingredients and other valuable products. A single NovoNutrients’ commercial manufacturing plant will capture and convert over 200,000 tons/yr of CO2 into over 100,000 tons/yr of high-protein feed. Key to the rapid and widespread deployment of the technology is maximization of its productivity and energy efficiency. Robust, physically based computational models of the technology will significantly increase productivity and efficiency, accelerating NovoNutrients’ technology to manufacturing scale. NREL has unique capabilities for creating and running such computational models. NREL's existing aerobic bioreaction computational fluid dynamics (CFD) models will be adapted to NovoNutrients’ gas fermentation (CO2, H2, O2) technology. The multiphysics CFD simulations require thousands of high-performance computing (HPC) node hours to simulate the complex geometries and contents of NovoNutrients’ industrial bioreactors. The experimentally validated CFD models were used to identify optimally efficient and productive bioreactor designs and operating conditions.

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

Universal liquid sample device and process for high resolution transmission electron microscope imaging and multimodal analyses of liquid sample materials

Liquid sample imaging devices and processes are disclosed for high resolution TEM imaging and multimodal analyses of liquid sample materials in situ under high vacuum that are compatible with standard type TEM chip membranes and TEM sample holders allowing TEM liquid sample imaging to be performed wherever a TEM instrument is accessible and at a substantially reduced cost compared to prior art systems and approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Self-grown NiCuO x hybrids on a porous NiCuC substrate as an HER cathode in alkaline solution

Electrocatalysts converted directly from the substrates hold the key to achieve high catalytic activity and durability due to their high bonding strength, intimate electronic contact, and tunable phase composition. Herein, an electrically conductive porous NiCuC substrate was adopted to develop a multi-porous NiCuO x /NiCuC hydrogen evolution reaction (HER) catalyst using a one-step oxidation method in this research. The NiCuO x hybrids were characterized as three typical layers: an outer layer with NiCuO 2 solid solution phase, a partially oxidized intermediate layer, and an inner layer with limited stable oxygen content. Here, the NiCuO x /NiCuC possesses abundant Ni(II)–Cu(II) sites to accelerate the Volmer step via the electrostatic effect with OH – , numerous metallic NiCu sites to facilitate the H adsorption, and highly accessible channels to promote the Heyrovsky step during HER catalysis. As a result, high electrocatalytic performance was obtained with an overpotential of 116 mV at a current density of 10 mA cm –2 in 1.0 M potassium hydroxide, and a stable catalytic performance during the HER process for more than 24 h.

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

Raspberry colloid-templated approach for the synthesis of palladium-based oxidation catalysts with enhanced hydrothermal stability and low-temperature activity

It is becoming increasingly urgent to develop and utilize novel, more efficient and stable materials for mobile and stationary emission control applications as the deleterious consequences of anthropogenic air pollution are becoming more evident and pressing. Tightening regulations, particularly related to automotive exhaust treatment, together with continued improvements in engine design, that result in lowering the engine operating temperatures and inadvertently lead to the release of an overwhelming proportion of pollutants during the cold start, present new challenges for materials design, specifically for oxidation catalysts. In particular, improvements in the low-temperature activity while maintaining catalyst stability at high temperatures are required from the next-generation catalyst. Typical catalysts for removal of pollutants from automotive exhaust streams incorporate platinum group metals (PGMs). They tend to be inefficient at low temperatures (below 250 °C), thus accounting for the cold start problem, yet sinter and lose their activity at high temperatures that are frequently encountered during catalyst operation. High PGM loadings are often employed to compensate for catalyst inefficiencies and fast degradation, ultimately resulting in high-cost catalytic converters. Here, we have developed a new approach for the design and formation of catalytic materials that allows for both significantly more efficient PGM incorporation and improved overall catalyst performance at reduced PGM loadings. The method provides control over the composition and geometry of the support through self-assembly of sacrificial composite template — “raspberry” polymeric colloids decorated with catalytic particles — accompanied by infiltration with metal-oxide precursor and subsequent removal of the colloids. This method simultaneously structures the porous network and organizes the catalytic particles within it. Uniquely, the resulting catalytic particles are partially embedded in the support matrix and partially exposed to the pore interior, producing catalytic sites that are both stable and accessible. Herein, the feasibility of this novel and versatile approach for automotive catalytic conversion is demonstrated: the studies include testing alumina-based raspberry-colloid-templated (RCT) catalysts containing Pd nanoparticles (RCT Pd/Al2O3) for oxidation of propane and carbon monoxide under simulated diesel exhaust conditions and hydrothermal aging at 800 °C for 50 h in the simulated stream. The RCT Pd/Al2O3 catalysts exhibit exceptional activity toward CO oxidation, reduced reaction onset temperature, and high stability to elevated temperatures (demonstrated through prolonged exposure to temperatures up to 950 °C) and reactive gas streams, without migration, sintering or loss of the precious metal NPs. Notably, the novel catalyst shows the same or slightly better performance than the commercial catalysts even when the PGM load is reduced by ~80 % compared to the commercial counterparts. These results provide confidence for the utilization of the RCT approach for the fabrication of robust nanostructured catalysts for next-generation, energy-efficient catalytic converters with improved performance at low and high temperatures and reduced costs. The RCT methodology is, in addition, highly generalizable, and can thus be applied for the design of a wide range of catalytic systems in the automotive sector and beyond.

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

Pilot-scale hydrotreating of catalytic fast pyrolysis biocrudes: process performance and product analysis

Catalytic fast pyrolysis (CFP) is a technology option for producing advanced biofuels from hydrocarbon-rich biocrude intermediates. The relatively high oxygen content of biocrudes compared to petroleum intermediates increases hydrogen consumption and the lower thermal stability accelerates catalyst deactivation and reactor fouling hindering the adaptation of hydrotreating technology for biocrude upgrading into biofuels. In this study, four chemically different biocrude feeds were upgraded in a pilot scale hydroprocessing unit at similar process conditions using a commercial hydrodeoxygenation (HDO) catalyst. The biocrude feeds and hydrotreated products were characterized using standard ASTM procedures and advanced analytical techniques (GCxGC-FID and GCxGC-MS). HDO catalyst activity was monitored by changes in physical properties and chemical composition of the upgraded products as a function of time on stream. Aliphatic acids, ketones, aldehydes, and furan derivatives were completely converted during the hydrotreating tests while the concentration of aromatics, aliphatic hydrocarbons and phenolics increased during the hydrotreating tests. The oxygen content, nitrogen content, specific gravity, viscosity and the heavy end of the boiling point range (determined by simulated distillation) of the upgraded products increased with increasing time on stream during hydrotreating. The deactivation rate was the lowest for the biocrude feed that contained the most aliphatic and aromatic hydrocarbons and was the highest for the biocrude feed that had the most anhydrosugars. Overall, the HDO deactivation rate correlates with the total amount of oxygen in the feed (17 wt% to 29 wt%, on a wet basis).

09 BIOMASS FUELS↗
DOE OSTI2026

A scalable framework for efficient coupling of thermal and microstructural simulations in additive manufacturing

Predicting microstructure evolution in metal additive manufacturing (AM) is important for process optimization, but spatiotemporal scale disparities between thermal transport and microstructure evolution create significant challenges for efficient data transfer between simulation codes. To address this, we present Stork, a scalable framework for coupling thermal and microstructural simulations. Stork uses a sparse data representation to identify and store active solidification sub-volumes, enabling highly parallel quad-linear interpolation from coarse thermal grids to fine microstructure grids without large intermediate storage. We demonstrate the framework by coupling the semi-analytic heat transfer code 3DThesis with the time-parallel cellular automata code Toucan. This approach achieves over two orders of magnitude reduction in data generation time and file size compared to prior workflows. Numerical studies show that quad-linear interpolation preserves grain morphology and crystallographic texture in laser powder bed fusion (LPBF) simulations for coarsening ratios up to 16. Overall, Stork provides a scalable pathway for high-throughput, component-scale AM simulations on modern high-performance computing systems.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Optimized multisectioned acoustic liners

A critical examination is presented of the use of optimized axially segmented acoustic liners to increase the attenuation of a liner. New calculations show that segmenting is most efficient at high frequencies with relatively long duct lengths where the attenuation is low for both uniform and segmented liners. Statistical considerations indicate little advantage in using optimized liners with more than two segments while the bandwidth of an optimized two-segment liner is shown to be nearly equal to that of a uniform liner. Multielement liner calculations show a large degradation in performance due to changes in assumed input modal structure. Finally, in order to substantiate previous and future analytical results, in-house (finite difference) and contractor (mode matching) programs are used to generate theoretical attenuations for a number of liner configurations for liners in a rectangular duct with no mean flow. Overall, the use of optimized multisectioned liners (sometimes called phased liners) fails to offer sufficient advantage over a uniform liner to warrant their use except in low frequency single mode application.

Baumeister, K. J.↗
NASA NTRS1983

Small Gas Turbine Combustor Primary Zone Study

A development process is described which consists of design, fabrication, and preliminary test evaluations of three approaches to internal aerodynamic primary zone flow patterns: (1) conventional double vortex swirl stabilization; (2) reverse flow swirl stabilization; and (3) large single vortex flow system. Each concept incorporates special design features aimed at extending the performance capability of the small engine combustor. Since inherent geometry of these combustors result in small combustion zone height and high surface area to volume ratio, design features focus on internal aerodynamics, fuel placement, and advanced cooling. The combustors are evaluated on a full scale annular combustor rig. A correlation of the primary zone performance with the overall performance is accomplished using three intrusion type gas sampling probes located at the exit of the primary zone section. Empirical and numerical methods are used for designing and predicting the performance of the three combustor concepts and their subsequent modifications. The calibration of analytical procedures with actual test results permits an updating of the analytical design techniques applicable to small reverse flow annular combustors.

Sullivan, R. E.↗
NASA NTRS1992

IRD dropout study

This final report describes work performed by SRS Technologies for the NASA Marshall Space Flight Center under Contract NAS8-39077, entitled 'Integrated Receiver-Decoder Dropout Study'. The purpose of the study was to determine causes of signal fading effects on ultra-high-frequency (UHF) range safety transmissions to the Space Shuttle during flyout. Of particular interest were deep fades observed at the External Tank (ET) Integrated Receiver-Decoder (IRD) during the flyout interval between solid rocket booster separation and ET separation. Analytical and simulation methods were employed in this study to assess observations captured in flight telemetry data records. Conclusions based on the study are presented in this report, and recommendations are given for future experimental validation of the results.

Yalowitz, Jeffrey S.↗
NASA NTRS1996

Aeroservoelastic Modeling and Validation of a Thrust-Vectoring F/A-18 Aircraft

An F/A-18 aircraft was modified to perform flight research at high angles of attack (AOA) using thrust vectoring and advanced control law concepts for agility and performance enhancement and to provide a testbed for the computational fluid dynamics community. Aeroservoelastic (ASE) characteristics had changed considerably from the baseline F/A-18 aircraft because of structural and flight control system amendments, so analyses and flight tests were performed to verify structural stability at high AOA. Detailed actuator models that consider the physical, electrical, and mechanical elements of actuation and its installation on the airframe were employed in the analysis to accurately model the coupled dynamics of the airframe, actuators, and control surfaces. This report describes the ASE modeling procedure, ground test validation, flight test clearance, and test data analysis for the reconfigured F/A-18 aircraft. Multivariable ASE stability margins are calculated from flight data and compared to analytical margins. Because this thrust-vectoring configuration uses exhaust vanes to vector the thrust, the modeling issues are nearly identical for modem multi-axis nozzle configurations. This report correlates analysis results with flight test data and makes observations concerning the application of the linear predictions to thrust-vectoring and high-AOA flight.

Brenner, Martin J.↗
NASA NTRS2008

Nondestructive Evaluation of Metal Fatigue Using Nonlinear Acoustics

Safe-life and damage-tolerant design philosophies of high performance structures have driven the development of various methods to evaluate nondestructively the accumulation of damage in such structures resulting from cyclic loading. Although many techniques have proven useful, none has been able to provide an unambiguous, quantitative assessment of damage accumulation at each stage of fatigue from the virgin state to fracture. A method based on nonlinear acoustics is shown to provide such a means to assess the state of metal fatigue. The salient features of an analytical model are presented of the microelastic-plastic nonlinearities resulting from the interaction of an acoustic wave with fatigue-generated dislocation substructures and cracks that predictably evolve during the metal fatigue process. The interaction is quantified by the material (acoustic) nonlinearity parameter extracted from acoustic harmonic generation measurements. The parameters typically increase monotonically by several hundred percent over the fatigue life of the metal, thus providing a unique measure of the state of fatigue. Application of the model to aluminum alloy 2024-T4, 410Cb stainless steel, and IN100 nickel-base superalloy specimens fatigued using different loading conditions yields good agreement between theory and experiment. Application of the model and measurement technique to the on-site inspection of steam turbine blades is discussed.

Cantrell, John H., Jr.↗
NASA NTRS2013

A Kinematic Calibration Process for Flight Robotic Arms

The Mars Science Laboratory (MSL) robotic arm is ten times more massive than any Mars robotic arm before it, yet with similar accuracy and repeatability positioning requirements. In order to assess and validate these requirements, a higher-fidelity model and calibration processes were needed. Kinematic calibration of robotic arms is a common and necessary process to ensure good positioning performance. Most methodologies assume a rigid arm, high-accuracy data collection, and some kind of optimization of kinematic parameters. A new detailed kinematic and deflection model of the MSL robotic arm was formulated in the design phase and used to update the initial positioning and orientation accuracy and repeatability requirements. This model included a higher-fidelity link stiffness matrix representation, as well as a link level thermal expansion model. In addition, it included an actuator backlash model. Analytical results highlighted the sensitivity of the arm accuracy to its joint initialization methodology. Because of this, a new technique for initializing the arm joint encoders through hardstop calibration was developed. This involved selecting arm configurations to use in Earth-based hardstop calibration that had corresponding configurations on Mars with the same joint torque to ensure repeatability in the different gravity environment. The process used to collect calibration data for the arm included the use of multiple weight stand-in turrets with enough metrology targets to reconstruct the full six-degree-of-freedom location of the rover and tool frames. The follow-on data processing of the metrology data utilized a standard differential formulation and linear parameter optimization technique.

Collins, Curtis L.↗
DOE OSTI2023

GOOML (Geothermal Operational Optimization with Machine Learning) [SWR-23-01]

The Geothermal Operational Optimization with Machine Learning (GOOML) is a partnership between NREL and Upflow, NZ, awarded in response to the U.S. Department of Energy's Geothermal Technologies Office's Funding Opportunity Announcement (FOA) to expand the role of advanced analytics and automation in geothermal operations through machine learning. Partnering with industry (Contact Energy Limited ("Contact"), Ngati Tuwharetoa Geothermal Assets Limited ("NTGA"), Ormat Technologies Inc. ("Ormat") and Flow State Solutions Limited ("FSS"), GOOML was created to improve the operational efficiency of geothermal power plant steam fields through the analysis of historical operational data and the application of custom machine learning algorithms. NREL's contributions include machine learning, coding, and data management expertise as well as access to high-performance compute solutions. GOOML can increase geothermal operational efficiency through development of a digital system twin that can be utilized to provide optimal geothermal operating conditions for real-world geothermal fields. GOOML allows users to analyze field production histories in detail, develop models, and train machine learning algorithms to identify opportunities for increased geothermal efficiency, detect potential trouble, and allow predictive scenario modeling. Preliminary experiments have demonstrated a potential to increase total generation by as much as 12% through ML optimization of the utilization of existing steam field resources.

Buster, Grant↗
DOE OSTI2024

QC-GN 2 oMS 2 : a Graph Neural Net for High Resolution Mass Spectra Prediction

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. Throughout this work, we evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2016

Global Optimization of N-Maneuver, High-Thrust Trajectories Using Direct Multiple Shooting

The performance of impulsive, gravity-assist trajectories often improves with the inclusion of one or more maneuvers between flybys. However, grid-based scans over the entire design space can become computationally intractable for even one deep-space maneuver, and few global search routines are capable of an arbitrary number of maneuvers. To address this difficulty a trajectory transcription allowing for any number of maneuvers is developed within a multi-objective, global optimization framework for constrained, multiple gravity-assist trajectories. The formulation exploits a robust shooting scheme and analytic derivatives for computational efficiency. The approach is applied to several complex, interplanetary problems, achieving notable performance without a user-supplied initial guess.

optimization↗
DOE OSTI2020

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

We report the design/discovery of new materials is highly nontrivial owing to the near-infinite possibilities of material candidates and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the forward problem. However, this approach is inefficient and severely constrained by the candidates that the human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the inverse problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature and high electric field, useful for critical structural, electrical, and energy storage applications. This approach to learn from and augment) human ingenuity is general and can be extended to discover polymers with other targeted properties and performance measures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗