Search NASA⌕ Search

SEARCH · Search NASA

Results for “mechanical efficiency”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A variational method for the sheath potential of hypersonic leading edges with space-charge limitations

Electron transpiration cooling for the leading edges (LE) of hypersonic aircraft utilizes thermionic emission; however, space-charge effects limit the electron emission rate, potentially diminishing the efficiency of this cooling mechanism. We develop a variational weak form of the Poisson equation that describes the sheath potential and then numerically solve it using the finite element method. This formulation has two main benefits: (1) the space-charge limit condition can be incorporated as a constraint and (2) it allows for the analysis of three-dimensional geometries with complex boundary conditions. We demonstrate that the current emitted from the surface of an LE is generally a small fraction of the Child–Langmuir limit due to space charge. We then propose several methods to enhance the emitted current from the surface and to boost the cooling effect of thermionic emission. These include increasing the plasma density, applying a negative surface potential, and using fringe fields under suitable geometric conditions. For a LaB6 emitting LE, the total emitted current is shown to be minimal and independent of the temperature of a surface with floating potential. However, when a negative potential is applied and the surface is heated, the emitted current follows the Richardson–Dushman relationship up to a critical temperature, beyond which it remains constant. At an applied surface potential of −5 V, the critical temperature is around 1700 K.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Cyanobacteria from marine oxygen-deficient zones encode both form I and form II Rubiscos

Cyanobacteria are highly abundant in the marine photic zone and primary drivers of the conversion of inorganic carbon into biomass. To date, all studied cyanobacterial lineages encode carbon fixation machinery relying upon form I Rubiscos within a CO 2 -concentrating carboxysome. Here, we report that the uncultivated anoxic marine zone (AMZ) IB lineage ofProchlorococcusfrom pelagic oxygen-deficient zones (ODZs) harbors both form I and form II Rubiscos, the latter of which are typically noncarboxysomal and possess biochemical properties tuned toward low-oxygen environments. We demonstrate that these cyanobacterial form II enzymes are functional in vitro and were likely acquired from proteobacteria. Metagenomic analysis reveals that AMZ IB are essentially restricted to ODZs in the Eastern Pacific, suggesting that form II acquisition may confer an advantage under low-O 2 conditions. AMZ IB populations express both forms of Rubisco in situ, with the highest form II expression at depths where oxygen and light are low, possibly as a mechanism to increase the efficiency of photoautotrophy under energy limitation. Our findings expand the diversity of carbon fixation configurations in the microbial world and may have implications for carbon sequestration in natural and engineered systems.

Science & Technology - Other Topics↗
DOE OSTI2020

Contrasting growth responses of Qilian juniper ( Sabina przewalskii ) and Qinghai spruce ( Picea crassifolia ) to CO2 fertilization despite common water-use efficiency increases at the northeastern Qinghai–Tibetan plateau

Abstract Rising atmospheric carbon dioxide (CO2) may enhance tree growth and mitigate drought impacts through CO2 fertilization. However, multiple studies globally have found that rising CO2 has not translated into greater tree growth despite increases in intrinsic water-use efficiency (iWUE). The underlying mechanism discriminating between these two general responses to CO2 fertilization remains unclear. We used two species with contrasting stomatal regulation, the relatively anisohydric Qilian juniper (Sabina przewalskii) and the relatively isohydric Qinghai spruce (Picea crassifolia), to investigate the long-term tree growth and iWUE responses to climate change and elevated CO2 using tree ring widths and the associated cellulose stable carbon isotope ratios (δ13C). We observed a contrasting growth trend of juniper and spruce with juniper growth increasing while the spruce growth declined. The iWUE of both species increased significantly and with similar amplitude throughout the trees’ lifespan, though the relatively anisohydric juniper had higher iWUE than the relatively isohydric spruce throughout the period. Additionally, with rising CO2, the anisohydric juniper became less sensitive to drought, while the relatively isohydric spruce became more sensitive to drought. We hypothesized that rising CO2 benefits relatively anisohydric species more than relatively isohydric species due to greater opportunity to acquire carbon through photosynthesis despite warming and droughts. Our findings suggest the CO2 fertilization effect depends on the isohydric degree, which could be considered in future terrestrial ecosystem models.

Wang, Wenzhi↗
DOE OSTI2021

Computational study of Li3BO3 and Li3BN2 I: Electrolyte properties of pure and doped crystals

Both Li3BO3 and Li3BN2 materials have promising properties for use in all-solid-state batteries and other technologies dependent on electrolytes with significant ionic conductivity. As the first of a two-part study, this paper reports the analysis of detailed simulations of Li ion diffusion in the monoclinic forms of these materials. Using both NEB and MD methods, it is clear that Li ion migration via vacancy mechanisms provides the most efficient ion transport in each material. While the results suggest that interstitial defects in these materials do not play a direct role in Li ion migration, their relative stability seems to enhance vacancy production via the formation of Frenkel-type defects. This may partially explain why the Li ion conductivities computed from MD simulations of samples initially containing a single Li ion vacancy are in reasonable agreement with measured values of this work for Li3BO3 and those reported in the literature for poorly crystalline samples of both materials. The possibility of increasing vacancy concentrations by substitutional doping (F for O in Li3BO3 and C for B in Li3BN2) is also examined, finding simulated conductivities comparable to those of the ideal vacancy model.

Li, Yan↗
DOE OSTI2024

Market Implications of Alternative Operating Reserve Modeling in Wholesale Electricity Markets

Pricing and settlement mechanisms are crucial for efficient resource allocation, investment incentives, market competition, and regulatory oversight. In the United States, Regional Transmission Operators (RTOs) adopts a uniform pricing scheme that hinges on the marginal costs of supplying additional electricity. This study investigates the pricing and settlement impacts of alternative reserve constraint modeling, highlighting how even slight variations in the modeling of constraints can drastically alter market clearing prices, reserve quantities, and revenue outcomes. Focusing on the diverse market designs and assumptions in ancillary services by U.S. RTOs, particularly in relation to capacity sharing and reserve substitutions, the research examines four distinct models that combine these elements based on a large-scale synthetic power system test data. Our study provides a critical insight into the economic implications and the underlying factors of these alternative reserve constraints through market simulations and data analysis.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data

Scientific simulations in high-performance computing (HPC) environments generate vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for postanalysis. Unlike traditional data reduction schemes such as deduplication or lossless compression, not only can error-controlled lossy compression significantly reduce the data size but it also holds the promise to satisfy user demand on error control. Pointwise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications with lossy compression since error control can adapt to the error bound in the dataset automatically. Pointwise relative-error-bounded compression is complicated and time consuming. In this article, we develop efficient precomputation-based mechanisms based on the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error-bounded compression with excellent compression ratios. In addition, we reduce traversing operations for Huffman decoding, significantly accelerating the decompression process in SZ. Experiments with eight well-known real-world scientific simulation datasets show that our solution can improve the compression and decompression rates (i.e., the speed) by about 40 and 80 p, respectively, in most of cases, making our designed lossy compression strategy the best-in-class solution in most cases.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Bacterial fitness for plant colonization is influenced by plant growth substrate

Despite advances in our understanding of bacterial plant colonization, the extent to which growth substrate influences the molecular mechanisms enabling bacteria to efficiently colonize plants remains poorly understood. To address this, we used randomly barcoded transposon mutagenesis sequencing (RB-TnSeq) in Paraburkholderia graminis OAS925, an efficient rhizosphere colonizer, and Brachypodium distachyon grown in six different substrates. Of the 382 rhizosphere colonization genes that we identified in OAS925, 348 genes (91.1%) are dependent on the growth substrate evaluated, and 34 genes (8.9%) are shared across all the substrates. Both the core and substrate-dependent colonization genes are from multiple functional categories, demonstrating the multifaceted and major impact that plant growth substrate has on bacterial colonization. The identified colonization genes and their varied importance across plant growth substrates could not be readily explained by differences in root exudate profiles, suggesting that the substrate environment itself plays an outsized role in the ability of a bacterium to colonize the rhizosphere. Our data confirm that bacterial fitness for plant colonization is strongly influenced by plant growth substrate type and highlights the importance of taking this parameter into consideration when engineering bacterial strains for improved host colonization.

Paraburkholderia↗
DOE OSTI2020

Assessment of software methods for estimating protein-protein relative binding affinities

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation

Interfaces play a pivotal role in dictating the performance and reliability of all-solid-state batteries (ASSBs), where complex electro-chemo-mechanical phenomena at grain boundaries (GBs) and interfaces can lead to degradation and failure. Traditional atomistic simulation methods, such as first-principles calculations and classical molecular dynamics, face limitations in modeling these interfaces due to either high computational cost or insufficient transferability to the diverse atomic environments evolving at interfaces. Machine-learning interatomic potentials (MLIPs) have emerged as a transformative approach, enabling large-scale, high-accuracy simulations of disordered and chemically complex systems by leveraging the predictability of machine learning models trained on first-principles data. Recent applications of MLIPs have demonstrated their ability to capture intricate behaviors at ASSB interfaces, including ion transport, interfacial evolution, and degradation mechanisms, with accuracy and efficiency unattainable by conventional methods. This prospective paper presents comprehensive analysis and practical guidance for MLIP development for GBs and interfaces in ASSBs, with a focus on three key pillars: data generation, model selection, and validation. Here, we review the current state of MLIP applications for GBs and interfaces in both general and ASSB-specific materials, highlighting best practices and challenges in constructing diverse and representative datasets, choosing appropriate machine learning architectures, and rigorously validating model performance. We also discuss emerging strategies and opportunities for improved reliability and efficiency of MLIPs to simulate realistic interfaces in ASSBs.

Energy - Storage↗
DOE OSTI2022

High Throughput Discovery of Thermoelectric Materials (Final Report)

New materials form the heart of advances in energy technologies; however, the actual synthesis and characterization of new materials remains an enormous bottleneck. In this award, the research team focused on developing new approaches overcome this bottleneck. The approach involved depositing at high (supersonic) velocity multiple crystalline materials at a substrate while controlling the resulting film composition. The film could then be annealed to create a homogeneous alloy that was suitable for characterization. High throughput characterization tools were developed in parallel. The research focused on thermoelectric materials, which convert the flow of heat into electrical power. The development of new thermoelectric materials with high power conversion efficiency could displace traditional mechanical heat engines (e.g. turbines).

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Performance contracting centers of expertise: a framework for federal implementation

This report examines the establishment and operation of Energy Performance Contracting (EPC) Centers of Expertise (COEs) within the federal government. EPCs, including Energy Savings Performance Contracts (ESPCs) and Utility Energy Service Contracts (UESCs), are critical mechanisms for advancing energy efficiency, resilience, and infrastructure modernization without the need for significant upfront appropriations. However, EPCs require specialized knowledge in project development, contracting, financing, legal parameters and technical project oversight. Currently federal agencies have varying levels of expertise and institutionalized policy to effectively and consistently use congressionally authorized EPCs which have decades of proven and impactful use. To address these challenges, several federal agencies have created COEs to centralize expertise, standardize practices, and streamline implementation. This report reviews statutory and policy drivers, highlights the benefits and challenges and presents case studies from the General Services Administration (GSA), the Department of Veterans Affairs (VA) and the U.S. Army Engineering and Support Center Huntsville (HNC). Recommendations are also provided for agencies considering the establishment of EPC COEs, which will bring much needed structure, consistency and lead to implementation of these energy and infrastructure building projects to save costs for U.S. taxpayers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Prong Segmentation using Point Set Transformers in Multiple View Neutrino Detectors

NOvA is a long-baseline neutrino experiment studying neutrino oscillations by detecting neutrinos from the NuMI beam at Fermilab. Its physics analysis relies on accurate prong segmentation, which involves matching each hit to its source particle and identifying the particle type. This task has commonly been addressed using a combination of traditional clustering algorithms and convolutional neural networks (CNNs). However, NOvA’s detector design presents data as two sparse and decoupled 2D images (XZ and YZ views) rather than a native 3D representation, posing a significant challenge for traditional CNN-based models. In this talk, we propose a novel neural network based on the Point Set Transformer. By treating detector hits as sparse point clouds and implementing a cross-view attention mechanism, our model enables efficient information mixing between both views. Evaluated on NOvA simulated data, our model achieves superior accuracy while requiring significantly fewer computational resources compared to other models. Furthermore, the model demonstrates great performance when applied to Liquid Argon Time Projection Chamber (LArTPC) data, which shows its potential as a universal prong segmentation algorithm for multiple view neutrino detectors.

Liu, Jiaxi [UC, Irvine]↗
DOE OSTI2020

Range-based memory system

A mechanism is provided for efficient coherence state modification of cached data stored in a range of addresses in a coherent data processing system in which data coherency is maintained across multiple caches. A tag search structure is maintained that identifies address tags and coherence states of cached data indexed by address tags. In response to a request from a device internal to or external from the coherence network, the tag search structure is searched to identify address tags of cached data for which the coherence state is to be modified and requests are issued in the data processing system to modify a coherence state of cached lines with the identified address tags. The request from the external device may specify a range of addresses for which a coherence state change is sought. The tag search structure may be implemented as search tree, for example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Multi-device cooling structure having assembly alignment features

A single device having both thermal and alignment features is provided to mechanically align and more efficiently cool multiple heat generating components. The device includes one or more thermal structures and assembly alignment features, in which the one or more thermal structures make thermal contact with a plurality of heat generating components. The assembly alignment features control the positions of the heat generating components during assembly of the heat generating components onto a common substrate.

42 ENGINEERING↗
DOE OSTI2023

Crosslinking of loose insulating powders

Described herein are materials and methods useful in the field of insulation, including building materials, refrigeration, cryogenics, and shipping, amongst others. Advantageously, the provided materials and method provide low thermal conductivities and increased mechanical strength, allowing for efficient insulating in a diverse range of applications. The provided materials and methods include individual particles connected by a polymer network that links individual particles and may include hollow or evacuated capsules and various strengthening agents.

Simpson, Lin Jay↗
DOE OSTI2022

Lead-Free Flexible Perovskite Solar Cells with Interfacial Native Oxide Have >10% Efficiency and Simultaneously Enhanced Stability and Reliability

Here we demonstrate an innovative compositional and interfacial engineering approach to achieve Pb-free flexible perovskite solar cells (f-PSCs) with an unprecedented combination of the highest efficiency reported to date (10.43%), together with enhanced operational stability and mechanical reliability. The key to this approach is alloying of an FASnI3 metal halide perovskite (MHP) thin film with Ge2+ to stabilize the Sn2+ oxidation state in the MHP, together with the use of a NiOx hole-transport layer. We show that this results in the in situ formation of a thin amorphous GeO2 native oxide layer at the NiOx/FASn0.9Ge0.1I3 interface. This layer not only passivates that critical interface but also enhances the interfacial mechanical bonding.

efficiency↗
DOE OSTI2023

Insights into palladium poisoning of Cu/SSZ-13 selective catalytic reduction catalysts

Here, the impacts of Pd poisoning to the activity, selectivity, and hydrothermal stability of Cu/SSZ-13 selective catalytic reduction (SCR) catalysts are reported. Pd lowers DeNOx efficiency of Cu/SSZ-13 via two mechanisms: (1) displacing SCR active sites in the form of isolated Pd-ions, and (2) catalyzing non-selective NH 3 oxidation in the form of PdO. The first mechanism works by the diffusion of isolated Pd-ions into chabazite cages to displace ZCu II OH SCR active species, and it occurs on Cu/SSZ-13 catalysts with and without external surface CuO clusters in similar fashion. In contrast, the second mechanism works differently with and without external surface CuO clusters. For Cu/SSZ-13 catalyst without external CuO clusters (i.e., primarily isolated Cu-ions), PdO leads to significant decrease of DeNOx efficiency at reaction temperatures above ~400°C due to its non-selective NH 3 oxidation activity at high temperatures. However, this poisoning effect becomes much less impactful on Cu/SSZ-13 catalyst containing CuO clusters. This is due to the formation of CuO-PdO solid solution via interactions between CuO and PdO, which reduces non-selective NH 3 oxidation potential. Furthermore, this solid solution formation even mitigates adverse effects caused by hydrothermal aging. Hence, the poisoning effects of Pd are closely related to Cu speciation and spatial distribution of a Cu/SSZ-13 catalyst. Finally, this study suggests a strategy in eliminating Pd poisoning, that is, the introduction of an oxide phase that effectively traps PdO but does not adversely influence SCR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Xenon doping of liquid argon in ProtoDUNE single phase

The Deep Underground Neutrino Experiment (DUNE) will be the next generation long-baseline neutrino experiment. The far detector is designed as a complex of four LAr-TPC (Liquid Argon Time Projection Chamber) modules with 17 kt of liquid argon each. The development and validation of the first far detector technology is pursued through ProtoDUNE Single Phase (ProtoDUNE-SP), a 770 t LAr-TPC at CERN Neutrino Platform. Crucial in DUNE is the photon detection system that will ensure the trigger of non-beam events — proton decay, supernova neutrino burst and BSM searches — and will improve the timing and calorimetry for neutrino beam events. Doping liquid argon with xenon is a known technique to shift the light emitted by argon (128 nm) to a longer wavelength (178 nm) to ease its detection. The largest xenon doping test ever performed in a LAr-TPC was carried out in ProtoDUNE-SP. From February to May 2020, a gradually increasing amount of xenon was injected to also compensate for the light loss due to air contamination. The response of such a large TPC has been studied using the ProtoDUNE-SP Photon Detection System (PDS) and a dedicated setup installed before the run. With the first it was possible to study the light collection efficiency with respect to the track position, while with the second it was possible to distinguish the xenon light (178 nm) from the LAr light (128 nm). The light shifting mechanism proved to be highly efficient even at small xenon concentrations (<20 ppm in mass) furthermore it allowed recovering the light quenched by pollutants. The light collection improved far from the detection plane, enhancing the photon detector response uniformity along the drift direction and confirming a longer Rayleigh scattering length for 178 nm photons, with respect to 128 nm ones. The charge collection by the TPC was monitored proving that xenon up to 20 ppm does not impact its performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗