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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Beyond Idealized Models of Nanoscale Metal Hydrides for Hydrogen Storage

Metal hydrides are attractive for compact, low-pressure hydrogen storage, yet a foundational understanding of factors governing their thermodynamics and kinetics is still lacking. Predictive modeling from the atomic to the microstructural scale plays a critical role in addressing these gaps, particularly for nanoscale materials, which promise improved performance but are difficult to probe. In this paper, we summarize strategies being developed within the Hydrogen Materials—Advanced Research Consortium (HyMARC) for going beyond conventional models to incorporate more complex physics, more realistic structures, and better approximation of operation conditions in simulations of nanoscale metal hydrides. We highlight four beyond-ideal factors that influence predicted performance: (1) surface anharmonic dynamics, (2) interface and surface energy penalties, (3) mechanical stress under confinement, and (4) the presence of native surface oxide. Approaches for addressing these factors are demonstrated on model materials representative of high-capacity hydrogen storage systems, and implications for understanding performance under operating conditions are discussed.

08 HYDROGEN↗
DOE OSTI2025

Implementation of Genetic Algorithms to Optimize Metal–Organic Frameworks for CO 2 Capture

Metal-organic frameworks (MOFs) are promising materials for CO 2 capture with the potential to use less energy than current industrial CO 2 capture methods. MOFs are highly versatile sorbents, and there is an almost unlimited number of MOFs that could be synthesized. In this work, we used a genetic algorithm (GA) and grand canonical Monte Carlo (GCMC) simulations to efficiently search for high-performing MOFs for CO 2 capture. We analyzed the effects of important GA parameters, including the mutation probability, the number of MOFs per generation and the number of GA generations, on the GA performance. Here, we performed GCMC simulations on-the-fly during the GA procedure to determine the performance of proposed MOFs and optimized their structures using multiple objective functions across different topologies. The GA was able to determine top-performing MOFs balancing CO 2 selectivity versus working capacity and reduced the cost of molecular simulations by a factor of 25 versus brute-force screening of an entire database of structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Rapidly Measuring Loop Footprints

Knowing a loop’s footprint — the unique data items it accesses — enables important locality and capacity analysis. Unfortunately, current methods for computing footprint cause integer factors of slowdown and are therefore difficult to use with realistic inputs. Current methods are slow because they are based on software tracing of load instructions and data addresses. We present an approach that combines lightweight measurements and static binary analysis. We use static analysis to reason about each load’s expected reuse. We then calculate the footprint for each loop using inference rules informed by measurements from common performance counters. We validate our method on benchmarks that vary access patterns (strided vs. unpredictable), reuse (varying and repeated accesses per element), and sparsity (all words in a cache line vs. some). For strided patterns, errors are within 1%; for unpredictable ones, errors are 5–10%. Our overheads are under 10%. A tool based on software tracing has an error of less than 1%, but either introduces at least 130× overhead or hangs.

Kilic, Ozgur O.↗
DOE OSTI2020

A Comfort Model Simplification for Tight Integration with Grid Service Optimizations

Localized generation, storage, and intelligent power electronics are increasingly being integrated into building. The excess storage, thermal, and generation capacity can be used to develop new markets for ancillary services such as peak reduction, voltage and frequency support, or load flattening. Widespread participation in these markets will make the power grid more resilient. However, widespread participation in these markets will require a simple and intuitive control over the excess capacity by the building occupants. Most importantly for adopting these technologies, participation in these markets should not require sacrificing occupant comfort. Many of the proposed control and optimization technologies for supplying the ancillary services do not explicitly incorporate occupant comfort into their models and when they do, generally utilize a rigid constraint on the indoor air temperature set a priori. This reduces flexibility in using excess thermal capacity. It also cannot take into account uncertainties in local factors that affect thermal comfort such as clothing insulation or relative humidity. In this paper we present and justify some assumptions that simplify the standard predictors of thermal comfort. We also develop a regression model of the thermal comfort that is linear with respect to the indoor air temperature. Next we develop the concept of thermal comfort variation that eliminates the dependence of the thermal comfort on uncertain thermal comfort factors. Then, we present a quadratic program utilizing the thermal comfort variation in both the objective function and as a constraint. Finally, we show the results of a time-of-use cost optimization that utilizes the simplified thermal comfort model.

Melin, Alexander↗
DOE OSTI2017

Carbon Fiber Manufacturing Facility Siting and Policy Considerations: International Comparison

Carbon fiber is increasingly used in a wide variety of applications due largely to its superior material properties such as high strength-to-weight ratio. The current global carbon fiber manufacturing industry is predominately located in China, Europe, Japan, and the United States. The carbon fiber market is expected to expand significantly through 2024 and to require additional manufacturing capacity to meet demand. Carbon fiber manufacturing facilities can offer significant economic development and employment opportunities as exemplified by the $1 billion investment and 500 jobs expected at a new Toray plant in Moore, South Carolina. Though the market is expected to expand, it is unclear where new manufacturing facilities will locate to meet demand. This uncertainty stems from the lack of research evaluating how different nations with significant carbon fiber manufacturing capacity compare as it relates to certain manufacturing facility siting factors such as costs of labor and energy as well as policy directed at supporting carbon fiber development, domestic deployment, and exports. This report fills these gaps by evaluating the top carbon fiber manufacturing countries, including China, European Union countries, Japan, Mexico, South Korea, Taiwan, and the United States. The report documents how the United States compares to these countries based on a range of manufacturing siting considerations and existing policies related to carbon fiber. It concludes with a discussion of various policy options the United States could adopt to both (1) increase the competitiveness of the United States as it relates to attracting new carbon fiber manufacturing and (2) foster broader end-use markets for deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1974

Capacity degradation due to interleaving and multiplexing

The capacity of an M-ary channel with N-ary coding is computed. It is found that the capacity of a very noisy channel is reduced by the factor (N-1)(log M)/(M-1)(log N). The results are applied to channels with noncoherent multifrequency shift keying and to concatenation of codes and interleaving.

Lyon, R. F.↗
NASA NTRS1980

Rolling-element bearings

In contrast to hydrodynamic bearings, which depend for low-friction characteristics on a fluid film between the journal and the bearing surfaces, roller-element bearings employ a number of balls or rollers that roll in an annular space. The paper briefly outlines the advantages and disadvantages of roller-element bearings as compared to hydrodynamic bearings. The discussion covers bearing types, rolling friction, friction losses in rolling bearings, contact stresses, deformations, kinematics (normal and high speeds), bearing dynamics including elastohydrodynamics, load distribution, lubrication (grease, solid oil, oil-air mist), specific dynamic capacity and life, specific static capacity, and fatigue or wearout (elastohydrodynamics, wear). Rolling bearing wear factor as a function of operating environment is plotted and discussed.

Anderson, W. J.↗
NASA NTRS1982

A high-resolution model of the planetary boundary layer - Sensitivity tests and comparisons with SESAME-79 data

A one-dimensional, planetary boundary layer (PBL) model is presented and verified using April 10, 1979 SESAME data. The model contains two modules to account for two different regimes of turbulent mixing. Separate parameterizations are made for stable and unstable conditions, with a predictive slab model for surface temperature. Atmospheric variables in the surface layer are calculated with a prognostic model, with moisture included in the coupled surface/PBL modeling. Sensitivity tests are performed for factors such as moisture availability, albedo, surface roughness, and thermal capacity, and a 24 hr simulation is summarized for day and night conditions. The comparison with the SESAME data comprises three hour intervals, using a time-dependent geostrophic wind. Close correlations were found with daytime conditions, but not in nighttime thermal structure, while the turbulence was faithfully predicted. Both geostrophic flow and surface characteristics were shown to have significant effects on the model predictions

Zhang, D.↗
NASA NTRS1986

Soil types and forest canopy structures in southern Missouri: A first look with AIS data

Spectral reflectance properties of deciduous oak-hickory forests covering the eastern half of the Rolla Quadrangle were examined using Thematic Mapper (TM) data acquired in August and December, 1982 and Airborne Imaging Spectrometer (AIS) data acquired in August, 1985. For the TM data distinctly high relative reflectance values (greater than 0.3) in the near infrared (Band 4, 0.73 to 0.94 micrometers) correspond to regions characterized by xeric (dry) forests that overlie soils with low water retention capacities. These soils are derived primarily from rhyolites. More mesic forests characterized by lower TM band 4 relative reflectances are associated with soils of higher retention capacities derived predominately from non-cherty carbonates. The major factors affecting canopy reflectance appear to be the leaf area index (LAI) and leaf optical properties. The Suits canopy reflectance model predicts the relative reflectance values for the xeric canopies. The mesic canopy reflectance is less well matched and incorporation of canopy shadowing caused by the irregular nature of the mesic canopy may be necessary. Preliminary examination of high spectral resolution AIS data acquired in August of 1985 reveals no more information than found in the broad band TM data.

Green, G. M.↗
NASA NTRS1993

Comparison between the land surface response of the ECMWF model and the FIFE-1987 data

An averaged time series for the surface data for the 15 x 15 km FIFE site was prepared for the summer of 1987. Comparisons with 48-hr forecasts from the ECMWF model for extended periods in July, August, and October 1987 identified model errors in the incoming SW radiation in clear skies, the ground heat flux, the formulation of surface evaporation, the soil-moisture model, and the entrainment at boundary-layer top. The model clear-sky SW flux is too high at the surface by 5-10 percent. The ground heat flux is too large by a factor of 2 to 3 because of the large thermal capacity of the first soil layer (which is 7 cm thick), and a time truncation error. The surface evaporation was near zero in October 1987, rather than of order 70 W/sq m at noon. The surface evaporation falls too rapidly after rainfall, with a time-scale of a few days rather than the 7-10 d (or more) of the observations. On time-scales of more than a few days the specified 'climate layer' soil moisture, rather than the storage of precipitation, has a large control on the evapotranspiration. The boundary-layer-top entrainment is too low. This results in a moist bias in the boundary-layer mixing ratio of order 2 g/Kg in forecasts from an experimental analysis with nearly realistic surface fluxes; this because there is insufficient downward mixing of dry air.

Betts, Alan K.↗
NASA NTRS1992

Problem: Thirst, Drinking Behavior, and Involuntary Dehydration

The phenomenon of involuntary dehydration, the delay in full restoration of a body water deficit by drinking, has been described extensively but relatively little is known about its physiological mechanism. It occurs primarily in humans when they are exposed to various stresses including exercise, environmental heat and cold, altitude, water immersion, dehydration, and perhaps microgravity, singly and in various combinations. The level of involuntary dehydration is approximately proportional to the degree of total stress imposed on the body. Involuntary dehydration appears to be controlled by more than one factor including social customs that influence what is consumed, the capacity and rate of fluid absorption from the gastrointestinal system, the level of cellular hydration involving the osmotic-vasopressin interaction with sensitive cells or structures in the central nervous system, and, to a lesser extent, hypovolemic-angiotensin II stimuli. Since humans drink when there is no apparent physiological stimulus, the psychological component should always be considered when investigating the total mechanisms for drinking.

Greenleaf, John E.↗
NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗
NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗
DOE OSTI2017

Retrofitting a Geothermal Plant with Solar and Storage to Increase Power Generation

Solar hybridization using concentrating solar power (CSP) can be an effective approach to augment the power generation and power cycle efficiency of a geothermal power plant with a declining resource. Thermal storage can further increase the dispatchability of a geothermal/solar hybrid system, which is particularly valued for a national grid with high renewable penetration. In this paper, a hybrid plant design with thermal storage is proposed based on the requirements of the Coso geothermal field in China Lake, California. The objective is to increase the power production by 4 MWe. In this system, a portion of the injection brine is recirculated through a heat exchanger with the solar heat transfer fluid, before being mixed with the production well brine. In the solar heating loop the brine should be heated to at least 155 degrees C to increase the net power. The solar field and storage were sized based on solar data for China Lake. Thermal storage is used to store excess power at the high-solar-irradiation hours and generate additional power during the evenings. The solar field size, the type and capacity of thermal storage and the operating temperatures are critical factors in determining the most economic hybrid system. Further investigations are required to optimize the hybrid system and evaluate its economic feasibility.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Providing Levelized Cost and Waiting Time Inputs for HDV Hydrogen Refueling Station Planning: A Case Study of U.S. I-75 Corridor

Widespread use of diesel fuels in freight transportation leads to greenhouse gas (GHG) emissions that play an important role in air pollution. The air pollution, as well as the energy crisis, drives the transition from diesel fuel to cleaner and more energy-efficient fuels such as hydrogen fuels. With such a transition, the strategic planning and optimization of supporting refueling infrastructure along the national highway for heavy-duty vehicles (HDVs) become a necessity. Cost and service level are two essential factors to be considered in hydrogen refueling station location and capacity optimization. The service level in this study is assessed by the delay (waiting time) at fueling stations as well as the fueling demand fulfilled. To this end, this study presents a methodology to provide waiting time (delay) and levelized hydrogen fuel cost inputs under different station configurations (number of dispensers and fill rates) for hydrogen refueling station planning. Determining the station configurations based on the trade-off between the two inputs is further discussed. Particularly, this study points out that the levelized cost and waiting time are affected by hourly demand patterns and estimates the two inputs under different peak hour fueling demand scenarios. The results suggest that, with the same daily demand, the cost, as well as the waiting time, increases obviously with the peak hour demand. In the case study, the cost grows at least 30% when the peak hour visit frequency of the daily total visits increases from 5% (evenly distributed pattern) to 10% (the most common pattern for existing diesel fueling stations along I-75). Accounting for this peak hour effect in hydrogen refueling station planning is recommended. Overall, the waiting time model and cost analysis provide key inputs for optimizing hydrogen refueling station location and configurations based on anticipated refueling demand patterns. The paper is the second in a series that aims to build a comprehensive modeling plan and optimize hydrogen refueling infrastructure along the Interstate 75 (I-75) corridor for HDVs.

Liu, Yuandong↗
DOE OSTI2026

Optimizing Heat Recovery with Storage: Control Validation and Sensitivity Analysis of the Time-Independent Energy Recovery Plant Using Modelica

Heat recovery in large building central plants saves energy but traditionally requires simultaneous heating and cooling. The Time-Independent Energy Recovery (TIER) plant shifts this paradigm by integrating thermal energy storage (TES) to enable heat recovery regardless of concurrent demand, offering a highly efficient, space-saving solution to achieve California’s energy goals. However, its integration of heat recovery chillers, cooling-only chillers, cooling towers, and trim air-source heat pumps (ASHPs) creates growing control and sizing complexity. To overcome this, this study employs high-fidelity Modelica dynamic simulation to validate TIER control sequences and optimize equipment sizing. We translated the written Sequences of Operation into executable Control Description Language (CDL) to test logic against sub-hourly loads. This verification workflow successfully identified and resolved critical vulnerabilities, such as thermal storage freezing and equipment short-cycling, in a virtual environment prior to physical deployment. Then, the study analyzes TIER plant performance across three simulated building types in three locations, and a real building load profile, ensuring variety in heating and cooling loads, and simultaneity factors and explores sizing rules for the TES and ASHP capacity. The analysis shows that the TIER plant operates equipment efficiently leading to a plant SCOP of around 7.5 across all scenarios, higher than a traditional ASHP plant, and a viable pathway to de-risk complex system design and control through simulation to identify optimal designs that maximize energy efficiency, minimize operational costs, and ensure robust operation in varied environmental conditions, thereby facilitating the broader adoption of such a solution for large buildings.

Zanetti, Ettore↗
DOE OSTI2018

Fast charge implications: Pack and cell analysis and comparison

This study investigates the effect of 50-kW (about 2C) direct current fast charging on a full-size battery electric vehicle's battery pack in comparison to a pack exclusively charged at 3.3 kW, which is the common alternating current Level 2 charging power level. Comparable scaled charging protocols are also independently applied to individual cells at three different temperatures, 20 °C, 30 °C, and 40 °C, to perform a comparative analysis with the packs. Dominant cell-level aging modes were identified through incremental capacity analysis and compared with full packs to gain a clear understanding of additional key factors that affect pack aging. While the cell-level study showed a minor impact on performance due to direct current fast charging, the packs showed a significantly higher rate of capacity fade under similar charging protocols. This indicates that pack-level aging cannot be directly extrapolated from cell evaluation. With this being said, delayed fast charging, completing shortly before discharge, was found to have less of an impact on battery degradation than conventional alternating current Level 2 charging

25 ENERGY STORAGE↗
DOE OSTI2018

The implications of fast charge in lithium ion battery performance and life: cell vs. pack

This study investigates the effect of 50-kW (about 2C) direct current fast charging on a full-size battery electric vehicle's battery pack in comparison to a pack exclusively charged at 3.3?kW, which is the common alternating current Level 2 charging power level. Comparable scaled charging protocols are also independently applied to individual cells at three different temperatures, 20?°C, 30?°C, and 40?°C, to perform a comparative analysis with the packs. Dominant cell-level aging modes were identified through incremental capacity analysis and compared with full packs to gain a clear understanding of additional key factors that affect pack aging. While the cell-level study showed a minor impact on performance due to direct current fast charging, the packs showed a significantly higher rate of capacity fade under similar charging protocols. This indicates that pack-level aging cannot be directly extrapolated from cell evaluation. Delayed fast charging, completing shortly before discharge, was found to have less of an impact on battery degradation than conventional alternating current Level 2 charging.

25 ENERGY STORAGE↗