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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Efficient, Compact, and Smooth Variable Propulsion Motor (Final Report)

In this project, a new architecture of highly efficient hydraulic motor was developed for the propulsion of off-highway vehicles. The motor uses an adjustable linkage driving a cam to vary the displacement of the piston, resulting in a Variable Displacement Linkage Motor (VDLM). The motor uses low friction rolling element bearings to significantly reduce mechanical friction, especially in the demanding low-speed high-torque conditions experienced by off-highway vehicles. The VDLM has high torque capabilities for its size due to the radial piston packaging and use of a multi-lobe cam. A VDLM is very smooth due to the ability to tune the torque ripple through the design of the cam profile. The project was divided into three periods. During the first period, a dynamic model was constructed of the motor to predict the performance of the motor and the vehicle. During the second period, a single-cylinder learning prototype was designed, built, and tested to validate the models constructed in the first period. In the third period, a multi-cylinder prototype motor was optimized, designed, fabricated, and tested. The motor demonstrated excellent mechanical efficiency (above 92.5% across the range of displacements), but the experimentally measure volumetric efficiency was lower than expected due to higher leakage rates created by the poor tolerance control on the prototype. To validate the dynamic models developed in the first period and better understand design trade-offs. In the third period a multi-cylinder concept demonstration prototype will be designed, fabricated, and tested. The final prototype will be tested on a motor dynamometer and will be utilized in hardware-in-the-loop testing to demonstrate its efficiency and performance impacts on the overall drive train. The experimental results were used in a drive train simulation of a compact track loader operating through a drive cycle. Using the VDLM in a hydrostatic circuit yielded 17.1% reduction in fuel consumption and 36.5% reduction in a series hybrid transmission.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Thermal Radiation Transport with Tensor Trains

We present a novel tensor network algorithm to solve the time-dependent, gray thermal radiation transport equation. The method invokes a tensor train (TT) decomposition for the specific intensity. The efficiency of this approach is dictated by the rank of the decomposition. When the solution is “low rank,” the memory footprint of the specific intensity solution vector may be significantly compressed. The algorithm, following a step-then-truncate approach of a traditional discrete ordinates method, operates directly on the compressed state vector, thereby enabling large speedups for low-rank solutions. To achieve these speedups, we rely on a recently developed rounding approach based on the Gram-SVD. We detail how familiar S N algorithms for (gray) thermal transport can be mapped to this TT framework and present several numerical examples testing both the optically thick and thin regimes. The TT framework finds low-rank structure and supplies up to ≃60× speedups and ≃1000× compressions for problems demanding large angle counts, thereby enabling previously intractable SN calculations and supplying a promising avenue to mitigate ray effects.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1980

Exercise training-induced hypervolemia - Role of plasma albumin, renin, and vasopressin

The purpose of the present study was twofold: (1) to determine the rate of induction and decay of exercise-training hypervolemia with a short-duration high-intensity training regimen; and (2) to assess the protein, osmotic, and endocrine responses that contribute to that mechanism. The test subjects were eight volunteer, healthy, trained college men (20-22 yr) engaged in isotonic exercise on a bicycle ergometer. Factors associated with plasma hypervolemia during training are identified. The results suggest that an efficient procedure for increasing plasma volume is the daily performance of high-intensity isotonic leg exercise for 2 h/day.

Convertino, V. A.↗
NASA NTRS1985

Actions for productivity improvement in crew training

Improvement of the productivity of astronaut crew instructors in the Space Shuttle program and beyond is proposed. It is suggested that instructor certification plans should be established to shorten the time required for trainers to develop their skills and improve their ability to convey those skills. Members of the training cadre should be thoroughly cross trained in their task. This provides better understanding of the overall task and greater flexibility in instructor utilization. Improved facility access will give instructors the benefit of practical application experience. Former crews should be integrated into the training of upcoming crews to bridge some of the gap between simulated conditions and the real world. The information contained in lengthy and complex training manuals can be presented more clearly and efficiently as computer lessons. The illustration, animation and interactive capabilities of the computer combine an effective means of explanation.

Miller, G. E.↗
NASA NTRS2014

A Semiautomated Multilayer Picking Algorithm for Ice-sheet Radar Echograms Applied to Ground-Based Near-Surface Data

Snow accumulation over an ice sheet is the sole mass input, making it a primary measurement for understanding the past, present, and future mass balance. Near-surface frequency-modulated continuous-wave (FMCW) radars image isochronous firn layers recording accumulation histories. The Semiautomated Multilayer Picking Algorithm (SAMPA) was designed and developed to trace annual accumulation layers in polar firn from both airborne and ground-based radars. The SAMPA algorithm is based on the Radon transform (RT) computed by blocks and angular orientations over a radar echogram. For each echogram's block, the RT maps firn segmented-layer features into peaks, which are picked using amplitude and width threshold parameters of peaks. A backward RT is then computed for each corresponding block, mapping the peaks back into picked segmented-layers. The segmented layers are then connected and smoothed to achieve a final layer pick across the echogram. Once input parameters are trained, SAMPA operates autonomously and can process hundreds of kilometers of radar data picking more than 40 layers. SAMPA final pick results and layer numbering still require a cursory manual adjustment to correct noncontinuous picks, which are likely not annual, and to correct for inconsistency in layer numbering. Despite the manual effort to train and check SAMPA results, it is an efficient tool for picking multiple accumulation layers in polar firn, reducing time over manual digitizing efforts. The trackability of good detected layers is greater than 90%.

Antarctic Ice-Sheet↗
NASA NTRS2016

Preliminary Results from the AFRL-NASA W/V-Band Terrestrial Link Experiment in Albuquerque, NM

Atmospheric propagation models and the measurements that train them are critical to the design of efficient and effective space-ground links. As communication systems advance to higher frequencies in search of higher data rates and open spectrum, a lack of data at these frequencies necessitates new measurements to properly develop, validate, and refine the models used for link budgeting and system design. In collaboration with the Air Force Research Laboratory (AFRL), NASA Glenn Research Center has deployed the WV-band Terrestrial Link Experiment (WTLE) in Albuquerque, NM to conduct a measurement campaign at 72 and 84 GHz, among the first atmospheric propagation measurements at these frequencies. WTLE has been operational since October 1, 2015, and the system design shall be herein discussed alongside preliminary results and performance.

propagation↗
DOE OSTI2023

A Hardware and Software Co-design Framework for Energy Efficient Neuromorphic Systems

Neuromorphic systems can be realized by a variety of algorithms and architectures. A common understanding is that spiking neuromorphic designs, which encode information into spatio-temporal spiking events, are both a biologically-accurate and efficient way of processing information. However, representing the information through timing relationships induces sophisticated circuit designs in traditional CMOS-based implementations. In recent years, high-capacity resistive memory (RRAM, aka, memristor) has demonstrated great potential in mimicking synaptic behaviors. Several RRAM-based spiking neuromorphic designs exist, most of which focus on rate coding schemes. These designs simplify circuit implementations of neuron models and explore challenges such as unsatisfactory speed, resolution, and performance. As an alternative, we will explore temporal coding spiking neuromorphic systems that encode information as the relative timing of neuron activations (spikes), which have been proven to be more adaptive and energy-efficient. Developing a neuromorphic system for spiking neural network (SNN) inference and online training, however, faces some major technical challenges: (1) It lacks circuit implementation support for temporal-coding SNN to achieve satisfying power efficiency and accuracy; (2) Although existing research works have investigated memristive synapse and neuron designs for spike-timing-dependent plasticity, the non-ideal conditions in implementation, such as device variations and signal degradation, degrade online learning accuracy of large scale systems; and (3) Non-optimized, inter-layer data traffic in SNNs, leads to unnecessary data communication costs. In this project, we plan to address these challenges by a hardware and software co-design framework that incorporates solutions at the circuit, architecture, and algorithm levels. At the circuit-level, we will elaborate on the in-situ SNN processing element designs for supporting both inference and online training modes. Variation-aware schemes will be studied to improve reliability. At the architecture level, we propose a pipelined, asynchronous architecture to retain the timing resolution of spikes. At the algorithm level, we will investigate an innovative SNN training algorithm for enabling activation sparsification and reducing unnecessary data communication costs. This neuromorphic system will provide an effective solution to real-life energy-constrained applications and significantly contribute to the exploration of next-generation high-performance computing systems under the DOE context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Circularly Polarized Attosecond Pulses Enabled by an Azimuthal Phase and Polarization Grating

High-harmonic generation (HHG) is an extreme nonlinear optical process that can map the properties of an infrared driving laser beam onto short wavelength attosecond pulse trains. However, current techniques for generating circularly polarized high harmonics for probing magnetic materials and chiral systems have limitations: two-color collinear counter-rotating driving lasers result in a low cutoff photon energy, while single-color non-collinear counter-rotating schemes suffer from low conversion efficiency. In this work, we generate circularly polarized attosecond pulse trains by using a structured laser driver which has a rotating polarization and phase grating along the azimuthal coordinate. Furthermore, our experimental and numerical results demonstrate the production of left and right circularly polarized harmonics, which naturally separate upon propagation. Our approach uses a single laser color in a collinear geometry, that can be scaled for high efficiency. Simulations show this scheme can extend into the soft x-ray region when driven by mid-infrared driving lasers, while preserving the same high phase-matching cutoff photon energy as for linearly-polarized high harmonics.

attosecond science↗
DOE OSTI2024

ResStock™ Data Visualization Tools - How to Use Them: A Demonstration [Slides]

This training from the National Renewable Energy Laboratory introduces Energy Efficiency Conservation Block Grant Program participants to ResStock, a modeling tool that can help states, municipalities, utilities, and manufacturers identify which residential building improvements save the most money and energy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Syracuse University Industrial Assessment Center (Final Report)

Syracuse University Industrial Assessment Center (SU-IAC) project is focused on the two primary objectives: (i) educate undergraduate and graduate students in engineering and associated disciplines on the concepts of energy sustainability and to provide hands-on training by performing energy assessment at industrial facilities to become future energy efficiency experts, and (ii) offer energy assessments to small and mid-sized manufacturing companies in SU-IAC 2 the greater New York State region, perform analytical engineering work in support of assessment recommendations, and to report the results of those analyses to the client companies, to DOE, and to Field office personnel Through this project period, SU-IAC successfully trained 77 engineering students who were undergraduate or graduate students at Syracuse University College of Engineering, or at SU- IAC’s satellite center at Clarkson University. Each of these students received comprehensive ‘in-class’ technical training, safety training, and hands-on field training. The students performed various tasks related to energy assessment at the manufacturing facilities, including billing analysis, energy use reduction recommendations, and report preparation. Through this project period, a total of 34 students have completed all requirements of the SU-IAC program and have received the US DoE Certificate on Energy Efficiency, with the remaining students continuing at various stages of field work or training. During this project period, due to Covid-related measures in New York State, Syracuse University campus and most of the manufacturing facilities were completely closed or were under severe restrictions for access, for an extended period from March 2020 through April 2021. The closures affected our project execution, with the project Sponsor offering structured relief to carry-out a modified project during the affected budget periods. During this project period, SU-IAC student teams, led by its Director, completed a total of 77 energy assessments at small- and medium-scale manufacturing facilities located in New York State. For these facilities, SU-IAC prepared and presented details for 569 assessment recommendations (ARs). The facilities implemented a total of 258 of these ARs, realizing annual average energy savings in the range of $\$ $3,300 to $\$ $55,000 per facility. Cumulatively, these facilities have reduced their “first year after implementation” energy use by a total of 4.37 million kwh, and fuel use by a total of 27.7 billion BTU. SU-IAC project has successfully met the two primary objectives (and the associated sub objectives) and helped the manufacturing facilities in New York State achieve quantitative energy use reductions as a result of this project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Hybrid Quantum–Classical Graph Transformers for Efficient Sentiment Analysis

Quantum Machine Learning (QML) offers a promising paradigm that leverages quantum computing principles to develop efficient and expressive models for learning from complex and structured data. Recent advances in natural language processing (NLP) and artificial intelligence (AI) have demonstrated capabilities in understanding, generating, and reasoning over linguistic and multimodal information. In this work, we present the Quantum Graph Transformer (QGT), a hybrid quantum–classical architecture that extends graph transformer capabilities through quantum self-attention. The QGT models variable-length sentences as token graphs, where both the embedding encoding and the self-attention mechanisms are implemented using parameterized quantum circuits (PQCs), enabling efficient contextual learning with significantly fewer trainable parameters. We train QGT using both fully connected and 𝑘 -nearest-neighbor graph structures and evaluate it on five benchmark sentiment-classification datasets. Experimental results show that QGT consistently achieves higher or comparable accuracy to existing quantum NLP models and outperforms a Classical Graph Transformer (CGT) baseline with identical architecture, achieving 29.4 × fewer parameters while requiring 3–5 × fewer samples to reach comparable performance. These findings highlight the potential of graph-based quantum models as scalable and data-efficient architectures for natural language understanding.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1999

Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

Decker, Arthur J.↗
DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗
NASA NTRS1995

The effects of Crew Resource Management (CRM) training in airline maintenance: Results following three year's experience

An airline maintenance department undertook a CRM training program to change its safety and operating culture. In 2 1/2 years this airline trained 2200 management staff and salaried professionals. Participants completed attitude surveys immediately before and after the training, as well as two months, six months, and one year afterward. On-site interviews were conducted to test and confirm the survey results. Comparing managers' attitudes immediately after their training with their pretraining attitudes showed significant improvement for three attitudes. A fourth attitude, assertiveness, improved significantly above the pretraining levels two months after training. The expected effect of the training on all four attitude scales did not change significantly thereafter. Participants' self-reported behaviors and interview comments confirmed their shift from passive to more active behaviors over time. Safety, efficiency, and dependability performance were measured before the onset of the training and for some 30 months afterward. Associations with subsequent performance were strongest with positive attitudes about sharing command (participation), assertiveness, and stress management when those attitudes were measured 2 and 12 months after the training. The two month follow-up survey results were especially strong and indicate that active behaviors learned from the CRM training consolidate and strengthen in the months immediately following training.

Taylor, J. C.↗
DOE OSTI2023

Children Of the Sun Solar Initiative (COSSI) (Final)

The Children Of the Sun Solar Initiative (COSSI) project is a pivotal step towards energy independence for the Spokane Tribe and the Spokane Indian Housing Authority (SIHA). It involves evaluating the solar potential of 32 SIHA and Tribal buildings, with a goal of achieving energy autonomy through renewable sources. By installing 652.5 kW of solar photovoltaic (PV) systems, the project significantly reduces carbon emissions, lowers energy costs, and enhances sustainability. Key Points: (1) COSSI's mission is to empower the Spokane Tribe and SIHA with clean energy solutions, aligning with their Long-Term Energy Vision. (2) The project includes solar installations on 32 buildings, generating approximately 700,000 kWh annually. (3) It also involves collaboration with SIHA's Weatherization Program and offers training opportunities for tribal members. (4) The installations aid energy efficiency and cost reduction for high-energy-use structures. (5) The initiative advances the Tribe's economic infrastructure, fostering renewable resources and sustainable practices. (6) COSSI serves as a model for leveraging federal funding and tax credits to establish clean energy projects, while generating training and job prospects. (7) The project contributes to energy independence through the adoption of evolving solar technologies. Overall, COSSI is a forward-looking effort to usher in clean energy, enhance infrastructure, and inspire similar projects across tribal and housing authorities.

14 SOLAR ENERGY↗
DOE OSTI2025

The tensor-train stochastic finite volume method for uncertainty quantification

The stochastic finite volume method offers an efficient one-pass approach for assessing uncertainty in hyperbolic conservation laws. Still, it struggles with the curse of dimensionality when dealing with multiple stochastic variables. Here, we introduce the stochastic finite volume method within the tensor-train framework to counteract this limitation. This integration, however, comes with its own set of difficulties, mainly due to the propensity for shock formation in hyperbolic systems. To overcome these issues, we have developed a tensor-train-adapted stochastic finite volume method that employs a global WENO reconstruction, making it suitable for such complex systems. This approach represents the first step in designing tensor-train techniques for hyperbolic systems and conservation laws involving shocks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Primal-Dual Differentiable Programming for Distribution System Critical Load Restoration: Preprint

Swift and reliable critical load restoration (CLR) can help make a distribution system resilient towards extreme events. To optimally achieve that, alongside practical concerns such as limiting online computational burden, some studies leverage model-free reinforcement learning (RL) to train control policies. Despite the advantages provided by RL algorithms, these approaches suffer from two issues: 1) the lack of a proper mechanism for constraint enforcement, and 2) poor sample efficiency. Therefore, in this paper, a primal-dual differentiable programming (PDDP) method is developed for guiding the training leading to a constraint-satisfying policy. Additionally, the model-based nature of the proposed method aims at improving sample efficiency. The experiment on a CLR problem demonstrates that PDDP can effectively train a control policy that both achieves desirable performance and satisfies required constraints.

differentiable programming↗
DOE OSTI2024

A novel methodology for gamma-ray spectra dataset procurement over varying standoff distances and source activities

The adoption of machine learning approaches for gamma-ray spectroscopy has received considerable attention in the literature. Many studies have investigated the deployment of various algorithm architectures to a specific task. However, little attention has been afforded to the development of the datasets leveraged to train the models. Such training datasets typically span a set of environmental or detector parameters to encompass a problem space of interest to a user. Variations in these measurement parameters will also induce fluctuations in the detector response, including expected pile-up and ground scatter effects. Fundamental to this work is the understanding that 1) the underlying spectral shape varies as the measurement parameters change and 2) the statistical uncertainties associated with two spectra impact their level of similarity. While previous studies attribute some arbitrary discretization to the measurement parameters for the generation of their synthetic training data, this work introduces a principled methodology for efficient spectral-based discretization of a problem space. A signal-to-noise ratio (SNR) respective spectral comparison measure and a Gaussian Process Regression (GPR) model are used to predict the spectral similarity across a range of measurement parameters. This innovative approach effectively showcased its capability by dividing a problem space, ranging from 5 cm to 100 cm standoff distances and 5 μCi–100 μCi of 137 Cs, into three unique combinations of measurement parameters. The findings from this work will aid in creating more robust datasets, which incorporate many possible measurement scenarios, reduce the number of required experimental test set measurements, and possibly enable experimental training data collection for gamma-ray spectroscopy.

data science↗