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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ~20%. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

galaxies: interactions↗
DOE OSTI2022

Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks

Advanced nuclear reactors often exhibit complex thermal-fluid phenomena during transients. To accurately capture such phenomena, a coarse-mesh three-dimensional (3-D) modeling capability is desired for modern nuclear-system code. In the coarse-mesh 3-D modeling of advanced-reactor transients that involve flow and heat transfer, accurately predicting the turbulent viscosity is a challenging task that requires an accurate and computationally efficient model to capture the unresolved fine-scale turbulence. In this work, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor. The model has a coarse mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data to ensure accuracy. A novel neural network architecture, combining a densely connected convolutional network and a long-short-term-memory network, is developed that can efficiently learn from the spatial temporal CFD transient simulation results. The neural network model was trained and optimized on a loss-of flow transient and demonstrated high accuracy in predicting the turbulent viscosity field during the whole transient. The trained model's generalization capability was also investigated on two other transients with different inlet conditions. The study demonstrates the potential of applying the proposed data-driven approach to support the coarse-mesh multi-dimensional modeling of advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2000

Team Training and Retention of Skills Acquired Above Real Time Training on a Flight Simulator

Above Real-Time Training (ARTT) is the training acquired on a real time simulator when it is modified to present events at a faster pace than normal. The experiments related to training of pilots performed by NASA engineers (Kolf in 1973, Hoey in 1976) and others (Guckenberger, Crane and their associates in the nineties) have shown that in comparison with the real time training (RTT), ARTT provides the following benefits: increased rate of skill acquisition, reduced simulator and aircraft training time, and more effective training for emergency procedures. Two sets of experiments have been performed; they are reported in professional conferences and the respective papers are included in this report. The retention of effects of ARTT has been studied in the first set of experiments and the use of ARTT as top-off training has been examined in the second set of experiments. In ARTT, the pace of events was 1.5 times the pace in RTT. In both sets of experiments, university students were trained to perform an aerial gunnery task. The training unit was equipped with a joystick and a throttle. The student acted as a nose gunner in a hypothetical two place attack aircraft. The flight simulation software was installed on a Universal Distributed Interactive Simulator platform supplied by ECC International of Orlando, Florida. In the first set of experiments, two training programs RTT or ART7 were used. Students were then tested in real time on more demanding scenarios: either immediately after training or two days later. The effects of ARTT did not decrease over a two day retention interval and ARTT was more time efficient than real time training. Therefore, equal test performance could be achieved with less clock-time spent in the simulator. In the second set of experiments three training programs RTT or ARTT or RARTT, were used. In RTT, students received 36 minutes of real time training. In ARTT, students received 36 minutes of above real time training. In RARTT, students received 18 minutes of real time training and 18 minutes of above real time training as top-off training. Students were then tested in real time on more demanding scenarios. The use of ARTT as top-off training after RTT offered better training than RTT alone or ARTT alone. It is, however, suggested that a similar experiment be conducted on a relatively more complex task with a larger sample of participants. Within the proposed duration of the research effort, the setting up of experiments and trial runs on using ARTT for team training were also scheduled but they could not be accomplished due to extra ordinary challenges faced in developing the required software configuration. Team training is, however, scheduled in a future study sponsored by NASA at Tuskegee University.

Ali, Syed Friasat↗
DOE OSTI2026

Opportunities to Expand Building Efficiency Programming at Community Colleges

According to the most recent U.S. Energy and Employment Report, more than 2.3 million workers in the United States are involved in activities that reduce energy usage in buildings. This workforce supports energy efficiency from the design of buildings and their systems through the manufacturing and trade of components and supplies involved in these systems to the installation, repair, and maintenance of these systems. Less than 10% of the workers in key building efficiency occupations have a bachelor’s or higher degree, compared to ~40% of the general workforce. Thus, the community college system is a key stakeholder in training and educating a large portion of the building efficiency workforce. Despite this, the literature review conducted for this report found almost no research focused on better understanding and supporting the role of community colleges as they train this workforce at scale. This report seeks to understand how and to what extent building efficiency and advanced building technology concepts are being addressed in community colleges as well as potential pathways for schools to consider to better prepare students to enter the building efficiency industry. The first section presents information from a literature review and data analysis to provide background on the building efficiency workforce, the types of building efficiency training and education available from community colleges, and the barriers and challenges that exist in the workforce. The second section offers a series of case studies that illustrate the various ways that building efficiency content can be addressed at community colleges. The final section provides an overview of the opportunities available to community colleges as well as considerations for schools that want to increase building efficiency programming.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Hindsight logging for model training

In modern Machine Learning, model training is an iterative, experimental process that can consume enormous computation resources and developer time. To aid in that process, experienced model developers log and visualize program variables during training runs. Exhaustive logging of all variables is infeasible, so developers are left to choose between slowing down training via extensive conservative logging, or letting training run fast via minimalist optimistic logging that may omit key information. As a compromise, optimistic logging can be accompanied by program checkpoints; this allows developers to add log statements post-hoc, and "replay" desired log statements from checkpoint---a process we refer to as hindsight logging. Unfortunately, hindsight logging raises tricky problems in data management and software engineering. Done poorly, hindsight logging can waste resources and generate technical debt embodied in multiple variants of training code. In this paper, we present methodologies for efficient and effective logging practices for model training, with a focus on techniques for hindsight logging. Our goal is for experienced model developers to learn and adopt these practices. To make this easier, we provide an open-source suite of tools for Fast Low-Overhead Recovery (flor) that embodies our design across three tasks: (i) efficient background logging in Python, (ii) adaptive periodic checkpointing, and (iii) an instrumentation library that codifies hindsight logging for efficient and automatic record-replay of model-training. Model developers can use each flor tool separately as they see fit, or they can use flor in hands-free mode, entrusting it to instrument their code end-to-end for efficient record-replay. Our solutions leverage techniques from physiological transaction logs and recovery in database systems. Evaluations on modern ML benchmarks demonstrate that flor can produce fast checkpointing with small user-specifiable overheads (e.g. 7%), and still provide hindsight log replay times orders of magnitude faster than restarting training from scratch.

Computer Science↗
NASA NTRS2014

Interfaces Visualize Data for Airline Safety, Efficiency

As the A-Train Constellation orbits Earth to gather data, NASA scientists and partners visualize, analyze, and communicate the information. To this end, Langley Research Center awarded SBIR funding to Fairfax, Virginia-based WxAnalyst Ltd. to refine the company's existing user interface for Google Earth to visualize data. Hawaiian Airlines is now using the technology to help manage its flights.

Source record↗
DOE OSTI2023

Community College Energy Code Training Program

The U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) funded a series of research and training programs under the Advanced Building Construction with Energy Efficient Technologies and Practices (ABC) initiative. The aim is to develop new training programs to support the development of a skilled workforce in the building energy efficiency industry. In response, the University of Illinois Smart Energy Design Assistance Center (SEDAC), in collaboration with State Energy Offices in Illinois, Hawaii, and Nevada, developed the Community College Energy Code Training Program. The program is designed to prepare a new generation of energy-literate professionals that understand energy efficiency and its relation to energy codes for new construction and existing buildings through: a) the development of a curriculum and innovative pedagogical materials, b) dissemination through a national network of state energy offices: c) outreach targeting Community College students and instructors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Physics-Informed Neural Network Modeling of Li-Ion Batteries: Preprint

Li-ion batteries (LIB) are a promising solution to enable storage of intermittent energy sources due to their high energy density. However, LIBs are known to significantly degrade after about 1000 charge-discharge cycles. LIBs degrade following different degradation modes and at a rate that depends on the operating conditions (external temperature, load). To plan the installation of batteries, appropriate understanding and prediction capabilities of their lifecycle is needed. High-fidelity numerical models of LIBs such as the pseudo-two-dimensional (P2D) model have been shown to accurately represent the charge-discharge-cycle of an LIB given ac- curate choice of the physical parameters. Given the large number of P2D parameters, adjusting them using forward runs is intractable. This work describes the development of a physics-informed neural network (PINN) as a surrogate substitute of the P2D model that captures parameter dependence. The PINN is advantageous as it needs little to no data, and can naturally encode the dependence of every model parameter. Here, a specific training procedure is adopted to efficiently cover parameter space, handle model stiffness and enforce boundary conditions. The trained PINN is validated against numerical solutions of the P2D model, and its applicability to battery degradation modeling is discussed.

battery degradation↗
NASA NTRS1987

Rotorcraft transmission

The NASA Lewis Research Center and the U.S. Army Aviation Systems Command share an interest in advancing the technology for helicopter propulsion systems. In particular, this presentation outlines that portion of the program that applies to the drive train and its various mechanical components. The major goals of the program are to increase the life, reliability, and maintainability; reduce the weight, noise, and vibration; and maintain the relatively high mechanical efficiency of the gear train. The current activity emphasizes noise reduction technology and analytical code development followed by experimental verification. Selected significant advances in technology for transmissions are reviewed, including advanced configurations and new analytical tools. Finally, the plan for transmission research in the future is presented.

Coy, John J.↗
DOE OSTI2023

Virtual Community Trains

When access to transit solutions is restricted to parts of a community, a division occurs what leads to disadvantaged socioeconomic conditions that only grows over time. This restriction causes difficulties in accessing needed resources such as medical facilities, employment, healthy food, education, healthy food, and more. To solve this restricted access, Labyrinth Smart Mobility and partners are developing a virtual community train that encourages scalability, energy efficiency, and equitability to all regardless of individual circumstances and limitation. The implementation of virtual community trains allows access to resources previously out of reach. The virtual community train enables one operator to drive a lead vehicle. This lead vehicle would then provide direction to a follow vehicle through vehicle-to-vehicle communication. Labyrinth Smart Mobility in conjunction with partners used various methods and resources to prove market feasibility, technical feasibility, economic need and viability, and more as well as design system requirements and architecture emphasis efficiency and safety. In doing so, Labyrinth Smart Mobility and partners will continue these advancements to further develop the virtual community train.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1988

Advanced transmission studies

The NASA Lewis Research Center and the U.S. Army Aviation Systems Command share an interest in advancing the technology for helicopter propulsion systems. In particular, this paper presents highlights from that portion of the program in drive train technology and the related mechanical components. The major goals of the program are to increase the life, reliability, and maintainability; reduce the weight, noise, and vibration; and maintain the relatively high mechanical efficiency of the gear train. The current activity emphasizes noise reduction technology and analytical code development followed by experimental verification. Selected significant advances in technology for transmissions are reviewed, including advanced configurations and new analytical tools. Finally, the plan for future transmission research is presented.

Coy, John J.↗
NASA NTRS1988

Advanced transmission studies

The NASA Lewis Research Center and the U.S. Army Aviation Systems Command share an interest in advancing the technology for helicopter propulsion systems. In particular, this paper presents highlights from that portion of the program in drive train technology and the related mechanical components. The major goals of the program are to increase the life, reliability, and maintainability; reduce the weight, noise, and vibration; and maintain the relatively high mechanical efficiency of the gear train. The current activity emphasizes noise reduction technology and analytical code development followed by experimental verification. Selected significant advances in technology for transmissions are reviewed, including advance configurations and new analytical tools. Finally, the plan for future transmission research is presented.

Coy, John J.↗
NASA NTRS1990

Rotorcraft transmissions

Highlighted here is that portion of the Lewis Research Center's helicopter propulsion systems program that deals with drive train technology and the related mechanical components. The major goals of the program are to increase life, reliability, and maintainability, to reduce weight, noise, and vibration, and to maintain the relatively high mechanical efficiency of the gear train. The current activity emphasizes noise reduction technology and analytical code development, followed by experimental verification. Selected significant advances in technology for transmissions are reviewed, including advanced configurations and new analytical tools. Finally, the plan for transmission research in the future is presented.

Coy, John J.↗
DOE OSTI2022

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

An Alternative Approach to Human Servicing of Crewed Earth Orbiting Spacecraft

As crewed spacecraft have grown larger and more complex, they have come to rely on spacewalks, or Extravehicular Activities (EVA), for mission success and crew safety. Typically, these spacecraft maintain all of the hardware and trained personnel needed to perform an EVA on-board at all times. Maintaining this capability requires volume and up-mass for storage of EVA hardware, crew time for ground and on-orbit training, and on-orbit maintenance of EVA hardware. This paper proposes an alternative methodology, utilizing launch on-need hardware and crew to provide EVA capability for space stations in Earth orbit after assembly complete, in the same way that one would call a repairman to fix something at their home. This approach would reduce ground training requirements, save Intravehicular Activity (IVA) crew time in the form of EVA hardware maintenance and on-orbit training, and lead to more efficient EVAs because they would be performed by specialists with detailed knowledge and training stemming from their direct involvement in the development of the EVA. The on-orbit crew would then be available to focus on the immediate response to the failure as well as the day-to-day operations of the spacecraft and payloads. This paper will look at how current unplanned EVAs are conducted, including the time required for preparation, and offer alternatives for future spacecraft. As this methodology relies on the on-time and on-need launch of spacecraft, any space station that utilized this approach would need a robust transportation system including more than one launch vehicle capable of carrying crew. In addition, the fault tolerance of the space station would be an important consideration in how much time was available for EVA preparation after the failure. Each future program would have to weigh the risk of on-time launch against the increase in available crew time for the main objective of the spacecraft.

Mularski, John R.↗
NASA NTRS2017

An Alternative Approach to Human Servicing of Crewed Earth Orbiting Spacecraft

As crewed spacecraft have grown larger and more complex, they have come to rely on spacewalks, or Extravehicular Activities (EVA), for assembly and to assure mission success. Typically, these spacecraft maintain all of the hardware and trained personnel needed to perform an EVA on-board at all times. Maintaining this capability requires up-mass, volume for storage of EVA hardware, crew time for ground and on-orbit training, and on-orbit maintenance of EVA hardware. This paper proposes an alternative methodology, utilizing either launch-on-need hardware and crew or regularly scheduled missions to provide EVA capability for space stations in low Earth orbit after assembly complete. Much the same way that one would call a repairman to fix something at their home these EVAs are dedicated to maintenance and upgrades of the orbiting station. For crew safety contingencies it is assumed the station would be designed such the crew could either solve those issues from inside the spacecraft or use the docked Earth to Orbit vehicles as a return lifeboat, in the same manner as the International Space Station (ISS) which does not rely on EVA for crew safety related contingencies. This approach would reduce ground training requirements for long duration crews, save Intravehicular Activity (IVA) crew time in the form of EVA hardware maintenance and on-orbit training, and lead to more efficient EVAs because they would be performed by specialists with detailed knowledge and training stemming from their direct involvement in the development of the EVA. The on-orbit crew would then be available to focus on the immediate response to any failures such as IVA systems reconfiguration or jumper installation as well as the day-to-day operations of the spacecraft and payloads. This paper will look at how current unplanned EVAs are conducted on ISS, including the time required for preparation, and offer an alternative for future spacecraft. As this methodology relies on the on-time and on-need launch of spacecraft, any space station that utilized this approach would need a robust transportation system, possibly including more than one launch vehicle capable of carrying crew. In addition, the fault tolerance of the future space station would be an important consideration in how much time was available for EVA preparation after the failure. Ideally the fault tolerance of the station would allow for the maintenance tasks to be grouped such that they could be handled by regularly scheduled maintenance visits and not contingency launches. Each future program would have to weigh the risk of on-time launch against the increase in available crew time for the main objective of the spacecraft. This is only one of several ideas that could be used to reduce or eliminate a station's reliance on rapid turnaround EVAs using on-board crew. Others could include having shirt-sleeve access to critical systems or utilizing low pressure temporarily pressurized equipment bays.

Mularski, John R.↗
NASA NTRS1988

Results of NASA/Army transmission research

Since 1970 the NASA Lewis Research Center and the U.S. Army Aviation Systems Command have shared an interest in advancing the technology for helicopter propulsion systems. In particular, that portion of the program that applies to the drive train and its various mechanical components are outlined. The major goals of the program were (and continue to be) to increase the life, reliability, and maintainability, reduce the weight, noise, and vibration, and maintain the relatively high mechanical efficiency of the gear train. Major historical milestones are reviewed, significant advances in technology for bearings, gears, and transmissions are discussed, and the outlook for the future is presented. The reference list is comprehensive.

Coy, John J.↗