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

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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Virtual Ultrasound Guidance for Inexperienced Operators

Medical ultrasound or echocardiographic studies are highly operator-dependent and generally require lengthy training and internship to perfect. To obtain quality echocardiographic images in remote environments, such as on-orbit, remote guidance of studies has been employed. This technique involves minimal training for the user, coupled with remote guidance from an expert. When real-time communication or expert guidance is not available, a more autonomous system of guiding an inexperienced operator through an ultrasound study is needed. One example would be missions beyond low Earth orbit in which the time delay inherent with communication will make remote guidance impractical. The Virtual Ultrasound Guidance system is a combination of hardware and software. The hardware portion includes, but is not limited to, video glasses that allow hands-free, full-screen viewing. The glasses also allow the operator a substantial field of view below the glasses to view and operate the ultrasound system. The software is a comprehensive video program designed to guide an inexperienced operator through a detailed ultrasound or echocardiographic study without extensive training or guidance from the ground. The program contains a detailed description using video and audio to demonstrate equipment controls, ergonomics of scanning, study protocol, and scanning guidance, including recovery from sub-optimal images. The components used in the initial validation of the system include an Apple iPod Classic third-generation as the video source, and Myvue video glasses. Initially, the program prompts the operator to power-up the ultrasound and position the patient. The operator would put on the video glasses and attach them to the video source. After turning on both devices and the ultrasound system, the audio-video guidance would then instruct on patient positioning and scanning techniques. A detailed scanning protocol follows with descriptions and reference video of each view along with advice on technique. The program also instructs the operator regarding the types of images to store and how to overcome pitfalls in scanning. Images can be forwarded to the ground or other site when convenient. Following study completion, the video glasses, video source, and ultrasound system are powered down and stored. Virtually any equipment that can play back video can be used to play back the program. This includes a DVD player, personal computer, and some MP3 players.

Caine, Timothy↗
NASA NTRS2022

DataSet: Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this e that it is feasible to classify sensor collected trajectories using a classifier trained flight controller data.

Chester V Dolph↗
NASA NTRS1993

Interactive displays for trajectory planning and proximity operations

Rendezvous, docking, and other Space Station proximity operations (PROX OPS) will be conducted routinely in space. Real-time interactive visual aids and planning tools will be helpful, if not necessary, for future missions both in preflight training and on orbit. Two such displays, eivaN and Navie, are currently available for examination and human factors testing. A study was conducted in which data were collected from eight test subjects. Solution times for both devices decreased rapidly with experience. Neither fuel usage nor the number of waypoints (burns) decreased with experience. With Navie, medians of solution time and fuel consumption totaled over all subjects peaked at one of two starting points above the V-bar with monotonically decreasing values in both directions. This pattern did not appear with eivaN values. Since the docking tasks were fundamentally different with each device, and because Navie imposed more constraints on the users than eivaN did, the orbital mechanics effects had a more pronounced effect on the Navie results than on the eivaN data.

Brody, Adam R.↗
NASA NTRS1996

Behavioral analysis of signals that guide learned changes in the amplitude and dynamics of the vestibulo-ocular reflex

We characterized the dependence of motor learning in the monkey vestibulo-ocular reflex (VOR) on the duration, frequency, and relative timing of the visual and vestibular stimuli used to induce learning. The amplitude of the VOR was decreased or increased through training with paired head and visual stimulus motion in the same or opposite directions, respectively. For training stimuli that consisted of simultaneous pulses of head and target velocity 80-1000 msec in duration, brief stimuli caused small changes in the amplitude of the VOR, whereas long stimuli caused larger changes in amplitude as well as changes in the dynamics of the reflex. When the relative timing of the visual and vestibular stimuli was varied, brief image motion paired with the beginning of a longer vestibular stimulus caused changes in the amplitude of the reflex alone, but the same image motion paired with a later time in the vestibular stimulus caused changes in the dynamics as well as the amplitude of the VOR. For training stimuli that consisted of sinusoidal head and visual stimulus motion, low-frequency training stimuli induced frequency-selective changes in the VOR, as reported previously, whereas high-frequency training stimuli induced changes in the amplitude of the VOR that were more similar across test frequency. The results suggest that there are at least two distinguishable components of motor learning in the VOR. One component is induced by short-duration or high-frequency stimuli and involves changes in only the amplitude of the reflex. A second component is induced by long-duration or low-frequency stimuli and involves changes in the amplitude and dynamics of the VOR.

NASA Discipline Neuroscience↗
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 NTRS2017

Updates and Overview of Spaceflight Medical Support in Russia and Kazakhstan

This panel presents recent updates to and a comprehensive overview of the operational medical support provided to ISS crewmembers in Star City, Russia and Kazakhstan as part of UTMB/KBRwyle's Human Health & Performance contract. With the current Soyuz training flow, physician support is required for nominal training evolutions involving pressure changes or other potential physical risks detailed in this presentation. In addition, full-time physician presence in Star City helps to address the disparity in access to health care in these relatively remote practice areas, while also developing and maintaining relationships with host nation resources. A unique part of standard training in Russia also involves survival training in both winter and water environments; logistic details and medical impacts of each of these training scenarios will be discussed. Following support of a successful training flow, UTMB/KBRwyle's Star City Medical Support Group (SCMSG) is also responsible for configuring medical packs in support of Soyuz launches and landings; we will present the rationale for current pack contents within the context of specific operational needs. With respect to contingency events, the group will describe their preparedness to respond appropriately by activating both local and global resources as necessary, detailing a specialized subset of the group who continually work and update these assets, given changes in international infrastructure and other impacts.

Chough, Natacha↗
DOE OSTI2022

Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling

Abstract Effective plasma transport modeling of magnetically confined fusion devices relies on having an accurate understanding of the ion composition and radiative power losses of the plasma. Generally, these quantities can be obtained from solutions of a collisional-radiative (CR) model at each time step within a plasma transport simulation. However, even compact, approximate CR models can be computationally onerous to evaluate, and in-situ evaluation of these models within a larger plasma transport code can lead to a rigid bottleneck. As a way to bypass this bottleneck, we propose deploying artificial neural network (ANN) surrogates to allow rapid evaluation of the necessary plasma quantities. However, one issue with training an accurate ANN surrogate is the reliance on a sufficiently large and representative training and validation data set, which can be time-consuming to generate. In this work we explore a data-driven active learning and training routine to allow autonomous adaptive sampling of the problem parameter space to ensure a sufficiently large and meaningful set of training data is assembled for the network training. As a result, we can demonstrate approximately order-of-magnitude savings in required training data samples to produce an accurate surrogate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗
DOE OSTI2019

Report on Task USA A 0931 (A.252) Implementation of Fast, Front-End Electronics for Improved Low-Dead Time Neutron Counting

The International Atomic Energy Agency's (IAEA) Department of Safeguards is working on the elaboration and implementation of advanced Non-Destructive Assay (NDA) methods, generally known as neutron noise analysis, for the verification of critical and sub-critical cores containing substantial quantities of direct use nuclear material. These verification methods rely on interpreting time properties of the neutron multiplication process, which are to be extracted from measured neutron pulse trains. The interpretations are highly dependent on the quality of the measured data, which is directly related to the capability of an employed neutron detector to resolve very close detection events originating from neutron bursts produced in a highly multiplying medium. Therefore, the application of detectors with minimum dead-time, pile-up, and other distortion effects is required. This report describes the work under Task USA A 0931 (A.252 Implementation of fast, front-end electronics for improved low-dead time neutron counting).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1961

Post University On-the-Job Training for Engineers

Our national need for qualified scientists and engineers is greater now than at any other time in our history. Fortunately, we can point with pride to this need as a measure of the impact of science and technology on our way of life. In effect, we have made such rapid strides In advancing established sciences and in opening new technological fields that we have proved the value of the scientist and engineer to society, and, as a-result, have created an expanding demand for their services which we must now attempt to satisfy. This demand we face is also due to the changing skills and high degree of specialization required to perform in these new technological fields. The colleges and universities are doing their part to provide current graduates with a modern technical foundation, but we cannot afford to ignore the thousands of experienced engineers and scientists already employed by private industry and government. As employers, we have an obligation to these men and women to see that they are provided with an understanding of the latest advances that modern technology has to offer; that we develop them in particular specialty areas characteristic of a given field of work; and, equally important, that we assist them in the transition from one field to another as the technological emphasis shifts. Practically all technological industries have experienced and continue to experience rapid changes in their activities. The aerospace business, in particular, has been characterized by extremely rapid, in fact revolutionary, changes during the relatively short period of its existence0 At the National Aeronautics and Space Administration, successor to the National Advisory Committee for Aeronautics, for example, we have encountered the fun impact of a changing science and technology. Indeed, as a research organization, we have undoubtedly contributed, in some measure, to this change. Within the NASAs Lewis Research Center, we have approximately 800 research scientists and engineers who have matured professionally in an environment which is essentially one of continuous learning - an experience which comes close to being a form of post graduate training in itself. This environment, in addition to providing continuous evolutionary changes, has also provided two major revolutions which have made this development picture more complex. We will describe these environmental changes which have occurred at the Lewis Research Center and discuss the various techniques and programs we have employed to provide for the professional development of our staff. The Lewis Research Center has had an Interesting and exciting l8-year history of aerospace propulsion research and development. It began during the early years of World War II as an expansion of the Power Plant Division of the NCA Langley Center with the mission of conducting research required for the development of improved reciprocating engines and to study the associated problems of subsonic propulsion aerodynamics, It was only a few years later, however, that turbojet and ramjet propulsion and supersonic flight research became our main concern. This transition to jet type engines and higher speeds was our first major technological change. The aerodynamics of propellers became the aerodynamics of high speed turbine and compressor blades; the fuel ignition and carbon deposition problems were transferred from a cyclical or Intermittent high compression combustion chamber to a continuous combustion zone within a thin-walled metal shell; aerodynamics problems were thrust into the supersonic range; and high temperature materials began to play an increasingly critical role. Although this transition still required the same basic knowledge and principles as before, the new engine types did involve a different emphasis and variety of consideration not generally familiar to our scientists and engineers.

Manganiello, Eugene J.↗
DOE OSTI2025

A Preferences Corpus and Annotation Scheme for Human-Guided Alignment of Time-Series GPTs

The process of time-series forecasting such as predicting trajectories of silicon content in blast furnaces is a difficult task. Most time-series approaches today focus on scalar-type MSE loss optimization. This optimization approach, while widely common, could benefit from the use of human expert or process-level preferences. In this paper, we introduce a novel alignment and fine-tuning approach that involves learning from a corpus of preferred and dis-preferred time-series prediction trajectories. Our contributions include (1) a preference annotation pipeline for time-series forecasts, (2) the application of Score-based Preference Optimization (SPO) to train decoder-only transformers from preferences, and (3) results showing improvements in forecast quality. The approach is validated on both proprietary blast furnace data and the UCI Appliances Energy dataset. The proposed preference corpus and training strategy offer a new option for fine-tuning sequence models in industrial settings.

DPO↗
DOE OSTI2022

Towards Fast and Accurate Predictions of Radio Frequency Power Deposition and Current Profile via Data-driven Modeling

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. The machine learning models use a database of 16,000+ GENRAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods ensure that the database covers the range of 9 input parameters ($n_{e0}$, $T_{e0}$, $I_p$, $B_t$, $R_0$, $n_{||}$, $Z_{eff}$, $V_{loop}$, $P_{LHCD}$) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to ~ms with high accuracy across the input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated Tracks

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this work indicate that it is feasible to classify sensor collected trajectories using a classifier trained on flight controller data.

Henry Holbrook↗
DOE OSTI2021

An adaptive adversarial domain adaptation approach for corn yield prediction

Recently, statistical machine learning and deep learning methods have been widely explored for corn yield prediction. Though successful, machine learning models generated within a specific spatial domain often lose their validity when directly applied to new regions. To address this issue, we designed an unsupervised adaptive domain adversarial neural network (ADANN). Specifically, through domain adversarial training, the ADANN model reduced the impact of domain shift by projecting data from different domains into the same subspace. Also, the ADANN model was designed to be trained in an adaptive way, which guaranteed the model can learn the domain-invariant features and perform accurate yield prediction simultaneously. Informative variables including time-series vegetation indices and sequential weather observations were first collected from multiple data sources and aggregated to the county level. Then, we trained the ADANN model with the extracted features and corresponding reported county-level corn yield from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated in four testing years 2016–2019. The U.S. corn belt was used as the study area and counties under study were grouped into two diverse ecological regions. Overall, the experimental results showed that the developed ADANN model had better performance than three other state-of-the-art machine learning models in both local experiments (train and test in the same region) and transfer experiments (train and test in different regions). As the first study using adversarial learning for crop yield prediction, this research demonstrates a novel solution for improving model transferability on crop yield prediction.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

BEAM Technology Flight Demonstration

As technologies advance, their growing complexity makes them harder to maintain. Detection methods for isolating and identifying impending problems are needed to balance this complexity. Through comparison of signal pairs from onboard sensors, the Beacon-based Exception Analysis For Multimissions (BEAM) algorithm can identify and help classify deviations in system operation from a data-trained statistical model. The goal of this task is to mature BEAM and validate its performance on a flying test bed. A series of F-18 flight demonstrations with BEAM monitoring engine parameters in real time was used to demonstrate in-the-field readiness. Captured F-18 and simulated F-18 engine data were used in model creation and training. The algorithm was then ported to the embedded system with a data buffering, file writing, and data-time-stamp monitoring shell to reduce the impact of embedded system faults on BEAM'S ability to correctly identify engine faults. Embedded system testing identified hardware related restrictions and contributed to iterative improvements in the code's runtime performance. The system was flown with forced engine flameouts and other pilot induced faults to simulate operation out of the norm. Successful detection of these faults, confirmed through post-flight data analysis, helped BEAM achieve TRL6.

Wang, David↗
DOE OSTI2024

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties

Machine learning-based data-driven modeling can allow computationally efficient time-dependent solutions of PDEs, such as those that describe subsurface multiphysical problems. In this work, our previous approach (Kadeethum et al., 2021d) of conditional generative adversarial networks (cGAN) developed for the solution of steady-state problems involving highly heterogeneous material properties is extended to time-dependent problems by adopting the concept of continuous cGAN (CcGAN). The CcGAN that can condition continuous variables is developed to incorporate the time domain through either element-wise addition or conditional batch normalization. Moreover, this framework can handle training data that contain different timestamps and then predict timestamps that do not exist in the training data. As a numerical example, the transient response of the coupled poroelastic process is studied in two different permeability fields: Zinn & Harvey transformation and a bimodal transformation. The proposed CcGAN uses heterogeneous permeability fields as input parameters while pressure and displacement fields over time are model output. Our results show that the model provides sufficient accuracy with computational speed-up. This robust framework will enable us to perform real-time reservoir management and robust uncertainty quantification in poroelastic problems.

97 MATHEMATICS AND COMPUTING↗