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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

42 ENGINEERING↗
DOE OSTI2021

Data-Driven Specification of Earth's Magnetic Field

As our society is increasingly dependent on technologies in space, predicting space weather becomes ever more important. We have come to rely on technologies such as cellphones, GPS, the Internet, and other commercial and military assets. In order to protect these systems a high priority is to develop better understanding of the harmful conditions of space weather encountered in the near-Earth radiation environment which are mostly dictated by the dynamics of energetic charged particles. The main objective of our proposal was to improve the specification and forecast of the particle distribution in the Earth’s inner magnetosphere using data assimilation together with machine learning (ML) algorithms in our ring current-atmosphere interactions model with self-consistent magnetic and electric fields (RAM-SCBE). Specifically, we explored the application of machine learning algorithms to emulate and improve, through infusion of observational data, the magnetic field (SCBE) of our ring current model. Our work advanced towards the characterization of the RAM-SCBE particle flux field and its potentially different simulation scenarios. We explored how the model behaves in a number of simulation scenarios in order to better understand how the model reacts to perturbations in the solar wind. We were able to capture this behavior through the simulation of an ensemble of RAM-SCB model solutions. The ensemble showed the important correlations between the particle fluxes and the magnetic field. We were able to start the training of a neural network (Autoencoder) to identify the dominant features of the magnetic field, and the main correlations (linear and non-linear) between the particle fluxes and the magnetic field. Our next steps consists of training a neural network to capture the time evolution of the dominant features and obtain a consistent surrogate model for the RAM-SCB.

42 ENGINEERING↗
NASA NTRSDate not supplied

Authoring, Analyzing, and Monitoring Requirements for a Lift-Plus-Cruise Aircraft

Requirements specification and analysis is widely applied to ensure the correctness of industrial systems in safety critical domains. Requirements are often initially written in natural language, which is highly ambiguous, and as a second step transformed into a language with rigorous semantics for formal analysis. [Question/problem] In this paper, we report on our experience in requirements creation and analysis, as well as run-time monitor generation using the Formal Requirement Elicitation Tool (FRET), on an industrial case study for a Lift-Plus-Cruise concept aircraft. [Principal ideas/results] We study the creation of requirements directly in the structured language of FRET without a prior definition of the same requirements in natural language. We focus on requirements describing state machines and discuss the challenges that we faced, in terms of creating requirements and generating monitors. We demonstrate how realizability, i.e., checking whether a requirements specification can be implemented, is crucial for understanding temporal interdependencies among requirements. [Contribution] Our study is the first complete attempt at using FRET to create industrial, realizable requirements and generate run-time monitors. Insight from lessons learned was materialized into new features in the FRET and JKind analysis frameworks.

Requirements engineering↗
NASA NTRSDate not supplied

Authoring, Analyzing, and Monitoring Requirements for a Lift-Plus-Cruise Aircraft

Requirements specification and analysis is widely applied to ensure the correctness of industrial systems in safety critical domains. Requirements are often initially written in natural language, which is highly ambiguous, and as a second step transformed into a language with rigorous semantics for formal analysis. In this paper, we report on our experience in requirements creation and analysis, as well as run-time monitor generation using the Formal Requirement Elicitation Tool (FRET), on an industrial case study for a Lift-Plus-Cruise concept aircraft. We study the creation of requirements directly in the structured language of FRET without a prior definition of the same requirements in natural language. We focus on requirements describing state machines and discuss the challenges that we faced, in terms of creating requirements and generating monitors. We demonstrate how realizability, i.e., checking whether a requirements specification can be implemented, is crucial for understanding temporal interdependencies among requirements. Our study is the first complete attempt at using FRET to create industrial, realizable requirements and generate run-time monitors. Insight from lessons learned was materialized into new features in the FRET and JKind analysis frameworks.

Requirements engineering↗
DOE OSTI2020

Air bearing heat exchanger methods

The various technologies presented herein relate to fabrication and operation of a heat exchanger that is configured to extract heat from an underlying substrate. Heat can be extracted by way of an air gap formed between an impeller and a baseplate. By utilizing a pump to create an initial air gap that is further maintained by rotation of the impeller relative to the baseplate, a spring can be utilized that can apply a force of greater magnitude to the impeller than is used in a conventional approach, thus enabling the weight of the impeller to be negligible with respect to a width of the air gap, thereby conferring the desirable feature of orientation independence with respect to gravity with no performance degradation.

42 ENGINEERING↗
NASA NTRS1976

The Boeing plastic analysis capability for engines

The current BOPACE program is described as a nonlinear stress analysis program, which is based on a family of isoparametric finite elements. The theoretical, user, programmer, preprocessing aspects are discussed, and example problems are included. New features in the current program version include substructuring, an out-of-core Gauss wavefront equation solver, multipoint constraints, combined material and geometric nonlinearities, automatic calculation of inertia effects, provision for distributed as well as concentrated mechanical loads, follower forces, singular crack-tip elements, the SAIL automatic generation capability, and expanded user control over input quantity definition, output selection, and program execution. BOPACE is written in FORTRAN 4 and is currently available for both the IBM 360/370 and the UNIVAC 1108 machines.

Vos, R. G.↗
DOE OSTI2020

CO 2 plume evolution in a depleted natural gas reservoir: Modeling of conformance uncertainty reduction over time

Uncertainty in the long-term fate of CO 2 injected for geologic carbon sequestration (GCS) is a significant barrier to the adoption of GCS as a greenhouse-gas emission-mitigation for industry and regulatory agencies alike. Here, we present a modeling study that demonstrates that the uncertainty in forecasts of GCS site performance decreases over time as monitoring data are used to update operational models. We consider a case study of GCS in a depleted natural gas reservoir, with CO 2 injection occurring over 20 years, with a 50-year post-injection site care period. We constructed a detailed model to generate the actual model output, which is considered synthetic observation data. A series of simpler operational models based on limited data and assumptions about how an operator would model such a site are then run and compared against actual model output at specific monitoring points after one year, two years, etc. The operational model is updated and improved using the synthetic observation data from the actual model at the same time intervals. Model parameter values and model features needed to be updated over time to improve matches to the actual model. These kinds of model adjustments would be a normal part of reservoir engineering and site management at GCS sites. Uncertainty in two key measures related to site performance decreases with time: extent of the CO 2 plume up-dip migration, and radial extent of the pressure pulse. This conclusion should help allay the concerns of industry and regulators about uncertainty in long-term fate of CO 2 at GCS sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Anti-distortion bioinspired camera with an inhomogeneous photo-pixel array

The bioinspired camera, comprising a single lens and a curved image sensor—a photodiode array on a curved surface—, was born of flexible electronics. Its economical build lends itself well to space-constrained machine vision applications. The curved sensor, much akin to the retina, helps image focusing, but the curvature also creates a problem of image distortion, which can undermine machine vision tasks such as object recognition. Here we report an anti-distortion single-lens camera, where 4096 silicon photodiodes arrayed on a curved surface in a nonuniform pattern assimilated to the distorting optics are the key to anti-distortion engineering. That is, the photo-pixel distribution pattern itself is warped in the same manner as images are warped, which correctively reverses distortion. Acquired images feature no appreciable distortion across a 120° horizontal view, as confirmed by their neural-network recognition accuracies. This distortion correction via photo-pixel array reconfiguration is a form of in-sensor computing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Targeted disruption of pi–pi stacking in Malaysian banana lectin reduces mitogenicity while preserving antiviral activity

Lectins, carbohydrate-binding proteins, have been regarded as potential antiviral agents, as some can bind glycans on viral surface glycoproteins and inactivate their functions. However, clinical development of lectins has been stalled by the mitogenicity of many of these proteins, which is the ability to stimulate deleterious proliferation, especially of immune cells. We previously demonstrated that the mitogenic and antiviral activities of a lectin (banana lectin, BanLec) can be separated via a single amino acid mutation, histidine to threonine at position 84 (H84T), within the third Greek key. The resulting lectin, H84T BanLec, is virtually non-mitogenic but retains antiviral activity. Decreased mitogenicity was associated with disruption of pi–pi stacking between two aromatic amino acids. To examine whether we could provide further proof-of-principle of the ability to separate these two distinct lectin functions, we identified another lectin, Malaysian banana lectin (Malay BanLec), with similar structural features as BanLec, including pi–pi stacking, but with only 63% amino acid identity, and showed that it is both mitogenic and potently antiviral. We then engineered an F84T mutation expected to disrupt pi–pi stacking, analogous to H84T. As predicted, F84T Malay BanLec (F84T) was less mitogenic than wild type. However, F84T maintained strong antiviral activity and inhibited replication of HIV, Ebola, and other viruses. The F84T mutation disrupted pi–pi stacking without disrupting the overall lectin structure. These findings show that pi–pi stacking in the third Greek key is a conserved mitogenic motif in these two jacalin-related lectins BanLec and Malay BanLec, and further highlight the potential to rationally engineer antiviral lectins for therapeutic purposes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Plasma confinement state classification via FPP relevant microwave diagnostics

We present a parsimonious and robust machine learning approach for identifying plasma confinement states in fusion power plants (FPPs) where reliable identification of the low-confinement and high-confinement regimes is critical for safe and efficient operation. Unlike research-oriented devices, FPPs must operate with a severely constrained set of diagnostics. To address this challenge, we demonstrate that a minimalist model, using only electron cyclotron emission (ECE) signals, can achieve accurate and reliable state classification. ECE provides electron temperature profiles without the engineering or survivability issues of in-vessel probes, making it a primary candidate for FPP-relevant diagnostics. Our framework employs ECE as input, extracts features using radial basis functions, and applies a gradient boosting classifier, achieving a test accuracy of 96% (correct predictions). Robustness analysis and feature importance analyzes confirm the approach’s reliability. These results demonstrate that state-of-the-art performance is attainable from a restricted diagnostic set, paving the way for minimalist yet resilient plasma control architectures for FPPs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Deep Reinforcement Learning-Based Edge Caching in Wireless Networks

With the purpose to offload data traffic in wireless networks, content caching techniques have recently been studied intensively. Using these techniques and caching a portion of the popular files at the local content servers, the users can be served with less delay. Most of the content replacement policies are based on the content popularity, that depends on the users' preferences. In practice, such information varies over time. Therefore, an approach to determine the file popularity patterns must be incorporated into caching policies. In this context, we study content caching at the wireless network edge using a deep reinforcement learning framework with Wolpertinger architecture. In particular, we propose deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching. For centralized edge caching, we aim at maximizing the cache hit rate. In decentralized edge caching, we consider both the cache hit rate and transmission delay as performance metrics. These proposed frameworks are assumed to neither have any prior information on the file popularities nor know the potential variations in such information. Via simulation results, the superiority of the proposed frameworks is verified by comparing them with other policies, including least frequently used (LFU), least recently used (LRU), and first-in-first-out (FIFO) policies.

42 ENGINEERING↗
DOE OSTI2025

Atlas-UI-3

SAND2025-14754O Atlas-UI-3 is the third iteration of the LLM chat user interface called Atlas. The tool does not share code with previous versions. It supports chat with large language models (LLMs), connects to retrieval-augmented generation (RAG) servers, features a marketplace/store for model and content providers (MCP), and enables tool calling. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Melander, Darryl [Sandia National Lab. (SNL-CA), L↗
NASA NTRS1977

Spin-Tunnel Investigation of the Spinning Characteristics of Typical Single-Engine General Aviation Airplane Designs. 1. Low-Wing Model A: Effects of Tail Configurations

The effects of tail design on spin and recovery were investigated in a spin tunnel. A 1/11-scale model of a research airplane which represents a typical low-wing, single engine, light general aviation airplane was used. A tail design criterion for satisfactory spin recovery for light airplanes was evaluated. The effects of other geometric design features on the spin and recovery characteristics were also determined. Results indicate that the existing tail design criterion for light airplanes, which uses the tail damping power factor as a parameter, cannot be used to predict spin-recovery characteristics.

Burk, S. M., Jr.↗
NASA NTRS1977

NASA Tech House: An early evaluation

An architect-engineering firm, as well as university participants, performed system studies, evaluated construction methods, performed cost effectiveness studies, and prepared construction drawings which incorporated the selected technology features into a final design. A Technology Utilization House (Tech House) based on this design was constructed at the NASA Langley Research Center in Hampton, Virginia. The Tech House is instrumented so that the performance of the design features and energy systems can be evaluated during a planned family live-in period. As such, the house is both a demonstration unit and a research laboratory. The Tech House is to demonstrate the kind of single-family residence that will probably be available within the next five years.

Source record↗
NASA NTRS1982

Development and operating characteristics of an advanced two-stage combustor

Results are presented from an experimental program aimed at optimizing the features of an advanced two-stage combustor that currently is being developed as part of the National Aeronautics and Space Administration/Pratt & Whitney Aircraft Energy Efficient Engine program. The combustor is designed to meet stringent goals for performance, emissions, durability and operational characteristics. An overview of the design selection process in light of these goals is discussed. Combustor rig test results, which highlight the techniques used to reduce emissions, development of the pilot and main power zone fuel injection systems, and methods of achieving efficient fuel staging, are presented.

Greene, W.↗
NASA NTRS1985

Experience with a software engineering environment framework

Experience with a software engineering environment framework tool called the Automated Product Control Environment (APCE) is described. The goals of the framework design, an overview of the major functions and features of the framework, and implementation and use of the framework are presented. Aspects of the framework discussed include automation and control; portability, distributability, and interoperability; cost/benefit analysis; and productivity. Results of using the framework are discussed and the framework approach is briefly compared to other software development environment approaches.

Blumberg, R.↗
NASA NTRS1995

Nonstandard finite difference schemes

The major research activities of this proposal center on the construction and analysis of nonstandard finite-difference schemes for ordinary and partial differential equations. In particular, we investigate schemes that either have zero truncation errors (exact schemes) or possess other significant features of importance for numerical integration. Our eventual goal is to bring these methods to bear on problems that arise in the modeling of various physical, engineering, and technological systems. At present, these efforts are extended in the direction of understanding the exact nature of these nonstandard procedures and extending their use to more complicated model equations. Our presentation will give a listing (obtained to date) of the nonstandard rules, their application to a number of linear and nonlinear, ordinary and partial differential equations. In certain cases, numerical results will be presented.

Mickens, Ronald E.↗