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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Machine-Vision Aids for Improved Flight Operations

The development of machine vision based pilot aids to help reduce night approach and landing accidents is explored. The techniques developed are motivated by the desire to use the available information sources for navigation such as the airport lighting layout, attitude sensors and Global Positioning System to derive more precise aircraft position and orientation information. The fact that airport lighting geometry is known and that images of airport lighting can be acquired by the camera, has lead to the synthesis of machine vision based algorithms for runway relative aircraft position and orientation estimation. The main contribution of this research is the synthesis of seven navigation algorithms based on two broad families of solutions. The first family of solution methods consists of techniques that reconstruct the airport lighting layout from the camera image and then estimate the aircraft position components by comparing the reconstructed lighting layout geometry with the known model of the airport lighting layout geometry. The second family of methods comprises techniques that synthesize the image of the airport lighting layout using a camera model and estimate the aircraft position and orientation by comparing this image with the actual image of the airport lighting acquired by the camera. Algorithms 1 through 4 belong to the first family of solutions while Algorithms 5 through 7 belong to the second family of solutions. Algorithms 1 and 2 are parameter optimization methods, Algorithms 3 and 4 are feature correspondence methods and Algorithms 5 through 7 are Kalman filter centered algorithms. Results of computer simulation are presented to demonstrate the performance of all the seven algorithms developed.

Menon, P. K.↗
NASA NTRS2021

Applying the Cognitive Space Gateway to Swarm Topologies

NASA's future vision for interplanetary networking includes a lunar network, Cube Satellite (CubeSat) constellations, and deep space robotic missions, comprising what could be viewed as a network of networks. Delay-tolerant networking (DTN) architecture and protocols provide a standard network layer among these varying scenarios and mitigate many challenges of the space environment, such as long delays, unplanned service interruptions, and asymmetric links. The Cognitive Space Gateway (CSG) is a routing method in a DTN architecture that uses spiking neural networks as the learning element to optimize routing decisions in a complex environment. This work aims to further develop cognitive networking technologies in several critical areas, including DTN, the CSG algorithm, CubeSat swarm topologies, and cloud services. To test the algorithm in a realistic scenario, the emulated network topology is based on a CubeSat swarm. The swarm may function as a mesh of nodes or as a hub-and-spoke network. An emulation environment will be built upon a commercial cloud service, such as Amazon Web Services (AWS) Elastic Compute Cloud. The cloud environment may enable a flexible, lower maintenance approach versus a multi-hop network based in a physical laboratory. The cloud platform will provide a secure environment allowing for collaboration among government and academic entities.

Ricardo Lent↗
NASA NTRS1988

Using AGNESS (A Generalized Network-based Expert System Shell) for matching images

The image correspondence problem is considered the most difficult step in both stereo and motion analysis. Stereo vision is useful in determining the 3-D positions of points on visible surface in a scene. Motion analysis is useful in determining the spatial and temporal relationships of objects in an environment. Besides stereo and motion analysis, there is the image correspondence problem. Most of this work is based on point or local area properties of the observed gray level values in 2-D images. A global and general approach to this problem is described by using a knowledge-based system. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions extracted from the given images. The physical component depends on features of the edge or region) in isolation. The spatial component involves the set of edges and regions adjacent to a given edge (or region) of the first image and the set of edges and regions adjacent to each potentially matching edge (or region) of the second image; thus the spatial context of each edge or region is considered. A computational network is used to represent this knowledge, it allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. An expert system shell called AGNESS (A Generalized Network-based Expert System Shell) is used to build a prototype system.

Pong, Ting-Chuen↗
DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Furthermore, studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Trustworthy Cyber Infrastructure for the Power Grid (TCIPG) Final Technical Report - November 20, 2015

The Trustworthy Cyber Infrastructure for the Power Grid project (TCIPG) was funded by DOE and DHS for a period of performance that ran from October 1, 2009 to August 31 2015. The partnership included the University of Illinois at Urbana-Champaign (lead institution) and partner institutions Arizona State University (replacing original partner UC Davis when faculty moved), Dartmouth College, and Washington State University. TCIPG was a unique public-private partnership of government, academia, and industry that was formed to meet the challenge of keeping our power grid secure. TCIPG followed from the earlier NSF-funded TCIP project, which kicked off in 2005. At that time, awareness of cyber security and resiliency in grid systems (and in control systems in general) was low, and the term “smart grid” was not in wide use. The original partnership was formed from a team of academic researchers with a shared vision for the importance of research in this area, and a commitment to producing more impactful results through early involvement of industry. From the TCIPG standpoint, “industry” meant both utilities (investor-owned as well as cooperatives and municipals) and system vendors (who sell technology to the utility sector). Although TCIPG was a university-led initiative, we have from the start stressed real-world impact and partnership with industry. That has led to real-world adoption of TCIPG technologies within the industry, achieving practical benefits. This report summarizes the achievements of TCIPG over its period of performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Molecular Vision - Multimodal, multitask retrieval of molecular structure from measured signatures for reference-free compound identification

We are currently at risk of generating false conclusions based on limited methods to identify small molecules in biological systems and in chemical forensics. By definition, the chemical structures of novel small molecules have not been determined, let alone measured or synthesized. Currently, unambiguous structure determination of small molecules is constrained by the time and effort needed to isolate compounds and perform de novo structure elucidation using laboratory-based methods, significantly extending the time to inform mitigation strategies. To address this gap, we have developed a deep learning approach to directly map molecular structure to experimental signatures. We aim to unify measurement technologies employed in untargeted small molecule identification studies—such as infrared (IR) spectrometry, tandem mass spectrometry (MS/MS), ion mobility spectrometry-derived collision cross section (CCS)—through use of a multimodal, multitask deep learning architecture. Where existing methods require direct generation of information-rich spectra and/or properties, an inherently difficult task, we will simplify molecular signature-based identification by posing the problem as a recognition or retrieval task. The model is thus presented with relevant endpoints – structure and one or more molecular signatures – and need only determine whether they are semantically related. Thus, our approach offers the following advantages over existing techniques: (i) circumvents difficulties associated with direct generation of molecular signatures from structure and structure from signatures; (ii) incorporates multiple molecular signatures simultaneously, as available, to support identification; and (iii) enables rapid computation of structural embeddings toward broad coverage of known chemical space. Taken together, the approach removes the need to explicitly obtain or compute reference spectra, representing a powerful method for compound identification that requires only experimentally observed signatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Autoregressive long-horizon prediction of plasma edge dynamics *

Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes such as SOLPS-ITER capture SOL physics with high accuracy, but their computational cost limits broad parameter scans and long transient studies. We present transformer-based, autoregressive surrogates for efficient prediction of 2D, time-dependent plasma edge state fields. Trained on SOLPS-ITER spatiotemporal data for the KSTAR tokamak, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons. We evaluate model variants trained with increasing autoregressive horizons (1–100 steps) on short- and long-horizon prediction tasks. Longer-horizon training systematically improves rollout stability and mitigates error accumulation, enabling stable predictions over hundreds to thousands of steps and reproducing key dynamical features such as the motion of high-radiation regions. Measured end-to-end wall-clock times show the surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration. Prediction accuracy degrades when the surrogate enters physical regimes not represented in the training dataset, motivating future work on data enrichment and physics-informed constraints. Overall, this approach provides a fast, accurate surrogate for computationally intensive plasma edge simulations, supporting rapid scenario exploration, control-oriented studies, and progress toward real-time applications in fusion devices.

autoregressive deep learning↗
DOE OSTI2022

Adaptive Urban Traffic Signal Control for Multiple Intersections: An LQR Approach

Traffic congestion leads to severe problems especially in urban traffic networks. It increases the chance of accidents, energy waste, and social costs. In order to address these problems, an adaptive linear quadratic regulator (LQR) approach is developed for traffic signal control at multiple intersections in an urban area. The proposed method controls the green time of the traffic signals to reduce traffic congestion and smooth traffic flow. Real-world data from vision-based traffic sensors are used to build the traffic network model, which mimics the real-world traffic behavior. In addition, the proposed control utilizes recursive least square parameter estimation, which is capable of tracking dynamic changes in traffic conditions. Simulation of Urban MObility (SUMO) is used to analyze the efficacy of the proposed method. Results of the simulation show that the proposed method outperforms pretimed control in various aspects.

adaptive LQR control↗
DOE OSTI2025

Large‐Scale 2D Perovskite Nanocrystals Photodetector Array via Ultrasonic Spray Synthesis

Abstract 2D perovskite (PVSK) single crystals have received significant attention due to their unique optical and optoelectronic properties. However, current synthesis methods face limitations, particularly in large‐area fabrication, which remain critical barriers to practical applications. In this study, the synthesis of red/green/purple‐blue‐colored 2D PVSK nanocrystals over a large area (4‐inch wafer) and the fabrication of high‐performance photodetector arrays are presented via a facile yet efficient spray‐coating approach with a liquid‐bridge transport effect. The photodetector array achieves 100% working yield, high photo‐responsivity (1.5 × 10 6 A W −1 ) and specific‐detectivity (1.1 × 10 16 Jones) with competitive photomapping characteristics. An intelligent vision system for automatic shape recognition is further demonstrated with a recognition rate exceeding 90%. This study provides significant advances in the scalable synthesis of nanoscale 2D PVSK crystals, their integration into large‐area optoelectronic devices, and their potential use in artificial‐intelligence systems.

Lee, Yoon Ho [Davidson School of Chemical Engineer↗
DOE OSTI2023

FAIR for AI: An interdisciplinary and international community building perspective

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

MEPHESTO: Modeling Energy-Performance in Heterogeneous SoCs and Their Trade-Offs

Integrated shared memory heterogeneous architectures are pervasive because they satisfy the diverse needs of mobile, autonomous, and edge computing platforms. Although specialized processing units (PUs) that share a unified system memory improve performance and energy efficiency by reducing data movement, they also increase contention for this memory since the PUs interact with each other. Prior work has investigated performance degradation due to memory contention, but few have studied the relationship of power and energy to memory contention. Moreover, a comprehensive solution that models memory contention for kernel placement on contemporary heterogeneous systems on chip (SoCs) in response to energy and performance has been largely unaddressed.This paper presents MEPHESTO, a novel and holistic approach for managing this balance. The authors characterize applications and PUs in terms of two memory contention factors - time factors and power factors - to achieve the desired trade-off between energy and performance for collocated kernel execution on heterogeneous systems. The authors believe that this investigation is the first to combine all of these factors and present a simple knob-based approach that expresses the target trade-off. The approach is evaluated on a diverse integrated shared memory heterogeneous system with a CPU, GPU, and programmable vision accelerator. By using an empirical model for memory contention that provides up to 92% accuracy, the kernel collocation approach can provide a near-optimal ordering and placement based on the user-defined, energy-performance trade-off parameter. Moreover, the dynamic programming-based heuristics provide up to 30% better energy or 20% performance benefits when compared with the greedy approaches commonly employed by previous studies.

Alaul haque monil, Mohammad↗
DOE OSTI2019

Adaptive Sample Preparation and Target Fabrication for High-Throughput Materials Science

High-repetition-rate brilliant and coherent light sources are significantly increasing the rate at which scientific experiments can be performed. An investment in transformative technologies to make samples of condensed matter could enable a paradigm shift for that field or open new exciting frontiers in high-energy density science (HEDS). This will especially be true for dynamic materials in extremes research or HEDS, where the repetition rate for placing samples into extreme environments is increasing and the samples or targets are irreversibly destroyed in each experiment. Research teams that are able to implement solutions to all aspects of the experimental process and enable a paradigm of high-throughput science will have the greatest success and impact. There are two different directions for adaptive samples. One is research when the number of experiments can be greatly increased with increased volume, such as in high energy density science. The other is when flexibility and agility can aid Bayesian optimization computational methods and speed up the discovery process, as with materials science. Conducting a similar equation-of-state (EOS) experiment took three years on the OMEGA laser facility, compared to five days of shifts at the LCLS XFEL. New materials with improved properties have been found by guided optimization of experimental science. Adaptive sample preparation and target fabrication can create a tremendous return on scientific investment. High-throughput enabling precision science is also a desirable goal. To address these issues, we sought a diverse set of innovative participants who had ideas of how to achieve this paradigm shift. We invited 42 researchers from a variety of disciplines and fields to attend a workshop in College Station, Texas, May 14–16, 2019. Many of the participants had never been in the same room before, and most found it very exciting to hear about new and different research. The goal was twofold: to define the challenges, i.e., “what would we like to be able to do and why it is difficult to do so,” and to describe the progress, approaches and capabilities, as well as the vision, “what we could do if we had more support.”

36 MATERIALS SCIENCE↗
DOE OSTI2021

Implementation of Additional Models into the MACCS Code for Nearfield Consequence Analysis

The NRC’s Non-Light Water Reactor Vision and Strategy report discusses the MACCS code readiness for nearfield analyses. To increase the nearfield capabilities of MACCS, the plume meander model from Ramsdell and Fosmire was integrated into MACCS and the MACCS plume meander model based on U.S. NRC Regulatory Guide 1.145 was updated. Test cases were determined to verify the plume meander model implementation into MACCS 4.1. The results using the implemented MACCS plume meander models match the comparisons with other codes and analytical calculations. This verifies that the additional MACCS plume meander models have been successfully implemented into MACCS 4.1. This report documents the verification of these model implementations into MACCS and a comparison of the results using

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Current and future directions in network biology

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Two-Armed, Mobile, Sensate Research Robot

The Anthropomorphic Robotic Testbed (ART) is an experimental prototype of a partly anthropomorphic, humanoid-size, mobile robot. The basic ART design concept provides for a combination of two-armed coordination, tactility, stereoscopic vision, mobility with navigation and avoidance of obstacles, and natural-language communication, so that the ART could emulate humans in many activities. The ART could be developed into a variety of highly capable robotic assistants for general or specific applications. There is especially great potential for the development of ART-based robots as substitutes for live-in health-care aides for home-bound persons who are aged, infirm, or physically handicapped; these robots could greatly reduce the cost of home health care and extend the term of independent living. The ART is a fully autonomous and untethered system. It includes a mobile base on which is mounted an extensible torso topped by a head, shoulders, and two arms. All subsystems of the ART are powered by a rechargeable, removable battery pack. The mobile base is a differentially- driven, nonholonomic vehicle capable of a speed >1 m/s and can handle a payload >100 kg. The base can be controlled manually, in forward/backward and/or simultaneous rotational motion, by use of a joystick. Alternatively, the motion of the base can be controlled autonomously by an onboard navigational computer. By retraction or extension of the torso, the head height of the ART can be adjusted from 5 ft (1.5 m) to 6 1/2 ft (2 m), so that the arms can reach either the floor or high shelves, or some ceilings. The arms are symmetrical. Each arm (including the wrist) has a total of six rotary axes like those of the human shoulder, elbow, and wrist joints. The arms are actuated by electric motors in combination with brakes and gas-spring assists on the shoulder and elbow joints. The arms are operated under closed-loop digital control. A receptacle for an end effector is mounted on the tip of the wrist and contains a force-and-torque sensor that provides feedback for force (compliance) control of the arm. The end effector could be a tool or a robot hand, depending on the application.

Engelberger, J. F.↗
NASA NTRS2005

Video-Camera-Based Position-Measuring System

A prototype optoelectronic system measures the three-dimensional relative coordinates of objects of interest or of targets affixed to objects of interest in a workspace. The system includes a charge-coupled-device video camera mounted in a known position and orientation in the workspace, a frame grabber, and a personal computer running image-data-processing software. Relative to conventional optical surveying equipment, this system can be built and operated at much lower cost; however, it is less accurate. It is also much easier to operate than are conventional instrumentation systems. In addition, there is no need to establish a coordinate system through cooperative action by a team of surveyors. The system operates in real time at around 30 frames per second (limited mostly by the frame rate of the camera). It continuously tracks targets as long as they remain in the field of the camera. In this respect, it emulates more expensive, elaborate laser tracking equipment that costs of the order of 100 times as much. Unlike laser tracking equipment, this system does not pose a hazard of laser exposure. Images acquired by the camera are digitized and processed to extract all valid targets in the field of view. The three-dimensional coordinates (x, y, and z) of each target are computed from the pixel coordinates of the targets in the images to accuracy of the order of millimeters over distances of the orders of meters. The system was originally intended specifically for real-time position measurement of payload transfers from payload canisters into the payload bay of the Space Shuttle Orbiters (see Figure 1). The system may be easily adapted to other applications that involve similar coordinate-measuring requirements. Examples of such applications include manufacturing, construction, preliminary approximate land surveying, and aerial surveying. For some applications with rectangular symmetry, it is feasible and desirable to attach a target composed of black and white squares to an object of interest (see Figure 2). For other situations, where circular symmetry is more desirable, circular targets also can be created. Such a target can readily be generated and modified by use of commercially available software and printed by use of a standard office printer. All three relative coordinates (x, y, and z) of each target can be determined by processing the video image of the target. Because of the unique design of corresponding image-processing filters and targets, the vision-based position- measurement system is extremely robust and tolerant of widely varying fields of view, lighting conditions, and varying background imagery.

Lane, John↗
DOE OSTI2025

Designing Future Energy Systems with Generative AI

Energy systems are experiencing various changes that impact the distribution, use, and reliability of energy. Local utilities and municipalities must respond and adapt to these changes, moving towards a future energy system with modernized infrastructure and other targeted investments and policy decisions. However, planning for and enacting these advancements requires significant effort from experts and engineers to develop strategies that ensure a reliable and secure energy future. This includes characterizing the current energy infrastructure, identifying areas for development, and engaging with local community members. Emerging generative artificial intelligence techniques can alleviate pain points and help support the development of the next generation of energy systems. Here, in this article, we highlight on-going generative AI work in the areas of atmospheric modeling, building energy management, and distribution network design, and we propose a vision for the role of generative AI that considers opportunities and identifies challenges inherent to this technology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗