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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

An intelligent training system for space shuttle flight controllers

An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The system was designed to train Mission Control Center (MCC) Flight Dynamics Officers (FDOs) to deploy a certain type of satellite from the Space Shuttle. The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system consists of five components: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The interface provides the trainee with information of the characteristics of the current training session and with on-line help. The domain expert (DeplEx for Deploy Expert) contains the rules and procedural knowledge needed by the FDO to carry out the satellite deploy. The DeplEx also contains mal-rules which permit the identification and diagnosis of common errors made by the trainee. The training session manager (TSM) examines the actions of the trainee and compares them with the actions of DeplEx in order to determine appropriate responses. A trainee model is developed for each individual using the system. The model includes a history of the trainee's interactions with the training system and provides evaluative data on the trainee's current skill level. A training scenario generator (TSG) designs appropriate training exercises for each trainee based on the trainee model and the training goals. All of the expert system components of PD/ICAT communicate via a common blackboard. The PD/ICAT is currently being tested. Ultimately, this project will serve as a vehicle for developing a general architecture for intelligent training systems together with a software environment for creating such systems.

Loftin, R. Bowen↗
NASA NTRS1988

An intelligent training system for space shuttle flight controllers

An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The system was designed to train Mission Control Center (MCC) Flight Dynamics Officers (FDOs) to deploy a certain type of satellite from the Space Shuttle. The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system consists of five components: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The interface provides the trainee with information of the characteristics of the current training session and with on-line help. The domain expert (Dep1Ex for Deploy Expert) contains the rules and procedural knowledge needed by the FDO to carry out the satellite deploy. The Dep1Ex also contains mal-rules which permit the identification and diagnosis of common errors made by the trainee. The training session manager (TSM) examines the actions of the trainee and compares them with the actions of Dep1Ex in order to determine appropriate responses. A trainee model is developed for each individual using the system. The model includes a history of the trainee's interactions with the training system and provides evaluative data on the trainee's current skill level. A training scenario generator (TSG) designs appropriate training exercises for each trainee based on the trainee model and the training goals. All of the expert system components of PD/ICAT communicate via a common blackboard. The PD/ICAT is currently being tested. Ultimately, this project will serve as a vehicle for developing a general architecture for intelligent training systems together with a software environment for creating such systems.

Loftin, R. Bowen↗
DOE OSTI2018

Accelerating the discovery of materials for clean energy in the era of smart automation

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Furthermore, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Swarm Intelligence Based Optimal Design of Local Volt/Var Control Function for Distributed Energy Resources

The increasing penetration of renewable based distributed energy resources (DERs) in distribution network (DN) leads to larger and more frequent voltage variation in distributions network (DN), thus posing challenges on voltage control. Real-time local voltage control method is a promising solution for the above issue. However, the local voltage control function needs to be customized and optimized according to real distribution system condition. In this paper, a swarm intelligence based Volt/Var control optimal design method (SO-VVC) is proposed to optimize the control function. Compared with existing approaches, the proposed method can not only represent the nonlinear behaviour of power flow but is also computation efficient. The performance of the proposed SO-VVC is demonstrated by case studies on a modified IEEE-123 bus system.

Zhang, Zhengfa [University of Tennessee, Knoxville↗
DOE OSTI2019

[Office of Basic Energy Sciences (BES)] Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning

To identify specific Priority Research Opportunities (PROs) for artificial intelligence and machine learning (AI/ML) at the US Department of Energy’s scientific user facilities, the Office of Basic Energy Sciences (BES) convened a roundtable of facility experts encompassing the fields of physics, chemistry, materials synthesis science, computational science, detector and accelerator technology, theory, modeling, simulation, and atomic-scale characterization techniques. The roundtable met on October 22–23, 2019, to identify coordinated, long-term AI/ML research efforts that will drive major advances in neutron, photon, and nanoscale sciences. This report describes the four PROs identified at the roundtable: PRO 1 on how AI/ML can extract high-value information from the large datasets; PRO 2 on how AI/ML can use such information in real time to maximize the facilities’ scientific output; PRO 3 on using AI/ML virtual laboratories (i.e., computational models of experimental facilities) to aid the facilities and user community in design and control of machine parameters and the design and execution of experiments, including training AI/ML models for PROs 1 and 2; and PRO 4 on how a shared scientific data infrastructure can provide tools to assemble and analyze the totality of data coming from user facilities. A section of the end of the report provides a summary on computer science and mathematics that highlights where enhanced AI/ML capabilities could be particularly impactful for BES user facilities.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1987

Man-machine interface issues in space telerobotics: A JPL research and development program

Technology issues related to the use of robots as man-extension or telerobot systems in space are discussed and exemplified. General considerations are presentd on control and information problems in space teleoperation and on the characteristics of Earth orbital teleoperation. The JPL R and D work in the area of man-machine interface devices and techniques for sensing and computer-based control is briefly summarized. The thrust of this R and D effort is to render space teleoperation efficient and safe through the use of devices and techniques which will permit integrated and task-level (intelligent) two-way control communication between human operator and telerobot machine in Earth orbit. Specific control and information display devices and techniques are discussed and exemplified with development results obtained at JPL in recent years.

Bejczy, A. K.↗
NASA NTRS1993

A parallel data management system for large-scale NASA datasets

The past decade has experienced a phenomenal growth in the amount of data and resultant information generated by NASA's operations and research projects. A key application is the reprocessing problem which has been identified to require data management capabilities beyond those available today (PRAT93). The Intelligent Information Fusion (IIF) system (ROEL91) is an ongoing NASA project which has similar requirements. Deriving our understanding of NASA's future data management needs based on the above, this paper describes an approach to using parallel computer systems (processor and I/O architectures) to develop an efficient parallel database management system to address the needs. Specifically, we propose to investigate issues in low-level record organizations and management, complex query processing, and query compilation and scheduling.

Srivastava, Jaideep↗
NASA NTRS2003

Free-Flowing Solutions for CFD

Licensed to over 1,500 customers worldwide, an advanced computational fluid dynamics (CFD) post-processor with a quick learning curve is consistently providing engineering solutions, with just the right balance of visual insight and hard data. FIELDVIEW is the premier product of JMSI, Inc., d.b.a. Intelligent Light, a woman-owned, small business founded in 1994 and located in Lyndhurst, New Jersey. In the early 1990s, Intelligent Light entered into a joint development contract with a research based company to commercialize the post-processing FIELDVIEW code. As Intelligent Light established itself, it purchased the exclusive rights to the code, and structured its business solely around the software technology. As a result, it is enjoying profits and growing at a rate of 25 to 30 percent per year. Advancements made from the earliest commercial launch of FIELDVIEW, all the way up to the recently released versions 8 and 8.2 of the program, have been backed by research collaboration with NASA's Langley Research Center, where some of the world's most progressive work in transient (also known as time-varying) CFD takes place.

Source record↗
NASA NTRS2013

Smart-Divert Powered Descent Guidance to Avoid the Backshell Landing Dispersion Ellipse

A smart-divert capability has been added into the Powered Descent Guidance (PDG) software originally developed for Mars pinpoint and precision landing. The smart-divert algorithm accounts for the landing dispersions of the entry backshell, which separates from the lander vehicle at the end of the parachute descent phase and prior to powered descent. The smart-divert PDG algorithm utilizes the onboard fuel and vehicle thrust vectoring to mitigate landing error in an intelligent way: ensuring that the lander touches down with minimum- fuel usage at the minimum distance from the desired landing location that also avoids impact by the descending backshell. The smart-divert PDG software implements a computationally efficient, convex formulation of the powered-descent guidance problem to provide pinpoint or precision-landing guidance solutions that are fuel-optimal and satisfy physical thrust bound and pointing constraints, as well as position and speed constraints. The initial smart-divert implementation enforced a lateral-divert corridor parallel to the ground velocity vector; this was based on guidance requirements for MSL (Mars Science Laboratory) landings. This initial method was overly conservative since the divert corridor was infinite in the down-range direction despite the backshell landing inside a calculable dispersion ellipse. Basing the divert constraint instead on a local tangent to the backshell dispersion ellipse in the direction of the desired landing site provides a far less conservative constraint. The resulting enhanced smart-divert PDG algorithm avoids impact with the descending backshell and has reduced conservatism.

Carson, John M.↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
DOE OSTI2020

Measuring Intelligence with the Sandia Matrices: Psychometric Review and Recommendations for Free Raven-Like Item Sets

The Sandia Matrices are a free alternative to the Raven’s Progressive Matrices (RPMs). This study offers a psychometric review of Sandia Matrices items focused on two of the most commonly investigated issues regarding the RPMs: (a) dimensionality and (b) sex differences. Model-data fit of three alternative factor structures are compared using confirmatory multidimensional item response theory (IRT) analyses, and measurement equivalence analyses are conducted to evaluate potential sex bias. Although results are somewhat inconclusive regarding factor structure, results do not show evidence of bias or mean differences by sex. Finally, although the Sandia Matrices software can generate infinite items, editing and validating items may be infeasible for many researchers. Further, to aide implementation of the Sandia Matrices, we provide scoring materials for two brief static tests and a computer adaptive test. Implications and suggestions for future research using the Sandia Matrices are discussed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Software development infrastructure for the HYBRID modeling and simulation project

One of the goals of the HYBRID modeling and simulation project is to assess the economic viability of hybrid systems in a market that contains renewable energy sources like wind. The idea is that it is possible for the nuclear plant to sell non-electric energy cushions, which absorb (at least partially) the volatility introduced by the renewable energy sources. This system is currently modeled in the Modelica programming language. To assess the economics of the system, an optimization procedure is trying to find the minimal cost of electricity production. The RAVEN code is used as a driver for the whole problem. It is assumed that at this stage, the HYBRID modeling and simulation framework can be classified as non-safety “research and development” software. The associated quality level is Quality Level 3 software. This imposes low requirements on quality control, testing and documentation. The quality level could change as the application development continues.Despite the low quality requirement level, a workflow for the HYBRID developers has been defined that include a coding standard and some documentation and testing requirements. The repository performs automated unit testing of contributed models. The automated testing is achieved via an open-source python script called BuildingsP from Lawrence Berkeley National Lab. BuildingsPy runs Modelica simulation tests using Dymola in an automated manner and generates and runs unit tests from Modelica scripts written by developers. In order to assure effective communication between the different national laboratories a biweekly videoconference has been set-up, where developers can report their progress and issues. In addition, periodic face-face meetings are organized intended to discuss high-level strategy decisions with management. A second means of communication is the developer email list. This is a list to which everybody can send emails that will be received by the collective of the developers and managers involved in the project. Thirdly, to exchange documents quickly, a SharePoint directory has been set-up. SharePoint allows teams and organizations to intelligently share, and collaborate on content from anywhere.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide–MHC Binding Predictions

The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell–based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2D b are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., Kd < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell–based vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Toward a systematic exploration of nano-bio interactions

Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability. - Highlights: • Nanomaterials studies make non-systematic alterations to nanoparticle properties. • Vast nanomaterials property spaces require systematic studies of nano-bio interactions. • Experimental design and modelling are efficient ways of exploring materials spaces. • We advocate systematic modification and computational analysis to probe nano-bio interactions.

60 APPLIED LIFE SCIENCES↗