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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
DOE OSTI2023

Energy Optimization of Light and Heavy-Duty Vehicle Cohorts of Mixed Connectivity, Automation and Propulsion System Capabilities via Meshed V2V-V2I and Expanded Data Sharing (Final Scientific and Technical Report)

Vehicle connectivity and automated driving technologies individually have the potential to decrease energy consumption and/or increase safety on light, medium or heavy duty vehicles to varying degrees depending on the traffic infrastructure and specific driving scenarios. Due to advances in sensing, perception and computing power, research and development emphasis in the mobility sector has shifted away from connectivity. Prior research has shown that driving automation with the absence of connectivity can in certain circumstances increase energy consumption. The effectiveness of synergizing connectivity and driving automation technologies is the focus of this work, specifically applied to vehicle cohorts of mixed composition, light and heavy duty, and powertrains ranging from all electric to conventional internal combustion engine. The project team is led by Michigan Technological University (MTU) and partnered with AVL Mobility Technologies Inc. (AVL), Borg Warner (BW), Traffic Technology Services (TTS), American Center for Mobility (ACM) and Navistar (NAV). The main thrusts for the team are to develop a micro-traffic simulation environment with specific VD&PT system attributes and CAV capabilities, 2) field a vehicle test fleet of mixed classification, propulsion and CAV capacity, 3) develop artificial intelligence (AI) and machine learning (ML) based multi-agent optimization methods for various traffic infrastructures, 4) integrate the virtual environment and the optimization methods then deploy the system as a CAV hardware in the loop (HiL) for the vehicle test fleet and 5) conduct closed track and public road testing to validate simulation and demonstrated energy and mobility improvements at multiple scales. For a cohort of mixed vehicles, the team will demonstrate a reduction of energy consumption of 10-50% at intersection, arterial roadway and limited access highway scenarios through connectivity and automation in simulation and at a closed test track. The energy reduction objectives of the project are summarized in Table 1, indicating the infrastructure and over what distances are relevant considered. Single scenario energy reductions are not relevant and thus, the research team took the approach to vary parameters associated with the infrastructure, vehicle cohort composition and dynamic behavior to generate energy consumption distributions for both unconnected and connected scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Data-driven Quasi-static Surrogate Model for Nuclear-powered Integrated Energy Systems

The integration of nuclear power into energy systems presents a promising avenue to address the growing global energy demands while minimizing greenhouse gas emissions. In this paper, we introduce a data-driven quasi-static surrogate model for nuclear-powered Integrated Energy Systems (IES) that comprises various components, including a small modular reactor (SMR), energy manifold (EM), balance of plant (BOP), high-temperature steam electrolysis (HTSE), and district heating (DH) system. Traditional physics-based models for these components often entail significant computational overhead and time consumption, necessitating the development of efficient surrogate models. The development of a complete surrogate model for the IES involves the creation of individual surrogate models for each component, leveraging machine learning techniques and simulated data. These isolated surrogate models are subsequently integrated, enabling a holistic view of the IES and reducing the computational burden associated with detailed physics-based simulations. This paper outlines the development process, validation, and the performance evaluation of the surrogate models. The findings shed light on the accuracy and applicability of the surrogate models in practical scenarios, demonstrating their potential to expedite the analysis of nuclear-powered IES and inform future research and development efforts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Ibis Networks/WattIQ (IN2 Final Report)

Ibis Networks is a full-stack cleantech company that provides plug-level energy monitoring and control to solve energy and asset management problems for the enterprise. During DATES – DATES, an NREL research team worked with the Ibis team to validate their product, the InteliSocket. The Ibis InteliSocket is a pass-through plug-load energy monitor and controller that is designed to reduce energy wasted by common 120 V plug-in devices in commercial office buildings, such as computer peripherals, conference room AV equipment, and break-room appliances. The system can shut off supply power to these end uses via remote control, manual switches, pre-set schedules, or automated control algorithms. The scope of this IN2 project was the development and refinement of “smart” learning behavior algorithms (LBAs), which could help installation processes and dramatically expand the sockets’ capabilities and energy-saving potential by suggesting suitable control schedules that are based on monitored use patterns. While Ibis has the analytical and software expertise for algorithm development, the lack of test data, both in a controlled laboratory setting and in real-life deployment scenarios, represented a key barrier toward commercialization of the product. Assistance through the IN2 program provided an opportunity to conduct the needed “trial and error” algorithm development. The project included baseline field-data collection, laboratory testing, and field validation components, all of which were conducted at the NREL campus between April 2017 and July 2019.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2010

Electrochromic Radiator Coupon Level Testing and Full Scale Thermal Math Modeling for Use on Altair Lunar Lander

In order to control system and component temperatures, many spacecraft thermal control systems use a radiator coupled with a pumped fluid loop to reject waste heat from the vehicle. Since heat loads and radiation environments can vary considerably according to mission phase, the thermal control system must be able to vary the heat rejection. The ability to "turn down" the heat rejected from the thermal control system is critically important when designing the system. Electrochromic technology as a radiator coating is being investigated to vary the amount of heat rejected by a radiator. Coupon level tests were performed to test the feasibility of this technology. Furthermore, thermal math models were developed to better understand the turndown ratios required by full scale radiator architectures to handle the various operation scenarios encountered during a mission profile for the Altair Lunar Lander. This paper summarizes results from coupon level tests as well as the thermal math models developed to investigate how electrochromics can be used to increase turn down ratios for a radiator. Data from the various design concepts of radiators and their architectures are outlined. Recommendations are made on which electrochromic radiator concept should be carried further for future thermal vacuum testing.

Bannon, Erika T.↗
DOE OSTI2021

Nominal and adversarial synthetic PMU data for standard IEEE test systems

GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus

Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications.

Boice, Emily N. (ORCID:0000000171802842)↗
NASA NTRS1998

Choosing Meteorological Input for the Global Modeling Initiative Assessment of High Speed Aircraft

The Global Modeling Initiative (GMI) science team is developing a three dimensional chemistry and transport model (CTM) to be used in assessment of the atmospheric effects of aviation. Requirements are that this model be documented, be validated against observations, use a realistic atmospheric circulation, and contain numerical transport and photochemical modules representing atmospheric processes. The model must also retain computational efficiency to be tractable to use for multiple scenarios and sensitivity studies. To meet these requirements, a facility model concept was developed in which the different components of the CTM are evaluated separately. The first use of the GMI model will be to evaluate the impact of the exhaust of supersonic aircraft on the stratosphere. The assessment calculations will depend strongly on the wind and temperature fields used by the CTM. Three meteorological data sets for the stratosphere are available to GMI: the National Center for Atmospheric Research Community Climate Model (CCM2), the Goddard Earth Observing System Data Assimilation System (GEOS DAS), and the Goddard Institute for Space Studies general circulation model (GISS). Objective criteria were established by the GMI team to identify the data set which provides the best representation of the stratosphere. Simulations of gases with simple chemical control were chosen to test various aspects of model transport. The three meteorological data sets were evaluated and graded based on their ability to simulate these aspects of stratospheric measurements. This paper describes the criteria used in grading the meteorological fields. The meteorological data set which has the highest score and therefore was selected for GMI is CCM2. This type of objective model evaluation establishes a physical basis for interpretation of differences between models and observations. Further, the method provides a quantitative basis for defining model errors, for discriminating between different models, and for ready re-evaluation of improved models. These in turn will lead to a higher level of confidence in assessment calculations.

Douglas, A. R.↗
NASA NTRS1999

Choosing Meteorological Input for the Global Modeling Initiative Assessment of High Speed Aircraft

The Global Modeling Initiative (GMI) science team is developing a three dimensional chemistry and transport model (CTM) to be used in assessment of the atmospheric effects of aviation. Requirements are that this model be documented be validated against observations, use a realistic atmospheric circulation, and contain numerical transport and photochemical modules representing atmospheric processes. The model must also retain computational efficiency to be tractable to use for multiple scenarios and sensitivity studies. To meet these requirements, a facility model concept was developed in which the different components of the CTM are evaluated separately. The first use of the GMI model will be to evaluate the impact of the exhaust of supersonic aircraft on the stratosphere. The assessment calculations will depend strongly on the wind and temperature fields used by the CTM. Three meteorological data sets for the stratosphere are available to GMI: the National Center for Atmospheric Research Community Climate Model (CCM2), the Goddard Earth Observing System Data Assimilation System (GEOS DAS), and the Goddard Institute for Space Studies general circulation model (GISS). Objective criteria were established by the GMI team to identify the data set which provides the best representation of the stratosphere. Simulations of gases with simple chemical control were chosen to test various aspects of model transport. The three meteorological data sets were evaluated and graded based on their ability to simulate these aspects of stratospheric measurements. This paper describes the criteria used in grading the meteorological fields. The meteorological data set which has the highest score and therefore was selected for GMI is CCM2. This type of objective model evaluation establishes a physical basis for interpretation of differences between models and observations. Further, the method provides a quantitative basis for defining model errors, for discriminating between different models, and for ready re-evaluation of improved models. These in turn will lead to a higher level of confidence in assessment calculations.

Douglass, A. R.↗
NASA NTRS1994

Fast Track NTR Systems Assessment for NASA's First Lunar Outpost Scenario

Integrated systems and mission study results are presented which quantify the rationale and benefits for developing and using nuclear thermal rocket (NTR) technology for returning humans to the moon in the early 2000's. At present, the Exploration Program Office (ExPO) is considering chemical propulsion for its 'First Lunar Outpost' (FLO) mission, and NTR propulsion for the more demanding Mars missions to follow. The use of an NTR-based lunar transfer stage, capable of evolving to Mars mission applications, could result in an accelerated schedule, reduced cost approach to moon/Mars exploration. Lunar mission applications would also provide valuable operational experience and serve as a 'proving ground' for NTR engine and stage technologies. In terms of performance benefits, studies indicate that an expendable NTR stage powered by two 50 klbf engines can deliver approximately 96 metric tons (t) to trans-lunar injection (TLI) conditions for an initial mass in low earth orbit (IMLEO) of approximately 199 t compared to 250 t for a cryogenic chemical TLI stage. The NTR stage liquid hydrogen (LH2) tank has a 10 m diameter, 14.8 m length, and 68 t LH2 capacity. The NTR utilizes a 'graphite' fuel form consisting of coated UC2 particles in a graphite substrate, and has a specific impulse capability of approximately 870 s, and an engine thrust-to-weight ratio of approximately 4.8. The NTR stage and its piloted FLO lander has a total length of approximately 38 m and can be launched by a single Saturn V-derived heavy lift launch vehicle (HLLV) in the 200 to 250 t-class range. The paper summarizes NASA's First Lunar Outpost scenario, describes characteristics for representative engine/stage configurations, and examines the impact on engine selection and vehicle design resulting from a consideration of alternative NTR fuel forms and lunar mission profiles.

Borowski, Stanley K.↗
DOE OSTI2020

Iodine Effects on Medical Isotope Producer Delay & Guard Bed Temperature and Performance in Support of NA-23 Program

In support of NA-23’s objective to accelerate the establishment of a reliable commercial United States (U.S.) domestic supply of 99Mo produced without the use of highly enriched uranium (HEU), the Pacific Northwest National Laboratory (PNNL) was tasked with modeling the performance of activated carbon delay and guard beds that may be typical for non-HEU 99Mo production processes. The specific tasks accomplished by PNNL included calculations of long-term effects of iodine on delay bed performance and development of a finite element simulation to better understand delay bed performance under various scenarios specifically with radioxenon and radioiodine decay heating of the delay beds. The main issue of interest in this study is understanding the effect of iodine on the abatement performance of the beds if iodine is not sufficiently removed from gases prior to reaching the delay beds. Finite element simulations of delay bed performance were developed using COMSOL Multiphysics® Software. These simulations are based on partial differential equations that describe xenon and iodine adsorption, heat transfer and decay of radioxenon and radioiodine isotopes. Heat transfer in the activated carbon beds is simulated with a heat source tied to the decay of radioactive xenon and iodine species. These simulations were used to investigate delay bed temperature and hold up time using radioxenon and radioiodine activities and flowrates expected from a non-HEU production process. A bounding case scenario was simulated, where all the iodine was released into the delay beds due to insufficient guarding of the delay beds. In addition, preliminary investigations of the guard beds were conducted.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario

Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work here focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. In conclusion, our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

BETO 2021 Peer Review - WBS 4.1.2.32: Bioeconomy Scenario Analysis

The Bioeconomy Scenario Analysis project uses systems thinking and analysis to assess current and/or prospective techno-economics, research and development, deployment strategies, policy, and market conditions and their impact on the potential development trajectories of the bioenergy industry over time. Results from this project include identification of opportunities and constraints to industrial development, quantification of multiple metrics (energy, economic, environmental) and informing researchers, decision makers, and industry of the steps needed for a sustainable, nationwide biofuels industry. Analyses from this project enable the creation of a bioenergy industry by (1) inciting policy-makers to explore scenarios for nationwide biofuels production, identifying policy actions consistent with pathways for growth; (2) improving industry’s understanding of industry growth potential under different technology and investment conditions, better targeting their development efforts; and (3) providing universities and other interested stakeholders with tools and analyses that can be adapted to meet research and teaching objectives, connecting students with careers that build the industry. One of the many modeling tools used in this project, the Biomass Scenario Model (BSM) is a publicly-available, unique, validated, state-of-the-art, award-winning, fourth-generation model of the domestic biofuels supply chain which explicitly focuses on how and under what conditions biofuel technologies might be deployed to contribute to the U.S. transportation energy sector. We use models like the BSM to examine the implications of policies and incentives as well as their potential side-effects. The BSM uses a system-dynamics simulation to model dynamic interactions and transitions across the supply chain; it tracks the deployment of biofuels given industrial learning and the reaction of the investment community in the context of land availability, projected oil markets, consumer demand for biofuels, and government policies over time. Under expected market conditions, analyses using the BSM suggest that the biofuels industry may require significant external actions in the early years to thrive. Interventions that accelerate the industrial learning process (e.g. operation of pre-commercial and commercial facilities) have been identified as having strong influence in starting the growth of a commercial biofuel industry. Policies which are coordinated across the whole supply chain in BSM foster the growth of the biofuels industry and production of tens of billions of gallons of biofuels may occur under sufficiently favorable conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2020

Lunar Transient Accelerations White Paper

Recently, the United States announced a plan to return astronauts to the moon by 2024 [8]. Lunar landing (and subsequent Mars landing) architecture was not considered when the current NASA standards and vehicle design requirements for crew injury risk were developed. Therefore a gap exists in protecting the crew in planetary landing scenarios. One of the interesting aspects of this design reference mission (DRM)is the consideration of having the crew stand during dynamic phases of flight. Although this approach was contemplated for the Apollo Lunar Module, current NASA standards do not address design solutions that allow the crew to stand. Currently, NASA uses several tools and associated limits to mitigate crew injury because of dynamic loads. Some of these tools are the Brinkley Dynamic Response Criterion (BDRC) model and Hybrid III Anthropomorphic Test Devices (ATDs) [10]; and these have several limitations for assessing spaceflight loading environments, as well as specific underlying assumptions that may not be applicable in planetary landing vehicles. The BDRC is a simple lumped mass parameter model developed by the U.S. military, and has been used primarily to evaluate injury risk associated with aircraft ejection systems. The model evaluates seat accelerations in each axis to determine injury risk. Because the model treats the human-seat-restraint system as a single system, it is contingent on a restraint system and seat with similar characteristics of the original test data underlying the model. In particular, the model requires a rigid seat with a minimum natural frequency of 15Hz, minimal seat pan padding, side supports, and multipoint harness. The BDRC model uses undamped natural frequency and damping coefficients based on these requirements and any deviations may render the model injury predictions void. In lunar landing, one expects that a minimal or even no seat with minimal restraints will be employed. In this case, the original model parameters are likely to not be applicable. For capsule-based spacecraft returning crew to Earth, the Hybrid III ATD in various sizes also is used to supplement the BDRC. This analytical tool was added to address limitations in the BDRC related to spacecraft landings while wearing a pressure garment and helmet. Although the Hybrid III ATD has additional measurement capability; head, neck,and lumbar spine responses were the only metrics included because of the limitations imposed by the model.Lunar landing acceleration limits must assume the crew is standing during landing, an orientation for which we have limited data.Although the Apollo missions did employ a standing orientation for the crew, much of the data is lost. Therefore data from othersources havebeen examined to inform lunar landing acceleration limits.

Jeffrey T Somers↗
DOE OSTI2026

Development of a 95-Year Solar Dataset for Resource Adequacy Studies

Long-term high-resolution solar data provides enhanced understanding of variability of solar generation and enhances our ability to develop strategies for a resilient and reliable electric grid under high deployment of solar energy. Therefore, it is important to develop long-term synthetic datasets that can provide multiple occurrences of various severe weather scenarios that are expected to test the limits of resource adequacy under scenarios contain various energy generation sources. Examples of such scenarios could be long periods of high temperatures when demand for electricity is high or periods where high winds could lead to a shut-down of transmission lines for long periods of time to ensure fire safety. NREL has developed the first version of such a dataset covering a 95-year period covering 2006-2100 at a 4km hourly resolution. This dataset contains all variables necessary to calculate solar generation. During development of this dataset, we focused on creating unbiased, high-resolution solar irradiance through statistical downscaling methods, using Regional Climate Model (RCM) simulations from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) as input. The National Solar Radiation Database (NSRDB) containing over 25 years of observations was used to calibrate the statistical downscaling models. This presentation will outline the primary steps in developing this dataset, including (1) regridding RCM data to a common grid at 20-km resolution, (2) correcting RCM biases with NSRDB, (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar and ancillary data. Additionally, we will present an evaluation of the downscaled data against the NSRDB across various zones in the CONUS. Lastly, we will present a user guide for accessing the datasets.

14 SOLAR ENERGY↗
NASA NTRS2000

Magnetic Flux Compression Concept for Aerospace Propulsion and Power

The objective of this research is to investigate system level performance and design issues associated with magnetic flux compression devices for aerospace power generation and propulsion. The proposed concept incorporates the principles of magnetic flux compression for direct conversion of nuclear/chemical detonation energy into electrical power. Specifically a magnetic field is compressed between an expanding detonation driven diamagnetic plasma and a stator structure formed from a high temperature superconductor (HTSC). The expanding plasma cloud is entirely confined by the compressed magnetic field at the expense of internal kinetic energy. Electrical power is inductively extracted, and the detonation products are collimated and expelled through a magnetic nozzle. The long-term development of this highly integrated generator/propulsion system opens up revolutionary NASA Mission scenarios for future interplanetary and interstellar spacecraft. The unique features of this concept with respect to future space travel opportunities are as follows: ability to implement high energy density chemical detonations or ICF microfusion bursts as the impulsive diamagnetic plasma source; high power density system characteristics constrain the size, weight, and cost of the vehicle architecture; provides inductive storage pulse power with a very short pulse rise time; multimegajoule energy bursts/terawatt power bursts; compact pulse power driver for low-impedance dense plasma devices; utilization of low cost HTSC material and casting technology to increase magnetic flux conservation and inductive energy storage; improvement in chemical/nuclear-to-electric energy conversion efficiency and the ability to generate significant levels of thrust with very high specific impulse; potential for developing a small, lightweight, low cost, self-excited integrated propulsion and power system suitable for space stations, planetary bases, and interplanetary and interstellar space travel; potential for attaining specific impulses approaching 10 (exp 6) seconds, which would enable missions to the outer planets within ten years and missions at interstellar distances within fifty years.

Litchford, Ron J.↗
NASA NTRS2019

The Role of SmallSats in Scientific Exploration and Commercialization of Space

Over the past decade, SmallSats have been established as having great potential for science exploration and commercialization of space. The SmallSat revolution aims to decrease the cost of space development, making space exploration accessible to students, educators, and public citizens. These efforts have focused on miniaturization of instruments and space platforms, as well as reducing their cost, mass, and needed power. In addition to enabling scientific exploration, SmallSats provide affordable means for the public to purchase remote sensing and communication products on a global scale. SmallSat mission concepts are particularly powerful when they are deployed in distributed architecture or constellations. For example, the most promising observation techniques for global science measurements of the Earth system and space weather require multi-point distributed observations of the Earth system at a feasible cost. The high cost of access to space has long been a barrier, especially with the prohibitive cost of large satellites. Affordable SmallSat constellations can be game-changers, enabling scientific exploration as well as commercial global data products. In this paper, we highlight investments made by NASA to date (specifically a study in developing and prototyping a SmallSat platform with standard interfaces), along with several example mission concept scenarios in Earth and space science (astrophysics, heliophysics, and planetary) applications that can be achieved using this platform.

technology development↗
NASA NTRS1991

Systematic methods for knowledge acquisition and expert system development

Nine cooperating rule-based systems, collectively called AUTOCREW which were designed to automate functions and decisions associated with a combat aircraft's subsystems, are discussed. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base and to assess the cooperation between the rule bases. Simulation and comparative workload results for two mission scenarios are given. The scenarios are inbound surface-to-air-missile attack on the aircraft and pilot incapacitation. The methodology used to develop the AUTOCREW knowledge bases is summarized. Issues involved in designing the navigation sensor selection expert in AUTOCREW's NAVIGATOR knowledge base are discussed in detail. The performance of seven navigation systems aiding a medium-accuracy INS was investigated using Kalman filter covariance analyses. A navigation sensor management (NSM) expert system was formulated from covariance simulation data using the analysis of variance (ANOVA) method and the ID3 algorithm. ANOVA results show that statistically different position accuracies are obtained when different navaids are used, the number of navaids aiding the INS is varied, the aircraft's trajectory is varied, and the performance history is varied. The ID3 algorithm determines the NSM expert's classification rules in the form of decision trees. The performance of these decision trees was assessed on two arbitrary trajectories, and the results demonstrate that the NSM expert adapts to new situations and provides reasonable estimates of the expected hybrid performance.

Belkin, Brenda L.↗
DOE OSTI2022

Exploiting Machine Learning in Multiscale Modelling of Materials

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.

42 ENGINEERING↗