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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

A-Train Datalist - A New GES DISC Service to Allow One-Stop Shopping for A-Train Data

The currently available services at the Goddard Earth Sciences Data Information Services Center (GES DISC) only allow users to select variables from a single data set at a time. Because entire variables from a data set are often displayed, user selection of variables of interest can be overwhelming. At the American Geophysical Union (AGU) 2016 Fall Meeting, GES DISC unveiled a new service called Datalist: a collection of predefined or user-defined data variables from one or more archived data sets. Our science support team has been curating Datalists and providing added value to the user community.Originally known as Afternoon Constellation, A-Train includes six currently on polar-orbiting Earth observation satellites: OCO-2, GCOM-W1, Aqua, CALIPSO, CloudSat, and Aura, which travel a few minutes apart from each other. This constellation arrangement has enabled coordinated science observations further forming comprehensive pictures of Earth weather and climate that are readily for use in crucial studies such as climate change.GES DISC Datalists are based on the software architecture of the new GES DISC website (also unveiled at the AGU 2016 Fall Meeting). The GES DISC science support team has created a Datalist to support the A-Train Data Depot (ATDD). Using pre-defined Datalist should hopefully save users significant effort in their data searches.

A-Train data ordering↗
DOE OSTI2024

Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning

Abstract Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an R 2 score of 0.85 and a root mean square error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real-time EELS/XAS analysis pipeline on mixtures of copper-containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.

36 MATERIALS SCIENCE↗
NASA NTRS2013

Kinect Engineering with Learning (KEWL)

According to a Nielsen survey at the time of this reporting, 41% of all households have a game console. This is one market in which NASA has been absent from education and outreach efforts. Kinect Engineering with Learning (KEWL) is made to enter into that market and bring NASA education and outreach to a very familiar venue. KEWL creates an education and outreach experience that is more participatory, both in a school and museum environment. KEWL is a set of applications that runs on an Xbox 360 using the Kinect controller used for education and outreach. These applications currently include: Train R2, a visual simulation of Robonaut 2 that allows students to control a virtual R2 in a game environment; Drive R2, an interface using the Xbox 360 and Kinect controller that allows students to control the real R2 using the methods they learned playing Train R2; ISS experience, a visual tour of the interior of the International Space Station where students use their body to fly through the virtual ISS; Gravity Ball, a simulation of throwing balls in the gravity of different planets; Solar Array repair, a simulation of the simplified STS-121 solar array repair mission; and PlaySpace, a Mars/Moon application that allows students to experience different aspects of Mars/Moon. Users can "fly through" the ISS using their body, allowing an experience similar to what an astronaut would have on orbit. In PlaySpace, users can fly over the surface of Mars and view surface data obtained by Mars rovers. Users of Train R2 and Drive R2 can experience what it is like to control a robot over a distance with a time delay, simulating the time delay that would occur between ground control and an on-orbit robot. The initial ISS experiences were built using parts of code from the NASA Enigma software. The models used in these experiences were also from the Integrated Graphics Operations and Analysis Lab model database. The PlaySpace experience incorporates surface data obtained from NASA rovers and satellites and was built by NASA JPL.

Goza, Sharon↗
DOE OSTI2022

Accurate Compression of Tabulated Chemistry Models with Partition of Unity Networks

Tabulated chemistry models are widely used to simulate large-scale turbulent fires in applications including energy generation and fire safety. Tabulation via piecewise Cartesian interpolation suffers from the curse-of-dimensionality, leading to a prohibitive exponential growth in parameters and memory usage as more dimensions are considered. Artificial neural networks (ANNs) have attracted attention for constructing surrogates for chemistry models due to their ability to perform high-dimensional approximation. However, due to well-known pathologies regarding the realization of suboptimal local minima during training, in practice they do not converge and provide unreliable accuracy. Partition of unity networks (POUnets) are a recently introduced family of ANNs which preserve notions of convergence while performing high-dimensional approximation, discovering a mesh-free partition of space which may be used to perform optimal polynomial approximation. In this work, we assess their performance with respect to accuracy and model complexity in reconstructing unstructured flamelet data representative of nonadiabatic pool fire models. Our results show that POUnets can provide the desirable accuracy of classical spline-based interpolants with the low memory footprint of traditional ANNs while converging faster to significantly lower errors than ANNs. For example, we observe POUnets obtaining target accuracies in two dimensions with 40 to 50 times less memory and roughly double the compression in three dimensions. We also address the practical matter of efficiently training accurate POUnets by studying convergence over key hyperparameters, the impact of partition/basis formulation, and the sensitivity to initialization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2021

Real-Time Culture-Independent Microbial Profiling Onboard the International Space Station using Nanopore Sequencing

For the past two decades, microbial monitoring of the International Space Station (ISS) has relied on culture-dependent methods that require return to Earth for analysis. This has a number of limitations, with the most significant being bias towards the detection of culturable organisms and the inherent delay between sample collection and ground-based analysis. In recent years, portable and easy-to-use molecular-based tools, such as Oxford Nanopore Technologies’ MinION™ sequencer and miniPCR bio’s miniPCR™ thermal cycler, have been validated onboard the ISS. Here, we report on the development, validation, and implementation of a swab-to-sequencer method that provides a culture-independent solution to real-time microbial profiling onboard the ISS. Method development focused on analysis of swabs collected in a low-biomass environment with limited facility resources and stringent controls on allowed processes and reagents. ISS-optimized procedures included enzymatic DNA extraction from a swab tip, bead-based purifications, altered buffers, and the use of miniPCR and the MinION. Validation was conducted through extensive ground-based assessments comparing current standard culture-dependent and newly developed culture-independent methods. Similar microbial distributions were observed between the two methods; however, as expected, the culture-independent data revealed microbial profiles with greater diversity. Protocol optimization and verification was established during NASA Extreme Environment Mission Operations (NEEMO) analog missions 21 and 22, respectively. Unique microbial profiles obtained from analog testing validated the swab-to-sequencer method in an extreme environment. Finally, four independent swab-to-sequencer experiments were conducted onboard the ISS by two crewmembers. Microorganisms identified from ISS swabs were consistent with historical culture-based data, and primarily consisted of commonly observed human-associated microbes. This simplified method has been streamlined for high ease-of-use for a non-trained crew to complete in an extreme environment, thereby enabling environmental and human health diagnostics in real-time as future missions take us beyond low-Earth orbit.

microbiology↗
DOE OSTI2020

Conventional and Next Generation Treatment Technologies for PFAS - 20067

In 2016, the United States Environmental Protection Agency (USEPA) established a Health Advisory Limit (HAL) for perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) (individually and the summation) of 70 nanograms per liter (ng/L) based on developing toxicological information. These two compounds are among several thousand per- and polyfluoroalkyl substances (PFAS). Due to a multitude of commercially beneficial physical and chemical properties, the availability of PFAS-relevant and practical water treatment technologies is limited. The use of conventional adsorbents, such as activated carbon (AC) and anion exchange (AIX) resins, have become a 'de facto' interim measure to immediately address drinking water above this criterion. However, these adsorbents can have marginal long-term efficiency and are relatively unproven against the diversity of polyfluorinated compounds. Additionally, geochemical and/or co-contaminant competition can significantly impede adsorption based PFAS removal. These challenges may be addressed using engineered filtration, such as reverse osmosis or nanofiltration (RO/NF); however, for larger flow systems RO/NF may have unacceptable reject ratios as high as 35% and the capital cost may preclude these technologies. Extending these technologies to natural waters, which have various degrees of geochemical and co-contaminant competition, often requires a treatment train, combining conventional adsorbents or engineered filtration with pretreatment and more innovative and emerging remediation solutions for PFAS. Conventional and Next Generation water treatment technologies for PFAS generally employ one of three mechanisms (adsorption, separation, or destruction). These mechanisms include many types of technologies for both municipal drinking water and extracted natural water applications. The previously mentioned AC, AIX, and RO/NF are commercially available technologies that are actively being deployed for PFAS treatment. Research and development around these technologies is focused on optimization, and ultimate destruction of PFAS is achieved through incineration. Next Generation water treatment technologies include PFAS-specific flocculants, foam fractionation, novel AIX resins, new engineered adsorptive media, electrochemical treatment, sonolysis, and photolysis, radiation, and plasma (forms of advanced reductive processes [ARP]). Research and development around these technologies is focused on proof of concept and assimilation to real world applications. As the PFAS-relevant destructive technologies (such as incineration, electrochemical treatment, sonolysis, and ARP) are energy intensive, the state of the practice for PFAS water treatment is to focus adsorption/separation based technologies on reducing and concentrating the volume of water requiring destructive treatment. This enables more flexibility with respect to circulation frequency, residence time, and more control over energy usage. Water treatment for PFAS presently requires multiple technologies (i.e. a treatment train) to protect human health in a cost-conscious manner. An investment in research and development to explore new technologies is part of a key initiative for efficient protection of human health. This presentation attempts to review PFAS-relevant water treatment technologies and provide perspective as to their status with respect to applicability and commercial relevance. (authors)

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRSDate not supplied

Earth-Independent Medical Operations (EIMO) Concept of Operations

In contrast to the current crew health paradigm for low-Earth orbit and Lunar missions, which depends on real-time communication with Mission Control, deep-space exploration missions will require a significant shift in medical operations. This shift is driven by the constraints of operating at a considerable distance from Earth, such as resource limitations—lack of resupply, restricted mass, power, volume, and data—as well as communication delays and the inability to evacuate back to Earth during emergencies. To move toward a more self-reliant medical model, a strategy is needed to gradually increase space-based crew autonomy and reduce risks to mission success in the challenging environment of deep space. This transformative change, known as "Earth-Independent Medical Operations" (EIMO), explores the gradual transfer of medical care and decision-making from Earth-based support to space-based systems. The goal of this transition is to enhance astronaut health and performance while minimizing mission risks. EIMO requires the development of a medical system that integrates seamlessly with mission planning, vehicle and spacesuit design, and data architecture. This integration is crucial for building a robust medical infrastructure that not only safeguards astronaut well-being but also ensures overall mission success. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has revised the EIMO model-based Concept of Operations (ConOps) which outlines an initial vision for EIMO. The ConOps, which is built on the stakeholders’ need, system goals, and objectives (NGOs), presents an array of in-mission scenarios that span a wide range of medical conditions demonstrating the system’s capabilities from basic to complex events. Developed by a multidisciplinary team of systems engineers, scientists, and clinicians within ExMC, the ConOps revision includes two new scenarios(Barotrauma and Self-Medical Management and Behavioral Health and Chronic Medical Care), and implementation of findings from EIMO technical interchange meetings that focused on data and training. The envisioned EIMO Medical System (MS) operates as a system of systems, gathering data from various sources such as reference databases, real-time wearable sensors, point-of-care diagnostics, and environmental controls. The MS also incorporates advanced training tools to support autonomous medical care, assisting the Crew Medical Officer (CMO) during medical events where Ground Support is either unavailable or communication-delayed beyond practicality. Furthermore, MS functions and capabilities were decomposed from the scenarios to establish foundational requirements for EIMO and traced to the NASA Spaceflight Human-System Standard(NASA-STD-3001, Volumes 1 and 2). These traces were performed to gain insights on the alignment of EIMO requirements with the NASA standard. This work serves as an initial recommendation to increase crew autonomy gradually and safely for Mars missions and future deep-space exploration.

medical system↗
DOE OSTI2024

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for >700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

HPC Analytics of Fused Thermal Plants Data to Optimize Operating Envelope

In this project, ORNL extensively reviewed the ORAP RAM data, and it guided us to develop machine learning models that can predict time to next failures and forecast failure trends, which will be useful for optimizing power plant operation strategies. More specifically, we trained multiple random forest models and evaluated the model accuracy to validate with 10+ years of historical data. In addition, we implemented a web-based graphical user interface system for the models to show how our models can be used in more intuitive ways. This proof of concept allowed exploration of model use with power plant operators in mind. Developed machine learning models will be helpful for managing risks, planning maintenance and operation, ultimately reducing the down time and increasing the service hours. For future work, there are several interesting research topics including but not limited to model enhancement, creating synergy with traditional failure modeling approaches, and data-driven actionable recommendation and suggestions.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2009

Impact of Lake Okeechobee Sea Surface Temperatures on Numerical Predictions of Summertime Convective Systems over South Florida

The NASA Short-term Prediction Research and Transition (SPoRT) Center, the Florida Institute of Technology, and the NOAA/NWS Weather Forecast Office at Miami, FL (MFL) are collaborating on a project to investigate the impact of using high-resolution, 2-km Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) composites within the Weather Research and Forecasting (WRF) prediction system. The NWS MFL is currently running WRF in real-time to support daily forecast operations, using the National Centers for Environmental Prediction Nonhydrostatic Mesoscale Model dynamical core within the NWS Science and Training Resource Center's Environmental Modeling System (EMS) software. Twenty-seven hour forecasts are run daily initialized at 0300, 0900, 1500, and 2100 UTC on a domain with 4-km grid spacing covering the southern half of Florida and adjacent waters of the Gulf of Mexico and Atlantic Ocean. The SSTs are initialized with the NCEP Real-Time Global (RTG) analyses at 1/12deg resolution. The project objective is to determine whether more accurate specification of the lower-boundary forcing over water using the MODIS SST composites within the 4-km WRF runs will result in improved sea fluxes and hence, more accurate e\~olutiono f coastal mesoscale circulations and the associated sensible weather elements. SPoRT conducted parallel WRF EMS runs from February to August 2007 identical to the operational runs at NWS MFL except for the use of MODIS SST composites in place of the RTG product as the initial and boundary conditions over water. During the course of this evaluation, an intriguing case was examined from 6 May 2007, in which lake breezes and convection around Lake Okeechobee evolved quite differently when using the high-resolution SPoRT MODIS SST composites versus the lower-resolution RTG SSTs. This paper will analyze the differences in the 6 May simulations, as well as examine other cases from the summer 2007 in which the WRF-simulated Lake Okeechobee breezes evolved differently due to the SST initialization. The effects on wind fields and precipitation systems will be emphasized, including validation against surface mesonet observations and Stage IV precipitation grids.

Case, Jonathan L.↗
NASA NTRS1993

Benefits gained and lessons learned from NASA's Small Explorer (SMEX) Program

NASA's Small Explorer Program (SMEX) is a sustained program of scientific satellites limited in mass to 200-300 kg (depending on orbital inclination) for a 500-km circular orbit. The SMEX program was undertaken to obtain the benefits of scientific yield, short development time, and high flight rate, with the goal to launch a mission every year. NASA also uses the program to train engineers and managers in designing and developing spacecraft. The phases of a SMEX mission life cycle are described including the competitive selection of missions through announcement of opportunity and evaluation of proposals, the definition of mission and system requirements, design and development of the spacecraft, testing, launch, and operations. Program content of some SMEX missions is then reviewed. The first SMEX mission was the Solar, Anomalous, and Magnetospheric Particle Explorer, which confirmed that anomalous cosmic rays are only partially ionized atoms. The Fast Auroral Snapshot Explorer was intended to measure the electric, magnetic, and time-variable fields and to record particle flow in the auroral acceleration region. A future mission is the Submillimeter Wave Astronomy Satellite to examine low-level molecular transitions.

Gilman, David↗
NASA NTRS1991

Capturing, using, and managing quality assurance knowledge for shuttle post-MECO flight design

Ascent initialization values used by the Shuttle's onboard computer for nominal and abort mission scenarios are verified by a six degrees of freedom computer simulation. The procedure that the Ascent Post Main Engine Cutoff (Post-MECO) group uses to perform quality assurance (QA) of the simulation is time consuming. Also, the QA data, checklists and associated rationale, though known by the group members, is not sufficiently documented, hindering transfer of knowledge and problem resolution. A new QA procedure which retains the current high level of integrity while reducing the time required to perform QA is needed to support the increasing Shuttle flight rate. Documenting the knowledge is also needed to increase its availability for training and problem resolution. To meet these needs, a knowledge capture process, embedded into the group activities, was initiated to verify the existing QA checks, define new ones, and document all rationale. The resulting checks were automated in a conventional software program to achieve the desired standardization, integrity, and time reduction. A prototype electronic knowledge base was developed with Macintosh's HyperCard to serve as a knowledge capture tool and data storage.

Peters, H. L.↗
DOE OSTI2024

Deep learning-enabled probing of irradiation-induced defects in time-series micrographs

Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.

Burns, Kory (ORCID:0000000158019909)↗
DOE OSTI2024

Resilient Control of Networked Microgrids Using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations

Improving system-level resiliency of networked microgrids against adversarial cyber-attacks is an important aspect in the current regime of increased inverter-based resources (IBRs). To achieve that, this paper contributes in designing a hierarchical control layer, in conjunction with the existing control layers, resilient to adversarial attack signals. Considering model complexities, unknown dynamical behaviors of IBRs, and privacy issues regarding data sharing in multi-party-owned microgrids, designing such a control layer is non-trivial. Here, to tackle these issues, a novel federated reinforcement learning (Fed-RL) method is proposed. To grasp the interconnected dynamics of networked microgrids, the paper develops Federated Soft Actor-Critic (FedSAC) algorithm following the vertical structure of implementing Fed-RL. Next, utilizing the OpenAI Gym interface, we built a custom set-up in GridLAB-D/HELICS co-simulation platform, named Resilient RL Co-simulation (ResRLCoSIM), to train the RL agents with IEEE 123-bus benchmark comprising 3 interconnected microgrids. Finally, the learned policies in the simulation are transferred to the real-time hardware-in-the-loop (HIL) test-bed developed using the high-fidelity Hypersim platform. Finally, experiments show that the simulator-trained RL controllers achieve desirable performance with the test-bed platform, validating the minimization of the sim-to-real gap.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Finding Electronic Structure Machine Learning Surrogates without Training

A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations – this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of machine-learning surrogate models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.

36 MATERIALS SCIENCE↗
NASA NTRS2024

Unveiling the Transferability of PLSR Models for Leaf Trait Estimation: Lessons from a Comprehensive Analysis with a Novel Global Dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least-squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47,393 records for >700 species and eight PFTs at 101 globally-distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55% and 7.0-33.12%) compared to nonspatial random cross-validation (NRCV). The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs and time due to biased validation schemes and provide guidance for future field sampling strategies and remote sensing applications.

Leaf traits↗
DOE OSTI2024

Deep learning based x-ray spectrometer for high repetition rate characterization of betatron radiation

Betatron radiation produced from a laser-wakefield accelerator is a broadband, hard x-ray (>1 keV) source that has been used in a variety of applications in medicine, engineering, and fundamental science. Further development and optimization of stable, high repetition rate (HRR) (>1 Hz) betatron sources will provide a means to extend their application base to include single-shot dynamical measurements of ultrafast processes or dense materials. Recent advances in laser technology used in such experiments have enabled increases in shot-rate and system stability, providing improved statistical analysis and detailed parameter scans. However, unique challenges exist at high repetition rate, where data throughput and source optimization are now limited by diagnostic acquisition rates and analysis. Here, we present the development of a machine-learning algorithm for the real-time analysis of betatron radiation. We report on the fielding of this deep learning algorithm for online source characterization at the Institut National de la Recherche Scientifique's Advanced Laser Light Source. By fine-tuning an algorithm originally trained on a fully synthetic dataset using a subset of experimental data, the algorithm can predict the betatron critical energy with a percent error of 7.2 % with a reconstruction time of 1.5 ms, providing a valuable tool for real-time, multi-objective optimization at HRR.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2016

Promoting A-Priori Interoperability of HLA-Based Simulations in the Space Domain: The SISO Space Reference FOM Initiative

Distributed and Real-Time Simulation plays a key-role in the Space domain being exploited for missions and systems analysis and engineering as well as for crew training and operational support. One of the most popular standards is the 1516-2010 IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA). HLA supports the implementation of distributed simulations (called Federations) in which a set of simulation entities (called Federates) can interact using a Run-Time Infrastructure (RTI). In a given Federation, a Federate can publish and/or subscribes objects and interactions on the RTI only in accordance with their structures as defined in a FOM (Federation Object Model). Currently, the Space domain is characterized by a set of incompatible FOMs that, although meet the specific needs of different organizations and projects, increases the long-term cost for interoperability. In this context, the availability of a reference FOM for the Space domain will enable the development of interoperable HLA-based simulators for related joint projects and collaborations among worldwide organizations involved in the Space domain (e.g. NASA, ESA, Roscosmos, and JAXA). The paper presents a first set of results achieved by a SISO standardization effort that aims at providing a Space Reference FOM for international collaboration on Space systems simulations.

Moller, Bjorn↗