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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Designing Scenarios for Controller-in-the-Loop Air Traffic Simulations

Within the Human Factors Division at NASA Ames Research Center the Airspace Operations Laboratory (AOL) is developing advanced automation concepts that help to transform the National Airspace System into NextGen, the Next Generation Air Transportation System. High-fidelity human-in-the-loop (HITL) simulations are used as a means to investigate and develop roles, responsibilities, support tools, and requirements for human operators and automation. This paper describes the traffic scenario design process and strategies as used by AOL researchers. Details are presented on building scenarios for specific simulation objectives using various design strategies. A focus is set on creating scenarios based on recorded real world traffic for terminal-area simulations.

scenario design↗
NASA NTRS1981

Group 1: Scenario design and development issues

All LOFT scenarios and flight segments should be designed on the basis of a detailed statement of specific objectives. These objectives must state what kind of situation is to be addressed and why. The origin, routing, and destination of a particular scenario should be dictated by the specific objectives for that scenario or leg. Other factors to be considered are the desired weather, climate, etc. Simulator visual system, as well as other capabilities and limitations must be considered at a very early stage of scenario design. The simulator navigation area must be apropriate and must coincide with current Jeppeson charts. Much of the realism of LOFT is destroyed if the crew is unable to use current manuals and other materials.

Sherwin, P.↗
NASA NTRS2013

Designing Scenarios for Controller-in-the-Loop Air Traffic Simulations

Well prepared traffic scenarios contribute greatly to the success of controller-in-the-loop simulations. This paper describes each stage in the design process of realistic scenarios based on real-world traffic, to be used in the Airspace Operations Laboratory for simulations within the Air Traffic Management Technology Demonstration 1 effort. The steps from the initial analysis of real-world traffic, to the editing of individual aircraft records in the scenario file, until the final testing of the scenarios before the simulation conduct, are all described. The iterative nature of the design process and the various efforts necessary to reach the required fidelity, as well as the applied design strategies, challenges, and tools used during this process are also discussed.

ATD1↗
DOE OSTI2024

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗
NASA NTRS1991

What makes a good LOFT scenario? Issues in advancing current knowledge of scenario design

An effort is made to ascertain which combinations of technical demands and crew coordination should be incorporated in training scenarios in order to maximize the effectiveness of training for crew members. Such high-fidelity simulation, which has come to be known as 'line-oriented flight training' or LOFT, involves the practice of both technical and crew coordination skills in a realistic setting, in conjunction with periodic reviews of performance via videotaped feedback. Attention is given to the integration of appropriate information, the measurement of objective task demands, the character of information from LOFT students, and the leeway allowed LOFT instructors.

Gregorich, Steven E.↗
NASA NTRS1992

Automated Simulation For Analysis And Design

Design Assistant Workstation (DAWN) software being developed to facilitate simulation of qualitative and quantitative aspects of behavior of life-support system in spacecraft, chemical-processing plant, heating and cooling system of large building, or any of variety of systems including interacting process streams and processes. Used to analyze alternative design scenarios or specific designs of such systems. Expert system will automate part of design analysis: reason independently by simulating design scenarios and return to designer with overall evaluations and recommendations.

Cantwell, E.↗
NASA NTRSDate not supplied

Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth-Moon system. Using Multi-Reward Proximal Policy Optimization, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.

Christopher J Sullivan↗
NASA NTRS2021

Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth- Moon system. Using Multi-Reward Proximal Policy Optimiza- tion, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.

Mashiku, Alinda K.↗
NASA NTRS2017

Design Considerations for Attitude State Awareness and Prevention of Entry into Unusual Attitudes

Loss of control - inflight (LOC-I) has historically represented the largest category of commercial aviation fatal accidents. A review of the worldwide transport airplane accidents (2001-2010) evinced that loss of attitude or energy state awareness was responsible for a large majority of the LOC-I events. A Commercial Aviation Safety Team (CAST) study of 18 worldwide loss-of-control accidents and incidents determined that flight crew loss of attitude awareness or energy state awareness due to lack of external visual reference cues was a significant causal factor in 17 of the 18 reviewed flights. CAST recommended that "Virtual Day-Visual Meteorological Condition" (Virtual Day-VMC) displays be developed to provide the visual cues necessary to prevent loss-of-control resulting from flight crew spatial disorientation and loss of energy state awareness. Synthetic vision or equivalent systems (SVS) were identified for a design "safety enhancement" (SE-200). Part of this SE involves the conduct of research for developing minimum aviation system performance standards (MASPS) for these flight deck display technologies to aid flight crew attitude and energy state awareness similar to that of a virtual day-VMC-like environment. This paper will describe a novel experimental approach to evaluating a flight crew's ability to maintain attitude awareness and to prevent entry into unusual attitudes across several SVS optical flow design considerations. Flight crews were subjected to compound-event scenarios designed to elicit channelized attention and startle/surprise within the crew. These high-fidelity scenarios, designed from real-world events, enable evaluation of the efficacy of SVS at improving flight crew attitude awareness to reduce the occurrence of LOC-I incidents in commercial flight operations.

Ellis, Kyle K. E.↗
DOE OSTI2023

Deep Learning for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting cold water into a subsurface fractures, which are in contact with the hot dry rock, and pulled through the production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. To develop economically viable EGS reservoirs, significant technical barriers (e.g., better stimulation technologies without adequate water and/or permeability) and non-technical barriers (e.g., land access and permitting) must be overcome. In this short conference paper, we present a workflow to address a part of this challenge – “How to develop economically viable EGS using existing technologies?”. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and cloud computing. This GTC framework is open-source and available at https://github.com/SmartTensors/GeoThermalCloud.jl. The GTC framework provides trained deep learning (DL) models to estimate the net present value of a given EGS design scenario. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. The database consists of EGS design parameters (inputs to DL model) and their net present value (output of DL model) in uncertain geologic systems. The EGS design parameters for constructing this training database are based on Utah FORGE but include the options of more wells and deeper depths. The DL models are trained by ingesting the EGS design parameters and estimating the corresponding net present value. Such an emulation allows us to screen various EGS designs quickly and identify good development strategies by coupling them with optimization techniques. Our preliminary results show promise in DL emulation of net present value. However, a lot more work is needed to improve the predictive capability of DL models (i.e., extensive hyperparameter tuning is necessary). This will be the primary focus of our future work.

artificial neural networks, geothermal↗
NASA NTRS2006

Solid Modeling of Crew Exploration Vehicle Structure Concepts for Mass Optimization

Parametric solid and surface models of the crew exploration vehicle (CEV) command module (CM) structure concepts are developed for rapid finite element analyses, structural sizing and estimation of optimal structural mass. The effects of the structural configuration and critical design parameters on the stress distribution are visualized, examined to arrive at an efficient design. The CM structural components consisted of the outer heat shield, inner pressurized crew cabin, ring bulkhead and spars. For this study only the internal cabin pressure load case is considered. Component stress, deflection, margins of safety and mass are used as design goodness criteria. The design scenario is explored by changing the component thickness parameters and materials until an acceptable design is achieved. Aluminum alloy, titanium alloy and an advanced composite material properties are considered for the stress analysis and the results are compared as a part of lessons learned and to build up a structural component sizing knowledge base for the future CEV technology support. This independent structural analysis and the design scenario based optimization process may also facilitate better CM structural definition and rapid prototyping.

Mukhopadhyay, Vivek↗
DOE OSTI2024

Tokamak divertor plasma emulation with machine learning

Abstract Future tokamak devices that aim to create conditions relevant to power plant operations must consider strategies for mitigating damage to plasma facing components in the divertor. One of the goals of MAST-U tokamak operations is to inform these considerations by researching advanced divertor configurations that aid stable plasma detachment. Machine design, scenario planning and detachment control would all greatly benefit from tools that enable rapid calculation of scenario-relevant quantities given some input parameters. This paper presents a method for generating large, simulated scrape-off layer data sets, which was applied to generate a data set of steady-state Hermes-3 simulations of the MAST-U tokamak. A machine learning model was constructed using a Bayesian approach to hyperparameter optimisation to predict diagnosable output quantities given control-relevant input features. The resulting best-performing model, which is based on a feedforward neural network, achieves high accuracy when predicting electron temperature at the divertor target and carbon impurity radiation front position and runs in around 1 ms in inference mode. Techniques for interpreting the predictions made by the model were applied, and a high-resolution parameter scan of upstream conditions was performed to demonstrate the utility of rapidly generating accurate predictions using the emulator. This work represents a step forward in the design of machine learning-driven emulators of tokamak exhaust simulation codes in operational modes relevant to divertor detachment control and plasma scenario design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Valuing Ecosystems in Equitable Energy Transition Planning

In this report we explore three areas of research and practice needed to scale clean energy sustainably: 1) advancing the predictive science to quantify positive and negative outcomes of energy development for the wellbeing of communities, environments, and disadvantaged groups; 2) scenario design using this science to shape renewable energy solutions for nation-wide decarbonization while delivering tangible, local benefits; and 3) participatory science-policy processes to understand what people want for the places where they live and work and how incorporating renewable energy can help or hinder their goals. Together these three components – quantifying social-ecological values, scenario design, and participatory processes – form the basis for a framework for valuing ecosystems to inform a more equitable energy transition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

NRIC Integrated Energy Systems Demonstration Pre-Conceptual Designs

This report presents pre-conceptual design scenarios for a potential multiphase demonstration program for innovative uses of nuclear energy with the National Reactor Innovation Center (NRIC) and the Crosscutting Technology Development Integrated Energy Systems (CTD IES) program in the U.S. Department of Energy’s Office of Nuclear Energy. The demonstration program would address the need for low-carbon energy sources among industry stakeholders by identifying and implementing high-impact advanced nuclear projects within a holistic systems perspective. Battelle Energy Alliance, LLC, the managing and operating contractor for the U.S. Department of Energy’s Idaho National Laboratory (INL) in Idaho Falls, Idaho, is seeking Expressions of Interest (EOI) for industry stakeholder participation in the potential demonstration program. Funding sources have not yet been identified for the demonstration program. Responses to the EOI will shape the development and funding requirements for the potential program and inform the down-selection of project designs for further planning and analysis from the wide set of pre-conceptual design scenarios shown in this report. This introductory section summarizes the need for low-carbon energy sources, describes the phases envisioned for the demonstration program, and outlines the organization of this report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Investigation into the instantaneous centre of rotation for enhanced design of floating offshore wind turbines

The dynamic behaviour of floating offshore wind turbines (FOWTs) involves complex interactions of multivariate loads from wind, waves, and currents, which result in complex motion characteristics. Although methods for analysing global motion responses are well-established, the time- and location-dependent kinematics remain underexplored. This paper investigates the instantaneous centre of rotation (ICR), a point of zero velocity at a time instance of general plane motion. Understanding and strategically positioning the ICR can reduce the dynamic motion in critical structural locations, enhancing the performance and structural robustness of FOWTs. The paper presents a method for computing the ICR using time-domain simulation results and proposes a statistical analysis approach suitable for design studies. Building on prior research, it examines the sensitivity of the ICR to external loading and design features, providing insights into how these factors influence motion response and how the motion response influences the statistics of the ICR, structural loads, and other performance metrics of interest. The study explores two FOWT configurations, a spar and a semisubmersible, identifying design variables that most effectively control the ICR statistics and identifying the ICR statistics most correlated with the responses of interest. Finally, through two case studies, we demonstrate how to apply these new insights in a practical design scenario. By adjusting the design variables most correlated with the ICR (fairlead vertical position and centre of mass for the spar and mooring line length and offset column diameter for the semisubmersible), we successfully modified the designs of the floating support structures to reduce the loads in the mooring lines, tower base, and blade roots, improving the ultimate strength and fatigue characteristics compared to the original designs.

17 WIND ENERGY↗
NASA NTRS2004

Virtual Mission Operation Framework (VMOF)

The Virtual Mission Operation Framework (VMOF) is one of the project lifecycle engineering process improvement efforts initiated by the institutional technology infrastructure program at JPL. The VMOF is composed of three frameworks: a model integration framework, a simulation framework, and a visualization framework. The model integration framework interfaces with spacecraft system design, mission design, and structure design. The simulation framework interfaces with the operation scenario design, environmental phenomena science, and science payload system design. The visualization framework interfaces with the flight system testbed, the ground system, and the science analysis. The three frameworks of the VMOF collaborate to create a comprehensive virtual mission operation that enables a "validation-in-the-loop" system design process and lifecycle-continuous science-return validation.This paper discusses the technical approaches for each framework implementation, challenges and approaches involved in streamlining mission information access, and on-going activities toward enabling Model-Based Engineering Design in a collaborative distributed environment.

virtual mission operations↗
NASA NTRSDate not supplied

Objective Structured Clinical Evaluation (OSCE) of an Artificial Intelligence (AI) Clinical Decision Support System (CDSS) Tool

BACKGROUND Objective Structured Clinical Evaluations (OSCEs) have long been established as a robust methodology for summative assessment of clinical skills and decision-making during medical education. The recent integration of Artificial Intelligence (AI) into clinical decision-making processes has prompted the need for novel evaluation frameworks to assess the efficacy and reliability of AI clinical decision support system (CDSS) tools. This abstract outlines the process of quantitatively evaluating a novel CDSS (“Doc in a Box” Google 2024) trained on curated medical spaceflight data in the psychomotor domain as it interfaces with a human volunteer acting as the crew medical officer (CMO). PURPOSE The AI CDSS under review was developed as part of the Lunar Command and Control Interoperability (LuCCI) project, which is intended to address a gap in how Lunar Surface Systems (LSS) would interoperate across multiple programs, commercial partners, and international partners. The project objective is to define, prototype, integrate, and evaluate an interoperable lunar command, control, data, and software reference architecture to enable autonomy and informatics capability through common standards across LSS. A multi-modal AI-based CDSS compatible with Federated LSS will assist clinicians in diagnosing and managing complex medical conditions by providing evidence-based recommendations through predictive analytics. Given the critical role of decision-support as NASA continues to evolve its Earth-independent medical operations (EIMO), it is imperative to ensure that such AI tools perform reliably and align with clinical standards during progressive lunar and Martian exploration class missions. METHODS The OSCE framework, traditionally used for evaluating human clinicians, was adapted to assess the AI tool's decision-making capabilities in simulated clinical scenarios. In this adapted OSCE, the AI CDSS was tested across a series of structured clinical scenarios designed to mimic real-life spaceflight patient cases. These scenarios included a range of conditions and complexities, allowing for comprehensive assessment of the tool's performance. Key evaluation metrics included accuracy of diagnosis, timeliness of decision-making, and appropriate recommendations for therapies. The OSCE was scored by human physician evaluators who assessed the AI's recommendations in comparison with expert clinicians' medical decision making to ensure alignment with best practices and the standard of care. RESULTS Preliminary results indicate that the AI CDSS demonstrated high accuracy in diagnostic recommendations and decision support across various scenarios. However, certain limitations were noted, such as occasional discrepancies in handling complex or nuanced cases that required a more contextual understanding. Additionally, the tool scored higher on the diagnostic portion of the rubric, with lower scores in the therapeutic recommendations. These findings highlight the importance of continuous refinement and validation of AI tools through rigorous evaluation frameworks like the OSCE. The adaptation of OSCEs for AI tools presents several advantages, including a structured and reproducible approach to evaluation, the ability to test AI systems in diverse clinical scenarios, and the opportunity to benchmark AI performance against established clinical standards to permit charting of future progress as aerospace medicine evolves as a discipline. Remaining challenges include ensuring that these evaluations capture the full spectrum of clinical decision-making scenarios that will be confronted by CMOs during missions and adequately reflecting real-world variability of the austere spaceflight environment. CONCLUSION Employing OSCEs to evaluate AI clinical decision support tools offers a promising approach to validating their clinical utility and efficacy. This methodology not only provides insights into the tool's performance but also fosters ongoing improvement and alignment with standard of care practices. Future research should focus on refining these evaluation processes and addressing limitations to enhance the integration of AI tools in clinical spaceflight settings. REFERENCES Scott S, Hearns V, Barker MA. Testing Clinical Skills: A Look at the OSCE and USMLE Clinical Skills Exams. S D Med. 2019 Oct;72(10):451-453. Majumder MAA, Kumar A, Krishnamurthy K, Ojeh N, Adams OP, Sa B. An evaluative study of objective structured clinical examination (OSCE): students and examiners perspectives. Adv Med Educ Pract. 2019 Jun 5;10:387-397. Karam VY, Park YS, Tekian A, Youssef N. Evaluating the validity evidence of an OSCE: results from a new medical school. BMC Med Educ. 2018 Dec 20;18(1):313.

Ariana M Nelson↗
NASA NTRS1992

Modeling and analysis of the Space Station Freedom Active Thermal Control System radiators using SINDA/FLUINT

Detailed SINDA/FLUINT models have been developed to assess the adequacy of the Space Station Freedom External Thermal Control System's radiator design, as well as to simulate the effects of various operational scenarios. Design margins can be deduced by quantifying such real-world effects as flow maldistribution due to line-pressure drop and uneven heat loads. The factor which most reduced the heat-rejection capacity is an uneven heat load split between the two low temperature loops.

Andish, Kambiz K.↗