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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sources NASA NTRS DOE OSTI

NASA NTRS2007

Generating Scenarios When Data Are Missing

The Hypothetical Scenario Generator (HSG) is being developed in conjunction with other components of artificial-intelligence systems for automated diagnosis and prognosis of faults in spacecraft, aircraft, and other complex engineering systems. The HSG accepts, as input, possibly incomplete data on the current state of a system (see figure). The HSG models a potential fault scenario as an ordered disjunctive tree of conjunctive consequences, wherein the ordering is based upon the likelihood that a particular conjunctive path will be taken for the given set of inputs. The computation of likelihood is based partly on a numerical ranking of the degree of completeness of data with respect to satisfaction of the antecedent conditions of prognostic rules. The results from the HSG are then used by a model-based artificial- intelligence subsystem to predict realistic scenarios and states.

Mackey, Ryan↗

NASA NTRS2020

Demand Forecast Model Development and Scenarios Generation For Urban Air Mobility Concepts

The purpose of this project is to estimate the demand for various Urban Air Mobility Concepts (UAM) of Operations and to generate scenarios for use in analysis and simulations. The demand forecast model, previously developed under NASA/NIA Contract No: NNL13AA08B; Task Order No: NNL16AA36T, for an urban on-demand air-taxi commuter concept is the basis for this work.

M. Rimjha↗

DOE OSTI2025

Developing an Automated Microscopic Traffic Simulation Scenario Generation Tool

Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. In conclusion, the calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.

autonomous vehicle↗

DOE OSTI2025

Scenario Generation for Built Environment Decision Support under Uncertainty: Case Studies of Airflow Modeling and Climate-Resilient Infrastructure System Design

When confronted with unforeseen challenges, practicing informed decision making is crucial for enhancing resilience in the built environment. While scan-to-building information modeling (BIM) is a well-established approach for creating detailed digital representations of physical assets, its application in assessing and improving infrastructure resilience remains underexplored. This study addresses this gap by proposing a novel application of scan-to-BIM, namely, scan-to-BIM-to-digital twin (S-BIM-DT) workflow. By integrating reality capture and digital twin technologies, this workflow creates continuously updated and accurate digital representations of physical assets, enabling the generation of various scenarios. Unlike traditional methods, the S BIM-DT workflow facilitates continuous model refinement, supporting informed resilience strategies. By combining these technologies into a cohesive process, the workflow facilitates decision making under uncertainty, enabling stakeholders to evaluate and respond to various scenarios effectively. We demonstrate the implementation of the S-BIM-DT workflow through two use cases that highlight its capability to enhance resilience at different scales. The first use case involves the Combined Transportation, Emergency, and Communications Center (CTECC) in Austin, Texas. BIM-enriched computational fluid dynamics (CFD) modeling simulates airflow and develops alternative scenarios for optimizing the heating, ventilation, and air conditioning (HVAC) systems. This approach enhances resilience against airborne health threats in a postCOVID context. The second use case focuses on designated areas within Beaumont, Texas, as part of the Southeast Texas Urban Integrated Field Laboratory (SETx-UIFL) research. By developing inundation maps to assess extreme weather events, this modeling aids in preparedness efforts and informs the development of climate-resilient infrastructure in vulnerable neighborhoods. Results indicate that the S-BIM-DT workflow effectively generates scenarios that enhance resilience in the built environment by facilitating informed decision making. Furthermore, this study serves as a bridge between advanced scan-to-BIM methodologies and the practical strategies needed to improve built infrastructure resilience.

Built environment↗

NASA NTRS2007

Hypothetical Scenario Generator for Fault-Tolerant Diagnosis

The Hypothetical Scenario Generator for Fault-tolerant Diagnostics (HSG) is an algorithm being developed in conjunction with other components of artificial- intelligence systems for automated diagnosis and prognosis of faults in spacecraft, aircraft, and other complex engineering systems. By incorporating prognostic capabilities along with advanced diagnostic capabilities, these developments hold promise to increase the safety and affordability of the affected engineering systems by making it possible to obtain timely and accurate information on the statuses of the systems and predicting impending failures well in advance. The HSG is a specific instance of a hypothetical- scenario generator that implements an innovative approach for performing diagnostic reasoning when data are missing. The special purpose served by the HSG is to (1) look for all possible ways in which the present state of the engineering system can be mapped with respect to a given model and (2) generate a prioritized set of future possible states and the scenarios of which they are parts.

James, Mark↗

NASA NTRS2018

Automated Scenario Generation for Human-in-the-Loop Simulations

Automated Multi-Aircraft Control System scenario generation for Human-in-the-Loop evaluations of air traffic management concepts is described. Methods for analyzing and comparing the seed-scenario generated using the automated process and the Human-in-the-Loop-scenario designed to meet the experiment objectives are discussed. The main findings are: (1) many of the characteristics of the seed-scenario used for constructing the Human-in-the-Loop-scenario are preserved in the Human-in-the-Loop Scenario, (2) landing characteristics of the traffic generated by the Multi-Aircraft Control System using the input scenario compare reasonably well with that intended in the input scenario, and (3) many of desired characteristics of the Human-in-the-Loop-scenario can be achieved by further automation.

SMART-NAS Testbed↗

NASA NTRS2018

Automated Scenario Generation for Human-in-the-Loop Simulations

Automated Multi-Aircraft Control System scenario generation for Human-in-the-Loop (HITL) evaluations of air traffic management concepts is described. The objective is to replace the difficult manual process with the automated process for creating an initial (seed) scenario that serves as a starting point for manual adjustments for creating the Human-in-the-Loop scenario. Methods for analyzing and comparing the seed-scenario generated using the automated process and the Human-in-the-Loop-scenario derived from it to meet the experiment objectives are discussed. Results of comparison of input Human-in-the-Loop-scenario with the Multi-Aircraft Control System output are also presented. The main findings are: (1) many of the characteristics of the seed-scenario used for constructing the Human-in-the-Loop-scenario are preserved in the Human-in-the-Loop-scenario, (2) landing rate profile of the traffic generated by the Multi-Aircraft Control System (MACS) using the input scenario compares reasonably well with that intended in the input scenario, and (3) many of the desired characteristics of the Human-in-the-Loop-scenario can be achieved by further automation.

Automated Scenario Generation↗

NASA NTRS2019

Automated Scenario Generation for Meeting Human-in-the-Loop Simulation Requirements

A two-step automated Multi-Aircraft Control System traffic scenario generation process for Human-in-the-Loop evaluations of air traffic management concepts is described. The first step of the two-step process employs the scenario generation capability currently available in NASA's Air Traffic Management Testbed. The second step refines the scenario by filtering flights from the traffic scenario based on route length, cruise speed, cruise altitude, entry time and the desired ratio of internal to external flights. A solution for achieving the desired ratio of internal to external flights, where internal flights are shorter flights and external flights are longer flights based on a distance threshold, is described. Finally, schedulers are described for shaping the hourly arrival traffic count as a function of time in response to airport capacity constraint or for increasing the traffic demand with respect to the available arrival capacity. Results generated for arrival traffic to the four major airports in the New York Metroplex on a busy day using the two-step procedure are discussed. These results show that traffic scenarios for Multi-Aircraft Control System that meet the Human-in-the-Loop and fast-time simulation requirements can be created automatically following the procedures described in the paper. The automated process will improve the accuracy and efficiency by eliminating the tedious manual process for scenario generation.

Automated Scenario Generation↗

NASA NTRS2019

Automated Scenario Generation for Meeting Human-In-The-Loop Simulation Requirements

Automated Scenario Generation↗

DOE OSTI2026

Agentic traffic intelligence: Augmented human-in-the-loop scenario generation for microscopic traffic simulation

Traditional microscopic traffic simulation generation often relies on static datasets and manual design, limiting its ability to simulate complex conditions easily. This paper presents a novel framework, Agentic Traffic Intelligence, which combines human approval large language models (LLMs), the Real-Twin tool, and multi-agent systems to perform realistic microscopic traffic simulation scenario generation. The proposed framework incorporates human-in-the-loop (HIL) control, retrieval-augmented generation (RAG), and multi-agent control mechanisms. HIL mechanisms are used to guide multiple LLMs focused on attributes for microscopic simulation generation and to improve the interpretability and transparency of LLM execution for users. RAG enhances context extraction by dynamically integrating external knowledge sources for traffic scenario generation foundations. A multi-agent architecture with supervisory control coordinates the interaction of simulation components, including traffic simulators, control logic, and calibration tools. This enables the synthesis of simulation-ready scenarios that reflect dynamic demand profiles and behavior controls. Furthermore, the framework fuses multisource traffic data with unstructured context and supports iterative refinement through interactive user feedback. Validated through microscopic simulation using Simulation of Urban Mobility, the generated scenarios demonstrate high-fidelity network generation with inflow and turn movement and behavioral calibration, offering a robust and efficient tool for stress-testing and optimizing urban mobility systems.

Hierarchical multi-agent control↗

NASA NTRS1989

Intelligent scenario generation for simulation-based training

A training scenario generator object database was developed to serve as a general-purpose mechanism for constructing the context needed to define a simulation scenario. It is found that the ability to automate the development of the input parameters required to produce a challenging simulation scenario targeted at a specific trainee can greatly enhance the efficiency of intelligent training systems. The approach described was used successfully in the payload-assist module deploy/intelligent computer-aided training system.

Loftin, R. Bowen↗

NASA NTRS2013

Appendix 2. Guide for Running AgMIP Climate Scenario Generation Tools with R in Windows, Version 2.3

This Guide explains how to create climate series and climate change scenarios by using the AgMip Climate team's methodology as outlined in the AgMIP Guide for Regional Assessment: Handbook of Methods and Procedures. It details how to: install R and the required packages to run the AgMIP Climate Scenario Generation scripts, and create climate scenarios from CMIP5 GCMs using a 30-year baseline daily weather dataset. The Guide also outlines a workflow that can be modified for application to your own climate data.

handbooks↗

DOE OSTI2026

Combining Generative Modeling and Advanced Control for Building Scenario Generation

Buildings make up a large portion of energy consumption in the U.S. today. Understanding their energy consumption patterns can improve their efficiency, but requires detailed models that rely on incomplete or unknown information. Previous work has shown that artificial intelligence (AI) can be used to predict missing information and even suggest upgrades to improve building efficiency. However, building upgrades may require undesirable upfront costs. Oppositely, advanced control could improve building efficiency with negligible upfront cost. To explore the tradeoffs between these two approaches, in this work we propose a workflow to compute optimal temperature setpoint schedules to minimize energy consumption and operational cost. Results show that modifying the temperature setpoints in a building using model predictive control (MPC) can effectively reduce its energy consumption and operational cost. This optimal operation cannot fully meet a desired goal. However, we show that by considering MPC in addition to component upgrades, a desired goal can be met with significantly less upfront costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2011

Optimizing Decision Preparedness by Adapting Scenario Complexity and Automating Scenario Generation

Klein's recognition-primed decision (RPD) framework proposes that experts make decisions by recognizing similarities between current decision situations and previous decision experiences. Unfortunately, military personnel arQ often presented with situations that they have not experienced before. Scenario-based training (S8T) can help mitigate this gap. However, SBT remains a challenging and inefficient training approach. To address these limitations, the authors present an innovative formulation of scenario complexity that contributes to the larger research goal of developing an automated scenario generation system. This system will enable trainees to effectively advance through a variety of increasingly complex decision situations and experiences. By adapting scenario complexities and automating generation, trainees will be provided with a greater variety of appropriately calibrated training events, thus broadening their repositories of experience. Preliminary results from empirical testing (N=24) of the proof-of-concept formula are presented, and future avenues of scenario complexity research are also discussed.

Dunne, Rob↗

NASA NTRS2007

Development of a Prototype Automation Simulation Scenario Generator for Air Traffic Management Software Simulations

A technique for automated development of scenarios for use in the Multi-Center Traffic Management Advisor (McTMA) software simulations is described. The resulting software is designed and implemented to automate the generation of simulation scenarios with the intent of reducing the time it currently takes using an observational approach. The software program is effective in achieving this goal. The scenarios created for use in the McTMA simulations are based on data taken from data files from the McTMA system, and were manually edited before incorporation into the simulations to ensure accuracy. Despite the software s overall favorable performance, several key software issues are identified. Proposed solutions to these issues are discussed. Future enhancements to the scenario generator software may address the limitations identified in this paper.

Khambatta, Cyrus F.↗

NASA NTRS2010

Automata learning algorithms and processes for providing more complete systems requirements specification by scenario generation, CSP-based syntax-oriented model construction, and R2D2C system requirements transformation

Systems, methods and apparatus are provided through which in some embodiments, automata learning algorithms and techniques are implemented to generate a more complete set of scenarios for requirements based programming. More specifically, a CSP-based, syntax-oriented model construction, which requires the support of a theorem prover, is complemented by model extrapolation, via automata learning. This may support the systematic completion of the requirements, the nature of the requirement being partial, which provides focus on the most prominent scenarios. This may generalize requirement skeletons by extrapolation and may indicate by way of automatically generated traces where the requirement specification is too loose and additional information is required.

Hinchey, Michael G.↗

DOE OSTI2025

Adaptive Dynamic Digital Twin for Test Scenario Generation

Vehicle testing has been an important part in the development of both highly automated vehicles (HAV) and advanced driving assistant systems (ADAS). Obtaining a good representation of the Vehicle Under Test (VUT) is crucial for test scenario library generation (TSLG). Current vehicle testing methods often involve calibrating car-following models using vehicle trajectory data to create static representations that cannot be dynamically updated. For instance, when multiple vehicle trajectories are collected, it is difficult to automatically determine whether a new trajectory improves the model's representativeness or degrades its accuracy. In this paper, we introduce a dynamically updated digital twin modeling framework featuring an adaptive mechanism that evaluates new trajectory data. This mechanism can decide whether to incorporate newly collected data into the current model or create a separate digital twin model when the trajectory significantly differs from prior data. Vehicle location, speed, and acceleration extracted from the newly collected trajectory data are used to support the dynamic update decision. By integrating this digital twin model into the test library generation process, we demonstrate its ability to assist in generating test libraries while effectively handling newly collected data.

Chen, Hanlin [ORNL] (ORCID:0000000165087715)↗

NASA NTRS1985

Traffic scenario generation technique for piloted simulation studies

Piloted simulation studies of cockpit traffic display concepts require the development of representative traffic scenarios. With the exception of specific aircraft interaction issues, most research questions can be addressed using traffic scenarios consisting of prerecorded aircraft movements merged together to form a desired traffic pattern. Prerecorded traffic scenarios have distinct research advantages, allowing control of traffic encounters with repeatability of scenarios between different test subjects. A technique is described for generation of prerecorded jet transport traffic scenarios suitable for use in piloted simulation studies. Individual flight profiles for the aircraft in the scenario are created interactively with a computer program designed specifically for this purpose. The profiles are then time-correlated and merged into a complete scenario. This technique was used to create traffic scenarios for the Denver, Colorado area with operations centered at Stapleton International Airport. Traffic scenarios for other areas may also be created using this technique, with appropriate modifications made to the navigation fix locations contained in the flight profile generation program.

Williams, David H.↗