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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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NASA NTRS2010

Modeling Regular Replacement for String Constraint Solving

Bugs in user input sanitation of software systems often lead to vulnerabilities. Among them many are caused by improper use of regular replacement. This paper presents a precise modeling of various semantics of regular substitution, such as the declarative, finite, greedy, and reluctant, using finite state transducers (FST). By projecting an FST to its input/output tapes, we are able to solve atomic string constraints, which can be applied to both the forward and backward image computation in model checking and symbolic execution of text processing programs. We report several interesting discoveries, e.g., certain fragments of the general problem can be handled using less expressive deterministic FST. A compact representation of FST is implemented in SUSHI, a string constraint solver. It is applied to detecting vulnerabilities in web applications

Fu, Xiang↗

NASA NTRS2014

OCSEGen: Open Components and Systems Environment Generator

To analyze a large system, one often needs to break it into smaller components.To analyze a component or unit under analysis, one needs to model its context of execution, called environment, which represents the components with which the unit interacts. Environment generation is a challenging problem, because the environment needs to be general enough to uncover unit errors, yet precise enough to make the analysis tractable. In this paper, we present a tool for automated environment generation for open components and systems. The tool, called OCSEGen, is implemented on top of the Soot framework. We present the tool's current support and discuss its possible future extensions.

User Specifications↗

NASA NTRS2012

Mars Science Laboratory Rover System Thermal Test

On November 26, 2011, NASA launched a large (900 kg) rover as part of the Mars Science Laboratory (MSL) mission to Mars. The MSL rover is scheduled to land on Mars on August 5, 2012. Prior to launch, the Rover was successfully operated in simulated mission extreme environments during a 16-day long Rover System Thermal Test (STT). This paper describes the MSL Rover STT, test planning, test execution, test results, thermal model correlation and flight predictions. The rover was tested in the JPL 25-Foot Diameter Space Simulator Facility at the Jet Propulsion Laboratory (JPL). The Rover operated in simulated Cruise (vacuum) and Mars Surface environments (8 Torr nitrogen gas) with mission extreme hot and cold boundary conditions. A Xenon lamp solar simulator was used to impose simulated solar loads on the rover during a bounding hot case and during a simulated Mars diurnal test case. All thermal hardware was exercised and performed nominally. The Rover Heat Rejection System, a liquid-phase fluid loop used to transport heat in and out of the electronics boxes inside the rover chassis, performed better than predicted. Steady state and transient data were collected to allow correlation of analytical thermal models. These thermal models were subsequently used to predict rover thermal performance for the MSL Gale Crater landing site. Models predict that critical hardware temperatures will be maintained within allowable flight limits over the entire 669 Sol surface mission.

Novak, Keith S.↗

NASA NTRS2012

Mars Science Laboratory Rover System Thermal Test

Novak, Keith S.↗

NASA NTRS2015

Generation of Simulated Tracking Data for LADEE Operational Readiness Testing

Operational Readiness Tests were an important part of the pre-launch preparation for the LADEE mission. The generation of simulated tracking data to stress the Flight Dynamics System and the Flight Dynamics Team was important for satisfying the testing goal of demonstrating that the software and the team were ready to fly the operational mission. The simulated tracking was generated in a manner to incorporate the effects of errors in the baseline dynamical model, errors in maneuver execution and phenomenology associated with various tracking system based components. The ability of the mission team to overcome these challenges in a realistic flight dynamics scenario indicated that the team and flight dynamics system were ready to fly the LADEE mission. Lunar Atmosphere and Dust Environment.

Tracking↗

NASA NTRS1996

A Remote Agent Prototype for Spacecraft Autonomy

NASA has recently announced the New Millennium Program (NMP) to develop

NMP↗

NASA NTRS2020

Integrating the Science Opportunity Analyzer with a Reusable Opportunity Search Framework

In our interactions with the Science Opportunity Analyzer (SOA) software, we recognized how its ability to search for geometric events in space is a need for robotic space missions in general. To satisfy this need, we propose the Tychonis framework, which is built upon the principles of: (1) separations of concerns, (2) extensibility, (3) reusability, and (4) independent verification and validation. Tychonis’ separation of concerns results in the availability of different constructs to model geometric events and search for them. These constructs can be extended by users as needed and reused across missions without changes. Given the low coupling between concerns, and the fact Tychonis can be augmented in isolation, its constructs can be validated independently from other pieces of software. This paper elaborates on these topics and presents an integration case study between Tychonis and SOA that relies on the concept of dynamic integration. Dynamic integration entails that augmentations of the framework are reflected automatically in the host application without any changes to the host application’s code. The SOA-Tychonis integration case study can be extrapolated to other tools that need to search for geometric events as the pattern repeats across implementations: (i) the manipulation of a user interface to model opportunities, (ii) the execution of algorithms to search for opportunities, and (iii) the presentation of search results to users. Overall, Tychonis’ is a story about how the application of proven software principles and good design choices can reduce risk and cost to space missions

Soria, Manel↗

NASA NTRSDate not supplied

Sensitivity of Optimal Midcourse Correction Scheduling for Robust Cislunar Trajectory Design

A new approach to optimal trajectory design is the determination of optimal trajectories that are robust to initial trajectory dispersions, navigation errors, maneuver execution errors, and environment modeling errors. This paper investigates the sensitivity of cislunar robust optimal trajectory design to launch date, duration of navigation measurement passes, and navigation measurement frequency. For a given cislunar trajectory from translunar injection (TLI) to lunar orbit insertion (LOI), the optimal locations of midcourse corrections, also known as trajectory correction maneuvers (TCM) are determined by minimizing the final 3-σ ∆v subject to a final 3-σ position dispersion constraint for a given launch date, specified measurement pass duration prior to each maneuver, and measurement frequency. Optical navigation (OpNav) is assumed, and OpNav field-of-view (FOV) and lighting constraints are employed. These constraints turn out to be important elements of the problem. The sensitivity of the optimal TCM locations are then investigated by varying the launch date, duration of OpNav measurement passes, and the OpNav measurement frequency, and then re-optimizing the locations of the TCMs. Given the problem parameters provided herein, results show that while the optimal TCM locations with respect to TLI vary greatly from one launch date to another, their locations with respect to LOI are nearly invariant over a 2-month launch window. Results also show that in all cases the optimal location of the last TCM is found to be at the point where the OpNav lunar FOV constraint is first violated. For all other TCMs, OpNav measurement pass duration and measurement frequency can have a moderate to large affect on the optimal TCM locations.

Linear Covariance Analysis↗

NASA NTRSDate not supplied

Turbulence Effects on Shaped Booms: Propagation Simulations Using KZKFourier

Upcoming X-59 aircraft flight tests as part of NASA’s Quesst Mission are expected to occur in a range of atmospheric conditions. Sensitivity of ground waveform acoustic metrics to turbulent perturbations during propagation complicates the determination of noise levels. A series of simulations through turbulence was executed using the KZKFourier model of Stout et al. to develop a database of results for expanding the functionality of NASA tools for estimating turbulence effects on shaped-boom ground waveforms. For 45 cases covering a seven-factor design space, multiple realizations of turbulence were generated to characterize statistical turbulence effects on levels of six acoustic metrics. An individual simulation in the study produced over one thousand waveforms across a virtual microphone array. Different measures were used to evaluate refinement of results with increasing number of simulations. Data suggest that mean effects and variability were most strongly influenced by propagation distance and velocity fluctuation intensity. Although local increases in acoustic metrics were common, the overall average result in all cases was a reduction in metric levels. Sensitivity of acoustic metrics varied, with mean reductions of up to 2 dB in Perceived Level occurring across the 45 cases.

turbulence↗

NASA NTRS2025

Airports as Energy Nodes Activity Summary

Advanced aircraft concepts that use non-traditional aviation energy storage methods such as batteries or cryogenic hydrogen are in development and expected to enter regular service at airports worldwide within the next decade. The energy needs for these aircraft may quickly overwhelm the existing energy infrastructure at airports, particularly at smaller and more remote facilities. Without energy upgrades, these airports will not be able to host these advanced vehicles, but without the advanced vehicle traffic, these airports will not have the rationale or funding to build up their energy infrastructure. The Airports as Energy Nodes (ÆNodes) activity, a collaboration between the National Aeronautics and Space Administration (NASA) and the National Renewable Energy Laboratory (NREL), was executed to understand and model the energy needs that advanced aircraft concepts may levy on these smaller airports, determine cost-effective approaches to enhance the airport energy infrastructure, and demonstrate the enhanced resilience of these energy infrastructure upgrades to the airport and surrounding community via “digital twin” simulation at relevant energy and dynamic time scales. The ÆNodes team also investigated future reference aircraft designs and materials to enable cryogenic hydrogen storage for aircraft. The ÆNodes team conducted analysis at two U.S. airport partner sites — Winchester Regional Airport in Winchester, Virginia, and Tweed/New Haven Airport in New Haven, Connecticut. The goal of this partnership was to develop data and reference infrastructure designs that could accommodate advanced aircraft in the future at these airports while also enhancing the resiliency of the energy supply to the surrounding airport community, which could be used to capture funding to enable the infrastructure upgrades. Over the course of the study, a method was developed to estimate air traffic requiring advanced energy services over the course of a year using a mix of historical data and companion studies on advanced aircraft transportation networks. The study has concluded at NASA but continues at NREL, who will develop a final report discussing the energy infrastructure upgrades and digital twin results. Preliminary results indicate that unrestricted adoption of advanced battery-electric aircraft may double traffic at these airports and increase peak daily power usage by an order of magnitude, while increase electricity energy needs by a factor of two to four. The infrastructure upgrades necessary to accommodate these increased energy needs could be used to provide enhanced energy services to the airport community to offset the cost and increase the utility of the upgrades, which will be described in the NREL final report.

Airports↗

DOE OSTI2020

Reliable cyber-threat detection in rapidly changing environments

In some embodiments, a plurality of monitoring nodes each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. An attack detection computer platform may receive the series of current monitoring node values and generate a set of current feature vectors including a current feature for capturing transients (e.g., local transients and/or global transients). The attack detection computer platform may also access an attack detection model having at least one decision boundary that was created using at least one of a set of normal feature vectors and/or a set of attacked feature vectors. The attack detection model may then be executed such that an attack alert signal is transmitted by the attack detection computer platform, when appropriate, based on the set of current feature vectors (including the current feature to capture transients) and the at least one decision boundary.

Abbaszadeh, Masoud↗

DOE OSTI2021

Cyber-attack detection, localization, and neutralization for unmanned aerial vehicles

In some embodiments, an Unmanned Aerial Vehicle (“UAV”) system may be associated with a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time that represent operation of the UAV system. An attack detection computer platform may receive the series of current monitoring node values and generate a set of current feature vectors. The attack detection computer platform may access an attack detection model having at least one decision boundary (e.g., created using a set of normal feature vectors a set of attacked feature vectors). The attack detection model may then be executed and the platform may transmit an attack alert signal based on the set of current feature vectors and the at least one decision boundary. According to some embodiments, attack localization and/or neutralization functions may also be provided.

Mestha, Lalit Keshav↗

DOE OSTI2023

Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments

The annual Workshop on Workflows in Support of Large-Scale Science (WORKS) is a premier venue for the scientific workflow community to present the latest advances in research and development on the many facets of scientific workflows throughout their life-cycle. The Lightning Talks at WORKS focus on describing a novel tool, scientific workflow, or concept, which are work-in-progress and address emerging technologies and frameworks to foster discussion in the community. This paper summarizes the lightning talks at the 2023 edition of WORKS, covering five topics: leveraging large language models to build and execute workflows; developing a common workflow scheduler interface; scaling uncertainty workflow applications on exascale computing systems; evaluating a transcriptomics workflow for cloud vs. HPC systems; and best practices in migrating legacy workflows to workflow management systems.

Titov, Mikhail↗

DOE OSTI2023

A Fine-grained Asynchronous Bulk Synchronous parallelism model for PGAS applications

The Partitioned Global Address Space (PGAS) model is well suited for executing irregular applications on cluster-based systems, due to its efficient support for short, one-sided messages. Separately, the actor model has been gaining popularity as a productive asynchronous message-passing approach for distributed objects in enterprise and cloud computing platforms, typically implemented in languages such as Erlang, Scala or Rust. To the best of our knowledge, there has been no past work on using the actor model to deliver both productivity and scalability to irregular PGAS applications with large number of small messages. In this paper, we introduce a new programming system for PGAS applications, in which point-to-point remote operations can be expressed as fine-grained asynchronous actor messages. In our approach, the programmer does not need to worry about programming complexities related to message aggregation and termination detection. Our approach can be viewed as extending the classical Bulk Synchronous Parallelism model with fine-grained asynchronous communications within a phase or superstep. Here, we believe that our approach offers a desirable point in the productivity-performance space for PGAS applications, with more scalable performance and higher productivity relative to past approaches. Specifically, for seven irregular mini-applications from the Bale Kernels and three graph kernels executed using 2048 cores in the NERSC Cori system, our approach shows geometric mean performance improvements of ≥ 20X relative to standard PGAS versions (UPC and OpenSHMEM) while maintaining comparable productivity to those versions.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2009

Functional Fault Modeling of a Cryogenic System for Real-Time Fault Detection and Isolation

When setting out to model and/or simulate a complex mechanical or electrical system, a modeler is faced with a vast array of tools, software, equations, algorithms and techniques that may individually or in concert aid in the development of the model. Mature requirements and a well understood purpose for the model may considerably shrink the field of possible tools and algorithms that will suit the modeling solution. Is the model intended to be used in an offline fashion or in real-time? On what platform does it need to execute? How long will the model be allowed to run before it outputs the desired parameters? What resolution is desired? Do the parameters need to be qualitative or quantitative? Is it more important to capture the physics or the function of the system in the model? Does the model need to produce simulated data? All these questions and more will drive the selection of the appropriate tools and algorithms, but the modeler must be diligent to bear in mind the final application throughout the modeling process to ensure the model meets its requirements without needless iterations of the design. The purpose of this paper is to describe the considerations and techniques used in the process of creating a functional fault model of a liquid hydrogen (LH2) system that will be used in a real-time environment to automatically detect and isolate failures.

Ferrell, Bob↗

DOE OSTI2019

Behavior, Energy, Autonomy, Mobility Modeling Framework

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility-maximizing evolutionary algorithm that minimizes each individual's cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within-day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing.

Bae, Sangjae↗

DOE OSTI2024

Behavior, Energy, Autonomy, Mobility Modeling Framework (BEAM) v1.0

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility- maximizing evolutionary algorithm that minimizes each individual’s cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within- day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing. It is designed to integrate with other open source transportation models, such as ActivitySim.

Lazarus, Jessica↗

DOE OSTI2020

Applying Machine Learning to the Classification of DC-DC Converters (Milestone 2 Deliverable Report)

Since extending the Autodetector to a convolutional neural network (CNN) machine learning classifier model, an effort has been executed to demonstrate its ability to distinguish not only a switching DC-DC converter as high voltage, but identify the make and model of a converter on which it was trained. This was achieved by collecting data in a noisy environment, pre-processing the time domain data to obtain composite images using a method that improves upon that of the prior research, then validating a trained CNN model to an accuracy of 100% on a selected candidate converter.

42 ENGINEERING↗