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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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- The primary goal of IAS is to mature Advanced Air Mobility (AAM) automation technologies via flight test and includes: - Aircraft automation technologies - Airspace automation technologies - CNSI technologies (sensors, comm & datalinks)
On August 14, 2021, a Mw 7.2 earthquake struck the Tiburon Peninsula of western Haiti triggering thousands of landslides. Three days after the earthquake on August 17, 2021, Tropical Storm Grace crossed shallow waters offshore of southern Haiti triggering more landslides worsening the situation. In the aftermath of these events, several organizations with disaster response capabilities or programs activated to provide information on the location of landslides to first responders on the ground. Utilizing remote sensing to support rapid response, one organization manually mapped initiation point of landslides and three automatically detected landslides. The 2021 Haiti event also provided a unique opportunity to test different automated landslide detection methods that utilized both SAR and optical data in a rapid response scenario where rapid situational awareness was critical. As the methods used are highly replicable, the main goal of this study is to summarize the landslide rapid response products released by the organizations, detection methods, quantify accuracy and provide guidelines on how some of the shortcomings encountered in this effort might be addressed in the future. To support this validation, a manually mapped polygon-based landslide inventory covering the entire affected area was created and is also released through this effort.
Abstract not provided.
In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.
This is the required paper summarizing the poster presentation material to be presented at The 25th European Microelectronics & Packaging Conference (EMPC 2025) (16 – 18 September 2025). The paper will be associated with the conference presentation in the conference proceedings
Range-extended hybrids are an attractive option for medium- and heavy-duty commercial vehicle fleets because they offer the efficiency of an electrified powertrain with the driving range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy (DOE)-funded project for Medium- and Heavy-Duty Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric Class 6 truck with a range-extending engine designed for pickup and delivery application. The National Renewable Energy Laboratory (NREL) assisted by developing representative workday drive cycles for Class 6 operation and an adapted cycle to enable vehicle track testing. A novel, automated driving system was utilized by Southwest Research Institute (SwRI) to improve the repeatability of the track testing conducted to quantify vehicle energy consumption. Cummins used a drivetrain model to design the hybrid control system for increased fuel savings. The control system functionality and fuel savings objective of 50% or more were confirmed by track testing described in this paper.
Two automated software verification and testing systems were developed for use in the analysis of computer programs. An evaluation of the static analyzer DAVE and the dynamic analyzer PET, which are used in the analysis of FORTRAN programs on Control Data (CDC) computers, are described. Both systems were found to be effective and complementary, and are recommended for use in testing FORTRAN programs.
We describe the conclusions of a technology and communities survey supported by concurrent and follow-on proof-of-concept prototyping to evaluate feasibility of defining a durable, versatile, reliable, visible software interface to support strategic modularization of test software development. The objective is that test sets and support software with diverse origins, ages, and abilities can be reliably integrated into test configurations that assemble and tear down and reassemble with scalable complexity in order to conduct both parametric tests and monitored trial runs. The resulting approach is based on integration of three recognized technologies that are currently gaining acceptance within the test industry and when combined provide a simple, open and scalable test orchestration architecture that addresses the objectives of the Automation Hooks task. The technologies are automated discovery using multicast DNS Zero Configuration Networking (zeroconf), commanding and data retrieval using resource-oriented Restful Web Services, and XML data transfer formats based on Automatic Test Markup Language (ATML). This open-source standards-based approach provides direct integration with existing commercial off-the-shelf (COTS) analysis software tools.
Starting with the NASA-sponsored STAEBL program, optimization methods based primarily upon the versatile program COPES/CONMIN were introduced over the past few years to a broad spectrum of engineering problems in structural optimization, engine design, engine test, and more recently, manufacturing processes. By automating design and testing processes, many repetitive and costly trade-off studies have been replaced by optimization procedures. Rather than taking engineers and designers out of the loop, optimization has, in fact, put them more in control by providing sophisticated search techniques. The ultimate decision whether to accept or reject an optimal feasible design still rests with the analyst. Feedback obtained from this decision process has been invaluable since it can be incorporated into the optimization procedure to make it more intelligent. On several occasions, optimization procedures have produced novel designs, such as the nonsymmetric placement of rotor case stiffener rings, not anticipated by engineering designers. In another case, a particularly difficult resonance contraint could not be satisfied using hand iterations for a compressor blade, when the STAEBL program was applied to the problem, a feasible solution was obtained in just two iterations.
The primary goal of this project was to design, construct, and test a full scale, high pressure simulated propellant feed system test bed that could evaluate the ability of the Reciprocating Feed System (RFS) to provide essentially constant flow rates and pressures to a rocket engine. The two key issues addressed were the effects of the transition of the drain cycle from tank to tank and the benefits of other hardware such as accumulators to provide a constant pressure flow rate out of the RFS. The test bed provided 500 psi flow at rates of the order of those required for engines in the 20,000 lbf thrust class (e.g., 20 to 40 lb/sec). A control system was developed in conjunction with the test article and automated system operation was achieved. Pre-test planning and acceptance activities such as a documented procedure and hazard analysis were conducted and the operation of the test article was approved by, and conducted in coordination with, appropriate NASA Marshall Space Flight Center personnel under a Space Act Agreement. Tests demonstrated successful control of flow rates and pressures.
This paper experimentally tests an implementation of a control barrier function (CBF) designed to guarantee a minimum time-gap in car following on an automated vehicle (AV) in live traffic, with a majority occurring on freeways. The CBF supervises a nominal unsafe PID controller on the AV’s velocity. The experimental testing spans two months of driving, of which 1.9 hours of data is collected in which the CBF and nominal controller are active. We find that violations of the guaranteed minimum time-gap are observed, as measured by the vehicle’s on-board radar unit. There are two distinct causes of the violations. First, in multi-lane traffic, Cut-ins from other vehicles represent external disturbances that can immediately violate the minimum guaranteed time gap provided by the CBF. When cut-ins occur, the CBF does eventually return the vehicle to a safe time gap. Second, even when cut-ins do not occur, system model inaccuracies (e.g., sensor error and delay, actuator error and delay) can lead to violations of the minimum time-gap. These violations are small relative to the violations that would have occurred using only the unsafe nominal control law.
For many decades, researchers at NASA Ames Research Center have worked to make the air-transportation system more efficient, predictable, and effective. Since about 2005 one important aspect of this research has been the development of an autonomous system for air-traffic control. This system, known as the Autoresolver, is designed to perform most of the roles that air-traffic controllers perform including ensuring separation between aircraft, creating routes around weather and other avoidance volumes, and sequencing and scheduling aircraft across points in space. The recent, rapid expansion of new aircraft operations and types, including urban air mobility aircraft and small unmanned aerial systems, have only increased the need for highly automated systems to control the predicted traffic demand. This talk will focus on the development of the Autoresolver - from concept to testing. It will also discuss the National Airspace (NAS) Digital Twin simulation platform, created to facilitate rapid testing and improvement of the algorithm and with the hope of proving the automation in a high-fidelity environment. An open question that will be discussed is how to ensure that the system-level emergent behavior of independently developed autonomous algorithms is what is desired.
Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software
The figure schematically illustrates a method and procedure for automated monitoring of an asset, as well as a hardware- and-software system that implements the method and procedure. As used here, asset could signify an industrial process, power plant, medical instrument, aircraft, or any of a variety of other systems that generate electronic signals (e.g., sensor outputs). In automated monitoring, the signals are digitized and then processed in order to detect faults and otherwise monitor operational status and integrity of the monitored asset. The major distinguishing feature of the present method is that the fault-detection function is implemented by use of a Bayesian sequential probability (BSP) technique. This technique is superior to other techniques for automated monitoring because it affords sensitivity, not only to disturbances in the mean values, but also to very subtle changes in the statistical characteristics (variance, skewness, and bias) of the monitored signals.
An effort was undertaken by NASA to develop an automated post-test, post-flight diagnostic system for rocket engines. The automated system is designed to be generic and to automate the rocket engine data review process. A modular, distributed architecture with a generic software core was chosen to meet the design requirements. The diagnostic system is initially being applied to the Space Shuttle Main Engine data review process. The system modules currently under development are the session/message manager, and portions of the applications section, the component analysis section, and the intelligent knowledge server. An overview is presented of a rocket engine data review process, the design requirements and guidelines, the architecture and modules, and the projected benefits of the automated diagnostic system.
An effort was undertaken by NASA to develop an automated post-test, post-flight diagnostic system for rocket engines. The automated system is designed to be generic and to automate the rocket engine data review process. A modular, distributed architecture with a generic software core was chosen to meet the design requirements. The diagnostic system is initially being applied to the Space Shuttle Main Engine data review process. The system modules currently under development are the session/message manager, and portions of the applications section, the component analysis section, and the intelligent knowledge server. An overview is presented of a rocket engine data review process, the design requirements and guidelines, the architecture and modules, and the projected benefits of the automated diagnostic system.
The objective of this project is to test connected and automated vehicles with both electrified and internal combustion engine powertrains to support updates and validation of modeling and simulation tools. This includes the development of the components and network architecture to execute and collect empirical data for multiple scenarios and traffic interactions. Specific program objectives include: • Translate Lab algorithms into vehicle and infrastructure controls • Conduct physical testing at realistic scale • Evaluate system performance • Improve models using empirical data • Improve control algorithms from lessons learned • Identify system and algorithm assumptions which need refinement It is important to note that the objective of this project was not to demonstrate the efficacy of the selected algorithms to improve energy efficiency but rather to validate and improve modeling and simulation tools using empirical data. While it is a desirable outcome to concurrently demonstrate improved energy efficiency through use of these algorithms, and in most cases that was the outcome, the success of this project was not predicated on the performance of the algorithm towards improving energy efficiency across all scenarios and test matrices.
The sensitivity and potential rapidity of the PIA test that was demonstrated during the feasibility study warranted continuing the effort to examine the possibility of adapting this test to an automated procedure that could be used during manned missions. The effort during this program has optimized the test conditions for two important respiratory pathogens, influenza virus and Mycoplasma pneumoniae, developed a laboratory model automated detection system, and investigated a group antigen concept for virus detection. Preliminary tests on the handling of oropharygeal clinical samples for PIA testing were performed using the adenovirus system. The results obtained indicated that the PIA signal is reduced in positive samples and is increased in negative samples. Treatment with cysteine appeared to reduce nonspecific agglutination in negative samples but did not maintain the signal in positive samples.