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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Strategic Deconfliction Performance: Results and Analysis from the NASA UTM Technical Capability Level 4 Demonstration

Unmanned Aircraft System (UAS) Traffic Management (UTM) refers to the service-based, cooperative approach to the management of small UAS in the National Airspace System that is safe, scalable, and fair. UTM provides the means to manage the airspace in a complementary manner that does not burden the current air traffic control workforce or infrastructure but allows the Air Navigation Service Provider to maintain its regulatory and operational authority of the airspace. A key feature of UTM is the ability to provide operators the means to strategically deconflict operations from others in the airspace through the digital exchange of information via supporting services. Through this approach, the four-dimensional operation volumes that encompass the intent of operators in a given area are discoverable and can be used for airspace awareness as well as planning conflict free operations that account for and avoid other operations. In certain cases, it is also possible to negotiate volume intersections for shared airspace use without the need to re-plan. In the NASA UTM concept, strategic deconfliction is the first layer of three in the overall conflict management model. The three layers of the conflict management model, which follow the International Civil Aviation Organization’s scheme [ICAO 2005] are: strategic conflict management, separate provision, and collision avoidance. In UTM, the strategic layer mostly occurs prior to departure, but is applicable to en route operations with sufficient planning horizon. The initial requirements for a strategic deconfliction capability within UTM are defined in a NASA publication [Rios 2018]. Within the concept and implementation of service-provided strategic deconfliction is the notion of priority. It is understood that there are instances in which an operation requires a priority designation within the UTM system and special handling accordingly to provide situation awareness and facilitate appropriate responses from other airspace users. Examples of situations requiring priority designation include: when an operator declares an emergency due to problems with the vehicle or its immediate surroundings; operations that are in support of certain organizations (e.g., public safety and first responders); or special missions that also require priority use of airspace (e.g., emergency medical deliveries). UAS Volume Reservations (UVRs) also relate to the topic of priority in the sense that the airspace that the volume encompasses has a different status or classification in which unassociated operations must vacate if inside, or avoid if outside, through strategic deconfliction with the volume. Operations that are specially permitted to access the UVR area are typically assigned priority status given the nature of their mission and their associated credentials. The ability to perform strategic deconfliction, handle certain operations with a priority distinction, and establish UVRs that are communicated throughout the UTM system, is predicated on an architecture that has been established through an evolutionary process in response to close collaboration with stakeholders from government and industry. Another important and influential aspect of these capabilities and architecture is the live, distributed flight tests that have been conducted across the Technical Capability Levels (TCLs) that culminated with a set of complex tests performed as part of TCL4 [Rios 2020]. The TCL4 flight test involved two FAA-designated UAS test sites building teams to collaborate with NASA’s UTM Project on the execution of several detailed, small UAS scenarios in urban environments.

conflict management↗
DOE OSTI2025

Performance Portability Evaluation of Fluid-Structure Interaction Simulations on Heterogeneous Platforms

The rapid proliferation of heterogeneous programming languages and multi-vendor hardware has underscored the critical need to evaluate the performance portability of scientific applications. In this work, we present the systematic porting and optimization of a massively parallel fluid-structure interaction code across multiple heterogeneous programming frameworks for deployment on leadership-class supercomputers from major vendors. Our analysis focuses on at-scale performance for simulations involving hundreds of millions of deformable cells, executed on a combination of CPUs and GPUs spanning thousands of nodes on exascale machines. We benchmark the performance of each implementation, highlighting the trade-offs inherent in adopting diverse programming models. Key insights regarding the portability of CUDA on multi-vendor platforms, the superior multi-core CPU performance from SYCL, and architectural considerations on performance optimization are distilled from our experience, offering guidance to other users of high performance computing based on our findings.

Martin, Aristotle [Duke University]↗
NASA NTRS2002

Autonomous planning and scheduling on the TechSat 21 mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting.

AI architectures applications intelligent agents m↗
NASA NTRSDate not supplied

Comparison of Multivariate Time Series Prediction Techniques for Emulating Noah-LSM Soil Moisture Outputs

Land surface models are crucial tools for many earth science applications including numerical weather prediction, water resource and crop monitoring, and climatological analysis. Given a set of atmospheric forcings, seasonal data, and static parameters, models like Noah-LSM solve for land surface quantities including skin temperature, sensible heat flux, and soil moisture. While these calculations are theoretically robust, they are often computationally expensive. Since artificial neural networks (ANNs) are universal function approximators, they can learn to emulate the output of a deterministic numerical model given a time series of input forcings, with the learned ANN having substantially shorter execution time. The ANN could efficiently parameterize other models, generate ensembles, and provide first-guess inputs for retrievals. As such, with the goal of developing a model that efficiently mimics the output of Noah-LSM given NLDAS2 forcings on a region covering much of the central US, we examine and compare several neural network architectures for the multi-horizon multivariate time series forecasting problem. Recent literature includes a diverse set of approaches including autoregressive architectures like LSTM and GRU, parametric and non-parametric statistical predictors (ForecastNet and MQRNN), self-attention (LSTM-attention-LSTM), and temporal convovlution (DeepTCN). We implement several of these models for the Noah-LSM prediction task, highlighting the features and challenges for each and providing practical insight on the training process.

Mitchell Dodson↗
NASA NTRS1999

Design Description of the X-33 Avionics Architecture

In this paper, we provide a design description of the X-33 avionics architecture. The X-33 is an autonomous Single Stage to Orbit (SSTO) launch vehicle currently being developed by Lockheed Martin for NASA as a technology demonstrator for the VentureStar Reusable Launch Vehicle (RLV). The X-33 avionics provides autonomous control of die vehicle throughout takeoff, ascent, descent, approach, landing, rollout, and vehicle safing. During flight the avionics provides communication to the range through uplinked commands and downlinked telemetry. During pre-launch and post-safing activities, the avionics provides interfaces to ground support consoles that perform vehicle flight preparations and maintenance. The X-33 Avionics is a hybrid of centralized and distributed processing elements connected by three dual redundant Mil-Std 1553 data buses. These data buses are controlled by a central processing suite located in the avionics bay and composed of triplex redundant Vehicle Mission Computers (VMCs). The VMCs integrate mission management, guidance, navigation, flight control, subsystem control and redundancy management functions. The vehicle sensors, effectors and subsystems are interfaced directly to the centralized VMCs as remote terminals or through dual redundant Data Interface Units (DIUs). The DIUs are located forward and aft of the avionics bay and provide signal conditioning, health monitoring, low level subsystem control and data interface functions. Each VMC is connected to all three redundant 1553 data buses for monitoring and provides a complete identical data set to the processing algorithms. This enables bus faults to be detected and reconfigured through a voted bus control configuration. Data is also shared between VMCs though a cross channel data link that is implemented in hardware and controlled by AlliedSignal's Fault Tolerant Executive (FTE). The FTE synchronizes processors within the VMC and synchronizes redundant VMCs to each other. The FTE provides an output-voting plane to detect, isolate and contain faults due to internal hardware or software faults and reconfigures the VMCs to accommodate these faults. Critical data in the 1553 messages are scheduled and synchronized to specific processing frames in order to minimize data latency. In order to achieve an open architecture, military and commercial off-the-shelf equipment is incorporated using common processors, standard VME backplanes and chassis, the VxWorks operating system, and MartixX for automatic code generation. The use of off-the-shelf tools and equipment helps reduce development time and enables software reuse. The open architecture allows for technology insertion, while the distributed modular elements allow for expansion to increased redundancy levels to meet the higher reliability goals of future RLVs.

Reichenfeld, Curtis J.↗
DOE OSTI2021

Comparing LLC-Memory Traffic between CPU and GPU Architectures

The cache hierarchy in modern CPUs and GPUs is becoming increasingly complex, which makes understanding the handshake between the memory access patterns and the cache hierarchy difficult. Moreover, the details of different cache policies are not publicly available. Therefore, the research community relies on observation to understand the relationship between memory access patterns and cache hierarchy. Our previous studies delved into the different microarchitectures of Intel CPUs. In this study, GPUs from NVIDIA and AMD are considered. Even though the execution models in CPUs and GPUs are distinct, this study attempts to correlate the behavior of the cache hierarchy of CPUs and GPUs. Using the knowledge gathered from studying Intel CPUs, the similarities and dissimilarities between CPUs and GPUs are identified. Through model evaluation, this study provides a proof of concept that traffic between last-level cache and memory can be predicted for sequential streaming and strided access patterns on GPUs.

Monil, M. A. H.↗
DOE OSTI2022

DRIPS: Dynamic Rebalancing of Pipelined Streaming Applications on CGRAs

Coarse-grained reconfigurable arrays (CGRAs) provide higher flexibility than application-specific integrated circuits (ASICs) and higher efficiency than fine-grained reconfigurable devices such as Field Programmable Gate Arrays (FPGAs). However, CGRAs are generally designed to support offloading of a single kernel. While their design, based on communicating functional units, appears to naturally suit data streaming applications composed of multiple cooperating kernels, current approaches only statically partition the resources across application kernels. However, emerging streaming applications at the edge (scientific instruments, sensor networks, network processing) perform much more than digital signal processing and often are data and input dependent. This leads to extremely variable kernel execution times, severely impacting the throughput of the entire pipeline if resources are only statically allocated. Therefore, in this paper, we propose DRIPS — a coarse-grained, dynamically, and partially reconfigurable array for data-dependent streaming applications. We present a unified compiler framework to facilitate the mapping of a given streaming application onto the DRIPS CGRA architecture. The experimental results show that DRIPS achieves an average throughput improvement of 1.46$\times$ across a set of representative applications over a statically partitioned solution. The additional area overhead to enable dynamic rebalancing consumes 16.34% of the entire area for a 5x5 CGRA prototype.

Tan, Cheng↗
NASA NTRS1979

Performance and economy of a fault-tolerant multiprocessor

The FTMP (Fault-Tolerant Multiprocessor) is one of two central aircraft fault-tolerant architectures now in the prototype phase under NASA sponsorship. The intended application of the computer includes such critical real-time tasks as 'fly-by-wire' active control and completely automatic Category III landings of commercial aircraft. The FTMP architecture is briefly described and it is shown that it is a viable solution to the multi-faceted problems of safety, speed, and cost. Three job dispatch strategies are described, and their results with respect to job-starting delay are presented. The first strategy is a simple First-Come-First-Serve (FCFS) job dispatch executive. The other two schedulers are an adaptive FCFS and an interrupt driven scheduler. Three failure modes are discussed, and the FTMP survival probability in the face of random hard failures is evaluated. It is noted that the hourly cost of operating two FTMPs in a transport aircraft can be as little as one-to-two percent of the total flight-hour cost of the aircraft.

Lala, J. H.↗
NASA NTRS1991

Architectures for intelligent machines

The theory of intelligent machines has been recently reformulated to incorporate new architectures that are using neural and Petri nets. The analytic functions of an intelligent machine are implemented by intelligent controls, using entropy as a measure. The resulting hierarchical control structure is based on the principle of increasing precision with decreasing intelligence. Each of the three levels of the intelligent control is using different architectures, in order to satisfy the requirements of the principle: the organization level is moduled after a Boltzmann machine for abstract reasoning, task planning and decision making; the coordination level is composed of a number of Petri net transducers supervised, for command exchange, by a dispatcher, which also serves as an interface to the organization level; the execution level, include the sensory, planning for navigation and control hardware which interacts one-to-one with the appropriate coordinators, while a VME bus provides a channel for database exchange among the several devices. This system is currently implemented on a robotic transporter, designed for space construction at the CIRSSE laboratories at the Rensselaer Polytechnic Institute. The progress of its development is reported.

Saridis, George N.↗
NASA NTRS2012

Integrating Existing Simulation Components into a Cohesive Simulation System

A tradition of leveraging the re-use of components to help manage costs has evolved in the development of complex system. This tradition continues on in the Joint Polar Satellite System (JPSS) Program with the cloning of the Suomi National Polar-orbiting Partnership (NPP) satellite for the JPSS-1 mission, including the instrument complement. One benefit of re-use on a mission is the availability of existing simulation assets from the systems that were previously built. An issue arises in the continual shift of technology over a long mission, or multi-mission, lifecycle. As the missions mature, the requirements for the observatory simulations evolve. The challenge in this environment becomes re-using the existing components in that ever-changing landscape. To meet this challenge, the system must: establish an operational architecture that minimizes impacts on the implementation of individual components, consolidate the satisfaction of new high-impact requirements into system-level infrastructure, and build in a long-term view of system adaptation that spans the full lifecycle of the simulation system. The Flight Vehicle Test Suite (FVTS) within the JPSS Program is defining and executing this approach to ensure a robust simulation capability for the JPSS multi-mission environment

McLaughlin, Brian J.↗
DOE OSTI2026

iDDS: intelligent distributed dispatch and scheduling for workflow orchestration

The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. We demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Economical graphics display system for flight simulation avionics

During the past academic year the focal point of this project has been to enhance the economical flight simulator system by incorporating it into the aero engineering educational environment. To accomplish this goal it was necessary to develop appropriate software modules that provide a foundation for student interaction with the system. In addition experiments had to be developed and tested to determine if they were appropriate for incorporation into the beginning flight simulation course, AERO-41B. For the most part these goals were accomplished. Experiments were developed and evaluated by graduate students. More work needs to be done in this area. The complexity and length of the experiments must be refined to match the programming experience of the target students. It was determined that few undergraduate students are ready to absorb the full extent and complexity of a real-time flight simulation. For this reason the experiments developed are designed to introduce basic computer architectures suitable for simulation, the programming environment and languages, the concept of math modules, evaluation of acquired data, and an introduction to the meaning of real-time. An overview is included of the system environment as it pertains to the students, an example of a flight simulation experiment performed by the students, and a summary of the executive programming modules created by the students to achieve a user-friendly multi-processor system suitable to an aero engineering educational program.

Source record↗
NASA NTRSDate not supplied

Towards Persistent Space Observations through Autonomous Multi-Agent Formations

Sensing platforms must advance in scale and sophistication in order to support increasingly ambitious missions across Earth and space science; intelligence, surveillance, and reconnaissance (ISR); and planetary exploration. Distributed, persistent observation platforms have the potential to play a pivotal role in next generation missions through improved area coverage, enhanced situational awareness, and faster identification of trends and changes. The Multi-Agent Clusters for Persistent Observations from Space (MACPOS) project at NASA Langley Research Center is developing key technologies for the autonomous, heterogeneous formations that will comprise such platforms. Research thrusts include dynamic formation negotiation for self-assembling clusters of agents, distributed motion planning, and coordinated trajectory execution. Adaptive leader-follower formation negotiation allows agents to cluster and break off as necessary to adapt to both nominal and new mission objectives. Coordinated motion planning and execution maintain the formation while ensuring safe separation distances among agents and obstacles in the environment. These capabilities align MACPOS with NASA’s initiative for space and surface in-situ assembly through fundamental technology development for autonomous multi-agent systems. This paper presents an overview and early progress for the MACPOS project. We describe the system architecture for both individual agents and the overall fleet. Design considerations are given for the planning, control, and metrology subsystems. Finally, we discuss planned project milestones and the expected course of development.

Matthew P Vaughan↗
DOE OSTI2026

Dual-loop Solvent-based CCS for Net Negative CO 2 Emissions with Lower Cost

This final technical report details the successful design, construction, and operational validation of an innovative dual-loop CO 2 capture technology designed to achieve deep decarbonization (>99%) from Natural Gas Combined Cycle (NGCC) power plants that results in the electricity with carbon intensity of approximate 42 kg CO 2 -eq/MWh, less than the electricity produced by solar PV. The integrated process couples a primary aqueous solvent (in this project, a water lean solvent – WLS) absorption loop for bulk CO 2 removal with a secondary potassium hydroxide (KOH) polishing loop featuring electrochemical regeneration. This architecture leverages the higher exergy efficiency of the primary loop while utilizing the fastest kinetic of the secondary loop to capture dilute residual CO 2 , achieving an overall capture rate of 99.9% and co-producing pure hydrogen after moisture being condensed and dehydrated. Technical feasibility was established through a comprehensive 2,000-hour experimental campaign on a bench-scale fully-integrated unit (using 4” absorber and 4” stripper) with the feeding flue gas flowrate in the range of 8-20 cfm, confirming the attainment of Technology Readiness Level (TRL) 4. The project executed extensive parametric testing followed by 1,000 hours of continuous steady-state testing, demonstrating exceptional process stability with the electrochemical regenerator exhibiting less than a 10% reduction in electrical conductivity over the duration of the campaign. Operational characterization gathering on the bench unit revealed distinct energy profiles for the hybrid system: the primary loop required approximately 280 kJ mol -1 for bulk removal, while the polishing loop required approximately 1,600 kJ mol -1 specifically when reducing dilute CO 2 concentrations from ~740 ppm down to <50 ppm. (Please note those energy values/numbers can only be viewed as relative relationship and should not be extrapolated as absolute values required for CO 2 capture). Furthermore, dynamic testing validated the system’s flexibility for utility applications, demonstrating a rapid process response time of <30 minutes to changes in flue gas flowrate. Emission monitoring confirmed that the dual-loop architecture effectively mitigates solvent losses, utilizing a water wash to remove entrained aerosols to <1 ppm. The Techno-Economic Analysis (TEA) indicates a cost of capture of $\$$59.3/tonne and a Levelized Cost of Electricity (LCOE) of 71.7 $\$$/MWh at the overall capture efficiency of 99.8% of total carbon in the flue gas stream, with sensitivity analysis identifying an economic optimum when the primary loop captures 97% of the total CO 2 . A Life Cycle Assessment (LCA) confirms the technology’s potential for net-negative emissions, determining a Global Warming Potential (GWP) of 52 kg CO 2 -eq/MWh—significantly lower than the baseline—which further decreases to 42 kg CO 2 -eq/MWh when crediting the displacement of conventional hydrogen production.

03 NATURAL GAS↗
DOE OSTI2025

Repositioning Quantum Cellular Automata for Dependable Quantum-Classical Systems

Quantum Cellular Automata (QCA) provides a structured model of distributed quantum computation with inherent locality and regularity properties that are suited to dependable execution. However, QCA remain largely absent from discussions on reproducibility, fault management, and orchestration in heterogeneous quantum-classical systems. We propose a dual-axis framework that situates QCA within both computation and physical realizability, revealing regions where robust, scalable, and hardware-constrained quantum dynamics may reside. By revisiting prior results through the lens of reproducibility and architecture resilience, we suggest that QCA offers a potential substrate for benchmarking and system-level co-design.

Stapleton, Nicholas [ORNL] (ORCID:0000000335305325↗
NASA NTRS2001

2000 Numerical Propulsion System Simulation Review

The technologies necessary to enable detailed numerical simulations of complete propulsion systems are being developed at the NASA Glenn Research Center in cooperation with industry, academia, and other government agencies. Large scale, detailed simulations will be of great value to the nation because they eliminate some of the costly testing required to develop and certify advanced propulsion systems. In addition, time and cost savings will be achieved by enabling design details to be evaluated early in the development process before a commitment is made to a specific design. This concept is called the Numerical Propulsion System Simulation (NPSS). NPSS consists of three main elements: (1) engineering models that enable multidisciplinary analysis of large subsystems and systems at various levels of detail, (2) a simulation environment that maximizes designer productivity, and (3) a cost-effective. high-performance computing platform. A fundamental requirement of the concept is that the simulations must be capable of overnight execution on easily accessible computing platforms. This will greatly facilitate the use of large-scale simulations in a design environment. This paper describes the current status of the NPSS with specific emphasis on the progress made over the past year on air breathing propulsion applications. Major accomplishments include the first formal release of the NPSS object-oriented architecture (NPSS Version 1) and the demonstration of a one order of magnitude reduction in computing cost-to-performance ratio using a cluster of personal computers. The paper also describes the future NPSS milestones, which include the simulation of space transportation propulsion systems in response to increased emphasis on safe, low cost access to space within NASA'S Aerospace Technology Enterprise. In addition, the paper contains a summary of the feedback received from industry partners on the fiscal year 1999 effort and the actions taken over the past year to respond to that feedback. NPSS was supported in fiscal year 2000 by the High Performance Computing and Communications Program.

Lytle, John↗
NASA NTRS2002

2001 Numerical Propulsion System Simulation Review

The technologies necessary to enable detailed numerical simulations of complete propulsion systems are being developed at the NASA Glenn Research Center in cooperation with industry, academia and other government agencies. Large scale, detailed simulations will be of great value to the nation because they eliminate some of the costly testing required to develop and certify advanced propulsion systems. In addition, time and cost savings will be achieved by enabling design details to be evaluated early in the development process before a commitment is made to a specific design. This concept is called the Numerical Propulsion System Simulation (NPSS). NPSS consists of three main elements: (1) engineering models that enable multidisciplinary analysis of large subsystems and systems at various levels of detail, (2) a simulation environment that maximizes designer productivity, and (3) a cost-effective, high-performance computing platform. A fundamental requirement of the concept is that the simulations must be capable of overnight execution on easily accessible computing platforms. This will greatly facilitate the use of large-scale simulations in a design environment. This paper describes the current status of the NPSS with specific emphasis on the progress made over the past year on air breathing propulsion applications. Major accomplishments include the first formal release of the NPSS object-oriented architecture (NPSS Version 1) and the demonstration of a one order of magnitude reduction in computing cost-to-performance ratio using a cluster of personal computers. The paper also describes the future NPSS milestones, which include the simulation of space transportation propulsion systems in response to increased emphasis on safe, low cost access to space within NASA's Aerospace Technology Enterprise. In addition, the paper contains a summary of the feedback received from industry partners on the fiscal year 2000 effort and the actions taken over the past year to respond to that feedback. NPSS was supported in fiscal year 2001 by the High Performance Computing and Communications Program.

Lytle, John↗