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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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A method for authenticating an additively manufactured (AM) object is disclosed herein. A computing device obtains a measurement that is indicative of a stochastic distribution of dopant incorporated into the AM object. The computing device generates authentication data for the AM object based upon the measurement, and authenticates the AM object based upon the authentication data.
Abstract Calibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems.
The goal of an intrusion tolerant network is to continue to provide predictable and reliable communication in the presence of a limited num ber of compromised network components. The behavior of a compromised network component ranges from a node that no longer responds to a nod e that is under the control of a malicious entity that is actively tr ying to cause other nodes to fail. Most current data communication ne tworks do not include support for tolerating unconstrained misbehavio r of components in the network. However, the fault tolerance communit y has developed protocols that provide both predictable and reliable communication in the presence of the worst possible behavior of a limited number of nodes in the system. One may view a malicious entity in a communication network as a node that has failed and is behaving in an arbitrary manner. NASA/Langley Research Center has developed one such fault-tolerant computing platform called SPIDER (Scalable Proces sor-Independent Design for Electromagnetic Resilience). The protocols and interconnection mechanisms of SPIDER may be adapted to large-sca le, distributed communication networks such as would be required for future Air Traffic Management systems. The predictability and reliabi lity guarantees provided by the SPIDER protocols have been formally v erified. This analysis can be readily adapted to similar network stru ctures.
Large-scale simulations of blood flow that resolve the 3D deformation of each comprising cell are increasingly popular owing to algorithmic developments in conjunction with advances in compute capability. Among different approaches for modeling cell-resolved hemodynamics, fluid structure interaction (FSI) algorithms based on the immersed boundary method are frequently employed for coupling separate solvers for the background fluid and the cells within one framework. GPUs can accelerate these simulations; however, both current pre-exascale and future exascale CPU-GPU heterogeneous systems face communication challenges critical to performance and scalability. In this paper, we describe, to our knowledge, the largest distributed GPU-accelerated FSI simulations of high hematocrit cell-resolved flows with over 17 million red blood cells. We compare scaling on a fat node system with six GPUs per node and on a system with a single GPU per node. Through comparison between the CPU- and GPU-based implementations, we identify the costs of data movement in multiscale multi-grid FSI simulations on heterogeneous systems and show it to be the greatest performance bottleneck on the GPU.
The Subsonic Single Aft Engine (SUSAN) Electrofan is a new single-aisle regional jet class transport aircraft being studied by NASA, which benefits from reduced fuel consumption through a reduction in the number of hydrocarbon fuel-burning engines from two to one. This is achieved through a hybrid-electric system architecture, where a series of generators are used to extract power from a fuel-consuming tail cone thruster to drive electric propulsors mounted on the wings. To support the development of these propulsion systems, which involve high levels of propulsion-airframe integration, this paper presents high-fidelity analyses through computational fluid dynamics (CFD) simulations provided by the Launch Ascent and Vehicle Aerodynamics (LAVA) framework, which also includes capabilities for modeling the effect of active propulsors. A trade space exploration is performed to investigate the advantages of several wing mounted distributed electric propulsion system configurations, including over-wing, under-wing, and trailing-edge arrangements. For the aft fuselage propulsor, focus is on the sizing of the inlet, core, and bypass ducts. High-fidelity aerodynamic shape optimization is also used to help minimize the inlet distortion intensity experienced by the aft propulsor fan through a reshaping of the fuselage.
Emerging system architectures are rapidly transforming in order to meet shifting requirements. Motivated by expanding data volumes, energy efficiency concerns, and the omnipresent need to improve performance, architectures are increasingly adopting a data-centric approach. At the core of this concept is the goal of minimizing data motion and instead processing data in-situ to the greatest degree possible. Therefore, data-centric designs, in contrast to conventional CPU-centric models, typically distribute compute capabilities throughout the architecture. As part of this paradigm shift, a novel class of devices known as data processing units (DPUs), alongside CPUs and GPUs, are quickly forming a third pillar of data-centric systems. These devices, which include smart network adapters and switches, seek to offload computation on data at the network edge as well as in-flight within the network fabric. The Open Smart Network API (OpenSNAPI) project seeks to develop a unified API for DPU devices. In our previous talks, we introduced the OpenSNAPI project and detailed our investigations regarding the viability of offloading compute intensive kernels to BlueField DPUs. In contrast, in this talk we detail our efforts to offload application-level file I/O to the DPU. We also discuss plans and early efforts to explore in-network compute capabilities. Finally, we describe our observations with respect to the evolving design of OpenSNAPI.
This paper proposes a reinforcement learning-based approach for distribution network reconfiguration(DNR) to enhance the resilience of the electric power supply. Resilience enhancements usually require solving large-scale stochastic optimization problems that are computationally expensive and sometimes infeasible. The exceptional performance of reinforcement learning techniques has encouraged their adoption in various power system control studies, specifically resilience-based real-time applications. In this paper, a single agent framework is developed using an Actor-Critic algorithm (ACA) to determine statuses of tie-switches in a distribution feeder impacted by an extreme weather event. The proposed approach provides a fast-acting control algorithm that reconfigures the feeder topology to reduce or even avoid load shedding. The problem is formulated as a discrete Markov decision process in such a way that a system state captures the system topology and its operational characteristics. An action is made to open or close a specific set of tie-switches after which a reward is calculated to evaluate the practicality and advantage of that action. The iterative Markov process is used to train the proposed ACA under diverse failure scenarios and is demonstrated on the 33-node distribution feeder system. Results show the capability of the proposed ACA to determine proper switching action of tie-switches with accuracy exceeding 93%.
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The application of robotics to coal mining machines is one approach pursued to increase productivity while providing enhanced safety for the coal miner. Toward that end, a network composed of microcontrollers, computers, expert systems, real time operating systems, and a variety of program languages are being integrated that will act as the backbone for intelligent machine operation. Actual mining machines, including a few customized ones, have been given telerobotic semiautonomous capabilities by applying the described network. Control devices, intelligent sensors and computers onboard these machines are showing promise of achieving improved mining productivity and safety benefits. Current research using these machines involves navigation, multiple machine interaction, machine diagnostics, mineral detection, and graphical machine representation. Guidance sensors and systems employed include: sonar, laser rangers, gyroscopes, magnetometers, clinometers, and accelerometers. Information on the network of hardware/software and its implementation on mining machines are presented. Anticipated coal production operations using the network are discussed. A parallelism is also drawn between the direction of present day underground coal mining research to how the lunar soil (regolith) may be mined. A conceptual lunar mining operation that employs a distributed communication and control network is detailed.
Efforts are under way to develop data-acquisition, data-processing, and data-communication systems for monitoring disasters over large geographic areas by use of uninhabited aerial systems (UAS) robotic aircraft that are typically piloted by remote control. As integral parts of advanced, comprehensive disaster- management programs, these systems would provide (1) real-time data that would be used to coordinate responses to current disasters and (2) recorded data that would be used to model disasters for the purpose of mitigating the effects of future disasters and planning responses to them. The basic idea is to equip UAS with sensors (e.g., conventional video cameras and/or multispectral imaging instruments) and to fly them over disaster areas, where they could transmit data by radio to command centers. Transmission could occur along direct line-of-sight paths and/or along over-the-horizon paths by relay via spacecraft in orbit around the Earth. The initial focus is on demonstrating systems for monitoring wildfires; other disasters to which these developments are expected to be applicable include floods, hurricanes, tornadoes, earthquakes, volcanic eruptions, leaks of toxic chemicals, and military attacks. The figure depicts a typical system for monitoring a wildfire. In this case, instruments aboard a UAS would generate calibrated thermal-infrared digital image data of terrain affected by a wildfire. The data would be sent by radio via satellite to a data-archive server and image-processing computers. In the image-processing computers, the data would be rapidly geo-rectified for processing by one or more of a large variety of geographic-information- system (GIS) and/or image-analysis software packages. After processing by this software, the data would be both stored in the archive and distributed through standard Internet connections to a disaster-mitigation center, an investigator, and/or command center at the scene of the fire. Ground assets (in this case, firefighters and/or firefighting equipment) would also be monitored in real time by use of Global Positioning System (GPS) units and radio communication links between the assets and the UAS. In this scenario, the UAS would serve as a data-relay station in the sky, sending packets of information concerning the locations of assets to the image-processing computer, wherein this information would be incorporated into the geo-rectified images and maps. Hence, the images and maps would enable command-center personnel to monitor locations of assets in real time and in relation to locations affected by the disaster. Optionally, in case of a disaster that disrupted communications, the UAS could be used as an airborne communication relay station to partly restore communications to the affected area. A prototype of a system of this type was demonstrated in a project denoted the First Response Experiment (Project FiRE). In this project, a controlled outdoor fire was observed by use of a thermal multispectral scanning imager on a UAS that delivered image data to a ground station via a satellite uplink/ downlink telemetry system. At the ground station, the image data were geo-rectified in nearly real time for distribution via the Internet to firefighting managers. Project FiRE was deemed a success in demonstrating several advances essential to the eventual success of the continuing development effort.
Presentation describes internal dosimetry as an intersection of disciplines involving Physiology, Anatomy, Physics, Mathematics and Computer Science, which some people call "The art of Internal Dosimetry". The basic components for internal dose calculations are: Biokinetic (metabolic) models describing the intake, distribution, retention and excretion of radionuclides in the body, Dosimetric models describing the interaction of radiation within the several body organs and tissues, and System of dose limitation.
The computation of two-electron repulsion integrals (ERIs) is often the most expensive step of integral-direct self-consistent field methods. Formally it scales as O(N 4 ), where N is the number of Gaussian basis functions used to represent the molecular wave function. In practice, this scaling can be reduced to O(N 2 ) or less by neglecting small integrals with screening methods. The contributions of the ERIs to the Fock matrix are of Coulomb (J) and exchange (K) type and require separate algorithms to compute matrix elements efficiently. We previously implemented highly efficient GPU-accelerated J-matrix and K-matrix algorithms in the electronic structure code TeraChem. Although these implementations supported the use of multiple GPUs on a node, they did not support the use of multiple nodes. This presents a key bottleneck to cutting-edge ab initio simulations of large systems, e.g., excited state dynamics of photoactive proteins. We present our implementation of multinode multi-GPU J- and K-matrix algorithms in TeraChem using the Regent programming language. Regent directly supports distributed computation in a task-based model and can generate code for a variety of architectures, including NVIDIA GPUs. We demonstrate multinode scaling up to 45 GPUs (3 nodes) and benchmark against hand-coded TeraChem integral code. Finally, we also outline our metaprogrammed Regent implementation, which enables flexible code generation for integrals of different angular momenta.
This paper introduces a generic distributed prognostic health management (PHM) architecture with specific application to the electrical power systems domain. Current state-of-the-art PHM systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to loss of functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become unsuitable for successful deployment, and efficient distributed architectures are required. A distributed architecture though, is not effective unless there is an algorithmic framework to take advantage of its unique abilities. The health management paradigm envisaged here incorporates a heterogeneous set of system components monitored by a varied suite of sensors and a particle filtering (PF) framework that has the power and the flexibility to adapt to the different diagnostic and prognostic needs. Both the diagnostic and prognostic tasks are formulated as a particle filtering problem in order to explicitly represent and manage uncertainties; however, typically the complexity of the prognostic routine is higher than the computational power of one computational element ( CE). Individual CEs run diagnostic routines until the system variable being monitored crosses beyond a nominal threshold, upon which it coordinates with other networked CEs to run the prognostic routine in a distributed fashion. Implementation results from a network of distributed embedded devices monitoring a prototypical aircraft electrical power system are presented, where the CEs are Sun Microsystems Small Programmable Object Technology (SPOT) devices.
The Ares Real-Time Environment for Modeling, Integration, and Simulation (ARTEMIS) has been developed for use by the Ares I launch vehicle System Integration Laboratory (SIL) at the Marshall Space Flight Center (MSFC). The primary purpose of the Ares SIL is to test the vehicle avionics hardware and software in a hardware-in-the-loop (HWIL) environment to certify that the integrated system is prepared for flight. ARTEMIS has been designed to be the real-time software backbone to stimulate all required Ares components through high-fidelity simulation. ARTEMIS has been designed to take full advantage of the advances in underlying computational power now available to support HWIL testing. A modular real-time design relying on a fully distributed computing architecture has been achieved. Two fundamental requirements drove ARTEMIS to pursue the use of high-fidelity simulation models in a real-time environment. First, ARTEMIS must be used to test a man-rated integrated avionics hardware and software system, thus requiring a wide variety of nominal and off-nominal simulation capabilities to certify system robustness. The second driving requirement - derived from a nationwide review of current state-of-the-art HWIL facilities - was that preserving digital model fidelity significantly reduced overall vehicle lifecycle cost by reducing testing time for certification runs and increasing flight tempo through an expanded operational envelope. These two driving requirements necessitated the use of high-fidelity models throughout the ARTEMIS simulation. The nature of the Ares mission profile imposed a variety of additional requirements on the ARTEMIS simulation. The Ares I vehicle is composed of multiple elements, including the First Stage Solid Rocket Booster (SRB), the Upper Stage powered by the J- 2X engine, the Orion Crew Exploration Vehicle (CEV) which houses the crew, the Launch Abort System (LAS), and various secondary elements that separate from the vehicle. At launch, the integrated vehicle stack is composed of these stages, and throughout the mission, various elements separate from the integrated stack and tumble back towards the earth. ARTEMIS must be capable of simulating the integrated stack through the flight as well as propagating each individual element after separation. In addition, abort sequences can lead to other unique configurations of the integrated stack as the timing and sequence of the stage separations are altered.
The Predictive Thermal Control (PTC) technology development project is a multiyear effort initiated in Fiscal Year (FY) 2017, to mature the Technology Readiness Level (TRL) of critical technologies required to enable ultra-thermally-stable telescopes for exoplanet science. During 2017/18 PTC has successfully progressed its three defined objectives: 1. Validate thermal optical performance models. 2. Derive thermal system stability specifications. 3. Demonstrate Predictive Thermal Control. by accomplishing or advancing three of its five quantifiable milestones: Milestone #1 (Complete): Created a high-fidelity STOP (Structural-Thermal-Optical-Performance) model of the1.5 meter ULE® AMTD (Advanced Mirror Technology Development)-2 mirror, including 3D CTE (Coefficient of Thermal Expansion) distribution and reflective coating, that predicts its optical performance response to steady-state and dynamic thermal gradients. Structural model was created using 3D X-Ray Computed Tomography; Milestone #2 (Complete): Derived specifications for thermal control system as a function of wavefront stability for a Vector Vortex Coronagraph; Milestone #3 (In-Process): Designed and started fabricating a predictive Thermal Control System for the AMTD-2 1.5 meter ULE® mirror that senses temperature changes and actively controls the mirror's thermal environment; Milestone #4 (In-Process): Performed preliminary STOP model validation tests of the 1.5-m ULE® mirror in a relevant thermal vacuum environment at the MSFC X-ray and Cryogenic Facility (XRCF) test facility; Milestone #5: Use validated model to perform trade studies to determine how thermo-optical performance can be optimized as a function of mirror design, material selection, mass, etc.
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The Imaging Neutral Particle Analyzer (INPA) at DIII-D is a diagnostic system used to accurately resolve the energy and spatial distributions of fast ions in fusion plasmas. A novel artificial intelligence (AI) technique named INPA-net is based on Reservoir Computing Networks and developed here to predict active and passive signals produced by charge-exchange reactions from injected and edge-cold neutrals, respectively, in magnetically confined fusion plasmas. This model is trained using a set of 21 time domain signals between 0 s to 3.35 s that includes injected beam and thermal plasma information, and 6444 real 2D experimental images of the INPA in 12 plasma discharges at DIII-D. The trained neural network is able to forecast experimental images in real-time. The model achieves an R-squared value of 0.91, which is higher than the 0.83 value achieved by a simple linear regression model. This improvement highlights the model's enhanced predictive accuracy for measured images from the validation set. This AI approach is valuable due to its rapid response times and potential for integration into real-time plasma control systems. A version of this model capable of generating syntehic images would be useful for the real-time monitoring of fast-ion transport. A comprehensive sensitivity study reveals that INPA-net maintains high performance even with variations in the input parameters, indicating the model's robustness and reliability. While developed for the INPA, the underlying architecture is adaptable and may be applied to various 2D imaging diagnostics in fusion research.
In this work we report on our experiences running OpenMP programs on a commodity cluster of PCs running a software distributed shared memory (DSM) system. We describe our test environment and report on the performance of a subset of the NAS Parallel Benchmarks that have been automaticaly parallelized for OpenMP. We compare the performance of the OpenMP implementations with that of their message passing counterparts and discuss performance differences.