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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

The fast linear accelerator modeling engine for FRIB online model service

Commissioning of a large accelerator facility like FRIB needs support from an online beam dynamics model. Considering the new physics challenges of FRIB such as modeling of non-axisymmetric superconducting RF cavities and multi-charge state acceleration, there is no readily available online beam tuning code. The design code of FRIB super-conducting linac, IMPACT-Z, is not suitable for online tuning because of its code design and running speed. Therefore, the Fast Linear Accelerator Modeling Engine (FLAME), specifically designed to fulfill FRIB’s online modeling challenges, is proposed. The physics model of FLAME, especially its novel way of modeling non-axisymmetric superconducting RF cavities using a multipole expansion based thin-lens kick model, is discussed in detail, and the benchmark results against FRIB design code is presented, after which the software design strategy of FLAME and its execution speed is presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Modeling the Effects of Artificial Drainage on Agriculture-dominated Watersheds using a Fully Distributed Integrated Hydrology Model: Datasets, scripts, model files

This model-data archive supports the research paper that demonstrates the integration of agricultural drainage features—specifically, narrow engineered ditches and tile drains—into a fully distributed, basin-scale integrated surface-subsurface hydrology model (ISSHM), Amanzi-ATS. The model employs innovative computational meshes aligned with agricultural ditches and incorporates the physically based Hooghoudt's drainage equation to simulate tile drainage, offering a novel strategy that enhances the accuracy of hydrological simulations.The archived dataset includes input parameters, model configurations, and select simulation outputs for the Amanzi-ATS model that successfully captured the streamflow patterns in the Portage River Watershed as validated by USGS gauge readings. Jupyter notebook for the preparation of model inputs and post-processing of outputs are also included. The model's predictive performance achieved a normalized Kling-Gupta Efficiency (KGE) of 0.81, surpassing SWAT without the necessity for site-specific calibration.The Amanzi-ATS model presented in this modeL-data archive allows for numerical experiments to explore the shifts in the flow structure under different drainage scenarios. As a tool for advancing the understanding of distributed hydrological responses and nutrient cycling, this archived model provides valuable insights for researchers, modelers, and decision-makers involved in watershed management and environmental modeling.The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include CSV and HDF5 files, which can be read through Python scripts. The input files for the ATS model, open-source integrated hydrology, and transport model, are in XML format and can be edited in any commonly used text editors.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Space applications of artificial intelligence; Proceedings of the Annual Goddard Conference, Greenbelt, MD, May 16, 17, 1989

Theoretical and implementation aspects of AI systems for space applications are discussed in reviews and reports. Sections are devoted to planning and scheduling, fault isolation and diagnosis, data management, modeling and simulation, and development tools and methods. Particular attention is given to a situated reasoning architecture for space repair and replace tasks, parallel plan execution with self-processing networks, the electrical diagnostics expert system for Spacelab life-sciences experiments, diagnostic tolerance for missing sensor data, the integration of perception and reasoning in fast neural modules, a connectionist model for dynamic control, and applications of fuzzy sets to the development of rule-based expert systems.

Rash, James L.↗
NASA NTRS2006

Verification of Autonomous Systems for Space Applications

Autonomous software, especially if it is based on model, can play an important role in future space applications. For example, it can help streamline ground operations, or, assist in autonomous rendezvous and docking operations, or even, help recover from problems (e.g., planners can be used to explore the space of recovery actions for a power subsystem and implement a solution without (or with minimal) human intervention). In general, the exploration capabilities of model-based systems give them great flexibility. Unfortunately, it also makes them unpredictable to our human eyes, both in terms of their execution and their verification. The traditional verification techniques are inadequate for these systems since they are mostly based on testing, which implies a very limited exploration of their behavioral space. In our work, we explore how advanced V&V techniques, such as static analysis, model checking, and compositional verification, can be used to gain trust in model-based systems. We also describe how synthesis can be used in the context of system reconfiguration and in the context of verification.

Brat, G.↗
DOE OSTI2018

Optimizing Tensor Contractions in CCSD(T) for Efficient Execution on GPUs

Tensor contractions are higher dimensional analogs of matrix multiplications, used in many computational contexts such as high order models in quantum chemistry, deep learning, nite element methods etc. In contrast to the wide availability of high-performance libraries for matrix multiplication on GPUs, the same is not true for tensor contractions. In this paper, we address the optimization of a set of symmetrized tensor contractions that form the computational bottleneck in the CCSD(T) coupled-cluster method in computational chemistry suites like NWChem. Some of the challenges in optimizing tensor contractions that arise in practice from the variety of dimensionalities and shapes for tensors include effective mapping of the high-dimensional iteration space to threads, choice of data buffering in shared-memory and registers, and tile sizes for multi-level tiling. Furthermore, in the case of symmetrized tensor contractions in CCSD(T), it is also a challenge to fuse contractions to reduce data movement cost by exploiting reuse of intermediate tensors. In this paper, we developed an efficient GPU implementation of the tensor contractions in CCSD(T) using shared-memory buffering, register tiling, loop fusion and register transpose. Experimental results demonstrate significant improvement over the current state-of-the-art.

Kim, Jinsung↗
DOE OSTI2020

Decovalex-2019 (Executive Summary)

The DECOVALEX Project is an on-going international research collaboration, established in 1992, to advance the understanding and modeling of coupled Thermal (T), Hydrological (H), Mechanical (M) and Chemical (C) processes in geological in geological systems. DECOVALEX was initially motivated by the recognition that prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel. Later it was realized that these processes also play a critical role in other subsurface engineering activities, such as subsurface CO 2 storage, enhanced geothermal systems, and unconventional oil and gas production through hydraulic fracturing. Research teams from many countries (e.g., Canada, China, Czech Republic, Finland, France, Germany, Japan, Republic of Korea, Spain, Sweden, Switzerland, Taiwan, United Kingdom, and the United States) various institutions have participated in the DECOVALEX Project over the years, providing a wide range of perspectives and solutions to these complex problems. These institutions represent radioactive waste management organizations, national research institutes, regulatory agencies, universities, as well as industry and consulting groups. The overall aim of DECOVALEX-2019 was to increase the understanding of various thermo-hydro-mechanical-chemical processes of importance for radionuclide release and transport from a repository to the biosphere and how they can be described and modelled using mathematical models. The scientific and technical objectives are: to increase the basic understanding of T-H-M-C coupled processes in fractured rocks (crystalline, sedimentary, argillaceous) and buffer materials; to investigate the predictive capabilities of different codes to field experiments and to perform verification of codes; to exchange experimental data, and improve the understanding of the constitutive behavior of crystalline and argillaceous rock masses and buffer materials; and to perform THMC calculations in a performance/safety assessment context.

58 GEOSCIENCES↗
NASA NTRS1977

Extended performance solar electric propulsion thrust system study. Volume 1: Executive summary

Several thrust system design concepts were evaluated and compared using the specifications of the most advanced 30 cm engineering model thruster as the technology base. The extensions in thruster performance required for the Halley's comet mission were defined and alternative thrust system concepts were designed. Confirmation testing and analysis of thruster and power-processing components were performed, and the feasibility of satisfying extended performance requirements was verified. A baseline design was selected from the alternatives considered, and the design analysis and documentation were refined. A program development plan was formulated that outlines the work structure considered necessary for developing, qualifying, and fabricating the flight hardware for the baseline thrust system within the time frame of a project to rendezvous with Halley's comet. An assessment was made of the costs and risks associated with a baseline thrust system as provided to the mission project under this plan. Critical procurements and interfaces were identified and defined. Results are presented.

Poeschel, R. L.↗
NASA NTRS1986

Phase 3 study of selected tether applications in space. Volume 1: Executive summary

A tethered launch assist from the Shuttle for payloads with up to 10,000 kg mass for the mission model and the tethering of a 15,000 kg science platform for the space station were addressed. Also encompassed was the design and cost analysis for a variable g device that could be placed on the tether and allow ultralow g or other types of experiments to be conducted. Numerous tether applications were examined and their theoretical feasibility and technology requirements were assessed.

Source record↗
NASA NTRSDate not supplied

Shock-Wave/Boundary-Layer Interaction (SWBLI) Experiments in the Presence of Transition-to-Turbulence on a Flat Plate Model in the NASA LaRC 31-Inch Mach 10 Air Tunnel

Historically, there have been a limited number of studies involving Transitional Shock-Wave/Boundary-Layer Interactions (XSWBLI) in hypersonic flows, the majority of which have been performed at Mach Numbers of 7.5 and below. Additionally, there have been even fewer such experiments which incorporate non-intrusive and optical diagnostics. NASA Langley Research Center (NASA LaRC), in collaboration with The University of Texas at San Antonio (UTSA) and The University of Tennessee Space Institute (UTSI), is currently planning and preparing for a series of experimental aerodynamic tests using the NASA LaRC 31-inch Mach 10 Air Tunnel. The experiments will primarily focus on studying the dynamics of Shock-Wave/Boundary-Layer Interactions (SWBLI) in the presence of transition (XSWBLI) and turbulence (SWTBLI) on a large, flat plate model at a freestream Mach number of 10. Besides continued interest in understanding the flow at high Mach numbers, executing such tests at Mach 10 avoids some known concerns and will aid in solving new problems. XSWBLI are a highly unsteady phenomena and the generation of XSWBLI at lower Mach numbers can prove to be a significant challenge. This concern will be somewhat mitigated in the Mach 10 flow, as boundary layers become increasingly stabilized, but some difficulty in achieving transition is still expected. Modelling SWBLI in the presence of transitional and turbulent boundary layers has also proven to be difficult, so a high-Mach number experiment which applies non-intrusive and optical diagnostics will aid in solving a unique problem as well as advancing the understanding and characterization of an aerodynamic surface at Mach 10.The NASA LaRC 31-inch Wind Tunnel incorporates a test section having a cross-section of 31-inches × 31-inches and provides optical access to the test section via three (3) ultra-violet (UV) transmitting windows on the top, side, and bottom. The Mach 10 operating conditions will consist of pressures (P0) ranging from 2.4 to 10MPa (348 to 1,450 psi) at a temperature (T0) of 1,000 K (1,800 °R). The NASA LaRC 31-inch wind tunnel will deliver 1-minute blow-down runs, of which 30-45 seconds will be consumed to reach the aforementioned test conditions. The pump down time between runs will be approximately 45-60 minutes, which will provide for 6-10 test runs per day over the course of a weeklong entry. The proposed flat plate model will be constructed of stainless steel with geometry consisting of a10-inch × 30-inch upper surface and a 2-inch thickness. The model will be designed to support multiple leading-edge inserts in order to examine blunt, round, and sharp leading edges at Mach 10. Adjustments will be made to manage boundary layer thickness as well as the strength and size of the shock interaction region for each test. Measurements and diagnostics will be performed though high-speed Schlieren, on-body high-speed pressure transducers, IR thermography, and oil flow visualization. Hypersonic vehicle applications have and will continue to emerge at the forefront of aerospace. The NASA-UTSA-UTSI team realizes the value of characterizing SWBLI, XSWBLI, and SWTBLI behaviors well beyond single-digit hypersonic Mach numbers. As hypersonic vehicle speed capabilities continue to increase, measurement and diagnostic methods for Mach numbers of 10 and beyond will provide the foundation for a firm understanding of flow field behavior which will directly influence the advancement of technologies towards the design and manufacturing of high-speed aerodynamic surfaces, controls, thermal protection systems, acoustic treatments, and structural components. The use of non-intrusive and optical diagnostic methods in such experimentation is pivotal in developing the visualization and empirical data necessary to advance the aforementioned technology areas.

Shockwave/Boundary-Layer Interactions↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

On measuring stimulated photon–photon scattering using multiple ultraintense lasers

Stimulated photon–photon scattering is a predicted consequence of quantum electrodynamics that has yet to be measured directly. Measuring the cross section for stimulated photon–photon scattering is the aim of a flagship experiment for NSF OPAL, a proposed laser user facility with two, 25-PW beamlines. We present optimized experimental designs for achieving this challenging and canonical measurement. A family of experimental geometries is identified that satisfies the momentum- and energy-matching conditions for two selected laser frequency options. Numerical models predict a maximum signal exceeding 1000 scattered photons per shot at the experimental conditions envisaged at NSF OPAL. Experimental requirements on collision geometry, polarization, cotiming and copointing, background suppression, and diagnostic technologies are investigated numerically. These results confirm that a beam cotiming shorter than the pulse duration and control of the copointing on a scale smaller than the shortest laser wavelength are needed to robustly scatter photons on a per-shot basis. Finally, we assess the bounds that a successful execution of this experiment may place on the mass scale of Born–Infeld nonlinear electrodynamics beyond the standard model of physics.

Beyond the Standard Model↗
DOE OSTI2023

Simulation toolkit for digital material characterization of large image-based microstructures

In this paper, an efficient image-based simulation toolkit for material characterization is presented, which is scalable to work from personal computers to workstations. The effective thermal conductivity, elasticity, and permeability are evaluated employing a computational homogenization framework based on the Finite Element Method (FEM). Two complementary open-source packages are presented: one developed in Python, which can convert digital images into voxel meshes (pyTomoviewer); the other developed in Julia, that can run numerical simulations to compute effective material properties (chpack). Also, a CUDA C version of chpack is provided (chfem_gpu). They were designed to deal with large multi-phase models, so strategies were devised to minimize their memory footprint, while avoiding a high toll on execution time. The voxel-based approach significantly simplifies the FEM meshes and allows efficient matrix-free implementations. In that sense, to handle large linear systems of equations, the element-by-element (EBE) technique is adopted, in conjunction with a low-memory implementation of the Preconditioned Conjugate Gradient (PCG) method. Finally, the code was thoroughly tested on an artificial geometry made of a square array of cylinders, for which analytical solutions exist, as well as on a real micro-tomographic reconstruction of FiberForm TM , a carbon preform commonly used in thermal protection systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Scalable Programming Workflows for Validation of Quantum Computers

Hybrid quantum-classical workflows have become standard methods for executing variational algorithms and other quantum simulation techniques, which are key applications for noisy intermediate scale quantum (NISQ) computers. Validating these simulations is an important task which helps gauge the progress of quantum computer development, and classical simulation can serve as a tool to this end. Both exact and more scalable approximate methods with quantifiable error bounds can be used in validation tasks where the applicable metrics include the distance from a calculable ground truth, the quality of an error model fit to data, etc. Here we present a library extension that includes methods for validation of quantum simulations based on scalable hybrid workflows executable on high performance computers. We provide examples that use approximate methods based on tensor networks and stabilizer simulators to bound the error of quantum simulations on NISQ hardware.

Nguyen, Thien↗
NASA NTRS2002

Maximally Expressive Task Modeling

Planning and scheduling systems organize "tasks" into a timeline or schedule. The tasks are defined within the scheduling system in logical containers called models. The dictionary might define a model of this type as "a system of things and relations satisfying a set of rules that, when applied to the things and relations, produce certainty about the tasks that are being modeled." One challenging domain for a planning and scheduling system is the operation of on-board experiment activities for the Space Station. The equipment used in these experiments is some of the most complex hardware ever developed by mankind, the information sought by these experiments is at the cutting edge of scientific endeavor, and the procedures for executing the experiments are intricate and exacting. Scheduling is made more difficult by a scarcity of space station resources. The models to be fed into the scheduler must describe both the complexity of the experiments and procedures (to ensure a valid schedule) and the flexibilities of the procedures and the equipment (to effectively utilize available resources). Clearly, scheduling space station experiment operations calls for a "maximally expressive" modeling schema. Modeling even the simplest of activities cannot be automated; no sensor can be attached to a piece of equipment that can discern how to use that piece of equipment; no camera can quantify how to operate a piece of equipment. Modeling is a human enterprise-both an art and a science. The modeling schema should allow the models to flow from the keyboard of the user as easily as works of literature flowed from the pen of Shakespeare. The Ground Systems Department at the Marshall Space Flight Center has embarked on an effort to develop a new scheduling engine that is highlighted by a maximally expressive modeling schema. This schema, presented in this paper, is a synergy of technological advances and domain-specific innovations.

Japp, John↗
DOE OSTI2022

DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware

Spiking Neural Networks (SNNs) are an emerging computation model that uses event-driven activation and bio-inspired learning algorithms. SNN-based machine learning programs are typically executed on tile-based neuromorphic hardware platforms, where each tile consists of a computation unit called a crossbar, which maps neurons and synapses of the program. However, synthesizing such programs on an off-the-shelf neuromorphic hardware is challenging. This is because of the inherent resource and latency limitations of the hardware, which impact both model performance, e.g., accuracy, and hardware performance, e.g., throughput. We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware. The proposed framework works in four steps. First, it analyzes a machine learning program and generates SNN workload using representative data. Second, it partitions the SNN workload and generates clusters that fit on crossbars of the target neuromorphic hardware. Third, it exploits the rich semantics of the Synchronous Dataflow Graph (SDFG) to represent a clustered SNN program, allowing for performance analysis in terms of key hardware constraints such as number of crossbars, dimension of each crossbar, buffer space on tiles, and tile communication bandwidth. Finally, it uses a novel scheduling algorithm to execute clusters on crossbars of the hardware, guaranteeing hardware performance. We evaluate DFSynthesizer with 10 commonly used machine learning programs. Our results demonstrate that DFSynthesizer provides a much tighter performance guarantee compared to current mapping approaches.

Computer Science↗
DOE OSTI2024

Deep Learning for Full Waveform Inversion of Elastic Active-Source Seismic Data to Estimate P-Wave Velocity Models

Seismic imaging methods are critical for Global Security and Energy & Homeland Security missions and activities that rely on subsurface characterization, but traditional methods remain computationally expensive and require significant labor hours and expertise to execute. Within the past few years, machine learning (ML), namely deep learning (DL), has been used to develop data-driven end-to-end full waveform inversion (FWI) methods to estimate 2D P-wave velocity (Vp) models in a fraction of the time as conventional FWI. These methods, however, are trained on simplistic acoustic wave seismic data and Vp models that are not realistic nor representative of real-world observations, leaving a large gap between the state-of-the-art and deployable, feasible, and practical DL FWI methods. Here, we generate a synthetic active-source, 3D, elastic wave seismic data set and a variety of Vp models with realistic geologic structure for training DL FWI methods. We evaluate six different methods that have performed well for acoustic DL FWI or medical imaging tasks using our more realistic dataset. We find that these six trained models do not match the performance of published acoustic end-to-end DL FWI methods, indicating more training data may be needed, physics may need to be incorporated to achieve good accuracy at the sacrifice of the end-to-end advantage, and/or novel methods need to be developed to enable end-to-end DL FWI methods to perform well for real-world seismic data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS2007

Build-up Approach to Updating the Mock Quiet Spike(TM)Beam Model

A crucial part of aircraft design is ensuring that the required margin for flutter is satisfied. A trustworthy flutter analysis, which begins by possessing an accurate dynamics model, is necessary for this task. Traditionally, a model was updated manually by fine tuning specific stiffness parameters until the analytical results matched test data. This is a time consuming iterative process. The NASA Dryden Flight Research Center has developed a mode matching code to execute this process in a more efficient manner. Recently, this code was implemented in the F-15B/Quiet Spike (Gulfstream Aerospace Corporation, Savannah, Georgia) model update. A build-up approach requiring several ground vibration test configurations and a series of model updates was implemented to determine the connection stiffness between aircraft and test article. The mode matching code successfully updated various models for the F-15B/Quiet Spike project to within 1 percent error in frequency and the modal assurance criteria values ranged from 88.51-99.42 percent.

Herrera, Claudia Y.↗
DOE OSTI2023

Machine Learning-Based Model Predictive Control of Two-Time-Scale Systems

In this study, we present a general form of nonlinear two-time-scale systems, where singular perturbation analysis is used to separate the dynamics of the slow and fast subsystems. Machine learning techniques are utilized to approximate the dynamics of both subsystems. Specifically, a recurrent neural network (RNN) and a feedforward neural network (FNN) are used to predict the slow and fast state vectors, respectively. Moreover, we investigate the generalization error bounds for these machine learning models approximating the dynamics of two-time-scale systems. Next, under the assumption that the fast states are asymptotically stable, our focus shifts toward designing a Lyapunov-based model predictive control (LMPC) scheme that exclusively employs the RNN to predict the dynamics of the slow states. Additionally, we derive sufficient conditions to guarantee the closed-loop stability of the system under the sample-and-hold implementation of the controller. A nonlinear chemical process example is used to demonstrate the theory. In particular, two RNN models are constructed: one to model the full two-time-scale system and the other to predict solely the slow state vector. Both models are integrated within the LMPC scheme, and we compare their closed-loop performance while assessing the computational time required to execute the LMPC optimization problem.

97 MATHEMATICS AND COMPUTING↗