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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

LDEXPT, an intelligent database system for the Composite Load Spectra project

The Composite Load Spectra project develops probabilistic models to simulate the probabilistic loads for selected components of a generic space propulsion system. Tremendous information such as engine load variables and their distributions is needed by the simulation program. An intelligent data base system was constructed and integrated with the probabilistic load simulation program to manage and maintain the knowledge base of the Composite Load Spectra project. The intelligent data base system takes care of the data retrieval and storage functions and has expert knowledge on engine load models and associated engine variables. The integration of the intelligent data base into the load simulation program achieves a smooth coupling between the numeric processing (load simulation calculation) and the symbolic processing (intelligent load information management).

Ho, H.↗
DOE OSTI2021

Solving inverse problems in stochastic models using deep neural networks and adversarial training

Inverse problems associated with stochastic models constitute a significant portion of scientific and engineering applications. In such cases the unknown quantities are distributions. The applicability of traditional methods is limited because of their demanding assumptions or prohibitive computational consumption; for example, maximum likelihood methods require closed-form density functions, and Markov Chain Monte Carlo needs a large number of simulations. We propose a new method that estimates the unknown distribution by matching the statistical properties between observed and simulated random processes. We leverage the expressive power of neural networks to approximate the unknown distribution and use a discriminative neural network for computing the statistical discrepancies between the observed and simulated random processes. Here we demonstrated numerically that the proposed methods can estimate both the model parameters and learn complicated unknown distributions.

42 ENGINEERING↗
NASA NTRS1966

Computer simulation program is adaptable to industrial processes

The Reaction kinetics ablation program /REKAP/, developed to simulate ablation of various materials, provides mathematical formulations for computer programs which can simulate certain industrial processes. The programs are based on the use of nonsymmetrical difference equations that are employed to solve complex partial differential equation systems.

Schultz, F. E.↗
NASA NTRS1974

A multiprocessor computer simulation model employing a feedback scheduler/allocator for memory space and bandwidth matching and TMR processing

A computer simulation model for a multiprocessor computer is developed that is useful for studying the problem of matching multiprocessor's memory space, memory bandwidth and numbers and speeds of processors with aggregate job set characteristics. The model assumes an input work load of a set of recurrent jobs. The model includes a feedback scheduler/allocator which attempts to improve system performance through higher memory bandwidth utilization by matching individual job requirements for space and bandwidth with space availability and estimates of bandwidth availability at the times of memory allocation. The simulation model includes provisions for specifying precedence relations among the jobs in a job set, and provisions for specifying precedence execution of TMR (Triple Modular Redundant and SIMPLEX (non redundant) jobs.

Bradley, D. B.↗
DOE OSTI2024

Expanding the representation of aerosol, cloud, and precipitation processes with graph network-based simulators

We explored a novel framework for simulating the small-scale processes that drive the evolution of aerosol, cloud, and precipitation particles, which are a critical gap in the predictive understanding of weather and climate. Particle-based methods have emerged as an effective tool for modeling aerosol-cloud-precipitation interactions, but existing particle-based models are computationally too expensive to simulate the large domains relevant for the atmosphere or to represent the full suite of relevant processes. The lack of a comprehensive and efficient reference model is a critical bottleneck in our understanding of cloud and precipitation processes and our ability to parameterize these processes for regional- and global-scale simulations. To address this need, we explored an approach to accelerate and expand particle-based models using a new machine learning approach, graph network-based simulators (GNS). Rather than modeling the evolution of the system by numerically integrating continuity equations, the GNS represents dynamics through learned message passing. Our aim was to develop fast and accurate surrogate models for particle-based simulations. We explored applying GNS to simulate cloud droplet transport, growth, and evaporation under turbulent conditions, but we found the GNS over-smoothed the simulations. We then applied the GNS to simulate aerosol dynamics through gas condensation and found the GNS was able to reproduce the benchmark, physics-based simulation with high accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

PUMA:POWDER UTILIZATION MODELING APPLICATION

SF-25-084 PUMA a high performance modeling framework to simulate powder processing. It provides a scalable tool for manufacturers to simulate powder pre- and post-processing. The tool can predict the distortion, residual stress, and (for reactive processes) reaction completion fraction of complex parts after curing/debinding, sintering, and infiltration processes. These predictions are key metrics industry uses to optimize these processes to produce dense, defect-free, stable components.

HU, TIANCHEN (GARY)↗
NASA NTRS1978

Data Management Symposium, Huntsville, Ala., October 18, 19, 1977, Proceedings

The transfer, processing, and use of satellite data are discussed. Topics relating to simulation and processing techniques include computer design assessments through simulation, data system dynamic simulation, and future timing accuracy requirements and procedures for data processing. Subjects relevant to data base management systems and users and their needs include an image-based information system architecture for correlation satellite and topological data base, efficient searching and sorting applications using an associative array processor, analysis of user's needs for a large technical data base, and variable length data formats. The technology development outlook was considered with attention to data systems technology outlook for supporting NASA programs, a user-oriented interactive information extraction system, and flow control and sorting techniques for telemetry packets.

Castelli, A.↗
NASA NTRS1991

Computational simulation of the creep-rupture process in filamentary composite materials

A computational simulation of the internal damage accumulation which causes the creep-rupture phenomenon in filamentary composite materials is developed. The creep-rupture process involves complex interactions between several damage mechanisms. A statistically-based computational simulation using a time-differencing approach is employed to model these progressive interactions. The finite element method is used to calculate the internal stresses. The fibers are modeled as a series of bar elements which are connected transversely by matrix elements. Flaws are distributed randomly throughout the elements in the model. Load is applied, and the properties of the individual elements are updated at the end of each time step as a function of the stress history. The simulation is continued until failure occurs. Several cases, with different initial flaw dispersions, are run to establish a statistical distribution of the time-to-failure. The calculations are performed on a supercomputer. The simulation results compare favorably with the results of creep-rupture experiments conducted at the Lawrence Livermore National Laboratory.

Slattery, Kerry T.↗
NASA NTRS1996

Parallel-Processing Test Bed For Simulation Software

Second-generation Hypercluster computing system is multiprocessor test bed for research on parallel algorithms for simulation in fluid dynamics, electromagnetics, chemistry, and other fields with large computational requirements but relatively low input/output requirements. Built from standard, off-shelf hardware readily upgraded as improved technology becomes available. System used for experiments with such parallel-processing concepts as message-passing algorithms, debugging software tools, and computational steering. First-generation Hypercluster system described in "Hypercluster Parallel Processor" (LEW-15283).

Blech, Richard↗
NASA NTRS2006

Precipitation processes developed during TOGA COARE (1992), GATE (1974), SCSMEX (1998), and KWAJEX (1999): 3D Cloud Resolving Model Simulation

Real clouds and cloud systems are inherently three-dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D). A few 3D CRMs have been used to study the response of clouds to large-scale forcing. In these 3D simulations, the model domain was small, and the integration time was 6 hours. Only recently have 3D experiments been performed for multi-day periods for tropical cloud systems with large horizontal domains at the National Center for Atmospheric Research (NCAR), NOAA GFDL, the U.K. Met. Office, Colorado State University and NASA Goddard Space Flight Center. An improved 3D Goddard Cumulus Ensemble (GCE) model was recently used to simulate periods during TOGA COARE (December 19-27, 1992), GATE (september 1-7, 1974), SCSMEX (May 18-26, June 2-11, 1998) and KWAJEX (August 7-13, August 18-21, and August 29-September 12, 1999) using a 512 by 512 km domain and 41 vertical layers. The major objectives of this paper are: (1) to identify the differences and similarities in the simulated precipitation processes and their associated surface and water energy budgets in TOGA COARE, GATE, KWAJEX, and SCSMEX, and (2) to asses the impact of microphysics, radiation budget and surface fluxes on the organization of convection in tropics.

Tao, W.-K.↗
DOE OSTI2024

wmpy-power: A Python package for process-based regional hydropower simulation

Hydropower is an important source of renewable energy in many parts of the world. The generation potential for a hydropower facility can vary greatly due to fluctuations in precipitation and snowmelt patterns impacting streamflow and reservoir storage. Human activities such as irrigation, manufacturing, and hydration can also influence water availability at nearby and downstream facilities. wmpy-power--the hydropower model described in this work--is process-based, leveraging explicit reservoir storage and release data to address impacts on hydropower from climate change and human adaptive behaviors to inform long-term planning and resource-adequacy considerations.

13 HYDRO ENERGY↗
NASA NTRS2012

The Role of Aerosols on Precipitation Processes: Cloud Resolving Model Simulations

Cloud microphysics is inevitably affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds. Therefore, size distributions parameterized as spectral bin microphysics are needed to explicitly study the effects of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. The formulation for the explicit spectral bin microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e. pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e., 33 bins). Atmospheric aerosols are also described using number density size-distribution functions. The model is tested by studying the evolution of deep cloud systems in the west Pacific warm pool region, the sub-tropics (Florida) and midlatitudes using identical thermodynamic conditions but with different concentrations of CCN: a low "clean" concentration and a high "dirty" concentration. Results indicate that the low CCN concentration case produces rainfall at the surface sooner than the high CeN case but has less cloud water mass aloft. Because the spectral-bin model explicitly calculates and allows for the examination of both the mass and number concentration of species in each size category, a detailed analysis of the instantaneous size spectrum can be obtained for these cases. It is shown that since the low (CN case produces fewer droplets, larger sizes develop due to greater condensational and collection growth, leading to a broader size spectrum in comparison to the high CCN case. Sensitivity tests were performed to identify the impact of ice processes, radiation and large-scale influence on cloud-aerosol interactive processes, especially regarding surface rainfall amounts and characteristics (i.e., heavy or convective versus light or stratiform types). In addition, an inert tracer was included to follow the vertical redistribution of aerosols by cloud processes. We will also give a brief review from observational evidence on the role of aerosol on precipitation processes.

Tao, Wei-Kuo↗
DOE OSTI2019

Report on plasma jet printer for sensor fabrication with process parameters optimized by simulation input

The In-Pile Instrumentation program conducts research to develop instrumentation that provides information regarding test conditions, as well as fuel and materials properties during irradiation. Such instruments must be miniaturized, robust to the nuclear reactor environment, and provide real-time data aligned with user needs. Towards this end, additive manufacturing techniques such as aerosol jet printing and Plasma Jet Printing, have demonstrated significant potential for the development of advanced in-pile instrumentation. Process control during the manufacturing process is critical in order to consistently create reliable sensors. This report details experimental and computational work aimed towards improved process control for robust sensor production using printing techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Parallel-in-Time Simulation Algorithm for Power Electronics: MMC-HVdc System

The complexity in simulating power electronics like modular multilevel converters (MMCs) requires simulation algorithms to speed-up the process. Existing simulation algorithms exploit spatial parallelism to speed-up simulation. With rise in complexity of power electronics and presence of increased number of states within them, there are limits in the speed-up using spatial parallelism. In this paper, a temporal parallelism algorithm based on parallel-in-time methods is developed for simulation of power-electronics-systems. The temporal parallelism algorithm is based on computation of power-electronics-states on coarse and fine time-steps using different models. The models of power-electronics-systems used in coarse and fine time-steps are average-value and detailed models, respectively. The updates to states on coarse time-step are computed serially and are used to initialize the states on fine time-step. The updates on fine time-step are computed in parallel. A translation method is proposed in this paper to update the states on fine time-step from the simulations in the coarse time-step, and vice-versa. The proposed algorithm is applied to simulate MMCs and is validated with respect to a detailed reference MMC model. The proposed algorithm recorded a speed-up of up to 3.47× with 5 cores.

42 ENGINEERING↗
DOE OSTI2024

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗
NASA NTRS1972

The use of a simulator in a synthetic aperture radar data processing tradeoff study.

A computer simulation of a synthetic aperture radar system is described. The objective in developing the simulator was to make it possible to assess the imaging performance of various configurations against a variety of target types. The simulation involves targets, the radar system itself, and the processor; several analysis and output options are provided. Targets may be internally generated or come from external sources, or externally produced (e.g., real) radar signals can be used. A number of parameters of the radar system can be varied. The characteristics of the processor can be adjusted to a considerable extent. Statistical analyses may be performed. Several output options are available, including imagery.

Lybanon, M.↗