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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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This is a variant of LaSDI where it minimizes both reconstruction loss terms of auto encoder and dynamics regression loss terms simultaneously. Plus, an active learning framework is adopted so that a physics-informed greedy sampling point can be added to enhance the performance of gLaSDI. It is written in python and several examples are included in this software.
The Abisko project aims to develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation. The SNN architecture explores novel neuromorphic devices that are based on resistive-switching materials, such as memristors and electrochemical RAM. Equally important, Abisko uses a deep codesign approach to pursue this goal by engaging experts from across the entire range of disciplines: materials, devices and circuits, architectures and integration, software, and algorithms. Here, the key objectives of our Abisko project are threefold. First, we are designing an energy-optimized high-performance neuromorphic accelerator based on SNNs. This architecture is being designed as a chiplet that can be deployed in contemporary computer architectures and we are investigating novel neuromorphic materials to improve its design. Second, we are concurrently developing a productive software stack for the neuromorphic accelerator that will also be portable to other architectures, such as field-programmable gate arrays and GPUs. Third, we are creating a new deep codesign methodology and framework for developing clear interfaces, requirements, and metrics between each level of abstraction to enable the system design to be explored and implemented interchangeably with execution, measurement, a model, or simulation. As a motivating application for this codesign effort, we target the use of SNNs for an analog event detector for a high-energy physics sensor.
This article describes the applications of the Spectrum(Tm) Solver in Multidisciplinary Analysis (MDA). Spectrum, a multiphysics simulation software based on the finite element method, addresses compressible and incompressible fluid flow, structural, and thermal modeling as well as the interaction between these disciplines. Multiphysics simulation is based on a single computational framework for the modeling of multiple interacting physical phenomena. Interaction constraints are enforced in a fully-coupled manner using the augmented-Lagrangian method. Within the multiphysics framework, the finite element treatment of fluids is based on Galerkin-Least-Squares (GLS) method with discontinuity capturing operators. The arbitrary-Lagrangian-Eulerian method is utilized to account for deformable fluid domains. The finite element treatment of solids and structures is based on the Hu-Washizu variational principle. The multiphysics architecture lends itself naturally to high-performance parallel computing. Aeroelastic, propulsion, thermal management and manufacturing applications are presented.
Although reuse is assumed to be especially valuable in building high quality software as well as in Object Oriented (OO) development, limited empirical evidence connects reuse with productivity and quality gains. The author's eight system study begins to define such benefits in an OO framework, most notably in terms of reduce defect density and rework as well as in increased productivity.
This paper proposes the development of intelligent sensors as part of an integrated systems approach, i.e. one treats the sensors as a complete system with its own sensing hardware (the traditional sensor), A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow them to get better with time. Under a project being undertaken at the NASA s Stennis Space Center, an integrated framework is being developed for the intelligent monitoring of smart elements. These smart elements can be sensors, actuators or other devices. The immediate application is the monitoring of the rocket test stands, but the technology should be generally applicable to the Integrated Systems Health Monitoring (ISHM) vision. This paper outlines progress made in the development of intelligent sensors by describing the work done till date on Physical Intelligent Sensors (PIS). The PIS discussed here consists of a thermocouple used to read temperature in an analog form which is then converted into digital values. A microprocessor collects the sensor readings and runs numerous embedded event detection routines on the collected data and if any event is detected, it is reported, stored and sent to a remote system through an Ethernet connection. Hence the output of the PIS is data coupled with confidence factor in the reliability of the data which leads to information on the health of the sensor at all times. All protocols are consistent with IEEE 1451.X standards. This work lays the foundation for the next generation of smart devices that have embedded intelligence for distributed decision making capabilities.
NASA's latest spacecraft Orion is in the development process of taking humans deeper into space. Orion is equipped with three main displays to monitor and control the spacecraft. To ensure the software behind the glass displays operates without faults, rigorous testing is needed. To conduct such testing, the Rapid Prototyping Lab at NASA's Johnson Space Center along with the University of Texas at Tyler employed a software verification tool, EggPlant Functional by TestPlant. It is an image based test automation tool that allows users to create scripts to verify the functionality within a program. A set of edge key framework and Common EggPlant Functions were developed to enable creation of scripts in an efficient fashion. This framework standardized the way to code and to simulate user inputs in the verification process. Moreover, the Common EggPlant Functions can be used repeatedly in verification of different displays.
The past two decades have seen a sustained increase in the use of high fidelity Computational Fluid Dynamics (CFD) in basic research, aircraft design, and the analysis of post-design issues. As the fidelity of a CFD method increases, the number of cases that can be readily and affordably computed greatly diminishes. However, computer speeds now exceed 2 GHz, hundreds of processors are currently available and more affordable, and advances in parallel CFD algorithms scale more readily with large numbers of processors. All of these factors make it feasible to compute thousands of high fidelity cases. However, there still remains the overwhelming task of monitoring the solution process. This paper presents an approach to automate the CFD solution process. A new software tool, AeroDB, is used to compute thousands of Euler and Navier-Stokes solutions for a 2nd generation glide-back booster in one week. The solution process exploits a common job-submission grid environment, the NASA Information Power Grid (IPG), using 13 computers located at 4 different geographical sites. Process automation and web-based access to a MySql database greatly reduces the user workload, removing much of the tedium and tendency for user input errors. The AeroDB framework is shown. The user submits/deletes jobs, monitors AeroDB's progress, and retrieves data and plots via a web portal. Once a job is in the database, a job launcher uses an IPG resource broker to decide which computers are best suited to run the job. Job/code requirements, the number of CPUs free on a remote system, and queue lengths are some of the parameters the broker takes into account. The Globus software provides secure services for user authentication, remote shell execution, and secure file transfers over an open network. AeroDB automatically decides when a job is completed. Currently, the Cart3D unstructured flow solver is used for the Euler equations, and the Overflow structured overset flow solver is used for the Navier-Stokes equations. Other codes can be readily included into the AeroDB framework.
Materials qualification of reactor structural materials is a critical step in rapid implementation of advanced nuclear reactor technologies, particularly to assess the corrosion performance in these designs. Accelerated qualification of reactor structural materials requires incorporating powerful computational toolsets, such as phase field modelling in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, to predict the evolution of structural materials due to corrosion. Accordingly, computational toolsets will require experimental data generated at appropriate length scales to validate accuracy. Focused ion beam (FIB) provides a high degree of control over manipulation of materials for analytical purposes, including capturing data on the evolution in the microstructure and elemental composition of materials at the mesoscale, an appropriate length scale for phase field modelling of intergranular diffusion phenomena using the MOOSE framework. For instance, the FEI Helios G4 UX dual beam plasma FIB microscope at the Irradiated Materials Characterization Laboratory (IMCL) is capable of backscatter diffraction (EBSD) and energy-dispersive x-ray spectroscopy (EDS) documenting the evolution in the microstructure and elemental composition, respectively. The Helios can perform EDS and EBSD three-dimensionally (3D) using tomography, which is then combined using different software packages to visualize 3D volumes correlating elemental composition to microstructural data. The purpose of this investigation was to develop a streamlined characterization and data processing workflow for 3D tomography studies on the FEI Helios G4 plasma FIB. The investigation is segmented into three parts: 1) Optimizing the data collection workflow, 2) identifying appropriate data processing and visualization software (i.e. DREAM.3D, MIPAR, and VGStudioMax), and 3) establishing an infrastructure for public release. The optimization of the data collection workflow is in collaboration with members of the U220 department to setup formal training on the tomography operation of the G4, through ThermoFisher Scientific, and exploring DREAM.3D, MIPAR, and VGStudioMax data processing/visualization software packages. VGStudioMax currently demonstrates the most promise for future use. Optimization of the data collection and processing workflow is still ongoing. A collaboration with INL High Performance Computing (HPC) established an open-source license for expediting the public release of FIB tomography datasets through HPC. FIB tomography data generated by the G4 will provide comprehensive data for validating 3D phase field mesoscale modelling tools within the MOOSE framework for accelerated qualification of reactor structural materials.
A numerical framework for analysis of complex valve systems supports testing of propulsive systems by simulating key valve and control system components in the test loop. In particular, it is designed to enhance the analysis capability in terms of identifying system transients and quantifying the valve response to these transients. This system has analysis capability for simulating valve motion in complex systems operating in diverse flow regimes ranging from compressible gases to cryogenic liquids. A key feature is the hybrid, unstructured framework with sub-models for grid movement and phase change including cryogenic cavitations. The multi-element unstructured framework offers improved predictions of valve performance characteristics under steady conditions for structurally complex valves such as pressure regulator valve. Unsteady simulations of valve motion using this computational approach have been carried out for various valves in operation at Stennis Space Center such as the split-body valve and the 10-in. (approx.25.4-cm) LOX (liquid oxygen) valve and the 4-in. (approx.10 cm) Y-pattern valve (liquid nitrogen). Such simulations make use of variable grid topologies, thereby permitting solution accuracy and resolving important flow physics in the seat region of the moving valve. An advantage to this software includes possible reduction in testing costs incurred due to disruptions relating to unexpected flow transients or functioning of valve/flow control systems. Prediction of the flow anomalies leading to system vibrations, flow resonance, and valve stall can help in valve scheduling and significantly reduce the need for activation tests. This framework has been evaluated for its ability to predict performance metrics like flow coefficient for cavitating venturis and valve coefficient curves, and could be a valuable tool in predicting and understanding anomalous behavior of system components at rocket propulsion testing and design sites.
Proper characterization of the precipitation drop size distribution (DSD) is integral to providing realistic and accurate space- and ground-based precipitation retrievals. Current technology allows for the development of DSD products from a variety of platforms, including disdrometers, vertical profilers and dual-polarization radars. Up to now, however, the dissemination or availability of such products has been limited to individual sites and/or field campaigns, in a variety of formats, often using inconsistent algorithms for computing the integral DSD parameters, such as the median- and mass-weighted drop diameter, total number concentration, liquid water content, rain rate, etc. We propose to develop a framework for the generation and dissemination of DSD characteristic products using a unified structure, capable of handling the myriad collection of disdrometers, profilers, and dual-polarization radar data currently available and to be collected during several upcoming GPM Ground Validation field campaigns. This DSD super-structure paradigm is an adaptation of the radar super-structure developed for NASA s Radar Software Library (RSL) and RSL_in_IDL. The goal is to provide the DSD products in a well-documented format, most likely NetCDF, along with tools to ingest and analyze the products. In so doing, we can develop a robust archive of DSD products from multiple sites and platforms, which should greatly benefit the development and validation of precipitation retrieval algorithms for GPM and other precipitation missions. An outline of this proposed framework will be provided as well as a discussion of the algorithms used to calculate the DSD parameters.
Understanding and controlling impurity behavior in actinide metal casting processes are foundational for efficient part production yet remain major challenges for researchers and industry. To help provide insight regarding impurity distribution during actinide metal casting, we have developed computational fluid dynamics (CFD) models for a laboratory-scale system using commercial and open-source code. Multiple simulation frameworks allow for improved confidence in the resulting outputs, while taking advantage of the maturity and convenience offered by commercial platforms and simultaneously maintaining the transparency and flexibility often provided by open-source software. Here, we describe multiple experiment-informed models designed to simulate a specific laboratory system in which uranium melt, containing a known starting concentration of carbon impurity, is electromagnetically stirred in an induction furnace. Here, the goal of the simulations is to predict the motion of uranium carbide microparticles in the velocity field of the melt. Prior to simulating the uranium-carbon system, numerical models were validated using a previously published nonradioactive experimental system. Effects of the size and shape of impurity particles in the models were investigated and agree with experimental findings. Simulation of smaller particles (< 50 µm) shows more homogenous distribution throughout the stirred melt. With increased particle size (100 µm), the body forces, which include the buoyancy force, dominate over the drag force, causing larger particles to move toward the crucible walls and upward in the system.
Here, this paper discusses a framework to design elements of the plant and control systems for floating offshore wind turbines in an integrated manner using linear parameter-varying models. Multiple linearized models derived from aero-elastic simulation software in different operating regions characterized by the incoming wind speed are combined to construct an approximate low-fidelity model of the system. The combined model is then used to generate open-loop, optimal control trajectories as part of a nested control co-design strategy that explores the system’s power production and stability using the platform pitch tilt as a proxy in the context of crucial plant and control design decisions. The radial distance between the central and outer columns and the diameter of the outer columns of the semisubmersible platform are the plant design variables. The platform stability and power production are studied for different plant design decisions. The effect of plant decisions on subsequent power production and stability response of the floating wind turbine is quantified in terms of the levelized cost of energy. The results show that the inner-loop constraints and the plant design decisions affect the turbine’s power and, subsequently, the cost of the system.
Solar photovoltaic (PV) resources are the most common form of distributed generation in residential and commercial customer premises within electric distribution networks. A higher penetration of PV generation in distribution circuits will impose challenges on maintaining service voltages within the range of industry standards, power quality, and power flow. Buildings consume 74% of the electricity produced in the United States, and a significant portion of the building load is dispatchable, making them responsive to electrical grid needs. Oak Ridge National Laboratory—in collaboration with Southern Company; the University of Tennessee, Knoxville; and the Georgia Institute of Technology—is examining the PV integration issues in distribution-level electrical grids and developing integrated demand-side control and communication systems to enable responsive loads. The proposed responsive loads mechanism performs renewable generation following to increase the penetration of solar PV within each feeder. The specific objectives of this project are to (1) examine distribution-level PV integration scenarios to understand requirements, (2) undertake an end-to-end simulation-based design of a distributed control strategy of loads geographically near the PV generation asset to minimize the effect on the distribution feeder, (3) deploy and demonstrate the control technology developed in partnership with utilities, and (4) perform a scalability analysis at the utility scale. This 3-year integrated project aims to develop, demonstrate, and validate demand-side control technology to enable increased the penetration of renewables while mitigating challenges that arise due to their intermittency. Activities in Budget Period (BP) 1 focused on a literature review and the formal design of a control system for integrating local distribution with generation and loads. The team used modeling and simulation to evaluate the impact of varying buildings loads, variable PV generation, and power flow dynamics on the distribution circuit. The dynamic models developed in BP 1 were used in BP 2 to develop a model-based control design and a test bed. The test bed has enabled the simulation-based testing and comparison of different control designs and formulations applied to different configurations of the distribution grid, PVs, and building loads. The control approaches developed in BP 2 were implemented in BP 3 in the form of hardware deployed at the Central Baptist Church (CBC) in Knoxville, Tennessee, for testing and evaluation. The outcome of this project was the development and demonstration of open-source, low-cost, low-touch sensing and control retrofits to distributed PV generation and building loads that, in a coordinated fashion, provide the load-shaping response needed to integrate high levels of renewable penetration. This research addresses the target metrics by dynamically controlling a load with solar generation variability to minimize the extent of two-way power flow, enhance reliability, facilitate high PV penetration (>100% of peak load in a line segment), and generate scalable software and hardware solutions adaptable to any penetration levels. The research and development activities are focused and designed to be impactful within the relevant 2020 targets time frame.An accurate open-source integration simulation framework for end-to-end control design was developed and deployed at the CBC facility for testing and evaluation. This final report provides a detailed review of the technical results achieved during this 3-year integrated project. A novel spectral analysis of PV data is demonstrated to derive the requirements of the control design. A detailed simulation-based analysis of PV integration at increasing penetration levels is presented using 1 year of PV data to demonstrate the impact on the distribution circuits. Two different control strategies were developed and demonstrated via simulation to track variable PV generation with adaptive load dispatch. The report concludes with a summary of accomplishments and recommendations for a path forward.
Lighting consumes significant energy in buildings, but current measurements of lighting efficiency are inadequate in capturing the spatial and temporal effectiveness of light. The most widely used lighting energy efficiency measure, luminous efficacy, quantifies the amount of light generated by individual light sources. While luminous efficacy provides manufacturers and designers with a useful metric for quantifying the energy efficiency of individual lighting devices, it does not fully address the use of lighting in architectural spaces. Instead, application efficacy, the relationship between the electrical power consumed by lighting hardware and the amount of light that contributes to the visual perception of building occupants, must be measured. Here, we propose a framework for calculating lighting application efficacy (LAE) based on the primary pathway of light in architectural settings: the generation and emission of light from a luminaire, the travel of the light throughout the space and into occupants’ eyes, and the process of visual perception. The LAE framework will include the development of computational methods for estimating the proportion of emitted light that is directed to task areas and areas corresponding to occupants’ visual fields using ray-tracing lighting design software. Future modeling and validation work will quantify energy efficiency of buildings.
The specification sets forth a technical framework within which to deal with the transfer of specific GOAL features to HAL/S. Key technical features of the translator are described which communicate with the data bank, handle repeat statements, and deal with software interrupts. GOAL programs, databank information, and GOAL system subroutines are integrated into one GOAL in HAL/S. This output is fully compatible HAL/S source ready for insertion into the HAL/S compiler. The Translator uses a PASS1 to establish all the global data needed for the HAL/S output program. Individual GOAL statements are translated in PASS2. The specification document makes extensive use of flowcharts to specify exactly how each variation of each GOAL statement is to be translated. The specification also deals with definitions and assumptions, executive support structure and implementation. An appendix, entitled GOAL-to-HAL Mapping, provides examples of translated GOAL statements.
This report describes a general-purpose Requirements Specification Language, RSL. The purpose of RSL is to specify precisely the external structure of a mechanized system and to define requirements that the system must meet. A system can be comprised of a mixture of hardware, software, and human processing elements. RSL is a hybrid of features found in several popular requirements specification languages, such as SADT (Structured Analysis and Design Technique), PSL (Problem Statement Language), and RMF (Requirements Modeling Framework). While languages such as these have useful features for structuring a specification, they generally lack formality. To overcome the deficiencies of informal requirements languages, RSL has constructs for formal mathematical specification. These constructs are similar to those found in formal specification languages such as EHDM (Enhanced Hierarchical Development Methodology), Larch, and OBJ3.
A simulation toolbox has been developed for the creation of both steady-state and dynamic thermodynamic software models. This paper describes the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS), which combines generic thermodynamic and controls modeling libraries with a numerical iterative solver to create a framework for the development of thermodynamic system simulations, such as gas turbine engines. The objective of this paper is to present an overview of T-MATS, the theory used in the creation of the module sets, and a possible propulsion simulation architecture. A model comparison was conducted by matching steady-state performance results from a T-MATS developed gas turbine simulation to a well-documented steady-state simulation. Transient modeling capabilities are then demonstrated when the steady-state T-MATS model is updated to run dynamically.
A simulation toolbox has been developed for the creation of both steady-state and dynamic thermodynamic software models. This presentation describes the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS), which combines generic thermodynamic and controls modeling libraries with a numerical iterative solver to create a framework for the development of thermodynamic system simulations, such as gas turbine engines. The objective of this presentation is to present an overview of T-MATS, the theory used in the creation of the module sets, and a possible propulsion simulation architecture.