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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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The System Advisor Model (SAM) is a free software that enables detailed performance and financial analysis for energy systems. • Conducts technoeconomic analysis of energy technologies to facilitate planning and decision making.
The redesigned solid rocket motor (RSRM) flight set 360L003 was launched on March 13, 1989 as part of NASA space shuttle mission STS-29R. As was the case with flight sets 360L001 and 360L002 (STS-26R and STS-27R), both motors (360L003A and 360L003B) performed in an excellent manner. Evaluation of the ground environment instrumentation measurements verified thermal model analysis data and showed agreement with predicted environmental effects. The right-hand aft field joint primary heater failed during the countdown; the secondary heater was activated and performed as designed. All other field joint heaters and aft skirt thermal conditioning systems had no anomalies. Shuttle thermal imager infrared readings compared favorably with measured ground environment instrumentation data. No thermal launch commit criteria violations occurred at any time. Evaluation of the development flight instrumentation showed exceptional propulsion performance. All ballistic parameters closely matched the predicted values and were well within the required specification levels. Girth and biaxial strain gage measurements compared closely with corresponding gages on previous flight motors, static tests, and with preflight predictions. Adequate safety factors were verified. (Some ignition transient spiking was noted in a few girth gages; the spiking was determined not to be representative of actual case behavior, but an instrumentation phenomena.) The accelerometers again measured high vibration amplitude levels during the ignition transient and the reentry Max Q phases. Postflight inspection showed that all combustion gas was contained by the insulation in the field and case-to-nozzle joints. No anomalous insulation erosion patterns were found, and the seals that did directly contain motor pressure showed no heat effects, erosion, or blowby. All anomalies identified were a result of splashdown damage, with the exception of fretting in the case field joint interference (nonsealing) surfaces and a prelaunch field joint heater failure. The disposition of all anomalies and the complete results are reported.
A computational structural/material analysis and design tool which would meet industry's future demand for expedience and reduced cost is presented. This unique software 'GENOA' is dedicated to parallel and high speed analysis to perform probabilistic evaluation of high temperature composite response of aerospace systems. The development is based on detailed integration and modification of diverse fields of specialized analysis techniques and mathematical models to combine their latest innovative capabilities into a commercially viable software package. The technique is specifically designed to exploit the availability of processors to perform computationally intense probabilistic analysis assessing uncertainties in structural reliability analysis and composite micromechanics. The primary objectives which were achieved in performing the development were: (1) Utilization of the power of parallel processing and static/dynamic load balancing optimization to make the complex simulation of structure, material and processing of high temperature composite affordable; (2) Computational integration and synchronization of probabilistic mathematics, structural/material mechanics and parallel computing; (3) Implementation of an innovative multi-level domain decomposition technique to identify the inherent parallelism, and increasing convergence rates through high- and low-level processor assignment; (4) Creating the framework for Portable Paralleled architecture for the machine independent Multi Instruction Multi Data, (MIMD), Single Instruction Multi Data (SIMD), hybrid and distributed workstation type of computers; and (5) Market evaluation. The results of Phase-2 effort provides a good basis for continuation and warrants Phase-3 government, and industry partnership.
The concept of (meta) modeling combines an intuitive way of formalizing the structure of an application domain with a high expressiveness that makes it suitable for a wide variety of use cases and has therefore become an integral part of many areas in computer science. While the definition of modeling languages through the use of meta models, e.g. in Unified Modeling Language (UML), is a well-understood process, their validation and the extraction of behavioral information is still a challenge. In this paper we present a novel approach for dynamic model analysis along with several fields of application. Examining the propagation of information along the edges and nodes of the model graph allows to extend and simplify the definition of semantic constraints in comparison to the capabilities offered by e.g. the Object Constraint Language. Performing a flow-based analysis also enables the simulation of dynamic behavior, thus providing an "abstract interpretation"-like analysis method for the modeling domain.
The operation of large US Department of Energy (DOE) research facilities, like the DIII-D National Fusion Facility, results in the collection of complex multi-dimensional scientific datasets, both experimental and model-generated. In the future, it is envisioned that integrated data analysis coupled with large-scale high performance computing (HPC) simulations will be used to improve experimental planning and operation. Practically, massive data sets from these simulations provide the physics basis for generation of both reduced semi-analytic and machine-learning-based models. Storage of both HPC simulation datasets (generated from US DOE leadership computing facilities) and experimental datasets presents significant challenges. In this paper, we present a vision for a DOE-wide data management workflow that integrates US DOE fusion facilities with leadership computing facilities. Data persistence and long-term availability beyond the length of allocated projects is essential, particularly for verification and recalibration of artificial intelligence and machine learning (AI/ML) models. Because these data sets are often generated and shared among hundreds of users across multiple leadership computing facility centers, they would benefit from cross-platform accessibility, persistent identifiers (e.g. DOI, or digital object identifier), and provenance tracking. Here, the ability to handle different data access patterns suggests that a combination of low cost, high latency (e.g. for storing ML training sets) and high cost, low latency systems (e.g. for real-time, integrated machine control feedback) may be needed.
The development of an advanced computational analysis of unsteady aerodynamic loads on isolated helicopter rotors in forward flight is described. The primary technical focus of the development was the implementation of a freely distorting filamentary wake model composed of curved vortex elements laid out along contours of constant vortex sheet strength in the wake. This model captures the wake generated by the full span of each rotor blade and makes possible a unified treatment of the shed and trailed vorticity in the wake. This wake model was coupled to a modal analysis of the rotor blade dynamics and a vortex lattice treatment of the aerodynamic loads to produce a comprehensive model for rotor performance and air loads in forward flight dubbed RotorCRAFT (Computation of Rotor Aerodynamics in Forward Flight). The technical background on the major components of this analysis are discussed and the correlation of predictions of performance, trim, and unsteady air loads with experimental data from several representative rotor configurations is examined. The primary conclusions of this study are that the RotorCRAFT analysis correlates well with measured loads on a variety of configurations and that application of the full span free wake model is required to capture several important features of the vibratory loading on rotor blades in forward flight.
Over the years, significant validation work for the neutronics code MPACT has been performed against zero-power critical benchmarks and measured data from operating nuclear power plants. As a part of the Modeling and Analysis of Exelon BWRs for Eigenvalue and Thermal Limits Predictability project, new validation efforts relevant to boiling water reactor (BWR) core applications have been performed. This paper presents the results of the critical experiment portion of the BWR validation efforts for MPACT. The Kritz-4 experiments and IPEN/MB-01 BWR-relevant configurations are modeled with MPACT. The Kritz-4 BWR critical experiments were performed at both cold and hot conditions, which is a unique feature among other critical experiment facilities. The MPACT results with linear source (LS) Method of Characteristics (MOC) have very good agreement with the measured criticality. When using the 60-group BWR library with LS and P2 scattering, the maximum k{sub eff} error is 109 pcm, and the average cold-hot bias is only -0.11 pcm/K. The fission rate distributions of MPACT are also verified with Serpent calculations. For the IPEN facility, two configurations are considered: one with a large central void, and one with a cruciform control rod. The MPACT k{sub eff} errors are less than 50 pcm for all IPEN problems. (authors)
Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks are very brittle and are difficult to deploy on resource-limited platforms. Model pruning, i.e., reducing the size of the network, is a widely adopted strategy that can lead to a more robust and compact model. Many heuristics exist for model pruning, but our understanding of the pruning process remains limited due to the black-box nature of a neural network model. Empirical studies show that some heuristics improve performance whereas others can make models more brittle. Here, this work aims to shed light on how different pruning methods alter the network’s internal feature representation and the corresponding impact on model performance. To facilitate a comprehensive comparison and characterization of the high-dimensional model feature space, we introduce a visual geometric analysis of feature representations. We evaluated a set of critical geometric concepts decomposed from the commonly adopted classification loss and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation. The proposed tool provides an environment for an in-depth comparison of pruning methods and a comprehensive understanding of how the model responds to common data corruption. By leveraging the proposed visualization, machine learning researchers can reveal the similarities between pruning methods and redundancy in robustness evaluation benchmarks, obtain geometric insights about the differences between pruned models that achieve superior robustness performance, and identify samples that are robust or fragile to model pruning and common data corruption.
This study presents a methodology for automated model generation and parameter estimation of building energy models using semantic modeling and Bayesian estimation. Semantic modeling techniques are used to represent the system components and their interactions, facilitating the automatic generation of a simulation model from dynamic component models. The proposed approach is applied to a case study of a ventilation system where a simulation model is generated, calibrated, and assessed through different performance metrics. These metrics demonstrate the accuracy and reliability of both model point estimates and probabilistic prediction intervals across all model outputs. Overall, the proposed methodology offers a systematic and automated approach to model development and calibration in building energy systems, with potential applications in building performance analysis, monitoring, and optimization.
This report presents the development of tools and methods for evaluating the reliability and performance of ceramic materials in high temperature solar receivers. As Concentrating Solar Power (CSP) technologies aim for higher operating temperatures to enhance efficiency and meet industrial process heat requirements, current high temperature metallic materials face challenges in maintaining structural integrity. This report explores advanced ceramics as a promising alternative, given their superior high temperature strength and lower thermal expansion, compared to metals. To address the need for effective ceramic receiver design tools, this report integrates statistical failure models of ceramics into the existing srlife tool: an open-source software package designed to estimate the life of high temperature CSP components. These failure models account for the inherent variability and flaw distribution in ceramics, as well as the impact of subcritical crack growth under high temperature cyclic loads. The report also presents experimental data collected for a commercially available ceramic material, SiC, and details the process of estimating reliability model parameters from these data. A comparative design analysis is then performed between ceramic (SiC) and metallic (current nickel-based superalloys A740H and A282) receiver. This comparison demonstrates that SiC receivers can achieve service life exceeding 30 years under high incident heat flux conditions, compared to just a few years for metallic receivers.
The design of heterogeneous catalysts relies on understanding the fundamental surface kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help the researcher in streamlining the process of catalyst design. Microkinetic modeling is used to identify critical reaction intermediates and rate-determining elementary reactions, thereby providing vital information for designing an improved catalyst. In this review, we summarize general procedures for developing microkinetic models using reaction kinetics parameters obtained from experimental data, theoretical correlations, and quantum chemical calculations. We examine the methods required to ensure the thermodynamic consistency of the microkinetic model. We describe procedures required for parameter adjustments to account for the heterogeneity of the catalyst and the inherent errors in parameter estimation. We discuss the analysis of microkinetic models to determine the rate determining reactions using the degree of rate control and reversibility of each elementary reaction. We introduce incorporation of Brønsted–Evans–Polanyi relations and scaling relations in microkinetic models and the effects of these relations on catalytic performance and formation of volcano curves. We review the analysis of reaction schemes in terms of the maximum rate of elementary reactions, and we outline a procedure to identify kinetically significant transition states and adsorbed intermediates. Here, we explore the application of generalized rate expressions for the prediction of optimal binding energies of important surface intermediates and to estimate the extent of potential rate improvement. We also explore the application of microkinetic modeling in homogeneous catalysis, electro-catalysis, and transient reaction kinetics. We conclude by highlighting the challenges and opportunities in the application of microkinetic modeling for catalyst design.
Flow accelerated corrosion (FAC) in nuclear power plant pipes is one of the leading causes of accidents, fatalities, damage and outages. Current FAC identification methods employ expensive sensing technology and are “active” methods, where the response of the piping system to an externally-generated thermal, mechanical or optical excitation must be measured. As a result, these techniques require a disruptive and time-consuming setup. Here we propose a method that utilizes pipe surface temperature measurements to passively monitor for FAC-induced pipe wall thinning without the need for expensive equipment or post-installation setup time. This diagnostic method utilizes a simulation data-driven diagnostic model to estimate the amount of thickness reduction in a pipe based on changes in measured steady-state pipe temperatures. In order to reduce the computational burden of generating large, simulation-based datasets, the behavior of the insulation of the pipe was modeled using a suitably calibrated heat transfer boundary parameter. Additionally, global sensitivity analysis was performed to determine system parameter(s), such as the temperature of water flowing inside the pipe, which significantly affect the steady state pipe wall temperature and could cause errors in diagnosis. Two diagnostic models, one using only the change in steady-state temperature as an indicator for FAC-induced pipe wall thinning and the other using water temperature as an additional diagnostic model input were evaluated for their ability to estimate thickness reductions in a pipe using simulated pipe wall temperature data. For the numerical experiments conducted in this work, both models estimated wall thickness with errors within 0.5 mm, indicating that the proposed technique can potentially be used as a low-cost, first-pass method for FAC monitoring.
The focus of the NASA / Marshall Space Flight Center (MSFC) Advanced Reusable Technologies (ART) project is to advance and develop Rocket-Based Combined-Cycle (RBCC) technologies. The ART project began in 1996 as part of the Advanced Space Transportation Program (ASTP). The project is composed of several activities including RBCC engine ground testing, tool development, vehicle / mission studies, and component testing / development. The major contractors involved in the ART project are Aerojet and Rocketdyne. A large database of RBCC ground test data was generated for the air-augmented rocket (AAR), ramjet, scramjet, and ascent rocket modes of operation for both the Aerojet and Rocketdyne concepts. Transition between consecutive modes was also demonstrated as well as trajectory simulation. The Rocketdyne freejet tests were conducted at GASL in the Flight Acceleration Simulation Test (FAST) facility. During a single test, the FAST facility is capable of simulating both the enthalpy and aerodynamic conditions over a range of Mach numbers in a flight trajectory. Aerojet performed freejet testing in the Pebble Bed facility at GASL as well as direct-connect testing at GASL. Aerojet also performed sea-level static (SLS) testing at the Aerojet A-Zone facility in Sacramento, CA. Several flight-type flowpath components were developed under the ART project. Aerojet designed and fabricated ceramic scramjet injectors. The structural design of the injectors will be tested in a simulated scramjet environment where thermal effects and performance will be assessed. Rocketdyne will be replacing the cooled combustor in the A5 rig with a flight-weight combustor that is near completion. Aerojet's formed duct panel is currently being fabricated and will be tested in the SLS rig in Aerojet's A-Zone facility. Aerojet has already successfully tested a cooled cowl panel in the same facility. In addition to MSFC, other NASA centers have contributed to the ART project as well. Inlet testing and parametrics were performed at NASA / Glenn Research Center (GRC) and NASA / Langley Research Center (LaRC) for both the Aerojet and Rocketdyne concepts. LaRC conducted an Air-Breathing Launch Vehicle (ABLV) study for several vehicle concepts with RBCC propulsion systems. LaRC is also performing a CFD analysis of the ramjet mode for both flowpaths based on GASL test conditions. A study was performed in 1999 to investigate the feasibility of performing an RBCC flight test on the NASA / Dryden Flight Research Center (DFRC) SR-71 aircraft. Academia involvement in the ART project includes parametric RBCC flowpath testing by Pennsylvania State University (PSU). In addition to thrust and wall static pressure measurements, PSU is also using laser diagnostics to analyze the flowfield in the test rig. MSFC is performing CFD analysis of the PSU rig at select test conditions for model baseline and validation. Also, Georgia Institute of Technology (GT) conducted a vision vehicle study using the Aerojet RBCC concept. Overall, the ART project has been very successful in advancing RBCC technology. Along the way, several major milestones were achieved and "firsts" accomplished. For example, under the ART project, the first dynamic trajectory simulation testing was performed and the Rocketdyne engine A5 logged over one hour of accumulated test time. The next logical step is to develop and demonstrate a flight-weight RBCC engine system.
PFE Auxiliary Systems conducted drop testing on SAVY-4000 containers to evaluate structural response under 12-foot drop conditions. In support of that effort, a finite-element modeling capability was developed to simulate drop response across multiple container sizes and impact orientations. The purpose of this work was to provide a consistent analysis framework that could support interpretation of testing, compare response trends across multiple configurations, and generate quantities of interest for later comparison with experimental data. More broadly, the analysis and testing were intended to assess whether the containers continued to perform their primary function after a 12-foot drop, namely maintaining structural integrity and containment of the contents. The modeling approach combined an implicit preload analysis with an explicit drop simulation so that each drop event began from a mechanically realistic assembled condition, including compression of the silicone O-ring. Separate models were developed for 2-quart, 5-quart, 12-quart, and 10-gallon containers. The results were evaluated in terms of strain-gauge response, collar-lid gap behavior, and accumulated plastic strain. In addition, parametric studies were performed on the 2-quart container to assess sensitivity to O-ring stiffness, friction, canister thickness, geometry tolerance, and mesh density. The simulations showed that predicted drop responses depended strongly on both container size and drop orientation. Gap metrics identified cases in which the predicted collar-lid opening exceeded the nominal O-ring cross-section threshold, while plastic strain metrics identified localized regions of elevated permanent deformation. Parametric studies showed that the predicted response was especially sensitive to the assumed O-ring stiffness and contact friction, while the geometry tolerance study produced smaller changes in the cases examined. The main value of this work was that it established a repeatable modeling and simulation workflow to support drop-test implementation, evaluate effects of future configuration changes, and understand modeling assumptions that most influenced predicted response. At the current stage, the results were viewed as preliminary model predictions rather than validated predictions. The next step would be to compare drop-test data to the model so that predictive values of the workflow could be refined and used with greater confidence to assess whether the containers maintained structural integrity and containment of the contents after a 12-foot drop.
The development of the Percheron Power (Percheron) composite Archimedes screw turbine (AST) is based upon employing newly developed theoretical mathematical models for optimizing the water volume carried through the AST for a given diameter, combined with minimizing efficiency losses due to bypass flow and friction. The key feasibility issues associated with producing the composite blades will be to understand the strength, fatigue, and wear performance of the AST blades and associated components, and how these factors change along the length of the turbine. To get to this understanding of material property requirements, Percheron needs free-surface computational fluid dynamics (CFD) models of the AST designs and to perform interactive modeling between the turbine blade optimization and component design based on finite element method stress analysis (FEA). The workscope and analyses requested by Percheron in the Small Business Voucher (SBV) program are well aligned with PNNL capability and recent work experience in performing CFD and FEA analysis for hydropower and marine energy turbines. The CFD analysis will provide estimates of power, efficiency, and surface forces on the blades. The forces on the blade will be transferred to the FEA analysis that will be used to evaluate the material stresses. The CFD and FEA analyses will be repeated for different AST designs provided to PNNL by Percheron. The work conducted in this SBV project will complement the existing Percheron FOA project by providing better defined parameters for design and material selection for the turbine unit prior to manufacture of initial test units.
Commercial buildings consume significant energy in the United States and exhibit high potential for energy use reduction through retrofits. Benchmarking and energy simulation are well established tools in the industry to identify potential improvements and measure performance. Analysis to identify most sensitive retrofit parameters to energy performance can optimize investment and available energy savings. Presented study demonstrates methodology using a static model to determine sensitivity of building design and retrofit parameters with respect to energy performance. Calibrated simulation energy models (eQUEST) of two distribution centers (A, B) are presented. A fractional factorial analysis is conducted on retrofit parameters of efficiency measures targeting the highest energy consumers, and the results are benchmarked using Energy Star® Portfolio Manager. A custom Microsoft Excel® based simulation model is created to simulate occupancy levels, lighting, plug loads, and other equipment used in various spaces throughout the day. For Building A, efficient lighting was the most influential parameter for energy savings, carbon savings and benchmarking score; whereas, for Building B, HVAC efficiency was most influential for energy and demand controlled ventilation and economizers was most influential for benchmarking score. While retrofit projects can save energy and carbon emissions, variation in source-site ratios and state grid emissions, benchmarking scores may not always reflect equivalent improvement. State grid emissions factors, natural gas composition are difficult to model and hence not considered in this study. In conclusion, the synergistic analysis presented, emphasizes the importance of benchmarking and efficiency retrofits in promoting sustainable building practices to reduce energy consumption.
Brief report summarizes results of experimental and theoretical studies of mass-transport properties of GeSe/Gel4 and Hg0.8Cd0.2Te systems in connection with growth of crystals in closed ampoules. Primary emphasis in studies was on thermochemical analyses, on development of mathematical models to predict diffusion-limited mass transport, and on comparison of theoretically predicted with experimental fluxes. Results applied to design, preparation, performance, and analysis of crystal-growth experiments of semiconducting materials on Earth and in outer space. Model extended to predict mass flux and overall composition of transport products of Hg0.8Cd0.2Te transport system.
Using a 3-D model for thermal infrared exitance and the Lowtran 7 atmospheric radiative transfer model, we compute the variation in brightness temperature with view direction and, in particular, the canopy thermal hot spot. We then perform a sensitivity analysis of surface energy balance components for a nominal case using a simple SVAT model given the uncertainty in canopy temperature arising from the thermal hot spot effect. Canopy thermal hot spot variations of two degrees C lead to differences of plus or minus 24% in the midday available energy.