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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 influence of nozzle size without wind, on wind penetration, the influence of nozzle pressure on wind penetration, wind velocity effects, nozzle pressure effects, nominal velocity, wind velocity effects on wind penetration, and nozzle azimuth angle effects are plotted.
The findings are reported regarding the safe operation of the NASA crystal growth furnace (CGF) and potential methods for detecting containment failures of the furnace. The main conclusions are summarized by ampoule leak detection, cartridge leak detection, and detection of hazardous species in the experiment apparatus container (EAC).
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Following the trend of creating flight code with state chart modeling and auto-code generation, NASA's Deep Impact (DI) project uses Stateflow for its Fault Protection Flight Software development.
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A system for enhanced power system model validation is provided. The system includes a computing device including at least one processor in communication with at least one memory device. The at least one processor is programmed to store a plurality of models for a plurality of devices and a plurality of input files associated with the plurality of models, receive, from a user, a selection of model of the plurality of models to simulate, retrieve one or more input files of the plurality of input files, perform a model validity check on the selected model, if the selected model passed the model validity check, perform a model calibration on the selected model, and if the selected model passed the model calibration, perform a post evaluation on the selected model.
Heat recovery from enhanced geothermal systems (EGS) is a complex process involving heat transport in both fracture networks and rock formations. A comprehensive understanding of and the ability to model the underlying heat transport mechanisms is important for the success of EGS but remains challenging in practice due to the generally insufficient characterization of EGS reservoirs. In the present study, we analyze an extensively monitored intermediate-scale EGS field experiment performed in a well-characterized testbed. The high-resolution, high-quality measurements from the field experiment enable the development of a high-fidelity model incorporating a well-constrained fracture network. Based on the field experiment, we investigate the complex heat transport processes in an EGS-relevant environment and validate the capability of a numerical approach in simulating these inherently coupled heat transport processes. A series of numerical simulations were performed to study the effects of different heat transport mechanisms, including thermal convection with fracture flow, thermal conduction in rock formations, and the Joule-Thomson effect. The agreement of thermal responses between field measurements and simulation results indicates that our numerical approach can appropriately model the heat transport processes pertaining to heat recovery from EGS reservoirs.
Validation and verification (V&V) are procedures used to evaluate system structure or behavior with respect to a set of requirements. Although expert systems are often developed as a series of prototypes without requirements, it is not possible to perform V&V on any system for which requirements have not been prepared. In addition, there are special problems associated with the evaluation of expert systems that do not arise in the evaluation of conventional systems, such as verification of the completeness and accuracy of the knowledge base. The criticality of most NASA missions make it important to be able to certify the performance of the expert systems used to support these mission. Recommendations for the most appropriate method for integrating V&V into the Expert System Development Methodology (ESDM) and suggestions for the most suitable approaches for each stage of ESDM development are presented.
Presented at the 16th International Conference on Greenhouse Gas Control Technologie in Lyons, France.
A cybersecurity infrastructure command validation system is provided herein for validating asset commands issued within an infrastructure network. The cybersecurity infrastructure command validation system can be integrated into an infrastructure network to monitor and validate infrastructure asset commands in real-time or while the infrastructure network is active. The cybersecurity infrastructure command validation system can receive or intercept commands issued by asset controllers. The cybersecurity infrastructure command validation system can validate the commands based on a command validation model. The command validation model can represent normal operating behavior of the infrastructure network. The cybersecurity infrastructure command validation system can provide valid commands to the intended infrastructure asset, or can reject invalid commands. The cybersecurity infrastructure command validation system can store validation results for use in updating the command validation model. The cybersecurity infrastructure command validation system can flag or otherwise warn the infrastructure network or administrators of invalid commands.
The validation of expert systems (ESs) has only recently become an active AI research topic. Current approaches have concentrated mainly on the validation of rule properties of such systems. The efforts presented improves on current methods by also exploiting the structural and semantic information of such systems. To increase programmer productivity, more and more companies have begun exploiting the advent of AI technology by developing applications using ES shells or other AI-based high level program generators. The architecture, functionality, and future goals of Expert Systems Validation are described along with the features that have been implemented for and in Automated Reasoning Tool, the ES shell presented.
The Expert Systems Validation Associate (EVA), a validation system under development at the Lockheed Artificial Intelligence Center for more than a year, provides a wide range of validation tools to check the correctness, consistency and completeness of a knowledge-based system. A declarative meta-language (higher-order language), is used to create a generic version of EVA to validate applications written in arbitrary expert system shells. The architecture and functionality of EVA are presented. The functionality includes Structure Check, Logic Check, Extended Structure Check (using semantic information), Extended Logic Check, Semantic Check, Omission Check, Rule Refinement, Control Check, Test Case Generation, Error Localization, and Behavior Verification.