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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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NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗
NASA NTRSDate not supplied

Thermal Data-driven Model Reduction for Enhanced Battery Health Monitoring

Electric aviation faces a major challenge of avoiding potentially catastrophic consequences of the battery’s thermal runaway while keeping the weight of the battery low. Detection of early warning signals of battery failures requires accurate monitoring of the battery’s health throughout its lifespan. However, identifying the parameters of the battery from field data is notoriously difficult. We investigate this problem within the framework of modeling the temperature dynamics of a Li-ion cell during tests simulating loading in electric aircraft flights. It is found that the parameters of a higher-fidelity physics-based thermal model cannot be identified from the simulated flight data. To resolve this issue, we reduce the higher-fidelity thermal model to a model with fewer parameters. The resulting reduced-order model can predict temperature dynamics accurately and is identifiable throughout the cell’s lifespan which allows using the model’s parameters to monitor the state-of-health of the aging cell and detect anomalies in thermal behavior.

Li ion batteries↗
NASA NTRSDate not supplied

Openet: Applications of Satellite-Based Evapotranspiration Data for Water Resources Management in the Western United States

Advancing water security in overallocated river basins globally requires consistent and reproducible information on consumptive use of water that can anchor the development of data-driven solutions to the challenge of balancing water supply and demand. OpenET is a fully automated system for field-scale (30 m), satellite-based mapping of evapotranspiration (ET) at daily, monthly and annual timesteps. OpenET currently provides spatially contiguous data throughout the 23 westernmost states in the continental US, and includes both current information as well as multi-year timeseries of ET. The OpenET consortium has implemented an ensemble of satellite-based ET models (ALEXI/DisALEXI, eeMETRIC, PT-JPL, geeSEBAL, SIMS and SSEBop) on Google Earth Engine, which provides a shared computing platform for collaboration on processing of data from Landsat and other satellites, land cover and meteorological inputs, leading to increased consistency and accuracy across the ensemble of models. Earth Engine also facilitates hosting and distribution of data via open data collections and an application programming interface. We provide updates on the OpenET framework, open data services and data access tools, approach to geographic expansion, recent accuracy assessments, and describe how a user-driven design approach has facilitated successful applications of OpenET data for a wide range of water resource management activities. Applications to date include: use of ET data to improve quantification of ET and consumptive use in Oregon, Utah and the Upper Colorado River Basin; streamlining of water use reporting requirements in the California Delta; support for calculation of water budgets for the implementation of the Sustainable Groundwater Management Act in California; and integration into decision support tools for irrigation management. The use cases demonstrate how satellite-derived ET data that are easily accessed and seen as broadly accepted can accelerate adoption of innovative water management practices at scale, and support advances in the sustainability of water supplies. Uptake and use of data by the OpenET science community has also led to advances in our understanding of the impacts of landcover change, irrigation intensification and wildfire events on hydrology and the water security.

Applications↗
NASA NTRSDate not supplied

A Combined Computational, Experimental, and Technology Development Approach to In-Space Laser Manufacturing Maturation at NASA Marshall Space Flight Center

In-space manufacturing (ISM) is emerging as a field vital to continued access and capabilities in the space environment. NASA Marshall Space Flight Center (MSFC) is advancing the frontier of in-space laser manufacturing (ISLM) techniques through work initially focused on maturing laser beam welding (LBW) and laser forming (LF) for use in space. Such techniques proffer the ability to assemble and join structures in space from sheet metal or other stock – extant satellites, in situ resource utilization of Lunar regolith, etc. – by forming to desired shapes and then joining via in-space welding (ISW). ISLM processes are useful for assembly, joining, modification, and repair of structures in free space and on the Lunar surface such as large observatories, antennas, trusses, blast/thermal/radiation shields, pressure vessels, and more. However, these techniques are not yet qualified & certified (Q&C) for regular application in space. It would be prohibitively expensive, laborious, and time-consuming to perform Q&C via traditional experimental approaches as data collection & experimentation in space is resource-intensive. As such, benchmark experiments and focused, properly instrumented technology demonstration efforts in space can collect sufficient data that – when combined with verified computational models in an integrated computational materials engineering (ICME) approach – can validate ICME tools capable of translating more readily obtained ground data to in-space, in situ, computationally informed Q&C of ISLM techniques. Several ISLM projects at MSFC are obtaining the data required to validate ICME tools through both ground and flight experiments. A parabolic flight experiment of LBW under vacuum is manifested for August 2024, including both microgravity and Lunar gravity profiles. This collaboration with the Ohio State University is investigating common aerospace alloys such as 316L stainless steel, 2219 aluminum alloy, and Ti64 titanium alloy. In situ data collection includes videography, thermography, and reference thermocouples to build a thermal model of the welds. This will elucidate the relevant physics when combined with post-flight microstructural examination and mechanical testing. MSFC is also progressing towards a suborbital flight experiment of LBW under vacuum, which could provide reams of data on ISW during sustained, high-quality reduced gravity. The effect of combined thermal (cryogenic and high-temperature) and vacuum exposure on both LBW (NASA-funded) and LF (DARPA-funded) is being investigated through ground experiments. In addition to the copious data collected during these ground experiments, ruggedization of LBW hardware will also be pursued. The datasets from these experiments will be used to validate computational models which will inform future ISLM efforts in an ICME framework. A variety of techniques across lengths scales, from CALPHAD-driven thermodynamics & kinetics to phase field modeling of solidification to kinetic Monte Carlo simulations of grain evolution at the mesoscale, will be employed to accelerate the infusion and eventual Q&C of LBW and LF for use in space. The development of data-driven surrogate models to bridge ground to flight experiments and thereby reduce the need for resource-intensive experiments in space will also be investigated. These ICME techniques, surrogate models, and datasets from ground testing can also be employed to advance manufacturing in terrestrial environments.

in-space welding↗