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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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Project 1: Developing a container for state variables in a physics code. Project 2: Setting up near-continuous performance and usage metrics monitoring of a physics code.
The Navigation and Meteorological Data from Multiple Sensors on Airborne Platform (NAVMET-AIR) value-added product encompasses the aafnaviwg data set in the ARM Data Center (ADC), known as the IWG file, named for the Inter-Agency Working Group for Airborne Data and Telemetry Systems (IWGADTS). Its purpose is to produce a suite of tools to promote standardization of instrument interface, data format, and data processing. This data set’s contents are navigational and meteorological state variables at 1 Hz. It also contains higher-order data flags created by ARM Aerial Facility (AAF) scientists to aid analysis such as periods when the aircraft is flying level, operating in maneuvers, or flying through cloud. Due to the nature of research flights, the payload of the aircraft consists of many duplicate and overlapping instruments to ensure, in case of instrument failure, there is a backup to record the data. Measurements from multiple instruments are consolidated into a single file. Each variable of this data set is carefully chosen from the onboard instrumentation and quality checked by AAF scientists to create this wholistic and most accurate data set for airborne research. This document is intended to be a hub towards the individual “read me” files produced for each campaign. For reference about what instrument was used for calculations on specific days, the read me files are located in the ARM IOP archive at the addresses listed, or access the IOP (intensive operational period) database at https://www.archive.arm.gov/.
Focal Area(s): 1. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising a hierarchy of models. 2. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: One major challenge that Earth system models (ESMs) face in providing credible prediction of the Earth system and its water cycle characteristics (e.g., mean state, variability, and extreme events) is to accurately simulate aerosol-cloud interactions (ACI). The physical, chemical, and dynamical processes affecting ACI are extremely complex and they range from nanoscale to planetary scale. In each model development cycle, scientists spend significant efforts investigating model deficiencies and uncertainties associated with aerosols (e.g., emissions, chemical processes, aerosol microphysics, and transport) and clouds (e.g., macrophysics, microphysics, turbulence, and large-scale circulation) in order to develop improved treatments. However, despite decades of active research, ACI is still a major source of uncertainty in climate projections, even though great progress has been made. Specific scientific challenges include: (i) Parameterizations are developed based on limited data; (ii) The complexity of a parameterization required for accurate predictions is not understood; (iii) Incomplete and unknown physics leads to errors in the fully coupled Earth system; and (iv) Complex physics is computationally too expensive to employ in ESMs.
The plastic deformation of metals is a dissipative process. Some fraction of the plastic work is converted to heat which, given the temperature dependent response of metals, produces a thermal-mechanical coupling. In various cases, for instance when the loading is dynamic, this interaction can impact the resulting response of a material and/or system. Thus, appropriately capturing the heat generation from plastic work is necessary for various solid mechanics analysis. Determination of the fraction of work converted to heat has been long studied. Recent developments have demonstrated that the fraction is not constant but depends on various state variables. Resolving these features requires combined modeling and experimental studies. To this end, 304L stainless steel – a poor thermal conductor – was recently subjected to such an investigation. Advanced modeling capabilities were deployed to assess novel thermomechanically coupled experiments. As a complement to that study, in the current work a similar investigation is performed on copper – a good thermal conductor – to assess performance on the opposite end of the spectrum. The current document discusses these modeling efforts.
Oxygen partial pressure is an important thermodynamic state variable that affects both the performance and degradation of solid oxide electrolysis cells. In this work, a 3D model developed from Virkar’s 1D model has been applied to reconstructed and synthetic microstructures of Ni-YSZ-GDC-LSCF cell. The effect of the microstructures, including the thickness of the YSZ and GDC layer, and the compositions of the hydrogen and oxygen electrode, on the distribution of oxygen partial pressure has been investigated. The results show that the maximum oxygen partial pressure may occur on the interface between oxygen electrode and GDC layer or between GDC layer and YSZ layer depending on the rate of oxygen ion exchange between GDC and YSZ. A thicker GDC layer lowers the maximum oxygen partial pressure in the cell, while a thicker YSZ layer lowers the maximum oxygen partial pressure in the hydrogen electrode. In addition, the Ni:YSZ ratio and porosity also affects the maximum partial pressure. These findings provide insights on mitigating degradation in solid oxide electrolysis cell by tuning the microstructures.
This project introduces an advanced deep learning (DL)-based surrogate modeling approach to enhance the efficiency and accuracy of large-scale geological carbon storage (GCS) simulations. Using the Illinois Basin Decatur Project (IBDP) dataset as training data, the study employs a residual U-Net architecture to predict critical state variables such as pressure and CO₂ saturation, as well as CO₂ plume migration. By incorporating key geological parameters (e.g., porosity, permeability, and rock facies) and physics-informed inputs like the diffusive time of flight and time step, the DL model effectively reduces computational complexity while maintaining robust physical constraints. Compared to traditional simulators like Eclipse, the DL model achieves remarkable accuracy, with a root mean square error (RMSE) of 1.57 psi for pressure and 0.007 for saturation, and dramatically reduces computational time from hours to just 69.9 seconds for 50-step simulations. These results demonstrate the potential of innovative DL methodologies to improve the predictivity and operational efficiency of GCS simulations, providing a reliable foundation for decision-making in CCS operations. Supported by the SMART initiative, this project underscores the success of leveraging computational innovations to advance CCS technologies.
This report documents the work performed to address several defects and features request related to the restart capability in MPACT. The following defects were fixed as a part of this milestone: • #3616 (Defect) Error in MPACT Restart Files with BPRs, • #3637 (Defect) Restart with Semi-PC Yields Inconsistent Results, • #3652 (Defect) Fix Incorrect Exposure Value on HDF File After Restart, • #3777 (Defect) Fix State Point Number when Using Restart File, • #3986 (Defect) Pin Exposures are Incorrect, • #4106 (Defect) Pin Exposures Equal to Zero After Restart, and • #4288 (Defect) Core Average Exposure Wrong when Reading Restart Files. The new features that were added are: • Axial Re-meshing capability, • Incorporation of MAMBA-1D data necessary to restart coupled MPACT/CTF/MAMBA calculations, and • Addition of state variables to the restart file. Numerous tests were also implemented to verify and protect these features and this report was prepared to document the work performed.
A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology and precipitation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology and precipitation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.