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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 main goal of this project is to develop accurate, efficient, and robust physics-based algorithms for modeling and simulation of flow and transport coupled with geomechanics in complex hydrological formations. We have developed an advanced modeling and simulation framework for multiphysics systems with multiscale and uncertain input parameters.
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The United States (U.S.) Department of Energy (DOE) Office of Fossil Energy (FE) at the National Energy Technology Laboratory (NETL) has developed the FE/NETL CO 2 Prophet Model, which is an updated version of the CO 2 Prophet. This document is a user’s manual for StrmtbFlow, which is one part of the FE/NETL CO 2 Prophet Model. CO 2 Prophet was originally developed in the 1990s by Texaco Exploration and Production Technology Department for DOE. The FE/NETL CO 2 Prophet Model is an oil reservoir simulator that is suitable for simulating water floods and supercritical carbon dioxide (CO 2 ) enhanced oil recovery (EOR). The FE/NETL CO 2 Prophet Model uses a number of assumptions to simplify the equations describing the flow of oil, water (or brine), and CO 2 in the oil reservoir.
An important part of a navigation system for a moving platform is the estimation of the rate of travel. This document presents a method for estimating the platform velocity in 3-dimensions using multiple antenna subarrays which could be used to augment navigation in a GPS-degraded environment. An advantage of this technique is that it does not require any knowledge of a positions of any landmarks. Results from radar data collected by the Sandia National Laboratories demonstration radar system are presented to illustrate the promise of this technique.
One critical issue related to shale oil/gas production is a rapid decline in wellbore production and a low recovery rate. Therefore, maximizing wellbore production and extending the production life cycle are crucial for the sustainability of shale oil/gas production. Shale oil/gas production starts with creating a fracture network by injecting a pressurized fluid in a wellbore. The induced fractures are then held open by proppant particles. During production, oil and gas release from the shale matrix, migrate to nearby fractures, and ultimately reach a production wellbore. Given the relatively high permeability of the induced fractures, oil/gas release and transport in low-permeability shale matrix are likely a limiting step for long-term wellbore production. This project is aimed to (1) fundamentally understand the disposition and release of complex hydrocarbon mixtures in nanopore networks of shale matrix and their transport from low-permeability matrix to hydrofracking-induced fractures under various reservoir conditions ranging from black oil to dry gas and (2) use machine learning to upscale and integrate the nanometer-scale understanding into reservoir-scale model simulations. The work will help develop new stimulation strategies to enable efficient resource recovery from fewer and less environmentally impactful wells.
Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applications in cloud and HPC. Owing to their unique compute and memory characteristics that come from an interplay between dense and sparse phases of computations, the emergence of reconfigurable dataflow (aka spatial) accelerators offers promise for acceleration by mapping optimized dataflows (i.e., computation order and parallelism) for both phases. The goal of this work is to characterize and understand the design-space of dataflow choices for running GNNs on spatial accelerators in order for the compilers to optimize the dataflow based on the workload. Specifically, we propose a taxonomy to describe all possible choices for mapping the dense and sparse phases of GNNs spatially and temporally over a spatial accelerator, capturing both the intra-phase dataflow and the inter-phase (pipelined) dataflow. Using this taxonomy, we do deep-dives into the cost and benefits of several dataflows and perform case studies on implications of hardware parameters for dataflows and value of flexibility to support pipelined execution.
The atmospheric pressure results presented show that n-heptane and iso-butanol mixtures had droplet diameter squares that decreased nearly linearly with time during burning while the flame standoff ratio increased throughout burning. Increasing the iso-butanol fraction decreased the burning-rate because of the accompanying increase in the density of the iso-butanol compared to heptane. At the same time, increasing iso-butanol mixture fraction appreciably reduced the maximum acetylene mole fraction and the sooting tendency. The mechanism is consistent with carbon atoms in iso-butanol binding to oxygen atoms that cannot then be transformed into soot. Computational and experimental results exhibited generally very good agreement. The simulated flame diameters bounded the measurements using definitions of flame diameter based on the maximum temperature and maximum OH mole fraction. With the simulations validated by the experiments, predictions of the peak acetylene concentrations as a soot precursor were made and found to occur very near the 1350 K isotherm as a soot inception temperature. Moreover, simulated greenhouse gas concentrations (CO2) were not appreciably changed with increasing iso-butanol concentration. As such, the results show a tradeoff with iso-butanol addition, namely that mixture droplets burn slower but produce less particulates and with little influence on formation of CO2 as a representative greenhouse gas.
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This innovation is based on advanced multi-dimensional extensions of ECVT sensors that involve ECVT, Displacement Current Phase Tomography (DCPT), Maxwell-Wagner-Sillars polarization effect (MWS), and velocimetry which exploit the variation of electric properties between the oil, water, and gas coming out of the well. Capacitance sensors are embedded on the inside of a pipe spool and placed in-line with the subsea oil line piping on the extraction end of the well. The difference in dielectric, dielectric loss, surface polarization, and velocity of each phase are used in the multi-dimensional algorithm to measure the volume fraction, distribution, velocity, mass flow rate, and flow regime of the mixture moving through the pipe.
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Explore the source record for details and available documents.