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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Rolling-element fatigue life with traction fluids and automatic transmission fluid in a high-speed rolling-contact rig

Rolling-element fatigue tests were run in standard and high-speed rolling-contact rigs at bar speeds from 5000 to 50,000 rpm to determine the effects of speed and lubricant film parameter on rolling-element fatigue life. AISI 52100 test bars were tested at a maximum Hertz stress of 4.83 GPa (700,000 psi) with three traction fluids and an automatic transmission fluid. Rolling-element fatigue life increased with speed, with the greatest increases occurring from 10,000 to 50,000 rpm. The life data tended to follow published life-versus-lubricant-film-parameter data up to a film parameter of approximately 3.

Parker, R. J.↗
DOE OSTI2020

Integrated Traction Drive Thermal Management: Keystone Project 3

The presentation was developed for the U.S. Department of Energy (DOE) Vehicle Technologies Program 2020 Annual Merit Review and Peer Evaluation Meeting. It reports on progress and accomplishments of DOE funded Integrated Traction Drive Thermal Management project. Highlighted are NREL's research accomplishments during FY 2020. Challenges associated with thermal management of integrated traction drives for electric vehicles arise due to the limited packaging space, coolant distribution, electrical connections, and ambient temperatures. For this project at NREL, research focuses on enabling thermal management technologies to support a modular, compact, self-contained integrated electric drive unit with motor and power electronics. Integration approaches, based on packaging location and coolant options for power electronics integrated with candidate motor technologies to reduce cables and connectors, are reviewed and compared using finite element analysis (FEA) tools. Automatic transmission fluid (ATF) jet impingement cooling characterization is carried out experimentally (measuring heat transfer coefficients) and using computational fluid dynamics (CFD) modeling.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Thermal Management for a Novel Non-Heavy Rare-Earth Interior Permanent Magnet Machine

The work presents a thermal management solution for a novel non-heavy rare-earth permanent magnet machine being developed at Oak Ridge National Laboratory. The motor has been designed to minimize losses while maximizing performance for a range of speeds and power ratings. The novel motor design reduces rare-earth magnet usage, thereby avoiding supply chain issues. The motor component heat losses are established for operating windows and desired performance. These heat losses, along with windage losses, are being used to develop cooling solutions for different components of this machine. A novel thermal management solution for stators and rotors has been developed, and progress is presented in this paper. The stator cooling is achieved with the help of water-ethylene glycol flowing over the finned aluminum stator jacket, and rotor cooling with automatic transmission fluid passing through novel channels designed in the rotor laminations. The attempt is to establish effective cooling of the stator winding, laminations, and rotor magnets. A 3D conjugate heat transfer model has been developed for overall thermal analysis to establish a down- selected thermal management solution for the machine. The model, in addition to estimated component heat losses, includes windage losses and its impact on rotor and stator cooling. Overall, the work presents a workable thermal solution for the interior permanent magnet machine with potential for further improvements. Future work will involve establishing end winding and refinement of other end parts of the machine with the aim of establishing a robust thermal management solution. The work will also focus on different shapes (e.g., round, non-round, presence of wedges) of rotor-stator gaps and investigate windage losses and their impact on thermal management for higher rotational speeds for the machine.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

CFD simulations of electric motor end ring cooling for improved thermal management

Proper thermal management of an electric motor for vehicle applications extends its operating range. One cooling approach is to impinge Automatic Transmission Fluid (ATF) onto the rotor end ring. Increased ATF coverage correlates to enhanced heat transfer. Computational Fluid Dynamics (CFD) analytical tools provide a mechanism to assess motor thermal management prior to hardware fabrication. The complexity of the fluid flow (e.g., jet atomization, interface tracking, wall impingement) and heat transfer makes these simulations challenging. Computational costs are high when solving these flows on high-speed rotating meshes. Typically, a Volume-of Fluid (VOF) technique (i.e., two-fluid system) is used to resolve ATF dynamics within this rotating framework. Suitable numerical resolution of the relevant physics for thin films under strong inertial forces at high rotor speeds is computationally expensive, further increasing the run times. In this work, a numerical study of rotor-ring cooling by ATF is presented using a patent automated Cartesian cut-cell based method coupled with Automatic Mesh Refinement (AMR). This approach automatically creates the Cartesian mesh on-the-fly and can effectively handle complex rotating geometries by adaptively refining the mesh based on local gradients in the flow field which results in better resolution of the air-ATF interface. A Single non-inertial Reference Frame (SRF) approach is used to account for the rotating geometry and to further improve the overall computational efficiency. Quasi-steady state conditions are targeted in the analysis of the results. Important physics such as ATF jet structure, velocity detail near the air-jet interface, ATF coverage/accumulation on the ring surface, and cooling capacity are presented for a low-resolution Reynolds averaged Navier-Stokes (RANS), high-resolution RANS, and high-resolution Large-Eddy Simulation (LES) models. Computations are scaled over hundreds of cores on a supercomputer to maximize turnaround time. Each numerical approach is shown to capture the general trajectory of the oil jet prior to surface impingement. The high-resolution LES simulation, however, is superior in capturing small scale details and heat transfer between the free jet and surrounding air.

42 ENGINEERING↗
DOE OSTI2021

On the feasibility of using physics-informed machine learning for underground reservoir pressure management

In this work, we evaluate the feasibility of using physics-informed machine learning (PIML) for underground energy-related pressure management. To this end, we develop a PIML framework to manage underground reservoir pressures by training neural networks to determine fluid extraction rates for dedicated extraction wells during fluid injection operations given a range of reservoir conditions (e.g., transmissivity and storativity). We implement an automatically-differentiable analytical physics model of fluid flow in porous media within the PIML framework as a proxy for more complicated models. This allows us to execute a sufficient number of training scenarios to fully evaluate the feasibility of using PIML to support pressure management activities. We quantify the number of physics-model parameters required for automatic differentiation to become more efficient than finite-difference gradient calculations. We use a simple scenario with a single injector, extractor, and critical location for our feasibility analysis. We evaluate the effect of the size of the training dataset (i.e., the number of reservoir condition samples) on the accuracy and efficiency of the PIML framework. For an equivalent number of model evaluations, the larger training dataset took less time to train and produced a neural network that was able to more accurately manage reservoir pressures. We also evaluate the effect of the training dataset batch size (i.e., number of reservoir condition samples used to update the neural network coefficients during training; i.e., how the training dataset is partitioned). While training ran faster with larger batch sizes, they produced neural networks that managed pressures less accurately. We demonstrate the approach on a more complex scenario involving 10 injectors, 10 extractors, and 4 critical locations (a relatively high well density of 20 wells/km2). We provide the number of forward and adjoint model evaluations required in each case as an indication of the feasibility of using PIML for pressure management when more complicated physics models with longer execution times are used.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Integrated Traction Drive Thermal Management: Keystone Project 3

The presentation represents a report of accomplishments during FY 2022 and is prepared for 2022 DOE VTO Annual Merit Review. The project goal is facilitation of achieving DOE 2025 target of 33 kW/L system power density for an electric traction drive through support of other EDT consortium members in design and development of thermal management systems for their respective integrated drive concepts. Namely, identifying pathways enabling high-performance, compact, and reliable integrated electric drives. Specific objectives are: 1) Research and evaluation of motor-integrated power electronics packaging technologies and thermal management approaches. 2) Development of thermal management system and its sub-components to enable integrated electric drive DOE power density targets in collaboration with project partners. 3) Supporting activities of DOE's Electric Drive Technologies (EDT) consortium members, Oak Ridge National Laboratory (ORNL) and University of Wisconsin research teams in thermal management component design and thermal modeling of their integrated traction drives. 4) Identifying candidate driveline fluids suitable for direct cooling of traction-drive components and high-voltage power electronics and evaluate their convective cooling performance.

ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2013

Wireless Integrated Microelectronic Vacuum Sensor System

NASA Stennis Space Center's (SSC's) large rocket engine test facility requires the use of liquid propellants, including the use of cryogenic fluids like liquid hydrogen as fuel, and liquid oxygen as an oxidizer (gases which have been liquefied at very low temperatures). These fluids require special handling, storage, and transfer technology. The biggest problem associated with transferring cryogenic liquids is product loss due to heat transfer. Vacuum jacketed piping is specifically designed to maintain high thermal efficiency so that cryogenic liquids can be transferred with minimal heat transfer. A vacuum jacketed pipe is essentially two pipes in one. There is an inner carrier pipe, in which the cryogenic liquid is actually transferred, and an outer jacket pipe that supports and seals the vacuum insulation, forming the "vacuum jacket." The integrity of the vacuum jacketed transmission lines that transfer the cryogenic fluid from delivery barges to the test stand must be maintained prior to and during engine testing. To monitor the vacuum in these vacuum jacketed transmission lines, vacuum gauge readings are used. At SSC, vacuum gauge measurements are done on a manual rotation basis with two technicians, each using a handheld instrument. Manual collection of vacuum data is labor intensive and uses valuable personnel time. Additionally, there are times when personnel cannot collect the data in a timely fashion (i.e., when a leak is detected, measurements must be taken more often). Additionally, distribution of this data to all interested parties can be cumbersome. To simplify the vacuum-gauge data collection process, automate the data collection, and decrease the labor costs associated with acquiring these measurements, an automated system that monitors the existing gauges was developed by Invocon, Inc. For this project, Invocon developed a Wireless Integrated Microelectronic Vacuum Sensor System (WIMVSS) that provides the ability to gather vacuum-gauge measurements automatically and wirelessly, in near-real time - using a low-maintenance, lowpower sensor mesh network. The WIMVSS operates by using a self-configuring mesh network of wireless sensor units. Mesh networking is a type of networking where each sensor or node can capture and disseminate its own data, but also serve as a relay to receive and transmit data from other sensors. Each sensor node can synchronize with adjacent sensors, and propagate data from one sensor to the next, until the destination is reached. In this case, the destination is a Network Interface Unit (NIU). The WIMVSS sensors are mounted on the existing vacuum gauges. Information gathered by the sensors is sent to the NIU. Because of the mesh networking, if a sensor cannot directly send the data to the NIU, it can be propagated through the network of sensors. The NIU requires antenna access to the sensor units, AC power, and an Ethernet connection. The NIU bridges the sensor network to a WIMVSS server via an Ethernet connection. The server is configured with a database, a Web server, and proprietary interface software that makes it possible for the vacuum measurements from vacuum jacketed fluid lines to be saved, retrieved, and then displayed from any Web-enabled PC that has access to the Internet. Authorized users can then simply access the data from any PC with Internet connection. Commands can also be sent directly from the Web interface for control and maintenance of the sensor network. The technology enabled by the WIMVSS decreases labor required for gathering vacuum measurements, increases access to vacuum data by making it available on any computer with access to the Internet, increases the frequency with which data points can be acquired for evaluating the system, and decreases the recurring cost of the sensors by using off-the-shelf components and integrating these with heritage vacuum gauges.

Krug, Eric↗
DOE OSTI2026

The Art of Automation: Translating Electron Microscopy Workflows Into Automated Processes

Acquiring data using a scanning transmission electron microscope (STEM) is a complex, multi-step process. The intricacy of the process depends on the type of sample, composition of the material, desired results of the experiment, resolution requirement and other experimental factors. Each experiment presents unique complications, such as sample drift and contamination, that the microscopist must consider when acquiring data. All these challenges are handled fluidly and expertly by experienced microscopists, but to reach new levels of innovation in material development, including greater reproducibility, throughput, and precision, the automation of these workflows is essential. The initial phase of this work involved translating intuition-based workflows into discrete, programmable steps. Some common key stages in STEM workflows are the initial tuning, scanning the sample for areas of interest, and then acquiring the data. Each stage can be broken further into specific parameter adjustments, such as aberration correction and dwell time optimization, depending on the experiment. When deconstructing various experiments each step was assessed for automation feasibility based on the amount of real time operator decisions. There are steps that lend themselves to automation more readily than others, such as course focusing and sample screening, but there is potential for full automation of all stages with time. As an initial step, an automated montage routine was developed, allowing for the efficient acquisition of large portions of the sample without requiring continuous intervention from the operator. The automation of this small process of the procedure demonstrates the value of this capability. A major challenge in automation arises from discrepancies between commanded, reported and actual stage movements. Using systematic tests, stage movement was quantified. This error can be corrected algorithmically for more accurate workflows in the future. Expanding automation capabilities would result in larger, more efficient data acquisition which allows for more robust statistical analysis. Additionally, this work lays the groundwork for a closed loop system where machine learning algorithms would intake automatically acquired data and make real time decisions. By progressively automating this instrument, this work establishes the foundation for fully automated experimentation in transmission electron microscopy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Design, Deployment, and Characterization of the World’s First Flexible Large Power Transformer

GE Research and its partner Prolec GE have designed, built and deployed in the field the world’s first flexible power transformer. The flexible power transformer is a transmission class 3-phase autotransformer configurable in impedance and in voltage which allows it to serve as a universal spare for multiple units in a given fleet. However, the key innovation in this new concept is the online adjustable leakage impedance which allows the transformer to change its impedance without interrupting the transmission line operation. The flexible power transformer can be designed with up to three low voltage transmission class ratings and up to 12 impedance values changeable both online and offline. This report provides an overview of the design, manufacturing, testing, and commissioning of the 165kV, 60MVA prototype built including the results of the field performance validation tests. The prototype was specified in collaboration with Cooperative Energy, the utility host. It was designed and tested in the factory according to IEEE standard C57.12.00 and followed all protocols for transportation, installation, and commissioning of a power transformer. In addition to the prototype, a flexible protection system capable of automatically adjusting its settings upon the transformer impedance was also developed and deployed in the for testing and validation. On September 3, 2021 the prototype was energized in Cooperative Energy’s substation in Columbia, Mississippi to become the world’s first flexible power transformer in operation. Its performances and impact on the grid operation were demonstrated through different field tests. Results obtained confirm that the impedance of the flexible transformer can be varied under load through its full range, from 4.3% to 9.3%, without adverse impacts on the line operation, the protection system, the transformer stability and health condition. Results also proved that the flexible transformer is very effective in controlling power transfer through the line or load sharing between units operating in parallel. Indeed, it was proven that higher impedances decrease the thruput power of the transformer while lower impedances increase it. Up to 26MW was controllable on a line loading of 45MVA. It was also possible to demonstrate that the variation of the transformer impedance has no effect on the circulating current between units in parallel, except a minor transient during the impedance change. It was also proven that the flexible protection relay can update its protection settings automatically when the impedance change was detected. The prototype has operated continuously for more than 12 months now with a peak load exceeding 50MVA corresponding to >80% of its ONAN power rating. No alarm, trip or sign of failure has been reported by the utility. In addition to the development and deployment of the flexible transformer prototype, investigations were carried out on new nanodielectric fluids to replace the mineral oil used in power transformers with the goal of reducing their footprint and weight. The key parameters that were targeted for improvement included the breakdown voltage to reduce clearances between windings and tank hence the footprint; viscosity and thermal conductivity to increase the cooling efficiency and therefore to reduce the winding material. Several nanodielectric mixtures with mineral oil including with alumina (Al2O3), titania (TiO2) and Borum Nitrate (BN) with different surfactants have been analyzed and tested. Unfortunately, despite encouraging results no nanofluid candidate has been found viable to replace mineral oil. With the formulations tested, breakdown voltages are generally similar to mineral oil at lower particle contents and worse at higher particle contents. Viscosity appreciably increased at particles concertation over 2 wt% and thermal conductivity increased slightly at 5wt% and appears to be 10-15% higher at 10 wt% particle content. It is recommended to continue investigations to find solutions that can help increase the power density of future flexible power transformers. Flexible power transformers can significantly help the future power grid by providing more flexibility and resiliency. Indeed, by providing voltage and impedance flexibility, flexible power transformers reduce the need for multiple spares, hence inventory costs for utilities. With their online controllable impedance, they can provide support to the grid and help manage short-circuit currents, power flow, line congestion, and grid stability which will become more important with higher penetrations of intermittent renewable resources. During the field validation tests, it was demonstrated that up to 26MW was controllable on a line loading of 45MVA when the transformer impedance was varied from its minimum to its maximum range. Also, with the impedance range, the short-circuit currents could be reduced by up to 38% at the load side of the transformer. With its controllable impedance, flexible transformers can be used in future strategies of grid resilience to help better prepare the grid to face forecasted severe events including storms, heat waves and contingencies. The flexible power transformers can also find role in other applications including high voltage transmission cables such as offshore wind farms where solutions for energizing the cables and managing the reactive power are of critical importance. The designed flexible power transformer is now fully validated and ready for commercialization. Further analysis on the benefits of flexible power transformers for grid stability and short-circuit management including current limiting capability, reclosure and line restoration, control of inrush current, sizing of flexible AC components (FACTS) would help its rapid adoption by the industry.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗