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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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At least 1,297 records · Page 72

Transient Efficiency, Flexibility, and Reliability Optimization of Coal-Fired Power Plants - Final Program Review

This program developed an advanced model-based monitoring and model-predictive control algorithms for a coal fired power plant (CFPP), and deployed these algorithms in a real-time platform to demonstrate performance benefits for transient flexibility and plant operation efficiency. More specifically, the objectives were successfully achieved through a combination of (i) developing a high-fidelity transient plant model in Apros, which was used as a high-fidelity plant simulation between $100-50\% TMCR$ where TMCR denotes the turbine maximum continuous rating, i.e., baseload, (ii) developing a very fast physics-based reduced-order model (ROM) of the plant, which ran more than $100\times$ faster than real-time, enabling its use as real-time embedded model for model-based estimation (MBE) and model predictive control (MPC) (iii) implementing a real-time MBE based on ROM using a robust unscented Kalman filter (UKF) to continuously tune the ROM to match the measurements from high-fidelity Apros plant model despite significant plant-model mismatch, and thus, obtain a Digital Twin of the plant (iv) designing and implementing a real-time MPC with dual objectives of transient plant load tracking with high ramp rates and minimizing coal consumption, i.e., improving plant efficiency in the baseload-partload operation range of $100-50\% TMCR$. Each key element above was developed and tested individually, and has been reported in corresponding Topical Reports. Finally, all the individual elements were integrated in an overall closed-loop system, that was successfully tested in desktop Simulink test harness simulations with ROM or high-fidelity model as the plant. Thereafter, the Simulink implementation was used to auto-generate C-code and deploy as real-time Docker microservice containers in Linux, and validate that they can run in real-time in the hardware-in-the loop (HIL) setup and produce the same results as in Simulink. The results of the integrated simulation tests in Simulink as well as the real-time HIL deployment are documented in this final report, showing good load tracking for load ramps at $3-4\%/min$ ramp rates, and achieving up to $5.5\%$ reduction in coal relative to baseline operation at $50\% TMCR$ load. The desktop and HIL simulations show successful performance of the overall model based estimation and control solution and achieve the key objectives of the program for flexible, efficient and reliable operation of sub-critical coal fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗

TomocuPy – efficient GPU-based tomographic reconstruction with asynchronous data processing

Fast 3D data analysis and steering of a tomographic experiment by changing environmental conditions or acquisition parameters require fast, close to real-time, 3D reconstruction of large data volumes. Here a performance-optimized TomocuPy package is presented as a GPU alternative to the commonly used central processing unit (CPU) based TomoPy package for tomographic reconstruction. TomocuPy utilizes modern hardware capabilities to organize a 3D asynchronous reconstruction involving parallel read/write operations with storage drives, CPU–GPU data transfers, and GPU computations. In the asynchronous reconstruction, all the operations are timely overlapped to almost fully hide all data management time. Since most cameras work with less than 16-bit digital output, the memory usage and processing speed are furthermore optimized by using 16-bit floating-point arithmetic. As a result, 3D reconstruction with TomocuPy became 20–30 times faster than its multi-threaded CPU equivalent. Full reconstruction (including read/write operations and methods initialization) of a 2048 3 tomographic volume takes less than 7 s on a single Nvidia Tesla A100 and PCIe 4.0 NVMe SSD, and scales almost linearly increasing the data size. To simplify operation at synchrotron beamlines, TomocuPy provides an easy-to-use command-line interface. Efficacy of the package was demonstrated during a tomographic experiment on gas-hydrate formation in porous samples, where a steering option was implemented as a lens-changing mechanism for zooming to regions of interest.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

An ion thruster module for primary propulsion systems.

The development of a 30 cm thruster module having the operational characteristics, weight, and structural integrity consistent with flight hardware is described. Elements of the program discussed in this paper are selection of an ion optical system design, development of the discharge chamber and its control, and the results of extensive performance mapping tests. The thruster system operates at 2750 sec specific impulse at 69% over-all efficiency and can be throttled from 2.0 A to 0.16 A beam current with a control system requiring a single electrical input. The 1 kV ion beam is formed by a high perveance, two grid ion optical system.

King, H. J.↗

Understanding the NASTRAN structural plotter

Functions of the plotting hardware and the operations performed by the NASTRAN structural plotter are summarized to provide the user an insight into how the program works. The processing performed in response to each plot command is described and the interrelationships of the various parameters are explained. To assist in the effective and efficient utilization of the system, examples are provided which also illustrate several not yet documented capabilities of the Level 16 structural plotter.

Kuusinen, L. R.↗

NASA Office of Space Flight (OSF) in-orbit servicing program

Tools, fixtures and systems being developed to improve the efficiency of extravehicular activity (EVA) servicing operations; and tools developed for EVA use being adapted for use in robot-assisted EVA, local robotic servicing, and remote robotic servicing, are presented. The basic types of satellite servicing operations, the various modes by which they can be accomplished, and the hardware which supports these modes are outlined. The program and hardware elements of the program infrastructure are reviewed.

Levin, George C.↗

Scalability and Portability of Two Parallel Implementations of ADI

Two domain decompositions for the implementation of the NAS Scalar Penta-diagonal Parallel Benchmark on MIMD systems are investigated, namely transposition and multi-partitioning. Hardware platforms considered are the Intel iPSC/860 and Paragon XP/S-15, and clusters of SGI workstations on ethernet, communicating through PVM. It is found that the multi-partitioning strategy offers the kind of coarse granularity that allows scaling up to hundreds of processors on a massively parallel machine. Moreover, efficiency is retained when the code is ported verbatim (save message passing syntax) to a PVM environment on a modest size cluster of workstations.

Phung, Thanh↗

Smart sensors using artificial intelligence for on-detector electronics and ASICs

Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated in next-generation experiments. Therefore, the need for near-sensor, at the data source, processing with more powerful algorithms is becoming increasingly important to more efficiently capture the right experimental data, reduce downstream system complexity, and enable faster and lower-power feedback loops. In this paper, we discuss the motivations and potential applications for on-detector AI. Furthermore, the unique requirements of particle physics can uniquely drive the development of novel AI hardware and design tools. We describe existing modern work for particle physics in this area. Finally, we outline a number of areas of opportunity where we can advance machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Recent Electric Propulsion Development Activities for NASA Science Missions

(The primary source of electric propulsion development throughout NASA is managed by the In-Space Propulsion Technology Project at the NASA Glenn Research Center for the Science Mission Directorate. The objective of the Electric Propulsion project area is to develop near-term electric propulsion technology to enhance or enable science missions while minimizing risk and cost to the end user. Major hardware tasks include developing NASA s Evolutionary Xenon Thruster (NEXT), developing a long-life High Voltage Hall Accelerator (HIVHAC), developing an advanced feed system, and developing cross-platform components. The objective of the NEXT task is to advance next generation ion propulsion technology readiness. The baseline NEXT system consists of a high-performance, 7-kW ion thruster; a high-efficiency, 7-kW power processor unit (PPU); a highly flexible advanced xenon propellant management system (PMS); a lightweight engine gimbal; and key elements of a digital control interface unit (DCIU) including software algorithms. This design approach was selected to provide future NASA science missions with the greatest value in mission performance benefit at a low total development cost. The objective of the HIVHAC task is to advance the Hall thruster technology readiness for science mission applications. The task seeks to increase specific impulse, throttle-ability and lifetime to make Hall propulsion systems applicable to deep space science missions. The primary application focus for the resulting Hall propulsion system would be cost-capped missions, such as competitively selected, Discovery-class missions. The objective of the advanced xenon feed system task is to demonstrate novel manufacturing techniques that will significantly reduce mass, volume, and footprint size of xenon feed systems over conventional feed systems. This task has focused on the development of a flow control module, which consists of a three-channel flow system based on a piezo-electrically actuated valve concept, as well as a pressure control module, which will regulate pressure from the propellant tank. Cross-platform component standardization and simplification are being investigated through the Standard Architecture task to reduce first user costs for implementing electric propulsion systems. Progress on current hardware development, recent test activities and future plans are discussed.

Pencil, Eric J.↗

Applications of the real-time data analysis system in the Ames 40- by 80-foot wind tunnel

The first major overhaul and refurbishment of the Ames 40- by 80-foot wind tunnel since it was put into operation in 1944 was recently completed. A substantial part of this refurbishment was devoted to providing this wind tunnel with a modern real time data acquisition and analysis system to increase the safety, efficiency, and accuracy of experimental investigations in this facility. Background leading to the requirements for the new system, major elements of the system, and some of the applications of the system are summarized. The potential of computerized data acquisition systems for wind tunnels in terms of long term trends in hardware and software costs, and the constraints which must be dealt with to achieve the full potential of computerized data acquisition systems are discussed.

Kelly, M. W.↗

Automotive Stirling Engine Development Project

The development and verification of automotive Stirling engine (ASE) component and system technology is described as it evolved through two experimental engine designs: the Mod 1 and the Mod 2. Engine operation and performance and endurance test results for the Mod 1 are summarized. Mod 2 engine and component development progress is traced from the original design through hardware development, laboratory test, and vehicle installation. More than 21,000 hr of testing were accomplished, including 4800 hr with vehicles that were driven more dm 59,000 miles. Mod 2 engine dynamometer tests demonstrated that the engine system configuration had accomplished its performance goals for power (60 kW) and efficiency (38.5%) to within a few percent. Tests with the Mod 2 engine installed in a delivery van demonstrated combined metro-highway fuel economy improvements consistent with engine performance goals and the potential for low emission levels. A modified version of the Mod 2 has been identified as a manufacturable design for an ASE. As part of the ASE project, the Industry Test and Evaluation Program (ITEP), NASA Technology Utilization (TU) project, and the industry-funded Stirling Natural Gas Engine program were undertaken to transfer ASE technology to end users. The results of these technology transfer efforts are also summarized.

Ernst, William D.↗

The Use of Human Factors Simulation to Conserve Operations Expense

In preparation for on-orbit operations, NASA performs experiments aboard a KC-135 which performs parabolic maneuvers, resulting in short periods of microgravity. While considerably less expensive than space operations, the use of this aircraft is costly. Simulation of tasks to be performed during the flight can allow the participants to optimize hardware configuration and crew interaction prior to flight. This presentation will demonstrate the utility of such simulation. The experiment simulated is the fluid dynamics of epoxy components which may be used in a patch kit in the event of meteoroid damage to the International Space Station. Improved configuration and operational efficiencies were reflected in early and increased data collection.

Hamilton, George S.↗

Demand Response Optimization and Management System for Real-TIme (DROMS-RT)

To design and demonstrate DROMS-RT, a highly distributed Demand Response Optimization and Management System for Real-Time (DROMS-RT) power flow control to support large scale integration of distributed renewable generation into the grid. AutoGrid developed a novel control and communications platform to allow highly dispatchable demand response (DR) services in time frames suitable for providing ancillary services to the transmission grid. These services will be substantially less expensive and more efficient than other forms of ancillary services options currently available to manage the intermittency associated with large-scale renewable integration. DROMSRT successfully leveraged Automated Demand Response (ADR) by fundamentally re-thinking the architecture of the DR platform from the ground up and by developing innovative new technologies in a number of areas related to DR. DROMS-RT leveraged the low-cost, open, interoperable DR signaling technology, OpenADR, and low-cost, internet-protocol based telemetry solutions to reduce the cost of hardware. This allowed DROMS-RT to provide dynamic price signals to millions of OpenADR clients. Statistically rigorous signal processing techniques were developed to reliably detect even small load reductions in the presence of noisy baseline profiles. Novel forecasting engines based on modern online machine learning algorithms enabled accurate individualized forecasts for customer loads in the presence of dynamic pricing signals, and a real-time decision engines enabled continuous optimization and optimal dispatch of DR resources across a large portfolio of heterogeneous loads that respond at varying time-scales. Moreover, the real-time optimization conducted by the decision engine can utilize grid physics to maximize load reduction at the transmission system in addition to the distribution sites, for more efficient grid operation. Finally, the Software-as-a-Service (SaaS) availability of the DROMS-RT platform has reduced the cost of deployment and enable participation of small commercial and residential customers in DR who otherwise would not be able to do so.

24 POWER TRANSMISSION AND DISTRIBUTION↗

James Webb Space Telescope Core 2 Test - Cryogenic Thermal Balance Test of the Observatorys Core Area Thermal Control Hardware

The James Webb Space Telescope (JWST), successor to the Hubble Space Telescope, will be the largest astronomical telescope ever sent into space. To observe the very first light of the early universe, JWST requires a large deployed 6.5-meter primary mirror cryogenically cooled to less than 50 Kelvin. Three scientific instruments are further cooled via a large radiator system to less than 40 Kelvin. A fourth scientific instrument is cooled to less than 7 Kelvin using a combination pulse-tube Joule-Thomson mechanical cooler. Passive cryogenic cooling enables the large scale of the telescope which must be highly folded for launch on an Ariane 5 launch vehicle and deployed once on orbit during its journey to the second Earth-Sun Lagrange point. Passive cooling of the observatory is enabled by the deployment of a large tennis court sized five layer Sunshield combined with the use of a network of high efficiency radiators. A high purity aluminum heat strap system connects the three instrument's detector systems to the radiator systems to dissipate less than a single watt of parasitic and instrument dissipated heat. JWST's large scale features, while enabling passive cooling, also prevent the typical flight configuration fully-deployed thermal balance test that is the keystone of most space missions' thermal verification plans. This paper describes the JWST Core 2 Test, which is a cryogenic thermal balance test of a full size, high fidelity engineering model of the Observatory's 'Core' area thermal control hardware. The 'Core' area is the key mechanical and cryogenic interface area between all Observatory elements. The 'Core' area thermal control hardware allows for temperature transition of 300K to approximately 50 K by attenuating heat from the room temperature IEC (instrument electronics) and the Spacecraft Bus. Since the flight hardware is not available for test, the Core 2 test uses high fidelity and flight-like reproductions.

JWST Thermal Core 2 Test↗

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

The recent development of deep learning has been mostly focusing on Euclidean data, such as images, videos, audios, etc. However, most real-world information and relation are often expressed as graphs. To efficiently learn from graph data, graph convolutional networks (GCNs) emerge as a promising approach, showing advantages in several practical applications such as social network analysis, knowledge discovery, 3D modeling, motion capturing, etc. Real-world graphs are usually extremely large and imbalanced, posting significant performance demand and design challenges on the hardware dedicated for GCN inference. In this paper, we propose an architecture design called UW-GCN to accelerate graph convolutional network inference. To tackle the major performance bottleneck from workload imbalance, we propose dynamic neighborhood stealing and remote chunk shuffling techniques, relying on hardware flexibility to achieve hardware auto-tuning under negligible area or delay overhead. Specifically, UW-GCN is able to smartly profile the sparse graph pattern while continuously adjusting the workload distribution via routing reconfiguration among parallel processing elements (PEs). The ideal configuration is then reused in the remaining iterations. To the best of our knowledge, this is the first accelerator design particularly for GCN and the first work relying on hardware auto-tuning, which is normally based on software, to achieve near-optimal workload balance in processing sparse structures.

Geng, Tong↗

The Case for Co-Designing Model Architectures with Hardware

While GPUs are responsible for training the vast majority of state-of-the-art deep learning models, the implications of their architecture are often overlooked when designing new deep learning (DL) models. As a consequence, modifying a DL model to be more amenable to the target hardware can significantly improve the runtime performance of DL training and inference. In this paper, we provide a set of guidelines for users to maximize the runtime performance of their transformer models. These guidelines have been created by carefully considering the impact of various model hyperparameters controlling model shape on the efficiency of the underlying computation kernels executed on the GPU. We find the throughput of models with “efficient” model shapes is up to 39% higher while preserving accuracy compared to models with a similar number of parameters but with unoptimized shapes.

Yin, Junqi↗

BeyondFingerprinting: AI-guided discovery of robust materials & processes

BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject-matter expertise with algorithms enriched with physics-based constraints to unearth process-structure-property correlations. Such algorithms, trained on high-throughput experiments and simulations, are shown to serve as surrogate models that efficiently detect key “fingerprints” in materials data, prognose material performance, and guide effective process improvements. To accelerate broader adoption across mission areas, this AI-guided approach was demonstrated with three complex process-centric exemplars: electroplating, physical vapor deposition, and laser powder bed fusion. Together, these exemplars impact nearly every hardware component relevant to DOE and NNSA national security missions.

36 MATERIALS SCIENCE↗