SEARCH · Search NASA
Results for “system of systems”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Comparison of Surface Tension Generation Methods in Smoothed Particle Hydrodynamics for Dynamic Systems
Developing robust numerical models of dynamic surface tension dominated multiphase systems is an ongoing challenge, especially in scenarios with large density and viscosity ratios. This is critical to the design and understanding of various physical and engineering systems, such as fluidized beds, fuel injectors, and drug delivery schemes. Much of the computational work in surface tension dominated multiphase flows has employed the continuum surface force method (CSF) of Brackbill et al. [1], which recasts surface tension from a surface force to a volumetric force that can be imposed in the vicinity of an interface. The CSF method produces accurate results across a variety of systems, however it relies on the identication of surface normals, which can be unreliable under certain conditions. Alternative methods of simulating surface tension have been proposed. Here the advantages and disadvantages of the CSF method in comparison to a pairwise forces (PF) method proposed by Tartakovsky and Panchenko [2] are explored. The CSF and PF methods are used in a smoothed particle hydrodynamics (SPH) framework to model dynamic systems. Results are compared to existing test cases from the literature and to analytic solutions derived from fundamental normal mode behavior of bubbles and droplets. A physical system for which the PF method is more stable and physically appropriate than the CSF method is identied.
Conversion of the LLNL/CAMS 1 MV biomedical AMS system to a semi-automated natural abundance 14C spectrometer: system optimization and performance evaluation
The Lawrence Livermore National Laboratory – Center for Accelerator Mass Spectrometry compact 1 MV biomedical accelerator mass spectrometer was repurposed and optimized for the semi-automated radiocarbon measurement of natural abundance environmental samples. Here we present results from 15 months of routine system operation and evaluate the system performance based on 30 sample wheels measured with consistent operating conditions over 7 months from August 2019 to March 2020. Additionally, the average measurement precision was 2.7 ± 0.7‰. System accuracy was assessed with measurements of 14 C-free materials, standard materials with 14 C-content spanning 0.5 to 1.5 F 14 C, and by comparison to split samples measured with the 10 MV AMS system. Overall, this system can routinely measure samples as small as 300 µgC and less than 33000 years without the need for size-specific correction protocols.
W-band system-on-chip electron cyclotron emission imaging system on DIII-D
Monolithic, millimeter wave “system-on-chip” (SoC) technology has been employed in heterodyne receiver integrated circuit radiometers in a newly developed Electron Cyclotron Emission Imaging (ECEI) system on the DIII-D tokamak for 2D electron temperature profile and fluctuation evolution diagnostics. Here, a prototype module operating in E-band (72-80 GHz) was first employed in a 2 x 10 element array that demonstrated significant improvements over the previous quasi-optical Schottky diode mixer arrays during the 2018 operational campaign of the DIII-D tokamak. For compatibility with International Thermonuclear Experimental Reactor (ITER) relevant scenarios on DIII-D, the SoC ECEI system was upgraded with 20 horn-waveguide receiver modules. Each individual module contains a University of California at Davis designed W-band (75 -110 GHz) receiver die that integrates a broadband low noise amplifier (LNA), a double balanced down-converting mixer, and a x4 multiplier on the local oscillator (LO) chain. A x2 multiplier and two IF amplifiers are packaged and selected to further boost the signal strength as well as downconvert the signal frequency. The upgraded W-band array exhibits > 30 dB additional gain and 20x improvement in noise temperature comparing with the previous Schottky diode radio frequency (RF) mixer input systems; an internal 8 times multiplier chain is used to bring down the LO frequency below 12 GHz, thereby obviating the need for a large aperture for quasi-optical LO coupling and replacing it with coaxial connectors. The horn-waveguide shielding housing avoids out-of-band noise interference on each individual module. The upgraded ECEI system plays an important role for absolute electron temperature evolution and fluctuation measurements for edge and core region transport physics studies.
Lighting System Control Data to Improve Design and Operation: Tunable Lighting System Data from NICU Patient Rooms
The advancement of LED and controls technology, computing capacity, and software provides new opportunities for researchers and designers to work together to further optimize spaces for occupant benefit. Here, lighting system control data from five neonatal intensive care unit patient rooms was collected over a 25-week monitoring period and analyzed to better understand occupant response to a tunable lighting system with automatic transitions throughout the day. Lighting systems are very rarely refined after installation based on actual use. Objective data detailing how the lighting system is used by the actual occupants highlights the opportunities for optimization after installation and provides insight for improving the next design. As use of the data becomes more commonplace, it can be leveraged for design recommendations. The collection of the data required no additional cost beyond the time for examining the data. The analysis revealed several clear opportunities for improvement, including adjustments to the default control setting at night, re-labeling of the control stations, and adjustments to the nighttime fade rate. The patient room occupants were active users of the different zones, dimming options, and manual overrides made available by the lighting system.
Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the Spallation Neutron Source
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory accelerates proton beams, which are directed toward a mercury target to generate the world’s most intense neutron beams via spallation. The target system consists of several interconnected subsystems and accounts for a major share of the facility’s overall downtime. Early detection of anomalies in the target system response can thus provide the possibility of taking corrective actions to reduce downtime. Accelerator facilities have largely focused on the beam side for data-driven fault prognostics. On the target side, SNS relies on operational shift technicians (OSTs), who respond to alarms and manually flag anomalies onto the System Tracking and Reliability (STAR) platform. This paper presents one of the first studies of using machine learning (ML) to automate anomaly detection in the target system. The study focused on the mercury process system as the first use case and employed reconstruction-based anomaly detection on minutely sampled time series signals. The pipeline was integrated into the STAR platform to autonomously rank and flag anomalies every week. The STAR platform provides a user interface for the OSTs to evaluate the flagged anomalies, thereby incorporating human feedback.
Quantifying the Window of Uncertainty for SSTDR Measurements of a Photovoltaic System
Not provided.
Beyond Price-Taker: Multiscale Optimization of Wind and Battery Integrated Energy Systems
Integrating renewable energy into the electric grid is challenging due to the intermittency and variability of wind and other non-dispatchable resources. Integrated energy systems (IESs) combine multiple energy technologies (e.g., fossil, nuclear, renewables, storage) to reduce costs and improve flexibility and reliability. However, standard techno-economic analysis (TEA) methods often overestimate the benefits of IESs because they fail to account for energy market adjustments. This paper systematically studies the limitations of the prevailing price-taker assumption for TEA and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 30.4%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.
Overview of High-Power Wireless Charging Systems and Analysis of Polyphase Wireless Charging System Phase Winding and Resonant Tuning Network Connection Configurations
In spite of the high energy efficiency and environmental benefits of electric vehicles (EVs), adoption rates are increasing at a relatively slow pace, primarily because of EVs’ limited range and long charging durations. To reduce EV charging times to be comparable to refueling times of conventional vehicles, extreme fast charging systems are required. Such charging systems would reduce drivers’ range anxiety and enable long-distance interstate travel with EVs. Here, this substantial target in charging rates with 15–20 minutes of recharging times, requires research and development from grid to batteries with advanced charging systems. Wireless power transfer systems for EV charging applications are flexible, convenient, and highly efficient, and they allow automated charging. This paper reviews the power electronics and winding and resonant tuning network configurations for polyphase wireless power transfer systems for high-power wireless charging applications.
Dialogue Between Lighting and HVAC Systems: Improving building system integration
Lighting systems have long been capable of sensing when someone enters or exits a room and using that knowledge to turn lights on or off. More recently, connected lighting systems with sensors integrated into every luminaire have become broadly available, facilitating highly granular occupancy detection. Similarly, HVAC systems have long been able to use an understanding of building occupancy to adjust temperature setpoints and reduce energy use without significant impacts to occupant comfort. Energy codes (e.g., ASHRAE/IES Standard 90.1, IECC, Title 24) now require “occupied standby HVAC control,” whereby systems adjust both temperature and ventilation setpoints in zones that are determined to be unoccupied during normal occupancy hours. Here, this article discusses current issues that stymie the integration of Lighting and HVAC systems, and DOE activities focused on addressing them.
System Response Characterization for a d–t Neutron Radiography System
We report the system response of a pixelated associated particle imaging (API) neutron radiography system. The detector readout currently consists of a 2x2 array of organic glass scintillator detectors, each with an 8x8 array of optically isolated pixels that match the size and pitch of the ARRAYJ-60035-64P-PCB Silicon Photomultiplier (SiPM) array from SensL/onsemi with 6x6 mm 2 SiPMs. The alpha screen of the API deuterium-tritium neutron generator is read out with the S13361-3050AE-08 from Hamamatsu, which is an 8x8 array of 3x3 mm 2 SiPMs. Data from the 320 channel system is acquired with the TOFPET2-based readout system. We present the predicted imaging capability of an eventual 5x5 detector array, the waveform-based energy and pulse shape characterization of the individual detectors, and the timing and energy response from the TOFPET2 system.
The Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2
Abstract. The potential for multiyear prediction of impactful Earth system change remains relatively underexplored compared to shorter (subseasonal to seasonal) and longer (decadal) timescales. In this study, we introduce a new initialized prediction system using the Community Earth System Model version 2 (CESM2) that is specifically designed to probe potential and actual prediction skill at lead times ranging from 1 month out to 2 years. The Seasonal-to-Multiyear Large Ensemble (SMYLE) consists of a collection of 2-year-long hindcast simulations, with four initializations per year from 1970 to 2019 and an ensemble size of 20. A full suite of output is available for exploring near-term predictability of all Earth system components represented in CESM2. We show that SMYLE skill for El Niño–Southern Oscillation is competitive with other prominent seasonal prediction systems, with correlations exceeding 0.5 beyond a lead time of 12 months. A broad overview of prediction skill reveals varying degrees of potential for useful multiyear predictions of seasonal anomalies in the atmosphere, ocean, land, and sea ice. The SMYLE dataset, experimental design, model, initial conditions, and associated analysis tools are all publicly available, providing a foundation for research on multiyear prediction of environmental change by the wider community.
System Modeling of the HTTR and Economic Dispatch Model of the Secondary System
High Temperature Gas-cooled Reactors (HTGRs) can be used for the generation of electricity and their process heat can be used to improve the efficiency of chemical processes such as hydrogen production. The JAEA-operated High-Temperature engineering Test Reactor (HTTR-GT/H2) is exploring using the reactor for electricity and hydrogen production. A RELAP5-3D model of the HTTR-GT/H2 secondary system has been developed using design information. The various components and heat exchangers in the secondary system were modeled and results were compared to the design conditions. The results for the sole-power generation mode were shown to fit the design conditions very well. The largest temperature difference was on the order of 7 K, and the largest pressure difference was on the order of 0.05 MPa. The results for the hydrogen cogeneration mode did not match the design conditions nearly as well. The largest temperature difference was about 39 K and the largest pressure difference was about 0.27 MPa at the compressor outlet. The larger differences for the hydrogen cogeneration mode are attributed to the various complex components and the flow being split in the secondary loop. A transient reduction in heat removal capability of the secondary system was investigated. Reactor temperatures are anticipated to rise as a result. The core reactivity response due to this increase in temperature is investigated and is expected to add negative reactivity to the reactor. An economic dispatch model was developed for a nuclear-driven iodine-sulfur cycle system to determine hydrogen sale prices that would make such a system profitable. The study focuses on the development of the economic model and the role that input data plays on final calculated values. It was found that the input electricity prices, whether using historical data or a host of synthetic time histories, produce significantly different breakeven hydrogen sale prices. As such, great care should be used in these economic dispatch analyses to select reasonable input assumptions.
Hybrid power system control and operating strategy based on power system state vector calculation
Controlling a hybrid power system includes calculating a power system state vector based on energy demand and a stored data array including a matrix defined by a power system hardware configuration. The control further includes producing a power request based on the power system state vector, and varying a flow of energy amongst energy devices using drive linkages in the hybrid power system based on the power request. Related apparatus, control logic and controller structure is disclosed.
Systems and methods for single-axis tracking via sky imaging and machine leanring comprising a neural network to determine an angular position of a photovoltaic power system
A system and method is disclosed for solar tracking and controlling an angular position of a photovoltaic power system. The solar tracking system includes an imaging device for capturing images of the sky; a solar position data generating module; and a control system comprising a neural network. The neural network has multiple convolutional layers to generate a first output associated with the images, and a solar position data module. A first dense layer module receives the solar position data and generates a second output. A second dense layer module receives the first output and the second output and generates a concatenated data sequence. A processor is programmed to generate a multi-planar irradiance signal (MPIS) in response to the concatenated data sequence, and determine an angular position of the PV power system and adjust the angular position in response to an angle of maximum irradiance.
Beyond Price Taker: Conceptual Design and Optimization of Integrated Energy Systems Using Machine Learning Market Surrogates
Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.