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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Optimal D-FACTS Placement in Moving Target Defense Against False Data Injection Attacks

Moving target defense (MTD) is a defense strategy to detect stealthy false data injection (FDI) attacks against the power system state estimation using distributed flexible AC transmission system (D-FACTS) devices. However, existing studies neglect to address a fundamental yet critical issue, i.e., the D-FACTS placement, by assuming that all lines are equipped with D-FACTS devices. Here, to tackle this problem, we first derive analytical necessary conditions and requirements on the D-FACTS placement for a complete MTD. Further, we propose sufficient conditions using a graph theory-based topology analysis to ensure that the MTD under the proposed D-FACTS placement has the maximum rank of its composite matrix, which is indicative of the MTD effectiveness. Based on the analytical conditions, we design D-FACTS placement algorithms by using the minimum number of D-FACTS devices to achieve the maximum MTD effectiveness. A novel MTD-based ACOPF model, in which the reactance of D-FACTS lines is introduced as decision variables, is proposed to find a trade-off between the system loss and the MTD effectiveness. Numerical results on 6-bus, IEEE 14-bus, and IEEE 118-bus systems show the efficacy of MTDs using the proposed D-FACTS placement algorithms in maximizing the composite matrix rank and detecting FDI attacks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Thermal Optimization and Experimental Demonstration of a Silicon Carbide, Half-Bridge Power Module

This paper describes modeling and experimental work to design and demonstrate a silicon carbide (SiC), single-side-cooled power module. Two SiC metal-oxide-semiconductor field-effect transistors (MOSFET) were used for each switch position and packaged on a metalized ceramic substrate. Thermal modeling was first conducted to optimize the module packaging to minimize junction-to-fluid thermal resistance and device temperature variations. This procedure identified the optimal device placement and baseplate thickness to maximize thermal performance. A heat exchanger - incorporating impinging jets of water-ethylene glycol - was designed to cool the module's baseplate to minimize thermal resistance and pumping power. The heat exchanger used relatively low fluid velocities (<1.5 m/s) to minimize erosion-corrosion effects and relatively large fluid channel sizes (>=1 mm) to prevent clogging. The power module concept was fabricated, and experiments were conducted to measure its thermal performance. Direct current was used to power the module devices, and junction temperatures were measured via device temperature-sensitive parameters. The experimental results were found to be in good agreement with model predictions (within 2%) and yielded junction-to-fluid thermal resistance values as low as about 15 mm2 K/W at relatively low pumping power values. The results demonstrate the improved performance associated with jet impingement cooling and represent an improvement over existing channel-flow-type cold plates, which can enable improvements to power density and reliability.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Comprehensive powertrain modeling for heavy-duty applications: A study of plug-in hybrid electric bus

A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed to achieve real-time optimization of plug-in hybrid electric buses while considering the real vehicle drivability and the practical operation of all powertrain components under real driving conditions. The control strategy is based on a supervisory control algorithm that alternately employs charge-dominant control and discharge-dominant control to manage multiple powertrain sources, and enable an optimal overall efficiency-based powertrain operation state by maximizing the inherent optimal powertrain efficiency through best all transmission gear choice and the blend of motor and/or engine power. In the model, a component energy efficiency database was developed to rapidly enable an smart and optimal operating state determined from a set of efficiency maps characterizing the component and powertrain control states as a function of the instantaneous vehicle operational condition. The results revealed that the energy savings achieved with the innovative powertrain control improved by 10%–30% compared with the baseline hybrid powertrain control strategy. The benefits of eco-driving with respect to energy consumption were also evaluated using the powertrain model. The powertrain control model was implemented into a real hybrid bus. Measured energy savings with the optimized control strategy were similar to the simulated results.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Combined Stress Testing of Perovskite Solar Cells for Stable Operation in Space

Perovskite solar cells (PSCs) could provide new low-cost opportunities for powering spacecraft. Here we are designing a PSC resilient to lunar surface conditions, focusing on contact/barrier layers combinations that maximize stability. Thermal vacuum stress shows cells with gold back-contacts and SiOx protection retain >90% of initial efficiency and indium tin oxide (ITO)-contacted cells retain ~100% of initial efficiency after five months of stress. SiO x mitigates mechanical aspects of contact stress, retaining 99% of initial efficiency, whereas cells with silicone/cover glass decreased 27%. We further exposed cells to combined illumination, 75 °C heat, and 10 -6 Torr vacuum for 24 h and found that ITO-contacted cells outperformed Au-contacted cells.

14 SOLAR ENERGY↗
DOE OSTI2023

Circuit-level design principles for transmission-mode microwave impedance microscopy

A recently developed technique of transmission-mode microwave impedance microscopy (T-MIM) has greatly extended the capabilities of standard reflection-mode MIM to novel applications, such as the in operando study of nanoscale electro-acoustic devices. As is common for new techniques, systematic design principles for boosting sensitivity and balancing bandwidth are lacking. Here, we show numerically and analytically that the T-MIM signal is proportional to the reflection-mode voltage enhancement factor η of the circuit, as long as the output impedance of the local voltage source is properly treated. We show that this proportionality holds in the currently achievable “weak sampling” regime and beyond, for which we demonstrate a realistic path with commercially available superconducting components and critically coupled impedance matching networks. Furthermore, we demonstrate that for these next-generation designs, the sensitivity is generally maximized at a slightly different frequency from the unloaded S 11 resonance, which can be explained by the maximum power transfer theorem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Advances in laser-based bremsstrahlung x-ray sources. II. Laser pulse propagation and guiding in nonuniform plasma media in the presence of self-focusing

An analytic Wentzel–Kramers–Brillouin model is presented of Gaussian laser pulse propagation through plasma with a quadratic transverse density profile and an arbitrarily varying, longitudinal density gradient under conditions of nonlinear self-focusing. From these solutions, it is shown that in the absence of nonlinear self-focusing and transverse nonuniformity, for exponential pre-plasma density profiles, the use of a low density coating of the laser target with electron density n0∼11 ncr (e.g., a CH foam of density 35 mg/cm3 for 1-micron laser light) maximizes laser intensity at best focus. Also, under laser and plasma conditions relevant to recent experiments on high-power laser systems, conditions are obtained for a Gaussian laser pulse to propagate stably through the pre-plasma medium. Such conditions would be expected to enhance the production of relativistic electrons from laser-target coupling, providing a possible explanation for the observed increase in MeV photon dose and enabling applications such as laser-based MeV X-ray radiography.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Scheduling and Control of Flexible Building Loads for Grid Services Based on a Virtual Battery Model

This paper presents a framework for modeling, scheduling, and controlling residential thermostatically controlled loads (TCLs) to provide multiple grid services, such as energy shifting, peak load reduction, and ancillary services. A modeling method is proposed to characterize the aggregate flexibility from heterogeneous TCLs using a battery-equivalent model. Based on the flexibility model, a multi-period optimal scheduling formulation is developed to best utilize the flexibility from building loads and maximize total benefits from stacked value streams. An algorithm is proposed to control individual TCLs to follow the desired power consumption in real-time. The proposed methods are illustrated and validated through simulations.

Wu, Di↗
DOE OSTI2026

The relative influences of hydrologic information and dams’ hydropower scheduling decisions on electricity price forecasts

Price dynamics in wholesale electricity markets are driven by supply and demand. In markets with hydroelectric dams, the timing and amount of hydropower offered can influence prices in similar ways to wind and solar power. Unlike variable renewable energy, however, the supply of hydropower in wholesale markets is a function of both water availability and operational decisions at dams. Dam operators maximize revenues in wholesale markets by aligning generation with the periods of highest expected prices, and these scheduling decisions may in turn influence prices. Here, we examine the relative importance of two types of information in predicting forward electricity prices: a) water availability at dams, in the form of short-to-medium-range hydrological forecasts; and b) hourly scheduling decisions at dams. Using softly coupled hydrologic, hydropower scheduling, and power systems models spanning the U.S. Western Interconnection, we quantify the importance of hydrologic forecast accuracy in correctly predicting wholesale electricity prices and compare this with the influence of dam operators’ own hourly scheduling decisions on realized market prices. We find that aligning hydropower generation schedules with the periods of high forecasted prices causes larger, inadvertent price forecast errors than imperfect hydrologic forecasts. This suggests that knowledge of how water is managed by dam operators within the week is more important than weekly inflow forecast errors when predicting forward electricity prices. Our findings have implications for optimal hydropower scheduling by region. Specifically, accounting for price effects is critical in markets dominated by hydropower capacity.

Electricity markets↗
DOE OSTI2024

“Max-tech FHR”: What is the maximum technically achievable water delivery capacity from a single 120 V plug?

Electrifying water heating will require affordable, direct drop-in replacements for existing gas water heaters that meet consumer expectations for hot water delivery. For many replacement scenarios, affordability requires a replacement option with like-for-like physical product dimensions, and existing consumer expectations presume a large delivery capacity from a small water heater. This work explores the technical potential for hot water delivery capacity for a heat pump water heater (HPWH) powered from a common 120 V plug. Delivery capability in this work is quantified by two metrics: the First Hour Rating (FHR) defined in the Code of Federal Regulations, and a real-world draw profile that resulted in hot water runout during field studies of 120 V HPWHs. The work focuses on the common “tall-and-slim” gas water heater product category, with external product dimensions of 20 inch diameter and 60 inch height. Using a simulation model in DOE/ORNL Heat Pump Design Model, validated by experimental baseline testing of a commercially available 120 V HPWH model, several measures were evaluated to maximize the delivery capacity of a 120 V HPWH. The 120 V HPWH was constrained by 1) external product dimensions of 20 in diameter and 60 inch height, 2) electrical power of 1.5 kW (85% of the available power from a dedicated 120 V, 15 A circuit), and 3) commercially-available components. The measures implemented included a large compressor, a pumped loop with plate heat exchanger to maximize tank stratification, enlarged heat exchangers, and relatively thin insulation to maximize the water storage volume.

Gluesenkamp, Kyle↗
DOE OSTI2021

The cosmological evolution of self-interacting dark matter

We study the evolution of cosmological perturbations in dark-matter models with elastic and velocity-independent self interactions. Such interactions are imprinted in the matter-power spectrum as dark acoustic oscillations, which can be experimentally explored to determine the strength of the self scatterings. Models with self interactions have similarities to warm dark matter, as they lead to suppression of power on small scales when the dark-matter velocity dispersion is sizable. Nonetheless, both the physical origin and the extent of the suppression differ for self-interacting dark matter from conventional warm dark matter, with a dark sound horizon controlling the reduction of power in the former case, and a free-streaming length in the latter. We thoroughly analyze these differences by performing computations of the linear power spectrum using a newly developed Boltzmann code. We find that while current Lyman-α data disfavor conventional warm dark matter with a mass less than 5.3 keV, when self interactions are included at their maximal value consistent with bounds from the Bullet Cluster, the limits are relaxed to 4.4 keV. Lastly, we make use of our analysis to set novel bounds on light scalar singlet dark matter.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energysharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.

aggregates↗
DOE OSTI2021

Practical implications of SFQ-based two-qubit gates

Scalability of today’s superconducting quantum computers is limited due to the huge costs of generating/routing microwave control pulses per qubit from room temperature. One active research area in both industry and academia is to push the classical controllers to the dilution refrigerator in order to increase the scalability of quantum computers. Superconducting Single Flux Quantum (SFQ) is a classical logic technology with low power consumption and ultra-high speed, and thus is a promising candidate for in-fridge classical controllers with maximized scalability. Prior work has demonstrated high-fidelity SFQ-based single-qubit gates. However, little research has been done on SFQ-based multi-qubit gates, which are necessary to realize SFQ-based universal quantum computing.In this paper, we present the first thorough analysis of SFQ-based two-qubit gates. Our observations show that SFQ-based two-qubit gates tend to have high leakage to qubit non-computational subspace, which presents severe design challenges. We show that despite these challenges, we can realize gates with high fidelity by carefully designing optimal control methods and qubit architectures. We develop optimal control methods that suppress leakage, and also investigate various qubit architectures that reduce the leakage. After carefully engineering our SFQ-friendly quantum system, we show that it can achieve similar gate fidelity and gate time to microwave-based quantum systems. Furthermore, the promising results of this paper show that (1) SFQ-based universal quantum computation is both feasible and effective; and (2) SFQ is a promising approach in designing classical controller for quantum machines because it can increase the scalability while preserving gate fidelity and performance.

42 ENGINEERING↗
DOE OSTI2023

Laminar Burning Speed Measurements of Hydrogen/Natural Gas Mixtures

Abstract To address the shortage of fossil fuels and the environmental impact of exhaust emissions, research efforts in combustion seek to identify candidates for sustainable fuels. Hydrogen is an example, from which water is produced as the main combustion product. However, when used as neat hydrogen, it is very difficult to control hydrogen combustion due to its high flame speed, pre-ignition propensity, and flashback characteristics. One method to control hydrogen combustion is to blend it with a well-established fuel. Natural gas is a common fuel for use in engines or gas turbines. Natural gas is characterized by slow flame speeds and poor lean-burn ability; thus, engine power is decreased in the lean-burn region. Consequently, natural gas engines are typically operated at stoichiometric conditions to maximize output. Blending hydrogen to natural gas has several advantages: (i) Control of hydrogen’s ignition characteristics, (ii) extension of Natural gas’s lower flammability limit — making it easier to ignite, and (iii) reduced greenhouse gas emissions. In this study, the laminar burning speed (LBS) for neat natural gas and natural gas/hydrogen mixtures at 5 atm and 296 K were measured. Schlieren optical imaging was utilized to validate laminar flame conditions and further investigate the structure of natural gas/hydrogen flames. Results indicate that replacing 50% of natural gas with hydrogen increases the peak laminar burning velocity by a factor of ∼1.6 times. LBS simulations were conducted with NUI 1.1 and UCF NG/H2 mechanisms; Predictions by both mechanisms were found to be satisfactory for neat NG mixtures. However, for 50% NG/ 50% H2 mixture, the true LBS for fuel-lean conditions was under-predicted by both models, while predictions for fuel-rich conditions were satisfactory. Additionally, a literature review and similar experimental analysis will be performed for methane, ammonia, and hydrogen fuel blends to better understand the combustion phenomena of natural gas fuels.

Yovino, Louis↗
DOE OSTI2022

Optimal band selection for target detection with a LWIR multispectral imager

Multispectral imaging can offer many benefits in cost, complexity, resolution, size, weight, and power, relative to hyperspectral imaging. When designing a multispectral system, spectral bandpasses can be selected using optimization algorithms configured to maximally separate target detection scores between target and background regions. A hyperspectral image (HSI) can serve as the source of data from which band groupings can be tested for optimality. The output of an adaptive cosine estimator target detection algorithm is used in an objective function. Three optimization algorithms are compared: particle swarm, dual annealing, and differential evolution. A global optimum is also found using a brute force approach on the Livermore Computing Syrah supercomputer. Three materials are investigated: calcite, gypsum, and limestone. This is done for 3-, 4-, and 5-band systems. The data originate from a longwave infrared HSI of a material display board. The optimization algorithms were run 30 times for every scenario. Performance statistics (maximum, minimum, mean, standard deviation, and median) based on the separation values are given. Additional characterization was performed using receiver operator characteristic (ROC) curves and the area under the ROC curve. While good performance was obtained for the three optimization algorithms, the dual annealing algorithm produced the highest and most consistent detection separation scores on average.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Integrating Variable Renewable Energy Into the Grid: Key Issues

To foster sustainable, low-emission development, many countries are establishing ambitious renewable energy targets for their electricity supply. The variability of solar and wind compared to traditional sources, coupled with growing demand for electricity, requires changes to power system planning and operations to meet these targets. Grid integration is the practice of developing efficient ways to deliver variable renewable energy (VRE), primarily wind and solar, to the grid. Good integration methods maximize the cost-effectiveness of incorporating VRE while maintaining or increasing system stability and reliability. To inform decarbonization strategies, policymakers, regulators, and system operators consider a variety of costs and opportunities associated with VRE integration, which can be organized into five topics: New renewable energy generation and transmission; Power system reliability; Transmission and distribution coordination; Cross-sectoral decarbonization opportunities; Energy equity and justice.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Autonomous Intelligent Charging/Discharging of Electric Vehicles using Distributed Multi-Agent ADMM Framework for Grid Ancillary Services

The increasing popularity of Electric Vehicles (EVs) in the distribution grid along with technological advancement in EV electronics such as vehicle to grid (V2G) technique has enabled them to participate in grid ancillary services. To achieve this, the EVs need to establish a contract with third-party aggregators and connect to a charging unit, either residential or commercial. At any time they are connected, the EVs can decide to take part in the ancillary services program offered to them by the aggregators. If agreed, the aggregators will use the EVs as a power source capable of charging/discharging power according to the input signal, and in return, they will be compensated. This inter-temporal nature of charging/discharging is also transforming the traditional optimal power flow (OPF) problem into a dynamic OPF problem. This chapter aims at developing a multi-layer time-dependent optimization algorithm to utilize EV potential and provide ancillary services while maximizing its utilization function. Specifically, in the upper layer, an autonomous distributed ADMM algorithm is developed to optimize the cost for charging/discharging EVs while using them to regulate the voltage at each bus in the distribution grid. The distributed ADMM algorithm is also expanded to the lower layer where the individual EVs active and reactive power is controlled for voltage regulation while maintaining the desired state of the charge of the vehicle at the end of the charging period. Here, the effectiveness and performance improvement of the proposed multi-layer algorithm is illustrated through analytical analysis and simulation results.

Rahman, Towfiq↗
DOE OSTI2020

Autonomous Intelligent Charging/Discharging of Electric Vehicles using Distributed Multi-Agent ADMM Framework for Grid Ancillary Services

The increasing popularity of Electric Vehicles (EVs) in the distribution grid along with technological advancement in EV electronics such as vehicle to grid (V2G) technique has enabled them to participate in grid ancillary services. To achieve this, the EVs need to establish a contract with third-party aggregators and connect to a charging unit, either residential or commercial. At any time they are connected, the EVs can decide to take part in the ancillary services program offered to them by the aggregators. If agreed, the aggregators will use the EVs as a power source capable of charging/discharging power according to the input signal, and in return, they will be compensated. This inter-temporal nature of charging/discharging is also transforming the traditional optimal power flow (OPF) problem into a dynamic OPF problem. This chapter aims at developing a multi-layer time-dependent optimization algorithm to utilize EV potential and provide ancillary services while maximizing its utilization function. Specifically, in the upper layer, an autonomous distributed ADMM algorithm is developed to optimize the cost for charging/discharging EVs while using them to regulate the voltage at each bus in the distribution grid. The distributed ADMM algorithm is also expanded to the lower layer where the individual EVs active and reactive power is controlled for voltage regulation while maintaining the desired state of the charge of the vehicle at the end of the charging period. The effectiveness and performance improvement of the proposed multi-layer algorithm is illustrated through analytical analysis and simulation results.

Rahman, Towfiq↗