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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
DOE OSTI2022

AN ESTIMATE OF SPENT NUCLEAR FUEL MECHANICAL LOADS IN THE GENERAL 30 CM PACKAGE DROP SCENARIO

The US Department of Energy Spent Fuel and Waste Science and Technology (SFWST) program is performing research to determine the mechanical loading conditions applied to spent nuclear fuel (SNF) during normal conditions of transport to inform mechanical tests of SNF and close an important knowledge gap related to the practical disposition of SNF in the US. Researchers at Pacific Northwest National Laboratory (PNNL) have completed an extensive finite element study to characterize and estimate the potential mechanical loads on SNF during a hypothetical 30 cm drop of an SNF transportation package. This modeling study is validated with test data collected by the SFWST program during a physical test campaign that included one-third scale package drop tests and full-scale single fuel assembly drop tests. The test campaign was led by Sandia National Laboratories (SNL) and included international collaboration with Equipos Nucleares S.A, S.M.E (ENSA) and Bundesanstalt für Materialforschung und -prüfung (BAM). The key contribution of the modeling study is to go beyond the limitations of the limited number of physical tests to estimate the impact response to variations in impact angle, initial gap conditions, fuel assembly design, burnup and other parameters that affect the mechanical loads. The methodology of this study included a classic parametric study to calculate the impact response of highly detailed fuel assemblies over many combinations of parameters. Models of a 17x17 pressurized water reactor fuel assembly and a generic 10x10 boiling water reactor fuel assembly were both used in this study to cover the major fuel assembly types in the US inventory. Over 2,000 impact responses were calculated. The results of the parametric study were evaluated using traditional methods and basic statistics. The results were also used to construct a damage model using multiple nonlinear regression techniques to predict the mechanical loads over the full range of all input parameters. The damage model was found to work very well for all impact angle cases where the cask came to rest on its side. It was concluded that end drop cases where the cask remained vertical (instead of tipping over onto its side) were not sufficiently characterized by the current set of parametric study cases to include in the damage model, but it was not a priority to fully investigate that range because the highest mechanical loads were observed in the broader range of side impact cases. This modeling work provides sufficient insight into the mechanical loads on SNF during a hypothetical 30 cm package drop that, when considered along with the physical test data collected by the SNL-led team, the SFSWT program can consider the knowledge gap closed.

Klymyshyn, Nicholas A.↗
DOE OSTI2024

Development of a Hardware-in-The-Loop Testbed for a Decentralized, Data-Driven Electric Vehicle Charging Control Algorithm

This study presents the design of an electric vehicle (EV)-grid integration (EVGI) hardware test-bed to implement smart EV charging algorithms. Here, the proposed test-bed also allows to create different grid events via flexible integration of other power hardware (e.g., controllable loads and battery energy storage systems) and test their impacts on EV charging. The design uses a real-time digital simulator to realize a complex distribution grid model with primary and secondary networks. A grid simulator physically realizes the selected nodes of the simulated grid to power an actual EV, forming a hardware-in-the-loop (HIL) test setup. The EV-grid integration is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox, operating as a custom EV Supply Equipment (EVSE). The HIL test-bed features a novel testing platform for accurate implementation and analysis of scalable charging algorithms. To this end, a data-driven, decentralized, model-free charging controller based on the Additive Increase and Multiplicative Decrease (AIMD) algorithm is presented and validated on an EV using the HIL test-bed. We tested the proposed algorithm under various case studies, and presented a comparison study with an existing droop-based, decentralized charging solution. The results showed that the EV successfully performed charging commands generated by the EVSE and regulated its charging power to effectively reduce the system loading caused by high EV penetration.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

On the Sensitivity of Coastal Hypoxia to Its External Physical Forcings

Abstract The development of low‐oxygen zones threatening marine life (hypoxia) occurs annually in multiple coastal regions of the world. The largest estuary of the continental United States, the Chesapeake Bay, typically has ≈10 km 3 of water with dioxygen concentrations <3 mg L −1 in July. As numerical methods for refining model resolutions in targeted areas are becoming common, there is interest in assessing the feasibility of simulating coastal hazards such as hypoxia in Earth System Models (ESMs). These coupled models are typically not constrained by observations and thus likely to feature systematic biases in their land, atmosphere, or ocean components. This study relies on four numerical experiments to evaluate the sensitivity of Chesapeake Bay hypoxia to changes or biases in its external physical forcings. Hypoxia exhibits only a minor decrease (−1.6%) after reducing the Bay's terrestrial freshwater discharge by 9.5% (but keeping terrestrial nutrient loadings the same). Changes in freshwater discharges have their largest impact on hypoxia during one extreme event (−37% during 2011 tropical storm Lee). Similarly, changing oceanic conditions on the shelf or their temporal frequency impact hypoxia by only 5%–6%, indicating that the latter is predominantly dictated by local conditions. Although these results are promising from the perspective of ESMs, additional components of ESMs will need to be evaluated before general conclusions can be reached. We notably speculate that the Bay's hypoxia would exhibit higher sensitivity to other forcings not examined here, notably air temperatures and nutrient loadings.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

U.S. Industrial and Commercial Motor System Market Assessment Report. Volume 3: Energy Saving Opportunity

This report seeks to inform the potential for motor systems to support decarbonization by estimating their potential energy, electricity cost, and CO 2 emissions reduction potential from adoption of proven energy efficiency actions and advanced technologies. This is the third and final report in a series of reports disseminating the findings of the U.S. Department of Energy’s (DOE’s) Motor System Market Assessment (MSMA). The MSMA and this report focus on polyphase motor systems greater than or equal to 1 horsepower (hp) in the industrial and commercial sectors. In the U.S. Industrial and Commercial Motor System Market Assessment Report Volume 1: Characteristics of the Installed Base (Volume 1 report), it was determined that these motor systems consume more than 1,000 terawatt-hours (TWh) annually. This equates to 29% of the U.S. electric grid load and results in 765 million metric tons (MMT) of CO 2 emissions and $166 billion in electricity costs. This report finds that substantial reductions to these energy, cost, and emissions impacts are possible, with three areas of significant opportunity being (1) improved load matching, (2) replacing older inefficient motors with more efficient motors, and (3) improving the condition of fluid (e.g., air, water, compressed air) distribution systems. A summary of the savings potential from these three opportunities placed within the context of the overall consumption for motor systems is illustrated in Figure ES 1 (industrial) and Figure ES 2 (commercial).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Robust Distribution System Load Restoration With Time-Dependent Cold Load Pickup

Service restoration is one of the critical functions to enable the future self-healing distribution system. To restore the distribution system in a timely and reliable manner, the realistic system operating conditions need to be accurately characterized. Here, two main factors that have great impacts on distribution system restoration (DSR) in practice are investigated. First, cold load pickup (CLPU), generally caused by thermostatically controlled loads (TCLs), is a common phenomenon after an outage and shaped by the outage duration. However, the time-dependent behaviors of CLPU are rarely considered in literature. In this paper, the operating state evolution of TCLs after an outage is analyzed to characterize time-dependent CLPU. And the time-dependent CLPU is analytically embedded in DSR to accurately represent the actual behaviors of the restored loads. Second, it is difficult to predict loads that fluctuate during DSR due to the lack of real-time measurement data. Accordingly, a robust DSR based on the information gap decision theory (IGDT) is proposed to address this challenge, fully considering the uncertainty of CLPU. The proposed models are tested in IEEE 13-node and 123-node test feeders. Simulation results demonstrate that the time-dependent CLPU model and the uncertainty modeling of CLPU can accurately capture the actual behaviors of loads with TCLs after an outage, which greatly improves DSR decisions in practice.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Role of α → ε → α phase transformation on the spall behavior of iron at atomic scales

Shock compression of iron microstructures above a threshold stress results in a α (BCC) → ε (HCP) transformation and the propagation of the release wave brings the metal back to the α phase following the ε→α transformation. Predicting failure behavior under shock loading conditions (spallation) relies on understanding the evolution of defects in the microstructure as it undergoes the α→ε→α phase transformation. This study uses molecular dynamics (MD) simulations to investigate the role of defect evolution during α→ε→α phase transformation on the spall strength values of single-crystal (sc) Fe microstructures. The MD simulations aim to characterize the ε phase fraction formed during shock compression and the defects during shock release for variations in loading orientations and shock stresses. The simulations are carried out for loading along the [100], [110], [111], and [112] orientations and for impact velocities ranging from 600 m/s to 1 km/s. The ε phase fractions during compression and defects (dislocations, twins) characterized during spall failure show an orientation dependence that affects the spall strength values. Here, the lowest value for spall strength is observed for the <110> loading orientation that shows a high density of twinning at the spall plane, whereas the highest value is observed for the <100> orientation and is associated with a α (BCC) → γ (FCC) transformation at the spall plane. The correlations of the spall strength values with the strain rates and with the ε phase fractions are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks

As a result of climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks; thus, the level of proactive preparation of the distribution system to be able to handle severe impacts of extreme weather events represents the system's resilience. One method that distribution system planners can use to prepare for future extreme events is to plan multiple microgrids which can use local generation as much as possible to supply critical loads. But partitioning an existing distribution system such that multiple feasible islands are planned and which are capable of supporting critical loads is still challenging for distribution systems - first, because of the size of the network graph partitioning problem and, second, because of the difficulty in properly formulating the desired attributes of such islands or microgrids. Therefore, this paper presents a genetic algorithm based approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems - node allocation and edge elimination - and it considers multiple topological and resilience-enhancing objectives. The performance of the proposed genetic algorithm-based approach is numerically evaluated on multiple test systems as well as on a real distribution feeder in Colorado, United States.

genetic algorithm↗
DOE OSTI2020

Impact analysis of personalized thermostat demand response

Demand response (DR) aims to curtail peak electric load to avoid running inefficient power plants, reduce costly transmission capacity increases, and improve grid reliability. One method of DR for residential settings involves the utility or third part remotely adjusting thermostat temperatures. Cooling setpoints may be increased 2°F – 6°F for hours, irrespective of occupant comfort. To maintain their comfort, occupants override these setpoint changes impacting the reliability of DR services provided to the grid. This paper presents a preliminary impact analysis of replacing the current DR approach of uniform thermostat setbacks for an entire population with personalized and context aware DR based on a dynamic model of occupant thermal comfort behavior. Over 151k interactions with ecobee thermostats are analyzed to predict the impact of such personalized DR on overrides, energy curtailment magnitude, and reliability.

Kane, Michael↗
DOE OSTI2022

Comparative critical mass calculations for NNL and ENDF/B-VIII.0 - Zirconium hydride thermal neutron scattering laws

Zirconium hydride (ZrH{sub x}) is a moderator material for TRIGA reactors and historical space reactor systems, such as SNAP-10A. Thermal neutron scattering laws (TSL) for two phases of this material, δ and ε, have been previously evaluated by Naval Nuclear Laboratory (NNL) and submitted to the National Nuclear Data Center (NNDC) for inclusion in the US national ENDF/B-VIII.1 nuclear data library. In contrast to the current ENDF/B-VIII.0 TSL evaluations, which consider only a single phase, the new evaluations are derived from separate ab initio calculations for both phases and include coherent elastic effects of the zirconium sublattice. To estimate the impact of these changes to the TSL evaluation of this material, comparative critical mass calculations were performed with MC21 for homogenous mixtures of high-enriched uranium (HEU) and ZrH{sub x} in bare and water reflected sphere configurations. These calculations yield an impact on the estimated critical mass as a function of {sup 235}U loading density with maximum differences as large as 1% - 5% for over-moderated thermal spectrum systems. Consequently, the NNL TSL evaluations are anticipated to have a small impact on criticality calculations of thermal reactor systems regardless of the material phase. Nevertheless, characteristic differences exist in the predicted thermal spectra as function of energy for the two sets of TSL evaluations, which are attributed to difference in the underlying phonon density of states of hydrogen bound in ZrH{sub x}. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Roadmap and Benchmarking: Privacy in Federated Load Forecasting

Data-driven techniques for energy demand forecasting continue to emerge with promising impacts on distribution grid planning. However, the development of robust and generalizable machine learning models requires that representative high quality training data are available. Distributed energy resources have begun to embed intelligence, gathering large amounts of data on customer demand, behavior, and household devices that are connected to the grid. Though utilities aggregate meter-level demand data for load shaping, demand response, outage management, reliability planning, and billing applications, there lies an inherent privacy concern in sharing consumption data that may identify individual consumer behavioral patterns. Hence, while sharing the data is crucial, the private sensitive customer data must be safeguarded from being exposed or manipulated. In this study, we propose a roadmap for implementing a based privacy preserving framework to support the advancement of data-driven analytics in data-sensitive distributed energy resources environments. The roadmap incorporates federated learning–a distributed training framework, differential privacy–a statistical framework that provides guarantees to safeguard the leakage of sensitive data, secure multiparty computation and homomorphic encryption– techniques for encrypting model gradients and applying secure aggregation on the server. Moreover, we perform baseline experiments on the federated short-term load forecasting (STLF) task using open-source residential load profile datasets, offering insights into the challenges of integrating differential privacy into federated learning.

Abebe, Waqwoya [Oak Ridge National Laboratory (ORN↗
DOE OSTI2021

Climate-Induced Tradeoffs in Planning and Operating Costs of a Regional Electricity System

Electricity grid planners design the system in order to supply electricity to end users reliably and affordably. Climate change threatens both objectives through potentially compounding supply- and demand-side climate-induced impacts. Uncertainty surrounds each of these future potential impacts. Given long planning horizons, system planners must weigh investment costs against operational costs under this uncertainty. Here, we developed a comprehensive and coherent integrated modeling framework combining physically-based models with cost-minimizing optimization models in the power system. We applied this modeling framework to analyze potential tradeoffs in planning and operating costs in the power grid due to climate change in the Southeast U.S. in 2050. We find that planning decisions that do not account for climate-induced impacts would result in a substantial increase in social costs associated with loss of load. These social costs are a result of under-investment in new capacity and capacity deratings of thermal generators when we included climate change impacts in the operation stage. Finally, these results highlight the importance of including climate change effects in the planning process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Volt-Var Curve Reactive Power Control Requirements and Risks for Feeders with Distributed Roof-Top Photovoltaic Systems

The benefits and risks associated with Volt-Var Curve (VVC) control for management of voltages in electric feeders with distributed, roof-top photovoltaic (PV) can be defined using a stochastic hosting capacity analysis methodology. Although past work showed that a PV inverter’s reactive power can improve grid voltages for large PV installations, this study adds to the past research by evaluating the control method’s impact (both good and bad) when deployed throughout the feeder within small, distributed PV systems. The stochastic hosting capacity simulation effort iterated through hundreds of load and PV generation scenarios and various control types. The simulations also tested the impact of VVCs with tampered settings to understand the potential risks associated with a cyber-attack on all of the PV inverters scattered throughout a feeder. The simulation effort found that the VVC can have an insignificant role in managing the voltage when deployed in distributed roof-top PV inverters. This type of integration strategy will result in little to no harm when subjected to a successful cyber-attack that alters the VVC settings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Assessing the Optical Performance Impact of Tracking Error in an Operational Concentrated Solar Power Plant Using Monte Carlo Ray-Tracing Simulation

Concentrating Solar Power (CSP) provides firm and dispatchable electricity due to its thermal storage and hybridization capabilities, which supports the decarbonization of our energy supply. Of the various CSP technologies, parabolic trough collectors are the most mature, with over 500 MW operating worldwide. The optical performance of parabolic trough systems is sensitive to tracking error, which is defined as the angular offset of a collector away from the sun position in the transversal plane. Tracking error commonly occurs due to non-continuous adjustment of the trough angle to point toward the sun, but other factors such as gravity, heating, and wind loading can also contribute to tracking error. Researchers have explored the impact of tracking error on optical performance both numerically and experimentally, but existing studies do not include measurements from operational utility-scale power plants. Tilt angle measurements of parabolic troughs at operational utility-scale power plants illustrate spatial variations in optical performance and include various sources of tracking error such as gravity, heating, and wind loading. To fully characterize wind driven loads on parabolic troughs, we are conducting a long-term field measurement campaign at the Nevada Solar One CSP plant located in Boulder City, NV, which has a nominal capacity of 72 MW and 0.5 hours of full-load storage. We record load measurements on four outer trough rows, collecting support structure bending moments, drive torque moments, dynamic accelerations of the spaceframe, mirror displacement, and tilt angles. Using the tilt measurements acquired at 20 Hz frequency, we calculate the deviation between the nominal sun position and the tracker angle. Using a Monte-Carlo ray-tracing simulation software, we assess the impact of the tracker angle deviation on optical performance throughout the diurnal cycle at various spatial locations within the CSP plant. In our view, this first-of-a-kind study will provide important guidance for future trough designs that reduce the impact of various sources of tracking error on performance.

concentrating solar power↗
DOE OSTI2020

Dynamic Tensile Characterization of Thin-Sheet Brittle Metallic Materials

Refractory metals are favorable materials in applications where high strength and ductility are needed at elevated temperatures. In some cases, operating temperatures may be near the melting point of the material. However, as temperature drops, refractory metals typically undergo a significant mechanical response change - ductile-to-brittle transition. These materials may be subjected to high strain rate loading at an ambient temperature state, such as an impact or crash. Knowledge of the high rate material properties are essential for design as well as simulation of impact events. The high rate stress-strain behavior of brittle metallic materials at ambient temperature is rarely studied because of experimental challenges, particularly when failure is involved. Failure typically occurs within the non-gage section of the material, which invalidates any collected stress-strain information. Here, a method to determine a specimen geometry which will produce failures in the gage section is presented. Pure tungsten in thin-sheet form was used as a trial material to select a specimen geometry for high rate Kolsky tension bar experiments. A finite element simulation was conducted to derive a strain correction for more accurate results. The room temperature stress-strain behavior of pure tungsten at a strain rate of 24 s –1 is presented. The outcome of this experimental technique can be applied to other brittle materials for dynamic tensile characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High spatial resolution temperature profile measurements of solid-oxide fuel cells

Temperature gradients resulting from local electrochemical reactions, current distribution and geometry of gas flow channels in solid oxide fuel cells (SOFCs) create thermal stresses, localized thermophysical property gradients and uneven property evolution, contributing to SOFC degradation. This paper presents a new method to perform temperature measurements (up to 800°C) at high spatial resolutions to monitor the operation of SOFCs. Using femtosecond laser irradiation, distributed fiber sensors were hardened for high temperature environment applications. Distributed fiber sensors were embedded in interconnected plates using an additive manufacturing method to perform temperature measurements with 4-mm spatial resolution during the operation of a planar fuel cell. The measurement revealed the impact of various H 2 fuel concentrations and current loads have on temperature profiles of the SOFC tested. Temperature variation on the anode side was found to be less than 5°C, and 3°C on the cathode side. The measurements were compared to results from a multiphysics fuel cell performance model simulating similar conditions. These simulations predicted similar temperature gradients, indicating the experimental data obtained is reasonable. The model also predicts that the effect of the embedded sensor has on the local temperature will be minimal and that the gradient of temperature in the gas channels will be captured despite the separation between the sensor and the gas flow. Finally, the high spatial resolution data harnessed by these distributed fiber sensors provides experimental support for model-based design and optimization to improve the operational efficiency and longevity of solid oxide fuel cells and fuel cell assemblies.

25 ENERGY STORAGE↗
DOE OSTI2021

The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices

Price-based demand response (PBDR) has recently been attributed great economic but also environmental potential. However, the determination of its short-term effects on carbon emissions requires the knowledge of marginal emission factors (MEFs), which compared to grid mix emission factors (XEFs), are cumbersome to calculate due to the complex characteristics of national electricity markets. This study, therefore, proposes two merit order-based methods to approximate hourly MEFs and applies them to readily available datasets from 20 European countries for the years 2017–2019. Based on the calculated electricity prices, standardized daily load shifts were simulated which indicated that carbon emissions increased for 8 of the 20 countries and by 2.1% on average. Thus, under specific circumstances, PBDR leads to carbon emissions increases, mainly due to the economic advantage fuel sources such as lignite and coal have in the merit order. MEF-based load shifts reduced the mean resulting carbon emissions by 35%, albeit with 56% lower monetary cost savings compared to price-based load shifts. Finally, by repeating the load shift simulations for different carbon price levels, the impact of the carbon price on the resulting carbon emissions was analyzed. The Spearman correlation coefficient between carbon intensity and marginal cost along the German merit order substantially increased with increasing carbon price. The coefficients were -0.13 for the 2019 carbon price of 24.9 €/t, 0 for 42.6 €/t, and 0.4 for 100.0 €/t. Therefore, with adequate carbon prices, PBDR can be an effective tool for both economical and environmental improvement.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Opportunities and challenges for flow-through hydrothermal pretreatment in advanced biorefineries

Hydrothermal pretreatment (HTP) using only water offers great potential to reduce the overall cost of the bioconversion process. However, traditional HTP performed in a batch has limitations in removing lignin and often needs to be performed under severe conditions to achieve reasonable pretreatment effects. Additionally, lignin left in the pretreated residue at these conditions is also highly condensed, thus possessing an even more adverse impact on the hydrolysis process, which requires high enzyme loadings. To address these technical challenges, HTP performed in a flow-through configuration was developed to simultaneously achieve near-complete hemicellulose recovery, high lignin removal and high sugar release. Despite facing challenges such as potentially large water usage, flow-through HTP still represents one of the most cost-effective and eco-friendly pretreatment methods. This review mainly covers the latest cutting-edge innovations of flow-through HTP along with structural and compositional changes of cellulose, hemicellulose, and lignin before and after pretreatment.

09 BIOMASS FUELS↗
DOE OSTI2024

Long-term effects of cycle time and volume exchange ratio on poly(3-hydroxybutyrate-co-3-hydroxyvalerate) production from food waste digestate by Haloferax mediterranei cultivated in sequencing batch reactors for 450 days

Food waste digestate was fed into a sequencing batch reactor (SBR) for Haloferax mediterranei (HM) to produce poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). This SBR was operated uninterruptedly for 450 days to test its stability, during which the cycle time and volume exchange ratio were varied to understand their impacts on the PHBV fermentation performance under ranged organic loading rates (OLR). Results showed that 1) PHBV productivity was proportional to OLR of food waste digestate; 2) substrate and product inhibitions were two limiting factors constraining substrate utilization and PHBV yields; 3) PHBV titer was dependent on the hydraulic retention time of the SBR while a volume exchange ratio lower than 0.5 is unfavorable due to the product inhibitor accumulation. Furthermore, this study for the first time demonstrated that the long-term stability of food waste-fed PHBV production by HM and revealed that inhibition effects could be barriers in SBR limiting the full-scale application of the technology.

09 BIOMASS FUELS↗