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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 19 records

Characterizing particle-based thermal storage performance using optical methods for use in next generation concentrating solar power plants

Concentrating Solar Power (CSP) generation is an attractive option for low-emission power generation; however, the high costs of thermal storage associated with concentrating solar create a large barrier for their use and adaptation into modern life. Lowering their operation costs, while maintaining high thermal storage and transfer performance is essential. Solid particle-based heat exchange systems can reduce CSP cost but are often less efficient. Efforts to increase their performance have led to use of binary size particle mixes. Presented is an optical-based thermal analysis technique used to measure near-wall thermal conductivity of particle beds essential in determining their heat exchanger efficiency. Modulated Photothermal Radiometry is used to make dynamic temperature measurements, allowing for the extraction of the most relevant thermal properties like thermal conductivity, specific heat, and effusivity. The system uses a modulated laser source causing a damped periodic heat flux, resulting in a frequency and thermal property dependent surface temperature, of which is measured using radiometry. Lock-In techniques are used to extrapolate the amplitude of the signal. Plotting the amplitude against the root angular frequency allows for effusivity measurement by ratio to a known sample. Using specific heat measurements from literature and density measurements, the thermal conductivity of the particle mixes can be calculated. The simplicity of MPTR to probe through the depth of the bed is ideal for use in CSP for dynamic thermal performance monitoring.

Corona, Javier↗

Probing Thermal Transport in Fluidized Bed Using Modulated Photothermal Radiometry

Abstract In concentrated solar power (CSP) applications, fluidized bed is a promising approach for high heat transfer coefficient (HTC) solar receivers and heat exchangers. However, the complexity of multiphase mixing has made it difficult to characterize and analyze the heat transfer mechanism. This paper presents an experimental study on simultaneously characterizing heat transfer in both the near-wall and the bulk regions of a fluidized bed using modulated photothermal radiometry (MPR). The MPR is a non-contact frequency-domain technique using an intensity-modulated laser as the heat source and surface infrared emission as thermometry. The thermal penetration depth of the laser heating is varied by controlling its modulation frequency, and thus the measurement can resolve the near-wall and the bulk thermal resistances. With the MPR technique, we measured fluidized silica sands with a mean size of 164 μm in a vertical channel of 6 mm depth. Our results show that the near-wall thermal resistance is substantially increased with increasing gas velocity, which partially offsets the benefit of higher HTC brought by stronger particle mixing during the fluidization. We also used the MPR to quantify the improvement in particle-wall heat transfer in an inclined channel. We found that an 8° inclination towards the heat exchanging side led to a lower near-wall thermal resistance and a higher HTC at high gas velocities. This work demonstrates that the MPR technique is a useful tool to quantify the important near-wall thermal resistance from a bulk particle bed, which not only advances our understanding of heat transfer in fluidized beds, but may also contribute to the design of fluidized bed heat exchangers with higher HTC.

14 SOLAR ENERGY↗

Advanced Permanent Magnet Generator Topologies Using Multimaterial Shape Optimization and 3D Printing: Preprint

A vast majority of utility-scale wind turbine generators in the United States are dependent on foreign- sourced rare-earth permanent magnets that are vulnerable to supply chain uncertainties. Many small wind original equipment manufacturers are motivated to pursue continuous improvements to the generator design to lower the material and production costs and improve performance by lowering cogging torque and increasing the efficiency. Traditional design and manufacturing offer limited opportunities. In this work, we demonstrate advanced design approaches for a 15-kW baseline wind turbine generator by making use of recent progress in three-dimensional (3D) printing of polymer- bonded magnets and, electrical and structural steel. We explore three methods of magnet parametrization using Bezier curves resulting in symmetric, asymmetric and multimaterial magnet designs. We employ a multiphysics approach combining parametric computer-aided design modeling, finite- element analysis and targeted sampling to identify novel designs with more opportunities for reducing rare-earth material, improving efficiency and minimizing cogging torque. The results show that asymmetric-pole design and multimaterial-pole designs offer a greater opportunity to minimize rare-earth magnet materials by up to 35% with similar performance as the baseline generator, suggesting newer opportunities with design freedom beyond traditional limits of symmetry and as allowed by 3D printing.

Bezier curves↗

Use of Grid-Forming Medium-Voltage Power Electronics Hub in a Microgrid Setting

This paper presents the application of a new design of a multiport, modular, medium-voltage power electronics hub (M3PE-HUB) in a microgrid setting. The M3PE-HUB system was modeled in a digital real-time simulator (DRTS) and integrated into the Banshee microgrid test system. This paper presents the preliminary DRTS simulation-based results of the M3PE-HUB system connected in a test microgrid system. Verification and validation of the M3PE-HUB architecture and controls in the test microgrid setting are the primary contributions of this work. The results of the operation during the islanding and resynchronization process indicate the feasibility of the proposed architecture in a microgrid setting. This paper also presents results for a system reconfiguration use case where the M3PE-HUB was used to reconfigure the system under a fault condition.

grid forming↗

Machine Learning Based Network Parameter Estimation Using AMI Data

The expansion of distribution power system and the growing penetration of distributed energy resources present new challenges for situational awareness. Calibrating the extended system model with sensor measurements and maintaining the usability is critical for utilities. This paper presents a distribution network parameter estimation (DNPE) approach using machine learning (ML) and metering data that improve the quality of extended distribution power system modeling. The reliability model can improve the ability of endpoint data to be translated into network-level situational awareness in real time and help distribution system operators (DSOs) solve branch flow and voltage problems. In addition, a data analytic and automate processing scheme is proposed to improve the sensor data quality and prevent misleading information. The effectiveness of the proposed method is verified with actual advanced metering infrastructure (AMI) data on a real utility feeder model, while considering the higher penetration of photovoltaic power generation. The test of DNPE and study results are demonstrated in this paper.

Parameter estimation, machine learning, power dist↗

Co-sputter deposition of Nb₃Sn layer into SRF cavity using Nb-Sn composite target

Nb₃Sn, with its superior superconducting critical temperature (Tc ~18.3 K) and superheating field (Hsh ~400 mT), is considered a promising material for superconducting radiofrequency (SRF) cavities, offering enhanced cryogenic performance compared to bulk niobium cavities. A Nb₃Sn coating technique has been developed for Nb SRF cavities using co-sputtering of Nb-Sn composite target in a DC cylindrical magnetron sputtering system. The composite target configuration and discharge conditions for co-sputtering were optimized to deposit Nb-Sn films on flat Nb substrates, followed by annealing to form Nb₃Sn. Multiple strategies have been explored to improve the surface homogeneity of the Nb₃Sn coating, including optimizing a two-step annealing process, annealing in Sn vapor, and a light Sn recoating process. A 1.5 µm Nb-Sn co-sputtered film was deposited on the interior of a 2.6 GHz Nb SRF cavity and annealed at 600 °C for 6 h, followed by 950 °C for 1 h. Cryogenic RF testing of the annealed cavity demonstrated a Tc of 17.8 K, confirming the formation of Nb₃Sn. Then, the annealed cavity underwent a light recoating treatment and attained a quality factor (Q0) of 8.5E+08 at 2.0 K.

Accelerator Physics↗

Investigation of Farm-to-Farm Interactions and Blockage Effects from AWAKEN using Large-Scale Numerical Simulations

A large-scale numerical computation of five wind farms was performed as a part of the American Wake Experiment (AWAKEN). This high-fidelity computation used the ExaWind/AMR-Wind LES solver to simulate a 100km × 100km domain containing 541 turbines under unstable atmospheric conditions matching previous measurements. The turbines were represented by Joukowski and OpenFAST coupled actuator disk models. Results of this qualitative comparison illustrate the interactions with wind farms with large scale ABL structures in the flow, as well as the extent of downstream wake penetration in the flow and blockage effects around wind farms.

Cheung, Lawrence C.↗

Mooring Load Monitoring of a Wave Energy Converter Using a Self-Synchronizing Underwater Acoustic Network

A self-synchronizing underwater acoustic network, designed for remote monitoring of mooring loads in Wave Energy Converters (WEC), has been developed and tested. This network uses Time Division Multiple Access and operates self-contained with the ability for users to remotely transmit commands to the network as needed. Each node is a self-contained unit, consisting of a protocol adaptor board, an underwater acoustic modem and a battery pack. A node can be connected to a load cell, to a topside user or to the WEC. Every node is swapable. The protocol adaptor board, named Protocol Adaptor for Digital LOad Cell (PADLOC) supports a variety of digital load cell message formats (CAN, MODBUS, custom ASCII) and underwater acoustic modem serial formats. PADLOC enables topside users to connect to separate load cells through a user-specific command.

acoustic↗

Electrical Fault Detection, Power Quality, Distributed Energy Resource Use Cases, and Cyber Event Applications with the Cyber Grid Guard System Using Distributed Ledger Technology

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation and are associated with distributed energy resources (DERs). The integrity and confidentiality of data from IEDs is crucial, and distributed ledger technology (DLT) could improve the resilience of microgrids by helping to make these data more secure. The most popular applications using blockchain technology for electrical utilities is in the field is based on energy trading. However, the dynamism of the penetration of customer owned DERs and the deployment of sensors with IEDs have led to the identification of new applications using DLT that are focused on other areas, such as monitoring, operation and management of the grid and its assets. In addition, the majority of studies on electrical grid applications with blockchain were validated with software simulations. Although general monitoring of power systems for using DLT could be evaluated in operational electric grids, other DLT research applications such as defense against cyber-attacks and/or electrical fault detection are not likely to be performed in a real infrastructure because of possible risks to the network/equipment security. This report summarizes the application of power system applications using distributed ledger technology (DLT), providing a secure DLT framework for collecting data from IEDs like power meters and protective relays inside and outside of an electrical substation and/or between two different electrical utilities. In this study, the use case scenarios were created and assessed for different power system application by using DLT. The electrical fault detection for faulted phases (1), power quality monitoring of phase voltage magnitudes, frequency levels and load power factor (2), DERs use case monitoring (3), and cyber-event applications (4) were performed in a test bed with a Cyber-Grid Guard (CGG) system using DLT. It had a real-time simulator with power meters and protective relays in-the-loop. The first section of this report presents a literature review of power system applications using blockchain at research level. The second section shows the theory and equations used on this report. The third section shows the description of the test bed, equipment, architecture, and electrical grid diagrams. The fourth section shows the experimental models and use case scenarios that were performed for the electrical fault detection, power quality, DERs use case, and cyber event applications with the CGG system using DLT. The fifth section shows the results collected from the tests based on comparing the time stamped events of the analog signals from the IEDs, DLT computer and real time simulator. The sixth section performed the discussion of the results for the use case scenarios. Finally, section seven presents the conclusions for this report were presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Calculating Average Hot Water Mixes of Residential Plumbing Fittings Using the ANSI 301-2019 Hot Water Draw Model and National Residential Data to Estimate Hot Water Use in Showerheads and Lavatory Faucets

Available studies of hot water use percentages may not necessarily be generalized to a national level given regional differences, varying methodological approaches, and limited sample sizes. While some publicly available usage data, reports, and surveys estimate actual household usage, a lack of water use data specific to end uses make more precise savings calculations difficult. Additionally, most hot water draw models tend to focus on overall household use and may not readily estimate lavatory fixture use. However, reviewing more recent studies and standards enables the U.S. Environmental Protection Agency’s WaterSense program to update its estimates for hot water use and consumers’ corresponding energy and monetary savings to better reflect realworld conditions. This report specifically focuses on improving hot water use and savings estimates of lavatory faucets and showerheads. To estimate the hot water use of lavatory faucets and showerheads, we employed the 2015 Residential Energy Consumption Surveys (RECS) microdata alongside the ANSI 301-2019 Hot Water Draw Model. These estimates account for regional differences in hot water use, including regional cold water inlet temperatures. As a result, the refinements presented in this report are more robust, more recent, and better describe the geographic variation than previous inputs used by the WaterSense program. In addition, the approach described in this paper can be updated over time or tailored to regionally specific needs given available inputs. We conclude that hot water percentages for showers and faucets calculated using publicly available national data are close to the percentages found by regional studies and are consistent with household-level models of water use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation inland-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine)rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover

58 GEOSCIENCES↗

Evaluating Probability of Containment Effectiveness at a GCS Sites using integrated assessment modeling approach with Bayesian decision Networks

Improved scientific and engineering understanding of the behavior of geologic CO2 storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large-scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long-term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and post-injection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring-informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post-injection phases. The National Risk Assessment Partnership’s open-source Integrated Assessment Model (NRAP-Open-IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO2 leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP-Open-IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long-term storage complex effectiveness informed by monitoring based on the NRAP-Open-IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information.

Bayesian network, Risk assessment, Monitoring, car↗

Investigations of Polymethacrylate Tribochemical Films Using X-Ray Spectroscopy and Optical Profilometry

This study investigates the elemental composition and surface morphology of solid tribochemical films formed on steel surfaces. The reversible addition-fragmentation chain transfer (RAFT) method was used to synthesize nine different metal-free polymers, which were blended into commercial base oils. The polymers were either homopolymers of dodecyl methacrylate and ethylhexyl methacrylate or were co-polymers of these monomers with six polar monomers. After tribological testing at 100 degrees C using the ball-on-flat geometry, the resulting tribochemical films were imaged using scanning electron microscopy (SEM) and optical microscopy. The resulting tribochemical films have thicknesses around 50-100 nm. Two of the films corresponding to small (P1-imidazole-containing copolymer) and large (P3-less polar homopolymer) wear were cross-sectioned using focused ion beam (FIB) and analyzed for elemental composition using energy-dispersive X-Ray (EDX) mapping. Oxygen and nitrogen enrichment was observed, consistent with the relative chemical composition of the precursor polymers. Transmission electron microscopy (TEM) evidence suggests that at the worn surface, some organic elements penetrate or are mixed into the steel substrate giving an interlocking appearance. The two samples examined with TEM showed that P1 tribofilm is diffused or mixed with the steel substrate more so than P3, suggesting a stronger affinity and contact during tribofilm formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

U.S. Manufacturing Water Use Data and Estimates: Current State, Limitations, and Future Needs for Supporting Manufacturing Research and Development

Water is essential to manufacturing operations; without it, many facilities could not operate or meet production demands. Physical, reputational, and regulatory risks to water supplies compounded by climate change-induced impacts on hydrological conditions threaten the adequacy of water supplies for manufacturing. Manufacturing water use has not been a major focus of either water or manufacturing-related research. Research and development (R&D) aimed at helping manufacturers use water more sustainably and adapt to changing water conditions is needed to ensure a thriving sector and economy. However, the ability to identify R&D needs is severely limited due to a lack of current, statistically representative data on manufacturing water use and its environmental implications. In this Perspective, we outline four key questions to inform R&D on manufacturing use and highlight how the current state of water data in the United States does not support the adequate investigation of these questions. We make recommendations for the water data characteristics needed to explore the research questions and knowledgeably inform R&D on manufacturing water use.

McCall, James↗

Symmetry-mode analysis for local structure investigations using pair distribution function data

Symmetry-adapted distortion modes provide a natural way of describing distorted structures derived from higher-symmetry parent phases. Structural refinements using symmetry-mode amplitudes as fit variables have been used for at least ten years in Rietveld refinements of the average crystal structure from diffraction data; more recently, this approach has also been used for investigations of the local structure using real-space pair distribution function (PDF) data. Here, the value of performing symmetry-mode fits to PDF data is further demonstrated through the successful application of this method to two topical materials: TiSe2, where a subtle but long-range structural distortion driven by the formation of a charge-density wave is detected, and MnTe, where a large but highly localized structural distortion is characterized in terms of symmetry-lowering displacements of the Te atoms. Here, the analysis is performed using fully open-source code within the DiffPy framework via two packages developed for this work: isopydistort, which provides a scriptable interface to the ISODISTORT web application for group theoretical calculations, and isopytools, which converts the ISODISTORT output into a DiffPy-compatible format for subsequent fitting and analysis. These developments expand the potential impact of symmetry-adapted PDF analysis by enabling high-throughput analysis and removing the need for any commercial software.

36 MATERIALS SCIENCE↗

An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data

This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using row-resolution (i.e., 15 minutes) smart meter data. We first demonstrate that the electricity consumption profiles on mild-temperature days can be used to approximate the base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed from the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and verify the proposed methods. Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers.

Kim, Hyeonjin↗