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DOE OSTI2021

Efficient and Reliable Power Takeoff for Ocean Wave Energy Harvesting

The project goal is to significantly improve the current ocean wave energy harvesting through innovative Power Take-off (PTO) design, advanced power electronics, and novel wave capture structures. The objective of the project is to design and demonstrate system-agnostic components for application across multiple MHK systems, and complete component designs, build scaled prototypes, and perform testing and analysis for metric validation of 25% increase in component rating/per unit cost and 50% reduction in failure rate. The major innovation of the PTO is the Mechanical Motion Rectifier (MMR) mechanism that rectifies the bi-directional oscillatory motion of the input from waves into a steady unidirectional rotation output to directly drive the electrical generator. Through this mechanism, the efficiency and the fatigue life of the PTO can be significantly improved to benefit the energy absorption and lifespan of the wave energy converters (WEC). During the period of performance, the component and system design are completed, the scaled prototypes are developed and performed testing. It is validated that the 25% increase in a component rating/per unit cost. The 50% reduction in failure rate is not directly validated by experiments, however, it can be explained qualitatively with analysis. Besides, 8 journal articles, 13 conference proceedings, 1 patent, 3 Master thesis and two Ph.D. dissertations are published based on the work related to this project. The list of all the publications can be found at the end of the project as an appendix. Over 30 students and postdocs were trained through this project. Three prototypes of 100W and 500W WECs and 10KW PTO were designed, built, and tested in ocean wave tank and using the NREL dynamometer. This project demonstrated 50-80% PTO efficiency, 90-98% power electronics efficiency, up to 66% capture width ratio in irregular waves, and 34% overall efficiency in regular waves.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Test Points for Online Monitoring of Quantum Circuits

Noisy Intermediate-Scale Quantum (NISQ) computers consisting of tens of inherently noisy quantum bits (qubits) suffer from reliability problems. Qubits and their gates are susceptible to various types of errors. Due to limited numbers of qubits and high error rates, quantum error correction cannot be applied. Physical constraints of quantum hardware including the error rates are used to guide the design and the layout of quantum circuits. The error rates determine the selection of qubits and their operations. The resulting circuit is executed on the quantum computer. This study explores the risk of unexpected changes in the error rates of NISQ computers post-calibration. We show that unexpected changes in error rates can alter the output state of a quantum circuit. To detect these changes, we propose the insertion of test points into the quantum circuit to enable online monitoring of the physical qubit behavior. We utilize classical, superposition, and uncompute test points. Furthermore, we use a gate error coverage metric to assess the quality of the tests. We verify the effectiveness of the proposed scheme on different IBM quantum computers (IBM Q), in addition to a noisy simulation that shows the scalability of the proposed approach.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Exxon Mobil-NETL Testing of DAC Materials

This joint initiative is aimed at extending our understanding of real DAC testing conditions at the NETL DAC testbed facility. Of particular focus will be: • Small Scale testing of powdered and formulated materials (supplied by ExxonMobil) to evaluate various performance metrics under DAC process cycles • Pilot scale testing of formulated materials at larger scales ExxonMobil will work with NETL to shake down equipment, validate testing methods, and define best practices for data analysis. Three tasks are proposed: 1. Validation of multi-cycle test data on powdered and formulated materials to benchmark various performance metrics of these materials. 2. Steam regeneration of powdered samples at small scale to define baseline performance under commercially relevant conditions. 3. Pilot scale testing of larger formulated materials to test commercially relevant samples under actual process cycles.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Proof-of-concept chemometric approach for environmental forensic sourcing of crude oil samples using SPME-GC-MS

Environmental exposure to crude oil through seepage and spillage poses risks to the immediate environment and the broader ecosystem as areas along the oil distribution path are affected by the influx of crude petroleum as well as the environmental, economic, and civil unrest that accompanies it. There is a large financial burden associated with the lost resources, including the cost of rehabilitation, and the affected sources of revenue for communities affected by oil spills. As such, it is crucial to determine the responsible parties. This work outlines an environmental forensics approach to determining the source of an un-weathered crude oil sample. The researchers employed solid phase microextraction coupled with gas chromatography mass spectrometry (SPME-GC-MS) to capture and analyze the gaseous components emitted by crude oil samples sourced from five locations. Samples were analyzed using Spearman's rank correlation and 3D covariance analysis. Both chemometric approaches yielded optimal performance results with no misclassifications, true positive rate (TPR) = 100 % and false positive rate (FPR) = 0 %. The similarity metrics calculated by each test noted clear delineations between the values of same-source and differently sourced samples. The Spearman's rank correlation test and 3D covariance calculations both demonstrated the ability to correctly identify sample source origin in this dataset. Finally, the authors outline an approach to the future application of these tests and suggest their joint use in future crude oil sourcing endeavors.

3D covariance mapping↗

DOE OSTI2022

Testing Hidden Assumptions of Representativeness in Reach-Scale Studies of Hyporheic Exchange

Field studies of hyporheic exchange in mountain systems are often conducted using short study reaches and a limited number of observations. It is common practice to assume these study reaches represent hyporheic exchange at larger scales or different sites and to infer general relationships among potential causal mechanisms from the limited number of observations. However, these assumptions of representativeness are rarely tested. In this study, we develop numerical models from four segments of mountain streams in different geomorphologic settings and extract shorter reaches to test how representative exchange metrics are in shorter reaches compared to their reference segments. We also map the locations of the representative reaches to determine if a pattern exists based on location. Finally, we compare variance of these shorter within-site reaches to 29 additional reaches across the same basin to understand the impacts of inferring causal mechanisms, for example, the expectation that wide and narrow valley bottoms will yield different hyporheic exchange patterns. Our results show that the location and length strategy of the study reach must be considered before assuming an exchange metric to be representative of anything other than the exact segment studied. Further, it is necessary to quantify within and between site variations before making causal inferences based on observable characteristics, such as valley width or stream morphology. Our findings have implications for future field practices and how those practices are translated into models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Indirect Tool Condition Monitoring Using Ensemble Machine Learning Techniques

Abstract Tool condition monitoring (TCM) has become a research area of interest due to its potential to significantly reduce manufacturing costs while increasing process visibility and efficiency. Machine learning (ML) is one analysis technique which has demonstrated advantages for TCM applications. However, the commonly studied individual ML models lack generalizability to new machining and environmental conditions, as well as robustness to the unbalanced datasets which are common in TCM. Ensemble ML models have demonstrated superior performance in other fields, but have only begun to be evaluated for TCM. As a result, it is not well understood how their TCM performance compares to that of individual models, or how homogeneous and heterogeneous ensemble models’ performances compare to one another. To fill in these research gaps, milling experiments were conducted using various cutting conditions, and the model groups were compared across several performance metrics. Statistical t-tests were also used to evaluate the significance of model performance differences. Through the analysis of four individual ML models and five ensemble models, all based on the processes’ sound, spindle power, and axial load signals, it was found that on average, the ensemble models performed better than the individual models, and that the homogeneous ensembles outperformed the heterogeneous ensembles.

Engineering↗

DOE OSTI2026

Design and Prototyping of the 162.5 MHz PIP-II RF Reference-Line Station

The Proton Improvement Plan II (PIP-II) Reference Line distributes phase-stable radio-frequency signals so accelerating cavities transfer energy to the proton beam at the correct point in each RF cycle. The full system contains 162.5, 325, and 650 MHz sections; this project focuses on the 162.5 MHz station. Commercial filters, amplifiers, splitters, mixers, and hardware were assembled into a compact, serviceable heat-plate layout and tested to verify performance metrics at 162.5 MHz.

Subedi, Harsheet [Unlisted, US]↗

DOE OSTI2022

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

High voltage converter modulators (HVCM) provide power to the accelerating cavities of the spallation neutron source (SNS) facility. HVCM experience catastrophic failures, which increase the downtime of the SNS and reduce beam time. The faults may occur due to different reasons including failures of the resonant capacitor, core saturation due to the magnetic flux, insulated-gate bipolar transistor (IGBT) failures, and others. We recently have setup a HVCM test stand to develop and test machine learning models for anomaly detection and fault prognostics. In this work, we propose binary classifiers and autoencoder architectures based on convolutional (CNN) and feedforward neural networks (FNN) to facilitate distinguishing normal from faulty waveforms coming from the HVCM during operation. The results indicate that the CNN binary classifier is the best model among the four showing very stable performance in the training and testing sets with impressive metrics of precision and recall reaching up to 99\% with a very small uncertainty. The FNN classifier shows the least performance with a large uncertainty in its metrics. The performances of the two autoencoders based on CNN and FNN were in between, showing very good performance nonetheless.

Radaideh, Majdi↗

DOE OSTI2024

2024 Photovoltaic Inverter Reliability Workshop Summary Report & Proceedings

The National Renewable Energy Laboratory (NREL) organized the 2024 Photovoltaic Inverter Reliability Workshop on April 11-12, 2024, hosted at NREL's South Table Mountain campus in Golden, Colorado. The workshop was organized around seven key topics, including the present state of inverter reliability; solutions for reliability challenges; life cycle cost and ownership issues; testing, standards, performance, and reliability metrics; data reporting, analytics, and sharing; and the future of PV inverter reliability research. Participants included inverter manufacturers, national laboratory researchers, academics, independent testing laboratories, and more. Over the course of the two-day workshop, attendees arrived at several key priorities and conclusions. This report summarizes these conclusions and then collects presentations from the workshop into a record of the workshop's proceedings.

14 SOLAR ENERGY↗

DOE OSTI2025

Performance Evaluation of a Single-Phase Grid-Forming Inverter Through Hardware Experiments

This study conducts hardware experiments to assess the performance of a commercial single-phase grid-forming (GFM) inverter using a purely hardware-based approach. We adhere to a testing protocol for the GFM inverter and enhance it by exploring the transient performance of GFM inverters across various grid dynamic events. Quantifiable performance metrics are established for each testing scenario to gain insights into the performance of the single-phase GFM inverters. Based on the comprehensive tests, the following observations are summarized: 1) The performance of the single-phase GFM inverter is satisfactory, and it is capable of being the islanding master for residence homes when the main grid is gone. 2) For frequency response, the inverter is able to stay connected but cannot inject the needed active power to support the grid. This may be improved by the manufacturer by using droop control. 3) The GFM inverter exhibits harmful transients in voltage and frequency during islanding operation, which can be enhanced by maintaining the same operating points before and after the breaker is open.

black start↗

DOE OSTI2024

Performance Evaluation of a Single-Phase Grid-Forming Inverter Through Hardware Experiments

dynamic performance↗

DOE OSTI2020

Comprehensive Approach to Measure the Mobility Energy Productivity of Freight Transport

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting has been gaining pace in recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, and shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

A Comprehensive Approach to Measure the Efficiency of Freight Transport: Freight Mobility Energy Productivity (F-MEP) Metric

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement is critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregated in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance of freight systems. Addressing this research need, this paper presents the 'Freight Mobility Energy Productivity' metric to quantify freight performance of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight performance. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight efficiency as a result of vehicle powertrain technology improvement. Results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the efficiency of existing as well as future freight trends and technologies.

25 ENERGY STORAGE↗

DOE OSTI2020

A Comprehensive Approach to Measure the Mobility Energy Productivity of Freight Transport: Preprint

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

Avista Turner Energy Storage System: Assessment of Battery Technical Performance

This report investigates the technical performance of the 1 MW, 3.2 MWh advanced vanadium flow battery energy storage system (FBESS), consisting of two 0.5 MW, 1.6 MWh strings, based on a number of reference performance and use case tests. The FBESS is located on the utility 12 kV distribution system, between the Turner Substation and SEL’s manufacturing facility in Pullman, Washington. Reference performance tests (RPTs) were intended to assess the general technical capability of the FBESS (e.g., stored energy capacity, ramp rate performance, ability to track varying charge/discharge commands, direct current (DC) battery internal resistance). Use case tests were used to examine the performance of the FBESS while engaged in specific grid services (e.g., arbitrage, frequency regulation, balancing, volt ampere reactive (var) support, dynamic peak shaving). The project determined performance metrics that are important for understanding FBESS performance when subjected to field operation for achieving economic benefits from measurements and/or calculations. These metrics were: • RTE with and without rest, with and without auxiliary loads • Auxiliary power consumption • Reference signal tracking • Temperature excursions beyond normal operating range • Parasitic power loss unaccounted by auxiliary load during rest, and • State of charge (SOC) excursions beyond normal operating range. These RPT and use case performance metrics were analyzed using recorded test results. In addition, the results and lessons presented are also beneficial for any task or effort that needs technical assessment on similar and different types of FBESS, and BESS in general, based on technical specifications and/or field deployment results, since the assessment methodology remains the same.

25 ENERGY STORAGE↗

DOE OSTI2019

Low-Cost Recuperative Heat Exchanger for Supercritical Carbon Dioxide Power Systems (Final Scientific/Technical Report)

Closed Brayton super-critical CO 2 power cycles are well suited to waste heat bottoming cycles, due to their increased efficiency and compactness, relative to Rankine steam bottoming cycles. Since waste heat applications would be retrofits, the power system compactness is important. To achieve high efficiency, these power cycles require high pressure recuperative type heat exchangers, of substantial heat duty. Current Printed Circuit Heat Exchangers (PCHE), originally developed for high pressure gas and oil applications, can be used as recuperators, but costs are higher than desired. Altex is developing a purpose-built high pressure and effectiveness recuperator to provide the reliability, compactness, performance and pressure capability of current recuperators, but at a reduced cost. The High Effectiveness Low Cost (HELC) recuperative heat exchanger design yields volume and weight metrics of .0024 m 3 /UA and 10.2 kg/UA, which are 4% and 77.3% below recuperator target metrics, respectively. A 50 kW test article was designed and fabricated. Performance tests on water and oil showed that the HELC design model could predict heat transfer, to within 10% of the measured value. This model was then used to project HELC performance, when operating on supercritical CO 2 . Besides performance tests, the test article was hydrostatically tested, for integrity at up to 4,000 psi pressure. At these conditions, some distortion of channels was encountered. To mitigate distortion, the inserts were redesigned and these results were used to project the cost of 500 kW and 5,083 kW HELC units. The cost metric for the 5,083 kW unit was determined to be $\$ $1,349/UA, which is 10% lower than the recuperator desired cost metric desired target of $\$ $1,500/UA. In addition, the 5,083 kW unit HELC cost of $\$ $61.09/kW is 33.6% lower than the $\$ $92/kW estimated cost for a PCHE. Hydrostatic pressure tests, at up to 4,000psi, showed that the unit did not leak. However, channels were distorted at this pressure differential. Design updates to minimize stress concentrations and distortion were prepared and analyzed, to show that distortion could be controlled, but to date tests have not been run to prove the design. Project results show the potential of the HELC approach, but more work is required to confirm this potential, at the larger scales of interest.

14 SOLAR ENERGY↗

DOE OSTI2020

Supercritical CO2 Recuperators, Presented at The ASME Turbo Expo 2017, June 29, 2017, by Dr. John Kelly, President, Altex Technologies Corporation

Closed Brayton super-critical CO2 power cycles are well suited to waste heat bottoming cycles, due to their increased efficiency and compactness, relative to Rankine steam bottoming cycles. Since waste heat applications would be retrofits, the power system compactness is important. To achieve high efficiency, these power cycles require high pressure recuperative type heat exchangers, of substantial heat duty. Current Printed Circuit Heat Exchangers (PCHE), originally developed for high pressure gas and oil applications, can be used as recuperators, but costs are higher than desired. Altex is developing a purpose-built high pressure and effectiveness recuperator to provide the reliability, compactness, performance and pressure capability of current recuperators, but at a reduced cost. The High Effectiveness Low Cost (HELC) recuperative heat exchanger design yields volume and weight metrics of .0024 m3/UA and 10.2 kg/UA, which are 4% and 77.3% below recuperator target metrics, respectively. A 50 kW test article was designed and fabricated. Performance tests on water and oil showed that the HELC design model could predict heat transfer, to within 10% of the measured value. This model was then used to project HELC performance, when operating on supercritical CO2. Besides performance tests, the test article was hydrostatically tested, for integrity at up to 4,000 psi pressure. At these conditions, some distortion of channels was encountered. To mitigate distortion, the inserts were redesigned and these results were used to project the cost of 500 kW and 5,083 kW HELC units. The cost metric for the 5,083 kW unit was determined to be $1,349/UA, which is 10% lower than the recuperator desired cost metric desired target of $1,500/UA. In addition, the 5,083 kW unit HELC cost of $61.09/kW is 33.6% lower than the $92/kW estimated cost for a PCHE. Hydrostatic pressure tests, at up to 4,000psi, showed that the unit did not leak. However, channels were distorted at this pressure differential. Design updates to minimize stress concentrations and distortion were prepared and analyzed, to show that distortion could be controlled, but to date tests have not been run to prove the design. Project results show the potential of the HELC approach, but more work is required to confirm this potential, at the larger scales of interest.

14 SOLAR ENERGY↗