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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

EVALUATION OF CONTROLLED END GAS AUTO IGNITION WITH EXHAUST GAS RECIRCULATION IN A STOICHIOMETRIC, SPARK IGNITED, NATURAL GAS ENGINE

Many stationary and heavy-duty on-road natural gas fueled engines today operate under stoichiometric conditions with a three-way catalyst. The disadvantage of stoichiometric natural gas engines compared to lean-burn natural gas and diesel engines is lower efficiency, resulting primarily from lower power density and compression ratio. Exhaust gas recirculation (EGR) coupled with advanced combustion controls can enable operation with higher compression ratio and power density, which yields higher efficiency. This also results in engine operation between the limits of knock and misfire. Operation between these limits has been named controlled end gas auto-ignition (C-EGAI) and can be used to improve the brake efficiency of the engine. Various methods of cylinder pressure-based knock quantification were explored to implement C-EGAI. The indicated quantification methods are used for the implementation of a control scheme for C-EGAI with a relation to the fractional heat release due to auto-ignition. A custom EGR system was built and the effect of EGR on the performance of a stochiometric, spark ignited, natural gas engine is evaluated. C-EGAI is implemented and the optimal parameters are determined for peak performance under EGR and C-EGAI conditions. In this study, knock detection is used for the recognition, magnitude, and location of the auto-ignition events. Cylinder pressure-based knock detection was the primary method for determining the occurrence and location of knock but was also used for implementing the ignition control scheme for controlled end gas auto-ignition. The combustion intensity metric (CIM) enabled parametric ignition timing control which allowed for the creation of a relationship between fractional heat release due to auto-ignition and CIM. Many stationary and heavy-duty on-road natural gas fueled engines today operate under stoichiometric conditions with a three-way catalyst. The disadvantage of stoichiometric natural gas engines compared to lean-burn natural gas and diesel engines is lower efficiency, resulting primarily from lower power density and compression ratio. Exhaust gas recirculation (EGR) coupled with advanced combustion controls can enable operation with higher compression ratio and power density, which yields higher efficiency. This also results in engine operation between the limits of knock and misfire. Operation between these limits has been named controlled end gas auto-ignition (C-EGAI) and can be used to improve the brake efficiency of the engine. Various methods of cylinder pressure-based knock quantification were explored to implement C-EGAI. The indicated quantification methods are used for the implementation of a control scheme for C-EGAI with a relation to the fractional heat release due to auto-ignition. A custom EGR system was built and the effect of EGR on the performance of a stochiometric, spark ignited, natural gas engine is evaluated. C-EGAI is implemented and the optimal parameters are determined for peak performance under EGR and C-EGAI conditions. In this study, knock detection is used for the recognition, magnitude, and location of the auto-ignition events. Cylinder pressure-based knock detection was the primary method for determining the occurrence and location of knock but was also used for implementing the ignition control scheme for controlled end gas auto-ignition. The combustion intensity metric (CIM) enabled parametric ignition timing control which allowed for the creation of a relationship between fractional heat release due to auto-ignition and CIM. Both exhaust gas recirculation and controlled end gas auto-ignition were analyzed with a cooperative fuel research (CFR) engine modified for boosted fuel/air intake. The data was interpreted to provide a proper evaluation of unique analytical methods to quantify the results of C_EGAI and characterize the live auto-ignition events. The control variables for this method of C-EGAI were optimized with EGR conditions to generate the point of peak performance on the CFR engine under stoichiometric, spark ignited, natural gas conditions.

Bayliff, Scott Michael↗
NASA NTRSDate not supplied

Elevating the Quality of Space Omics Sequencing Data: Innovations and Methodologies from NASA GeneLab Sample Processing Laboratory

NASA’s GeneLab, part of the NASA Open Science Data Repository, is a space-related database that hosts a diverse range of transcriptomics, proteomics, epigenomics and genomics data. The NASA GeneLab Sample Processing Laboratory (SPL) generates omics data from biological experiments conducted aboard the International Space Station, Space Shuttle and space related ground experiments, this omics data then hosted on the GeneLab repository. Samples generated such experiments pose numerous technical challenges such as small experimental sample size, variance in dissection times, limited tissue preservation methods, prolonged storage time, and more. GeneLab SPL team had developed specialized expertise in nucleic acid extraction, library preparation and sequencing of such biological samples via extensive training and years of experience. In order to ensure data accuracy and consistency across experiments, SPL has developed standardized protocols for each species and tissue type. These protocols in conjunction with quality control metrics and data standards are crucial in generating of high-quality data. SPL protocols and standards have been developed in collaboration with the scientific community and had been made publicly available on the GeneLab portal, guaranteeing comparability of datasets across spaceflight experiments. To ensure reliability of data generation, SPL leverages cutting-edge innovations in laboratory automation for sample processing. By leveraging these state-of-the-art platforms, SPL achieves high levels of data reproducibility while significantly minimizing sources of bias and variability, especially across experiments with large numbers of samples. Over the past few years, the space biology investigator community has accessed SPL-generated data from the Open Science Data Repository for a myriad of data re-analysis and re-use studies. We observe a trend that in-house SPL-generated data consistently outperforms outsourced sequencing data in terms of technical standards, quality control metrics, timeliness of data delivery, and sequencing and reagent efficiency. Superior data generation has and will continue to enable discoveries in disease, diagnostic tools, and the biological effects of long duration spaceflight.

GeneLab↗
NASA NTRSDate not supplied

Elevating the Quality of Space Omics Sequencing Data: Innovations and Methodologies from NASA GeneLab Sample Processing Laboratory

NASA’s GeneLab, part of the NASA Open Science Data Repository, is a space-related database that hosts a diverse range of transcriptomics, proteomics, epigenomics and genomics data. The NASA GeneLab Sample Processing Laboratory (SPL) generates omics data from biological experiments conducted aboard the International Space Station, Space Shuttle and space related ground experiments, this omics data then hosted on the GeneLab repository. Samples generated such experiments pose numerous technical challenges such as small experimental sample size, variance in dissection times, limited tissue preservation methods, prolonged storage time, and more. GeneLab SPL team had developed specialized expertise in nucleic acid extraction, library preparation and sequencing of such biological samples via extensive training and years of experience. In order to ensure data accuracy and consistency across experiments, SPL has developed standardized protocols for each species and tissue type. These protocols in conjunction with quality control metrics and data standards are crucial in generating of high-quality data. SPL protocols and standards have been developed in collaboration with the scientific community and had been made publicly available on the GeneLab portal, guaranteeing comparability of datasets across spaceflight experiments. To ensure reliability of data generation, SPL leverages cutting-edge innovations in laboratory automation for sample processing. By leveraging these state-of-the-art platforms, SPL achieves high levels of data reproducibility while significantly minimizing sources of bias and variability, especially across experiments with large numbers of samples. Over the past few years, the space biology investigator community has accessed SPL-generated data from the Open Science Data Repository for a myriad of data re-analysis and re-use studies. We observe a trend that in-house SPL-generated data consistently outperforms outsourced sequencing data in terms of technical standards, quality control metrics, timeliness of data delivery, and sequencing and reagent efficiency. Superior data generation has and will continue to enable discoveries in disease, diagnostic tools, and the biological effects of long duration spaceflight.

GeneLab↗
DOE OSTI2022

Storage Futures Study: Grid Operational Impacts of Widespread Storage Deployment

This report, the fifth in the Storage Futures Study series, uses cost-driven scenarios from the ReEDS model as a starting point to examine the operational impacts of grid-scale storage deployment and relationships between this deployment and the contribution of variable renewable energy. We use commercial production cost modeling software to evaluate hourly operation of five scenarios that reach between 210 gigawatts (GW) and 930 GW of installed storage by 2050. We find that storage plays an important role in these power systems between now and 2050 - by storing the lowest-marginal cost generation (often, overgeneration from solar or wind plants) and generating energy during the highest net load periods of the day and year. Storage helps with the integration of variable renewable energy and by providing an important resource to provide continued reliable power.

25 ENERGY STORAGE↗
NASA NTRS1990

The sixth generation robot in space

The knowledge based simulator developed in the artificial intelligence laboratory has become a working test bed for experimenting with intelligent reasoning architectures. With this simulator, recently, small experiments have been done with an aim to simulate robot behavior to avoid colliding paths. An automatic extension of such experiments to intelligently planning robots in space demands advanced reasoning architectures. One such architecture for general purpose problem solving is explored. The robot, seen as a knowledge base machine, goes via predesigned abstraction mechanism for problem understanding and response generation. The three phases in one such abstraction scheme are: abstraction for representation, abstraction for evaluation, and abstraction for resolution. Such abstractions require multimodality. This multimodality requires the use of intensional variables to deal with beliefs in the system. Abstraction mechanisms help in synthesizing possible propagating lattices for such beliefs. The machine controller enters into a sixth generation paradigm.

Butcher, A.↗
DOE OSTI2021

Nonlinear ultrasonic technique for the characterization of microstructure in additive materials

This study employs nonlinear ultrasonic techniques to track microstructural changes in additively manufactured metals. The second harmonic generation technique based on the transmission of Rayleigh surface waves is used to measure the acoustic nonlinearity parameter, β. Stainless steel specimens are made through three procedures: traditional wrought manufacturing, laser-powder bed fusion, and laser engineered net shaping. The β parameter is measured through successive steps of an annealing heat treatment intended to decrease dislocation density. Dislocation density is known to be sensitive to manufacturing variables. In agreement with fundamental material models for the dislocation-acoustic nonlinearity relationship in the second harmonic generation, β drops in each specimen throughout the heat treatment before recrystallization. Geometrically necessary dislocations (GNDs) are measured from electron back-scatter diffraction as a quantitative indicator of dislocations; average GND density and β are found to have a statistical correlation coefficient of 0.852 showing the sensitivity of β to dislocations in additively manufactured metals. Moreover, β shows an excellent correlation with hardness, which is a measure of the macroscopic effect of dislocations.

42 ENGINEERING↗
DOE OSTI2021

Demonstrating SolarPILOT’s Python API Through Heliostat Optimal Aimpoint Strategy Use Case

SolarPILOT is a software package that generates solar field layouts and characterizes the optical performance of concentrating solar power (CSP) tower systems. SolarPILOT was developed by the National Renewable Energy Laboratory (NREL) as a stand-alone desktop application but has also been incorporated into NREL’s1 System Advisor Model (SAM) in a simplified format. Prior means for user interaction with SolarPILOT have included the application’s graphical interface, the SAM routines with limited configurability, and through a built-in scripting language called “LK.” This paper presents a new, full-featured, Python-based application programmable interface (API) for SolarPILOT, which we hereafter refer to as CoPylot. CoPylot provides access to all SolarPILOT’s capabilities to generate and characterize power tower CSP systems seamlessly through Python. Supported capabilities include (i) creating and destroying a model instance with message reporting tools; (ii) accessing and setting any SolarPILOT variable including custom land boundaries for field layouts; (iii) programmatically managing receiver and heliostat objects with varied attributes for systems with multiple receiver or heliostat types; (iv) generating, assigning, and modifying solar field layouts including the ability to set individual heliostat locations, aimpoints, soiling rates, and reflectivity levels; (v) simulating solar field performance; (vi) returning detailed results describing performance of individual heliostats, the aggregate field, and receiver flux distribution; and, (vii) exporting Python-based model instances to multiple file formats. CoPylot enables Python users to perform detailed CSP tower analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. In addition to CoPylot’s functionality, Python users have access to the over 100,000 open-source libraries to develop, analyze, optimize, and visualize power tower CSP research. This enables CSP researchers to perform analysis that was previously not possible through SolarPILOT’s existing interfaces. This paper discusses the capabilities of CoPylot and presents a use case wherein we demonstrate optimal solar field aiming strategies.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Demonstrating SolarPILOT's Python API Through Heliostat Optimal Aimpoint Strategy Use Case: Preprint

SolarPILOT is a software package that generates heliostat field layouts and characterizes the optical performance of concentrating solar power (CSP) tower systems. SolarPILOT was developed by the National Renewable Energy Laboratory (NREL) as a stand-alone desktop application but has also been incorporated into NREL's System Advisor Model (SAM) in a simplified format. Prior means for user interaction with SolarPILOT have included the application's graphical interface, the SAM routines with limited configurability, and through a built-in scripting language called "LK." This paper presents a new, full-featured Python-based application programmable interface (API) for SolarPILOT, which we hereafter refer to as CoPylot. CoPylot provides access to all SolarPILOT's capabilities to generate and characterize power tower CSP systems seamlessly through Python. Supported capabilities include (i) creating and destroying a model instances with message reporting tools; (ii) accessing and setting any SolarPILOT variable including custom land boundaries for field layout; (iii) programmatically managing receiver and heliostat objects with varied attributes for systems with multiple receiver or heliostat types; (iv) generating, assigning, and modifying heliostat field layouts including the ability to set individual heliostat locations, aimpoints, soiling rates, and reflectivity levels; (v) simulating heliostat field performance; (vi) returning detailed results describing performance of individual heliostats, the aggregate field, and receiver flux; and, (vii) exporting Python-based model instances to multiple file formats. CoPylot enables Python users to perform detailed tower CSP analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. In addition to CoPylot's functionality, Python users have access to the over 100,000 open-source libraries to develop, analyze, optimize, and visualize CSP tower research.

41 EE - Solar Energy Technologies Office (EE-4S)↗
NASA NTRS1988

Advanced sensible heat solar receiver for space power

NASA Lewis, through in-house efforts, has begun a study to generate a conceptual design of a sensible heat solar receiver and to determine the feasibility of such a system for space power applications. The sensible heat solar receiver generated in this study uses pure lithium as the thermal storage medium and was designed for a 7 kWe Brayton (PCS) operating at 1100 K. The receiver consists of two stages interconnected via temperature sensing variable conductance sodium heat pipes. The lithium is contained within a niobium vessel and the outer shell of the receiver is constructed of third generation rigid, fibrous ceramic insulation material. Reradiation losses are controlled with niobium and aluminum shields. By nature of design, the sensible heat receiver generated in this study is comparable in both size and mass to a latent heat system of similar thermal capacitance. The heat receiver design and thermal analysis was conducted through the combined use of PATRAN, SINDA, TRASYS, and NASTRAN software packages.

Bennett, Timothy J.↗
NASA NTRS1988

Advanced sensible heat solar receiver for space power

NASA Lewis, through in-house efforts, has begun a study to generate a conceptual design of a sensible heat solar receiver and to determine the feasibility of such a system for space power applications. The sensible heat solar receiver generated in this study uses pure lithium as the thermal storage medium and was designed for a 7 kWe Brayton (PCS) operating at 1100 K. The receiver consists of two stages interconnected via temperature sensing variable conductance sodium heat pipes. The lithium is contained within a niobium vessel and the outer shell of the receiver is constructed of third generation rigid, fibrous ceramic insulation material. Reradiation losses are controlled with niobium and aluminum shields. By nature of design, the sensible heat receiver generated in this study is comparable in both size and mass to a latent heat system of similar thermal capacitance. The heat receiver design and thermal analysis were conducted through the combined use of PATRAN, SINDA, TRASYS, and NASTRAN software packages.

Bennett, Timothy J.↗
DOE OSTI2024

Microbial Community Changes across Time and Space in a Constructed Wetland

Constructed wetlands are artificial ecosystems designed to replicate natural wetland processes. Microbial communities play a pivotal role in cycling essential elements, particularly sulfur, which is crucial for trace metal fixation and remobilization in these ecosystems. By their response to their environment, microbial communities act as biological indicators of the wetland performance. To address knowledge gaps pertinent to the changes in trace metal bioavailability in relation to microbial activities in the H-02 constructed wetland, we performed this study to investigate temporal and spatial variations in microbial communities by using molecular biology tools. Quantitative polymerase chain reaction and next generation sequencing techniques were employed to analyze archaeal and bacterial groups associated with sulfur and methane cycling. Alpha diversity indices were used to assess species richness, evenness, and dominance. Results indicated high gene abundance of Desulfuromonas (5.37 × 10 6 g.cell –1 ), methane oxidizing bacteria (6.92 × 10 6 g.cell –1 ), and methanogenic microorganisms (3.02 × 10 5 g.cell –1 ) during cool months. Warm months were marked by sulfate reducing bacteria dominance (3.31 × 10 6 g.cell –1 ), potentially due to competitive interactions and environmental conditions, higher temperatures, and lower redox potential. Spatial variability among microbial groups was insignificant, but trends in gene abundance indicated complex factors influencing these groups. Next generation sequencing data demonstrated Firmicutes as the most abundant phylum with over 50% regardless of the season or sampling location. Cool months exhibited higher alpha diversity than warm months. Overall, this study showed that seasonal changes significantly impacted the microbial communities in the H-02 constructed wetland that are associated with the sulfur cycle and eventually trace metal biogeochemistry, revealing two distinct mechanisms of the sulfur cycle between the two main seasons, whereas spatial variability effects were not conclusive.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Probabilistic Forecasting of Generators Startups and Shutdowns in the MISO System Based on Random Forest

Solving security constrained unit commitment (SCUC) problems to plan an economical generation schedule for day-head electricity market has been an important research topic in recent years. Mixed integer programming method (MIP), the-state-of-art approach for solving SCUC problem, is known computationally hard when the number of binary status variables is large. In this paper, a machine learning-based algorithm - random forest (RF), was applied to forecast the startups (SU) and shutdowns (SD) hours of generators, based on historical hourly system condition observations in the Midcontinent Independent System Operator (MISO) system. The main purpose is to reduce the number of binary status variables, by fixing the SU/SD hours to a narrow range of high confidence. This would significantly reduce the size of the decision space, and therefore speed up SCUC solutions with reduced uncertainty.

Lin, Xinming↗
DOE OSTI2026

Structural Design of Bismuth Telluride Nanoplates through Process Variables

Binary pnictogen chalcogen compounds, primarily bismuth tellurides and selenides, are of great interest due to their applications in emerging quantum devices, as well as thermoelectric generators. The performance of bismuth telluride in these roles depends on its structure at the nanoscale, particularly the size, shape, and crystallinity of its nanocrystalline forms. However, current methods for controlling these features are often slow, inconsistent, or difficult to scale. Here, we demonstrate that through a solvothermal synthesis and hot injection process, precise control over the morphology of bismuth telluride nanoplates is possible with independent tuning of process variables, such as temperature and reaction time. We find that the nanoplate shape and internal porosity vary systematically with synthesis temperature and that the same morphological outcomes can be rapidly achieved at a fixed temperature by adjusting reaction duration. These results reveal that both the temperature and time can independently direct bismuth telluride morphological features, allowing for rapid, tunable synthesis strategies. Our approach offers a scalable framework, not only for bismuth telluride but also for related layered chalcogenides used in energy harvesting and quantum technologies.

Ackley, Jordan [Boise State Univ., ID (United Stat↗
DOE OSTI2022

Evaluating Grid Strength under Uncertain Renewable Generation

The increasing displacement of synchronous generators with renewable resources such as wind and solar via power electronic interfaces causes a reduction in short-circuit strength and weak grid issues. The variation and uncertainty of renewable energy increase challenges for identifying weak grid conditions. This paper proposes an efficient method to analyze the impact of uncertain renewable energy on grid strength. The proposed method uses the probabilistic collocation method (PCM) to approximate the results of grid strength assessment under uncertain renewable generation, in order to reduce computational burden without compromising result accuracy when compared with traditional Monte Carlo simulation (MCS). To improve the accuracy of the approximation results, the proposed method integrates the K-means clustering technique with PCM to select the approximation samples of input variables. The efficacy of the proposed method is demonstrated by comparison with MCS on the modified IEEE 9-bus system and modified IEEE 39-bus system with multiple renewable generators.

grid strength↗
DOE OSTI2026

Transverse Kinematic Imbalance in MicroBooNE's New nue CC0pi Measurements

Neutrino-nucleus cross section measurements require accurate modelling of neutrino interactions. Neutrino beams are not monoenergetic, and the energy of each interaction must instead be modelled using nuclear interaction assumptions. This introduces significant systematic uncertainty into cross section measurements. Effects such as Fermi motion, nuclear correlations, and final-state interactions (FSI) smear the underlying quasi-elastic scattering signal, making it difficult to disentangle genuine quasi-elastic kinematics from nuclear effects across the full range of interaction channels (QE, MEC, RES, DIS) probed in these measurements. Transverse Kinematic Imbalance (TKI) variables, such as $\delta p_T$ and $\delta \alpha_T$, probe this same phase space by exploiting the fact that the incoming neutrino has zero transverse momentum ($\vec{p}_T^{\,\nu} = 0$). Any measured transverse imbalance in the final state therefore arises from nuclear effects rather than from uncertainty in the incident neutrino energy, allowing cross section measurements to select a phase space that is rich in quasi-elastic-like events with minimal contamination from FSI and other nuclear effects, independent of energy reconstruction. Recent unfolded MicroBooNE cross section measurements of electron-neutrino charged-current interactions with zero pions and at least one proton ($\nu_e$ CC0$\pi$, 1eNp0$\pi$) show that several leading nuclear interaction generators (including GENIE variants, NuWro, GiBUU, and NEUT) reproduce the differential cross section in electron energy reasonably well, but consistently struggle to describe the differential cross section in the cosine of the leading proton's angle, yielding lower $p$-values across all seven generators tested. This tension points to a more fundamental, kinematics-driven disagreement between data and generators that is not visible in energy-only cross section observables. This is precisely the regime TKI variables are designed to probe. Following previous TKI cross section measurements with muon-neutrino data in MicroBooNE, this poster presents the case for extending the TKI framework to electron-neutrino cross section measurements as a next step to isolate and characterize the source of the observed generator tension in proton kinematics.

Burridge, Jessica [U. Manchester (main)] (ORCID:00↗
DOE OSTI2022

Evaluation of Ocean Biogeochemistry and Carbon Cycling in CMIP Earth System Models With the International Ocean Model Benchmarking (IOMB) Software System

Abstract The International Ocean Model Benchmarking (IOMB) software package is a new community resource that we use here to evaluate surface and upper ocean biogeochemical variables and integrated anthropogenic carbon uptake from earth system models (ESMs) contributing to the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). IOMB generates graphics and tables for systematically comparing model predictions against multiple datasets. Our analysis reveals some improvement in the multi‐model mean from CMIP5 to CMIP6 for most of the variables we examined. Compared to data‐constrained estimates of ocean anthropogenic carbon uptake for the 1994–2007 period, negative biases exist for many models between 30 and 50°S. Global model estimates of anthropogenic carbon uptake for the same period do not change significantly from CMIP5 to CMIP6, with the combined ensemble mean estimate of 27.8 ± 0.5 Pg C lower than a data‐constrained estimate of 33.0 ± 4.0 Pg C. At the same time, the change in the natural carbon inventory from CMIP is estimated to be a source of 0.7 ± 0.3 Pg C, which is considerably smaller in magnitude than a data‐constrained estimate of 5.0 ± 3.0 Pg C. With chlorofluorocarbon (CFC) predictions available for several models, we demonstrate that negative anthropogenic dissolved inorganic carbon biases coincide with negative biases in CFC concentration, highlighting the importance of weak exchange between the surface and interior ocean in regulating rates of anthropogenic carbon uptake. To examine the robustness of this attribution across the CMIP models, we calculate the global vertical temperature gradient between 200 and 1,000 m as a metric for global stratification and exchange between the surface and deeper waters. We find a linear relationship between the bias of the vertical temperature gradients and the bias in global anthropogenic carbon uptake, consistent with the hypothesis that model biases in anthropogenic carbon uptake are related to biases in surface‐to‐interior exchange by physical processes.

58 GEOSCIENCES↗
DOE OSTI2025

Machine learning inversion from scattering for mechanically driven polymers

A machine learning inversion method is developed for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration and off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the machine learning inversion. Finally, we train a Gaussian process regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters.

Gaussian process regressors↗
DOE OSTI2024

Performance Testing of an Integrated Magnetic Power Take-Off

A wave energy converter (WEC) and the power take-off (PTO) generator system can be represented by using a mass-spring-dampener model. By incorporating a negative stiffness spring within the PTO, the overall stiffness of the PTO can be lowered allowing the impedance of the PTO to be more closely matched with the WEC. By designing for impedance matching the WEC can greatly enhance its power generation capability. This project has involved the design, fabrication and testing of a new type of linear-stroke length and rotary stroke length adjustable negative stiffness magnetic springs for use within a wave energy converter (WEC). The magnetic springs were studied by using 3-D finite element analysis with the objective of creating high energy density and a long linear stroke length. After the construction and testing of both a proof-of-principle adjustable linear and rotary magnetic spring prototype the rotary (torsional) magnetic spring was selected for scaling up analysis. The selected scaled-up proof-of-principle magnetic spring had a peak torque of 850 Nm with a ±45 degree stroke length. By translating the inner rotor relative to the outer rotor, the stiffness could be adjusted to be either negative or positive stiffness. During this project a magnetic lead screw was also studied, and it was shown that by combining the magnetic lead screw with a linearly translating magnetic spring a very long rotary stroke length could be attained. However, as the magnetic lead screw increased complexity and reduced overall energy density relative to a rotary (torsional) spring, this design approach was not further pursed. Dynamometer testing of the variable stiffness magnetic springs was first completed by Portland State University and following this the magnetic spring performance was independently verified by Sandia National Laboratory (Sandia). A WEC analysis when using a variable stiffness magnetic spring was completed by using the WecOptTool. WecOptTool is an open-source WEC optimization software developed by Sandia that supports efficient power take-off (PTO) and control optimization. Wave condition data from the Oregon PacWave test-site was used in this study. The analysis showed that a WEC with a tunable stiffness value could consistently achieve about 80% of its maximum theoretical power production. The use of a tunable stiffness WEC, rather than zero-stiffness or a constant stiffness WEC was also shown to lead to a smaller maximum PTO force requirement. The variable stiffness magnetic spring was integrated into an experimental WEC developed by Sandia, called a Wave-Bot. Sandia successfully completed water-tank testing of the Wave-Bot at the Navy’s Carderock, Maryland, wave-basin test site. The wave-basin testing helped to experimentally demonstrate the operating capabilities and increased power generation capability of a WEC containing an integrated variable stiffness magnetic spring. A WEC capacity factor analysis was also completed. The capacity factor was defined as the ratio of annual average WEC generator power to the maximum (RMS) generated power. This capacity factor provided a means of identifying the ratio of potential revenue to cost. It was calculated that if a tunable variable stiffness magnetic spring has RMS constraints it could operate with a capacity factor above 30%. This is comparable to a wind turbine’s capacity factor.

16 TIDAL AND WAVE POWER↗