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DOE OSTI2021

Developing Technology Performance Level Assessments for Early-Stage Wave Energy Converter Technologies: Preprint

The advantage of using Technology Performance Level (TPL) in conjunction with Technology Readiness Level (TRL) assessments in guiding technology development trajectories to successful outcomes in less time, at less overall cost, and with less encountered risk has been well articulated in the literature. In partnership with industry and international collaborators, a TPL assessment methodology for grid-connected applications has been developed through the application of the systems engineering approach. Metrics under seven different categories have been developed, weighted based on their relative relevance, and combined to yield a composite score. The methodology has been implemented in a spreadsheet tool plus a web application specifically aimed at assessing early stage (TRL 1-3) concepts. The target use cases are (a) technology developers improving their design, to find fatal flaws early, to get feedback on current design, to identify areas of improvement that will yield the highest return on investment, (b) reviewers assessing technologies in competitions or for making funding decisions, (c) investor or project developer doing due diligence, (d) policy makers landscaping the technology domain for formulating R&D strategy. The methodology and the tools are undergoing continuous improvement based on the experience and lessons learnt from applying it to internal and external marine energy technology development projects. The methodology is also being adapted for assessing WECs servicing markets outside the continental grid - broadly categorized as Powering the Blue Economy (PBE) applications. Such applications have vastly different functional requirements entailing a modification of the methodology to account for their higher risk tolerance, reduced price sensitivities, lower power needs, different permitting protocols, etc. This paper presents the latest status of the TPL assessment methodology and tools, describes its adaptation to select PBE markets, and explores its extension to other domains where it could provide a comprehensive and holistic measure of a nascent or disruptive technology's technoeconomic performance potential.

metrics↗

DOE OSTI2021

Developing Technology Performance Level Assessments for Early-Stage Wave Energy Converter Technologies

metrics↗

DOE OSTI2024

On the Representativity of Electrode Microstructure Parameters and Their Electrochemical Response for Lithium Ion Batteries

Lithium-ion battery electrochemical models require an accurate description of the electrodes microstructures to be predictive that can be achieved through nanoscale imaging. Such observations are however limited by their field of view (FOV), as they provide only a subset of the whole electrode volume that does not necessarily represent the whole electrode microstructure heterogeneity, and therefore can bias the microstructure analysis. A microstructure scale electrochemical model was used to investigate lithium plating onset, material non-uniform utilization, and in-plane heterogeneities for an NMC-graphite full cell. To evaluate the representativeness, and thus relevance, of these model predictions, a coupled representativity analysis has been performed on the microstructure parameters and, in a novel way, on the full cell electrochemical response. Electrode microstructure parameters representativeness has been first quantified using the representative volume element (RVE) methodology. The RVE major flaw is that ultimately it can only conclude if a FOV contains representative subvolumes of the FOV, but not if the FOV itself is representative of the electrode volume. Analysis can conclude negatively ('FOV is not representative'), but not positively ('FOV is representative'). One major contribution of this work was to quantify the convergence of the RVE size with the FOV, to actually investigate the FOV representativeness and thus partly remedy this intrinsic limitation. The analysis determined that performing a standard RVE calculation, without exploring its FOV convergence, is likely to strongly underestimate the actual RVE size. The new RVE methodology has been automated in the NREL open-source Microstructure Analysis Toolbox (MATBOX) and is available to the battery community. Representativeness of microstructure parameters is however only an intermediate step, as the end-results of an electrochemical model are performances predictions. Indeed, what is the practical consequence of a given deviation for a microstructure parameter? The microstructure parameter deviation propagations to the 3D microstructure scale electrochemical response have been then quantified for different charge rates. This defines a threshold for the microstructure parameters FOV for a desired maximum deviation of the electrochemical response. Such deviation propagation analysis is analogous to error propagation analysis and is necessary to determine the relevance of microstructure scale model predictions for macroscale predictions. Electrochemical model shows cell representative section areas are increasing with C-rate, due to higher in-plane heterogeneities, indicating larger FOVs are required specifically for fast charge modeling. Therefore, we introduced the novel concept of electrochemical RVE (eRVE) that is a function of the operating conditions (thus defined as a dynamic RVE), with an increasing dependence with the C-rate. Representativity analysis of the investigated cell determined a FOV of 144.4 x 54.4 m2 is large enough to establish a convergence on the representative section areas for low to intermediate C-rate (=2.5C), but not large enough to conclude for higher rates. This work aims to emphasize the importance of representativity analysis for LIB electrode microstructures, as it is required to estimate the error, and thus the relevance, of microstructure parameters intended to be used in macroscale models. The methodology and results can help researchers to select the relevant imaging and associated FOV required to provide accurate enough microstructure parameters.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2017

Developing a Descriptor-Based Approach for CO and NO Adsorption Strength to Transition Metal Sites in Zeolites

The discovery of new materials tailored for a given application typically requires the screening of a large number of compounds, and this process can be significantly accelerated by computational analysis. In such an approach the performance of a compound is correlated to a materials property, a so-called descriptor. Here we develop a descriptor-based approach for the adsorption of CO and NO to Cu, Ni, Co, and Fe sites in zeolites. Here we start out by discussing a possible design strategy for zeolite catalysts, define the studied test set of sites in the zeolites SSZ-13 and mordenite, and define a set of appropriate descriptors. In a subsequent step we use these descriptors in single-parameter, two-parameter, and multiparameter regression analysis and finally use a machine-learning genetic algorithm to reduce the number of variables. We find that one or two descriptors are not sufficient to accurately capture the interactions between molecules and metal centers in zeolites, and indeed a multiparameter approach is necessary. Even though many of the descriptors are directly correlated, we identify the position of the s orbital and the number of valence electrons of the active site as well as the HOMO–LUMO gap of the adsorbate as most important descriptors. Furthermore, the reconstruction of the active sites upon adsorption plays a crucial role, and when it is explicitly included in the analysis, correlations improve significantly. In the future we expect that the fundamental methodology developed here will be adapted and transferred to selected problems in adsorption and catalysis and will assist the rational design of materials for the given application.

36 MATERIALS SCIENCE↗

NASA NTRS1975

Systems cost performance analysis (study 2.3). Volume 1: Executive summary

A methodology for developing balanced designs of spacecraft subsystems which interrelates cost, performance, safety, and schedule considerations was developed. The methodology consists of a two-step process: selection of all hardware designs which satisfy the given performance and safety requirements; and estimating the cost and schedule required to design, build, and operate each spacecraft design. Aggregate equations were written to describe the performance, safety (reliability), cost, and schedule required for one type of stabilization and control subsystem in terms of the equipment used. The methodology was applied to unmanned, automated spacecraft subsystems, and the resulting model was implemented as a digital computer program.

Source record↗

DOE OSTI2016

ROM-based Subset Selection Algorithm for Efficient Surrogate Modeling

This summary discusses a new methodology to support the construction of surrogate models for computationally expensive models with many input parameters and output responses. In realistic engineering calculations, such as thermal-hydraulics simulation, the cost of the simulation is typically expensive, especially when detailed models are employed to describe system behavior, thereby justifying the need for surrogate models. The cost of surrogate model construction is typically a function of the number of model parameters and the degree of model nonlinearity. For sufficiently complex models, the cost of constructing the surrogate model could be by itself overwhelmingly expensive, thereby diminishing its value. One key requirement of surrogate model construction is the ability to preselect the best parametric form to describe the model behavior over the expected range of parameter variations. This summary presents a new algorithm to help select the best approximation out of a template of functions using recent advances in reduced order modeling (ROM) techniques. The idea of selecting functions out of a template is typically referred to as subset selection in the statistical and applied mathematics community. Different from existing subset selection algorithms, we rely on ROM to help identify the most influential functions for the surrogate model. Previous work has shown that ROM can help reduce the effective dimensionality of the parameter space, which can help reduce the cost of surrogate model construction. In this summary, we adopt a different philosophy, where the fitting functions are treated as pseudo parameters and ROM is applied on the pseudo parameter space, with the result now being a ranked list of the pseudo parameters along with their contribution to the quality of the surrogate model fit. This approach allows the analyst to discard all terms that have negligible impact on the quantity of interest. A relap5 model is employed to demonstrate the application of the proposed methodology. (authors)

42 ENGINEERING↗

DOE OSTI2020

Leveraging Real-World Driving Data for Design and Impact Evaluation of Energy Efficient Control Strategies

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies. In this study, two methods are presented of leveraging real-world data in both design optimization of energy efficient control strategies and in evaluating the real-world impact of those control strategies upon large-scale deployment. Through these methodologies, strategies with highest impact on energy savings were selected to be implemented as control algorithms. The developed algorithms were incorporated into a vehicle dynamics and powertrain control architecture implemented on a Cadillac CT6 demonstration vehicle. The control algorithms were then exercised on real-world driving scenarios to determine their impact on collective energy savings. The methodology utilizes the large-scale driving data sets maintained by the National Renewable Energy Laboratory to extract real-world driving scenarios and efficient simulation software tools. The insights obtained through this research help in guiding technology selection for energy efficient driving controls.

ARPA-E↗

DOE OSTI2023

A ranking methodology for the coupling of pressurized water nuclear reactors and molten salt thermal energy storage

Thermal energy storage (TES) systems are a proposed solution to electricity market demand fluctuations and can be coupled to nuclear power plants to enable load following. Here, this work focuses on developing a methodology for evaluating potential designs for integrating a TES system into an existing pressurized water nuclear power plant. The proposed ranking methodology allows a group of experts to postulate and weigh designs based on ranking criteria derived from literature. The methodology developed in this study can aid in the final selection of a TES design for an existing nuclear power plant. The same process could be used to analyze other TES and nuclear reactor designs. The determined optimum design from this methodology was to place the TES system after the steam generator and to utilize the steam generated from the nuclear power plant to heat a molten salt TES unit. Another conclusion of this research was that human bias is prevalent during design selection, and actions such as standardized ranking criteria and large groups of experts should be used to minimize this error.

25 ENERGY STORAGE↗

DOE OSTI2022

WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling

Online deep learning (ODL) has become an important methodology for modeling time-based performance of computer systems. An open problem is the intelligent selection of features from raw workload traces of computer systems. The best methods are overly sensitive to noisy data, causing frequent feature changes and re-training. Using all available features inflates training time and introduces model artifacts if some features should have been dropped. We present WinnowML, a method for automatically determining the most relevant feature subset for a predictive time-series model. WinnowML combines existing feature ranking algorithms and a history of each feature's ranking to iteratively rank a feature set to lower prediction error and maximize long term relevance. From this ranked feature set, the most relevant and stable subset is selected to train a model. Experimentally, we show how WinnowML can lower a model's mean absolute relative error up to 42% on average compared to the closest performing approach. Additionally, we lower the fluctuation in feature ranking and selection up to 65%. We also demonstrate how to combine WinnowML and a model search tool to provide improvements in performance of up to 14.5% when compared to using all the feature available.

Bel, Oceane MS↗

DOE OSTI2014

TU-C-BRE-09: Performance Comparisons of Patient Specific IMRT QA Methodologies Using ROC Analysis

Purpose: To evaluate the ability of a selection of patient-specific QA methods to accurately classify IMRT plans as acceptable or unacceptable based on a multiple ion chamber (MIC) phantom. Methods: Twenty-four IMRT plans were selected (20 previously failed the institutional QA), and were measured on a MIC phantom to assess their dosimetric acceptability. These same plans were then measured using film (Kodak EDR2) and ion chamber (Wellhofer cc04), ArcCheck (Sun Nuclear), and MapCheck (Sun Nuclear) (delivered AP field-by-field, AP composite, and with original gantry angles). All gamma analyses were performed at 2%/2mm, 3%/3mm, and 5%/3mm. By using the MIC results as a gold standard, the sensitivity and specificity were calculated across a range of cut-off thresholds (% pixels passing for gamma analysis, and % dose difference for ion chamber), and were used to form ROC curves. Area under the curve (AUC) was used as a metric to quantify the performance of the various QA methods. Results: Grouping device’s AUC’s revealed two statistically significant different groups: ion chamber (AUC of 0.94), AP composite MapCheck (AUC of 0.85), ArcCheck (AUC of 0.84), and film (AUC of 0.82) were in the better performing group, while original gantry angles and AP field-by-field MapCheck (AUC of 0.65 and 0.66, respectively) matched less well with the gold standard results. Optimal cut-offs were also assessed using the ROC curves. We found that while often 90% of pixels passing is used as a criteria, the differing sensitivities of QA methods can lead to device and methodology-based optimal cutoff thresholds. Conclusion: While many methods exist to perform the same task of patient-specific IMRT QA, they utilize different strategies. This work has shown that there are inconsistencies in these methodologies in terms of their sensitivity and specificity to dosimetric acceptability. This work was supported by Public Health Service grants CA010953, CA081647, and CA21661 awarded by the National Cancer Institute, United States Department of Health and Human Services.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Viability of Miniature Discs in LFA-427

Miniature disc samples pose challenging measuring conditions for laser flash analysis. With reductions in signal from lower radiosity and lower half-rise times from low thicknesses, optimal selections of coatings and laser parameters are critical in a successful measurement. This report explains the methodology in successfully measuring two different material types—metals and ceramics—in proper coating selection, laser parameters, and post analysis for each material, as well as applying it to un-irradiated uranium samples of each type (U-metal, UO 2 ). This was done through a combination of analysis techniques combining automatic commercial methods (Neztech) and manual methods for correcting laser pulse impact aberrations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1993

A COMPACT model reduction methodology for articulated, multi-flexible bodies structures

To simulate the dynamical motion of articulated, multiflexible body structures, one can use multibody simulation packages such as DISCOS. To this end, one need appropriate reduced-order models for all the flexible components involved. The component modes projection and assembly model reduction methodology is one way to construct these reduced-order component models. However, the generated reduced-order system models typically contain high frequency extraneous modes. The presence of high frequency extraneous modes in a reduced-order system model forces us to use a small stepsize in the numerical integration of that model, resulting in an increase in the elapsed time of the integration process. To eliminate these high-frequency modes, we first identify and discard the component modes that 'contributed' to the generations of these modes. A sensitivity analysis is then used to determine how the remaining component modes should be perturbed in order to still closely capture the selected system modes. The effectiveness of the proposed methodology has been successfully verified using a finite-element model of the Galileo spacecraft.

Lee, Allan Y.↗

NASA NTRS2010

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy

Simon, Donald L.↗

NASA NTRS1983

A preliminary damage tolerance methodology for composite structures

The certification experience for the primary, safety-of-flight composite structure applications on the F-16 is discussed. The rationale for the selection of delamination as the major issue for damage tolerance is discussed, as well as the modeling approach selected. The development of the necessary coupon-level data base is briefly summarized. The major emphasis is on the description of a full-scale fatigue test where delamination growth was obtained to demonstrate the validity of the selected approach. A summary is used to review the generic features of the methodology.

Wilkins, D. J.↗

DOE OSTI2012

Assessing the Predictive Capability of the LIFEIV Nuclear Fuel Performance Code Using Sequential Calibration

This report considers the problem of calibrating a numerical model to data from an experimental campaign (or series of experimental tests). The issue is that when an experimental campaign is proposed, only the input parameters associated with each experiment are known (i.e. outputs are not known because the experiments have yet to be conducted). Faced with such a situation, it would be beneficial from the standpoint of resource management to carefully consider the sequence in which the experiments are conducted. In this way, the resources available for experimental tests may be allocated in a way that best 'informs' the calibration of the numerical model. To address this concern, the authors propose decomposing the input design space of the experimental campaign into its principal components. Subsequently, the utility (to be explained) of each experimental test to the principal components of the input design space is used to formulate the sequence in which the experimental tests will be used for model calibration purposes. The results reported herein build on those presented and discussed in [1,2] wherein Verification & Validation and Uncertainty Quantification (VU) capabilities were applied to the nuclear fuel performance code LIFEIV. In addition to the raw results from the sequential calibration studies derived from the above, a description of the data within the context of the Predictive Maturity Index (PMI) will also be provided. The PMI [3,4] is a metric initiated and developed at Los Alamos National Laboratory to quantitatively describe the ability of a numerical model to make predictions in the absence of experimental data, where it is noted that 'predictions in the absence of experimental data' is not synonymous with extrapolation. This simply reflects the fact that resources do not exist such that each and every execution of the numerical model can be compared against experimental data. If such resources existed, the justification for numerical models would be reduced considerably. The authors note that the PMI is primarily intended to provide a high-level, quantitative description of year-to-year (or version-to-version) improvements in numerical models, where these descriptions can be used as a means of justifying funding requests to support further model development research. It is in this context that the present report should be considered: the availability of data from experimental tests should be viewed as a time-dependent variable, where experiments are added to the calibration suite as resources become available. For the present report, the experimental data is of course already available (permitting demonstration of the proposed methodology). Furthermore, the authors are not proposing this methodology as the answer to the question of how to allocate resources for experimental tests, and readers are directed to [5] and the references contained in Section 1 of [5] for additional information on the subject. However, the strength of this methodology is that it offers a means by which to select the sequence of experiments in a pre-arranged experimental campaign (a situation for which the methods discussed in [5] are less appropriate). The report is organized as follows. Section 2 describes the methodology employed to formulate the sequences of experiments for the calibrations performed for this study. Section 3 then presents the results associated with two sequences; supplementary results are provided in the Appendix. The report then concludes in Section 4 with a brief summary.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2013

Phosphorylated mesoporous carbon as effective catalyst for the selective fructose dehydration to HMF

Phosphorylated mesoporous carbons (PMCs) have been synthesized using an already reported one pot methodology. These materials have been applied as acidic catalysts in the dehydration of fructose to hydroxymethylfurfural (HMF). PMCs showed better selectivity to HMF compared to sulfonated carbon catalyst (SC) despite lower activity. The concentration of P-O groups correlates to the activity/selectivity of the catalysts; the higher the P-O concentration the higher the activity. However, the higher the P-O content the lower the selectivity to HMF. Indeed a lower concentration of the P-O groups (and even the acidic groups) minimized the degradation of HMF to levulinic acid and the formation of by-products, such as humines. Stability tests showed that these systems deactivate due to the formation of humines, water insoluble by-products derived from the dehydration of fructose, blocking the active site of the catalyst. Increasing the amount of P-O groups, higher amount of humines are formed; therefore carbons containing lower amount of phosphorylated groups, such as P/N-0.25, are less prone to deactivation. Keywords: Phosphorylated mesoporous carbons; fructose dehydration; HMF

Villa, Alberto↗

DOE OSTI2019

Scenario Development: Proposal of a Methodology for Different Host Rocks and Disposal Concepts - 19007

Scenario development is an essential part of the assessment of the long-term safety of a repository for radioactive waste. The new act on site selection in Germany in 2017 requires that salt, clay and crystalline rock are studied as possible sites for repositories. So far, a comprehensive methodology for scenario development has been developed only for a site in salt rock in Germany within the Preliminary Safety Analysis (VSG). The question arose whether this methodology is transferable to other host rocks and disposal concepts and which modifications may be necessary. The transferability was shown in the project ANSICHT for clay rock sites while introducing some modifications of the methodology. This project studied the transferability of crystalline rock sites, however, there is no suitable safety and disposal concept or even a generic site yet. To study the transferability, initial barriers and FEP for different concepts were selected and the first steps of the methodology for crystalline rock were applied and discussed. It was concluded that the definition of barriers as containment providing rock zone (CPRZ) is difficult but important for the application of the methodology. Concepts for crystalline rock can foresee a CPRZ within the host rock or above. If the concept is using engineered barriers only and no CPRZ, the number of initial barriers is low. This will subsequently lead to a more complex scenario development since long FEP chains have to be considered. Based on the discussion so far, the transferability of the methodology seems to be feasible in general. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Low-energy mass-selected ion beam production of fragments produced from hexamethyldisilane for SiC film formation

We have proposed an experimental methodology which makes it possible to deposit silicon carbide (SiC) films on Si substrates with a low-energy mass-selected ion beam system using hexamethyldisilane (HMD) as a gas source. In this study, one of the fragment ions produced from HMD, SiCH{sub 4}{sup +}, was mass-selected. The ion energy was approximately 100 eV. Then, the SiCH{sub 4}{sup +} ions were irradiated to a Si(100) substrate. When the temperature of the Si substrate was set at 800 °C during the ion irradiation, the X-ray diffraction and Raman spectroscopy of the substrate following the completion of ion irradiation experiment demonstrated the occurrence of 3C-SiC deposition.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗