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DOE OSTI2025

A Deep Learning-Aided Workflow for Decoding the Stress Regime of Southern Nevada

The Rock Valley fault zone in southern Nevada has a notable history of seismic activity and is the site of a future direct comparison experiment of explosion and earthquake sources. This study aims to gain insight into regional tectonic processes by leveraging recent advances in seismic monitoring capabilities to elucidate the local stress regime. A crucial step in this investigation is the accurate determination of P-wave first-motion polarities, which play a vital role in resolving earthquake focal mechanisms of small earthquakes. Here, we deploy a deep learning-based method for automatic determination of first-motion polarities to vastly expand the polarity dataset beyond what has been reviewed by human analysts. By the integrating P-wave polarities with new measurements of S/P amplitude ratios, we obtain robust focal mechanism estimates for 1306 earthquakes with a local magnitude of 1 and above occurring between 2010 and 2023 in southern Nevada. We then use the focal mechanism catalog to examine the regional stress orientation, confirming an overall trans-tensional stress regime with smaller scale complexities illuminated by individual earthquake sequences. These findings demonstrate how detailed analyses of small earthquakes can provide fundamental information for understanding earthquake processes in the region and inform future experiments at the Nevada National Security Site.

58 GEOSCIENCES↗

DOE OSTI2026

Constructing a High‐Resolution Aftershock Catalog for the 2017 Mw 8.2 Tehuantepec Earthquake Sequence Using a Machine Learning–Based Workflow

The 8 September 2017 Mw 8.2 Tehuantepec earthquake was the largest instrumentally recorded normal‐faulting earthquake in Mexico. The mainshock occurred offshore within the Tehuantepec seismic gap, generating >30,000 aftershocks in the following year. We applied an open‐source, machine learning (ML)–assisted workflow to construct a high‐resolution aftershock catalog using data from temporary and permanent seismic networks in southern Mexico. The workflow integrates PhaseNet for phase detection; GaMMA for phase association; and VELEST, HypoInverse, and HypoDD for velocity modeling and relocation. We processed seven months of continuous waveform data from 29 broadband stations, including a temporary rapid‐response deployment that improved station coverage of the offshore rupture zone. To evaluate performance, we compared our results against analyst‐reviewed picks and event locations from the Servicio Sismológico Nacional catalog. The resulting catalog contains 11,374 relocated earthquakes and represents the most comprehensive published dataset for this sequence, incorporating the first full use of the temporary network. Relocated hypocenters show improved depth control and align well with the Slab2.0 subduction geometry, revealing clearer separation between offshore slab events and onshore crustal seismicity. This study demonstrates that combining ML‐based detection with established methods provides a scalable and reproducible approach for constructing high‐quality earthquake catalogs in tectonically complex environments and offers practical guidance for adapting similar workflows to other earthquake sequences.

Garcia, Marc [The University of Texas at El Paso, ↗

DOE OSTI2020

Accuracy of HVAC Load Predictions: Validation of EnergyPlus and DOE-2 using FLEXLAB Measurements

The aim of the project reported here was to better understand the level of accuracy of three building energy simulation (BES) engines (‘engines’) — EnergyPlus™, DOE-2.1e, and DOE-2.2 — by identifying and investigating significant deviations between the performance predicted by these engines and actual performance as measured in the FLEXLAB® test facility at Lawrence Berkeley National Laboratory (LBNL). The specific test conditions included some of those prescribed in ANSI/ASHRAE Standard 140 - Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Detailed measurements of FLEXLAB performance, including indoor temperatures and heat fluxes and air-flow and water flow rates and temperatures in the Heating, Ventilating and Air Conditioning (HVAC) system, together with hourly weather data, were recorded and used in analyzing the simulation results from EnergyPlus v8.8, DOE-2.2 v3.65 and DOE-2.1e v127. These engines are commonly used in the United States for building energy code compliance, federal, state, and utility incentives programs, as well as energy efficient design of new buildings and energy retrofit of existing buildings. Seven conventional overhead mixing ventilation scenarios were tested and each engine was found to have a similar level of agreement with the measurements of space-level heating and sensible cooling loads. These results provide useful information regarding the accuracy of these engines in predicting the cooling and heating load elements of whole building energy performance. This information is intended for practitioners who are concerned about transitioning between simulation tools with different engines and for managers of utility programs leveraging these tools for evaluating and/or projecting measure savings to be incentivized under their programs. The results of the comparisons of simulated and measured performance indicate that the predictions from all three engines are not significantly different. The 24-hour average value of the absolute mean bias indicates the likely magnitude of the error in any particular case. The average mean bias is reduced by cancelation of overprediction in one case by underprediction in another. The daytime absolute mean biases, which may be more important for both energy performance and occupant comfort, are ~6%, presumably because of the greater complexity involved in simulating in the presence of solar radiation. EnergyPlus typically overpredicts the cooling load and/or underpredicts the heating load by ~1.5% and the DOE-2 engines typically underpredict the cooling load by approximately the same amount. The Root Mean Square Error is relatively more sensitive to shorter term variations in the difference between predicted and measured loads; the three engines have similar values, ~10%, suggesting that the uncertainties in their predictions of peak loads may also be similar in magnitude. The implication of these results is that users, both designers and program analysts, can use EnergyPlus, DOE-2.1e, or DOE-2.2 to model conventional commercial buildings equipped with overhead mixing ventilation with a similar level of confidence. Further work is required to better understand the variability in the level of agreement between the engine predictions and FLEXLAB measurements, where a particular engine will agree well with FLEXLAB in some cases and not so well in others and another engine will agree or disagree in different cases. As the sources of this variability are identified and eliminated or reduced significantly, it is recommended that the experimental capabilities and methods developed in the study reported here should be applied to validating heating and cooling load calculations for spaces with different types of furniture and miscellaneous loads. These methods should then be applied to low energy space conditioning systems in EnergyPlus including, in particular, radiant slab and radiant ceiling panel cooling and heating systems and ‘mixed mode’ systems that combine mechanical cooling and natural ventilation systems, focusing on controls, including control of thermal mass. The work reported here addresses the conventional method of heating and cooling occupied spaces; other methods, such as the use of radiant heating and cooling systems have the potential to provide equivalent occupant comfort, or better, with lower energy consumption. These systems are addressed more explicitly in EnergyPlus but there is a need for empirical validation to give users the same level of confidence in modeling these systems that they have, or should have, in modeling conventional systems, based on the results presented here.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

OpenOA: An Open-Source Codebase For Operational Analysis of Wind Farms

OpenOA is an open source framework for operational data analysis of wind energy plants, implemented in the Python programming language. OpenOA provides a common data model, high level analysis workflows, and low-level convenience functions that engineers, analysts, and researchers in the wind energy industry can use to facilitate analytics workflows on operational data sets. OpenOA contains documentation, worked out examples in Jupyter notebooks, and a corresponding example dataset from the Engie Renewable’s La Haute Borne Dataset.

17 WIND ENERGY↗

DOE OSTI2011

Final report for LDRD project 11-0029 : high-interest event detection in large-scale multi-modal data sets : proof of concept.

Events of interest to data analysts are sometimes difficult to characterize in detail. Rather, they consist of anomalies, events that are unpredicted, unusual, or otherwise incongruent. The purpose of this LDRD was to test the hypothesis that a biologically-inspired anomaly detection algorithm could be used to detect contextual, multi-modal anomalies. There currently is no other solution to this problem, but the existence of a solution would have a great national security impact. The technical focus of this research was the application of a brain-emulating cognition and control architecture (BECCA) to the problem of anomaly detection. One aspect of BECCA in particular was discovered to be critical to improved anomaly detection capabilities: it's feature creator. During the course of this project the feature creator was developed and tested against multiple data types. Development direction was drawn from psychological and neurophysiological measurements. Major technical achievements include the creation of hierarchical feature sets created from both audio and imagery data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2011

Model and Analytic Processes for Export License Assessments

This paper represents the Department of Energy Office of Nonproliferation Research and Development (NA-22) Simulations, Algorithms and Modeling (SAM) Program's first effort to identify and frame analytical methods and tools to aid export control professionals in effectively predicting proliferation intent; a complex, multi-step and multi-agency process. The report focuses on analytical modeling methodologies that alone, or combined, may improve the proliferation export control license approval process. It is a follow-up to an earlier paper describing information sources and environments related to international nuclear technology transfer. This report describes the decision criteria used to evaluate modeling techniques and tools to determine which approaches will be investigated during the final 2 years of the project. The report also details the motivation for why new modeling techniques and tools are needed. The analytical modeling methodologies will enable analysts to evaluate the information environment for relevance to detecting proliferation intent, with specific focus on assessing risks associated with transferring dual-use technologies. Dual-use technologies can be used in both weapons and commercial enterprises. A decision-framework was developed to evaluate which of the different analytical modeling methodologies would be most appropriate conditional on the uniqueness of the approach, data availability, laboratory capabilities, relevance to NA-22 and Office of Arms Control and Nonproliferation (NA-24) research needs and the impact if successful. Modeling methodologies were divided into whether they could help micro-level assessments (e.g., help improve individual license assessments) or macro-level assessment. Macro-level assessment focuses on suppliers, technology, consumers, economies, and proliferation context. Macro-level assessment technologies scored higher in the area of uniqueness because less work has been done at the macro level. An approach to developing testable hypotheses for the macro-level assessment methodologies is provided. The outcome of this works suggests that we should develop a Bayes Net for micro-level analysis and continue to focus on Bayes Net, System Dynamics and Economic Input/Output models for assessing macro-level problems. Simultaneously, we need to develop metrics for assessing intent in export control, including the risks and consequences associated with all aspects of export control.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2011

EMP Attachment 2: DOE-SC PNNL Site Data Management Plan

This Data Management Plan (DMP) describes the data management processes and activities under the Pacific Northwest National Laboratory (PNNL) Site Environmental Monitoring Plan. The activities currently in the Plan are limited to offsite environmental surveillance of PNNL Site radiological releases to the air. The DMP provides guidance on data capture, processing and transmittal, and database configuration management. The requirements for the PNNL Site Environmental Monitoring (SEM) Database and associated records are documented in order to assure that vital data are recorded accurately, stored in a manner that retains data integrity, and are suitable for analyst to use. Protocols and procedures must ensure the data will be adequate and robust.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Rapid Exploitation and Analysis of Documents

Analysts are overwhelmed with information. They have large archives of historical data, both structured and unstructured, and continuous streams of relevant messages and documents that they need to match to current tasks, digest, and incorporate into their analysis. The purpose of the READ project is to develop technologies to make it easier to catalog, classify, and locate relevant information. We approached this task from multiple angles. First, we tackle the issue of processing large quantities of information in reasonable time. Second, we provide mechanisms that allow users to customize their queries based on latent topics exposed from corpus statistics. Third, we assist users in organizing query results, adding localized expert structure over results. Forth, we use word sense disambiguation techniques to increase the precision of matching user generated keyword lists with terms and concepts in the corpus. Fifth, we enhance co-occurrence statistics with latent topic attribution, to aid entity relationship discovery. Finally we quantitatively analyze the quality of three popular latent modeling techniques to examine under which circumstances each is useful.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2011

Strategic Plan for Nuclear Energy – Knowledge Base for Advanced Modeling and Simulation (NE-KAMS)

NE-KAMS knowledge base will assist computational analysts, physics model developers, experimentalists, nuclear reactor designers, and federal regulators by: (1) Establishing accepted standards, requirements and best practices for V&V and UQ of computational models and simulations, (2) Establishing accepted standards and procedures for qualifying and classifying experimental and numerical benchmark data, (3) Providing readily accessible databases for nuclear energy related experimental and numerical benchmark data that can be used in V&V assessments and computational methods development, (4) Providing a searchable knowledge base of information, documents and data on V&V and UQ, and (5) Providing web-enabled applications, tools and utilities for V&V and UQ activities, data assessment and processing, and information and data searches. From its inception, NE-KAMS will directly support nuclear energy research, development and demonstration programs within the U.S. Department of Energy (DOE), including the Consortium for Advanced Simulation of Light Water Reactors (CASL), the Nuclear Energy Advanced Modeling and Simulation (NEAMS), the Light Water Reactor Sustainability (LWRS), the Small Modular Reactors (SMR), and the Next Generation Nuclear Power Plant (NGNP) programs. These programs all involve computational modeling and simulation (M&S) of nuclear reactor systems, components and processes, and it is envisioned that NE-KAMS will help to coordinate and facilitate collaboration and sharing of resources and expertise for V&V and UQ across these programs. In addition, from the outset, NE-KAMS will support the use of computational M&S in the nuclear industry by developing guidelines and recommended practices aimed at quantifying the uncertainty and assessing the applicability of existing analysis models and methods. The NE-KAMS effort will initially focus on supporting the use of computational fluid dynamics (CFD) and thermal hydraulics (T/H) analysis for M&S of nuclear reactor systems, components and processes, and will later expand to include materials, fuel system performance and other areas of M&S as time and funding allow.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2011

US National Climate Assessment (NCA) Scenarios for Assessing Our Climate Future: Issues and Methodological Perspectives Background Whitepaper for Participants

This whitepaper is intended to provide a starting point for discussion at a workshop for the National Climate Assessment (NCA) that focuses on the use and development of scenarios. The paper will provide background needed by participants in the workshop in order to review options for developing and using scenarios in NCA. The paper briefly defines key terms and establishes a conceptual framework for developing consistent scenarios across different end uses and spatial scales. It reviews uses of scenarios in past U.S. national assessments and identifies potential users of and needs for scenarios for both the report scheduled for release in June 2013 and to support an ongoing distributed assessment process in sectors and regions around the country. Because scenarios prepared for the NCA will need to leverage existing research, the paper takes account of recent scientific advances and activities that could provide needed inputs. Finally, it considers potential approaches for providing methods, data, and other tools for assessment participants. We note that the term 'scenarios' has many meanings. An important goal of the whitepaper (and portions of the workshop agenda) is pedagogical (i.e., to compare different meanings and uses of the term and make assessment participants aware of the need to be explicit about types and uses of scenarios). In climate change research, scenarios have been used to establish bounds for future climate conditions and resulting effects on human and natural systems, given a defined level of greenhouse gas emissions. This quasi-predictive use contrasts with the way decision analysts typically use scenarios (i.e., to consider how robust alternative decisions or strategies may be to variation in key aspects of the future that are uncertain). As will be discussed, in climate change research and assessment, scenarios describe a range of aspects of the future, including major driving forces (both human activities and natural processes), changes in climate and related environmental conditions (e.g., sea level), and evolution of societal capability to respond to climate change. This wide range of scenarios is needed because the implications of climate change for the environment and society depend not only on changes in climate themselves, but also on human responses. This degree of breadth introduces and number of challenges for communication and research.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Review of the WECC EDT phase 2 EIM benefits analysis and results report.

A region-wide Energy Imbalance Market (EIM) was recently proposed by the Western Electricity Coordinating Council (WECC). In order for the Western Area Power Administration (Western) to make more informed decisions regarding its involvement in the EIM, Western asked Argonne National Laboratory (Argonne) to review the EIM benefits study (the October 2011 revision) performed by Energy and Environmental Economics, Inc. (E3). Key components of the E3 analysis made use of results from a study conducted by the National Renewable Energy Laboratory (NREL); therefore, we also reviewed the NREL work. This report examines E3 and NREL methods and models used in the EIM study. Estimating EIM benefits is very challenging because of the complex nature of the Western Interconnection (WI), the variability and uncertainty of renewable energy resources, and the complex decisions and potentially strategic bidding of market participants. Furthermore, methodologies used for some of the more challenging aspects of the EIM have not yet matured. This review is complimentary of several components of the EIM study. Analysts and modelers clearly took great care when conducting detailed simulations of the WI using well-established industry tools under stringent time and budget constraints. However, it is our opinion that the following aspects of the study and the interpretation of model results could be improved upon in future analyses. The hurdle rate methodology used to estimate current market inefficiencies does not directly model the underlying causes of sub-optimal dispatch and power flows. It assumes that differences between historical flows and modeled flows can be attributed solely to market inefficiencies. However, flow differences between model results and historical data can be attributed to numerous simplifying assumptions used in the model and in the input data. We suggest that alternative approaches be explored in order to better estimate the benefits of introducing market structures like the EIM. In addition to more efficient energy transactions in the WI, the EIM would reduce the amount of flexibility reserves needed to accommodate forecast errors associated with variable production from wind and solar energy resources. The modeling approach takes full advantage of variable resource diversity over the entire market footprint, but the projected reduction in flexibility reserves may be overly optimistic. While some reduction would undoubtedly occur, the EIM is only an energy market and would therefore not realize the same reduction in reserves as an ancillary services market. In our opinion the methodology does not adequately capture the impact of transmission constraints on the deployment of flexibility reserves. Estimates of flexibility reserves assume that forecast errors follow a normal distribution. Improved estimates could be obtained by using other probability distributions to estimate up and down reserves to capture the underlying uncertainty of these resources under specific operating conditions. Also, the use of a persistence forecast method for solar is questionable, because solar insolation follows a deterministic pattern dictated by the sun's path through the sky. We suggest a more rigorous method for forecasting solar insolation using the sun's relatively predictable daily pattern at specific locations. The EIM study considered only one scenario for hydropower resources. While this scenario is within the normal range over the WI footprint, it represents a severe drought condition in the Colorado River Basin from which Western schedules power. Given hydropower's prominent role in the WI, we recommend simulating a range of hydropower conditions since the relationship between water availability and WI dispatch costs is nonlinear. Also, the representation of specific operational constraints faced by hydropower operators in the WI needs improvements. The model used in the study cannot fully capture all of the EIM impacts and complexities of power system operations. In particular, a primary benefit of the EIM is a shorter dispatch interval; namely, 5 minutes. However, the model simulates the dispatch hourly. Therefore it cannot adequately measure the benefits of a more frequent dispatch. A tool with a finer time resolution would significantly improve simulation accuracy. When the study was conducted, the rules for the EIM were not clearly defined and it was appropriate to estimate societal benefits of the EIM assuming a perfect market without a detailed specification of the market design. However, incorporating a more complete description of market rules will allow for better estimates of EIM benefits. Furthermore, performing analyses using specific market rules can identify potential design flaws that may be difficult and expensive to correct after the market is established. Estimated cost savings from a more efficient dispatch are less than one percent of the total cost of electricity production.

14 SOLAR ENERGY↗

DOE OSTI2012

Final Progress Report: Isotope Identification Algorithm for Rapid and Accurate Determination of Radioisotopes Feasibility Study

This is the final report of the project titled, 'Isotope Identification Algorithm for Rapid and Accurate Determination of Radioisotopes,' PMIS project number LA10-HUMANID-PD03. The goal of the work was to demonstrate principles of emulating a human analysis approach towards the data collected using radiation isotope identification devices (RIIDs). It summarizes work performed over the FY10 time period. The goal of the work was to demonstrate principles of emulating a human analysis approach towards the data collected using radiation isotope identification devices (RIIDs). Human analysts begin analyzing a spectrum based on features in the spectrum - lines and shapes that are present in a given spectrum. The proposed work was to carry out a feasibility study that will pick out all gamma ray peaks and other features such as Compton edges, bremsstrahlung, presence/absence of shielding and presence of neutrons and escape peaks. Ultimately success of this feasibility study will allow us to collectively explain identified features and form a realistic scenario that produced a given spectrum in the future. We wanted to develop and demonstrate machine learning algorithms that will qualitatively enhance the automated identification capabilities of portable radiological sensors that are currently being used in the field.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2012

Hydra-TH User's Manual, Dated: December 1, 2011 (V.LA-CC-11-120)

Hydra-TH is a hybrid finite-element/finite-volume code built using the Hydra toolkit specifically to attack a broad class of incompressible, viscous fluid dynamics problems prevalent in the thermal-hydraulics community. The purpose for this manual is provide sufficient information for an experience analyst to use Hydra-TH in an effective way. The Hydra-TH User's Manual present a brief overview of capabilities and visualization interfaces. The execution and restart models are described before turning to the detailed description of keyword input. Finally, a series of example problems are presented with sufficient data to permit the user to verify the local installation of Hydra-TH, and to permit a convenient starting point for more detailed and complex analyses.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Pathway Controlled Penetration (PcP) [Slides]

The technical approach employs advanced computational simulation tools to demonstrate how current assets can destroy RWK-RFI-12-0001's HDBT, a tunnel complex with two portals built into the base of a granite mountain. The granite over layer is assumed to be 60 meters thick over both portals and 80 meters over the facility's mission space. Key S&T is the completed development of a highly innovative viscoplastic fracture material model, 3D parallel gas-fracture capabilities into FDEM, and a stochastic handling of the material properties. Phase I - Develop and validate code simulation tools: (1) develop, incorporate and validate AZ-Frac material model for granite; and (2) Develop and incorporate gas-driven-fracture modeling into LANL's FDEM MUNROU code; (3) Develop and incorporate stochastic features into FDEM modeling. Phase II - Conduct PcP analysis on above HDBT: (1) Acquire HDBT design data, develop simulation model; and (2) Evaluate and select most promising defeat alternative. Phase III - Deliver code, train Service target analysts, and conduct simulations against real world HDBTs. PcP uses advanced computer simulations to enhance HDBT functional defeat efforts. Newly developed material models that account for fractural energy coupled with the finite discrete element methodology (FDEM) will provide targeting packages that will create penetration avenues for current or future lethality options. This novel computational approach requires full 3D geologic and structure characterization as well as significant high performance computing capabilities. The goal is to distinctively alter the targeting paradigm by leveraging critical DoD assets along with insitu geologic strata. In other words, assets will utilize underground rock structure to their benefit by creating rubbilization zones that will allow pathway controlled penetration.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2012

Robustness of RISMC Insights under Alternative Aleatory/Epistemic Uncertainty Classifications: Draft Report under the Risk-Informed Safety Margin Characterization (RISMC) Pathway of the DOE Light Water Reactor Sustainability Program

The Risk-Informed Safety Margin Characterization (RISMC) pathway is a set of activities defined under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability Program. The overarching objective of RISMC is to support plant life-extension decision-making by providing a state-of-knowledge characterization of safety margins in key systems, structures, and components (SSCs). A technical challenge at the core of this effort is to establish the conceptual and technical feasibility of analyzing safety margin in a risk-informed way, which, unlike conventionally defined deterministic margin analysis, would be founded on probabilistic characterizations of uncertainty in SSC performance. In the context of probabilistic risk assessment (PRA) technology, there has arisen a general consensus about the distinctive roles of two types of uncertainty: aleatory and epistemic, where the former represents irreducible, random variability inherent in a system, whereas the latter represents a state of knowledge uncertainty on the part of the analyst about the system which is, in principle, reducible through further research. While there is often some ambiguity about how any one contributing uncertainty in an analysis should be classified, there has nevertheless emerged a broad consensus on the meanings of these uncertainty types in the PRA setting. However, while RISMC methodology shares some features with conventional PRA, it will nevertheless be a distinctive methodology set. Therefore, the paradigms for classification of uncertainty in the PRA setting may not fully port to the RISMC environment. Yet the notion of risk-informed margin is based on the characterization of uncertainty, and it is therefore critical to establish a common understanding of uncertainty in the RISMC setting.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2012

The Future Potential of Waver Power in the United States

The theoretical ocean wave energy resource potential exceeds 50% of the annual domestic energy demand of the United States, is located close to coastal population centers, and, although variable in nature, may be more consistent and predictable than some other renewable generation technologies. As a renewable electricity generation technology, ocean wave energy offers a low air pollutant option for diversifying the U.S. electricity generation portfolio. Furthermore, the output characteristics of these technologies may complement other renewable technologies. This study addresses the following: (1) The theoretical, technical and practical potential for electricity generation from wave energy (2) The present lifecycle cost profile (Capex, Opex, and Cost of Electricity) of wave energy conversion technology at a reference site in Northern California at different plant scales (3) Cost of electricity variations as a function of deployment site, considering technical, geo-spatial and and electric grid constraints (4) Technology cost reduction pathways (5) Cost reduction targets at which the technology will see significant deployment within US markets, explored through a series of deployment scenarios RE Vision Consulting, LLC (RE Vision), engaged in various analyses to establish current and future cost profiles for marine hydrokinetic (MHK) technologies, quantified the theoretical, technical and practical resource potential, performed electricity market assessments and developed deployment scenarios. RE Vision was supported in this effort by NREL analysts, who compiled resource information, performed analysis using the ReEDSa model to develop deployment scenarios, and developed a simplified assessment of the Alaska and Hawaii electricity markets.

16 TIDAL AND WAVE POWER↗

DOE OSTI2012

The verification and uncertainty quantification of surrogate models used for structural analysis

High fidelity modeling of complex systems can require large finite element models to capture the physics of interest. Typically these high-order models take an excessively long time to run. For important studies such as model validation and uncertainty quantification, where probabilistic measures of the response are required, a large number of simulations of the high fidelity model with different parameters are necessary. In addition, some environments, such as an extensive random vibration excitation, require a long simulation time to capture the entire event. A process that produces a highly efficient model from the original high order model is necessary to enable these analyses. These highly efficient models are referred to as surrogate models, for their purpose is to represent the main physics that is of importance, but decrease the computational burden. A critical aspect of any surrogate model is how faithfully the efficient model represents the original high-order model. This paper describes the process for verifying a surrogate model using response quantities of interest and quantifying the introduced uncertainties in the use of the surrogate model. This is done to help any analyst if they use any surrogate model; such has, Craig-Bampton Reduc tions, POD, etc.

Ross, Michael↗