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DOE OSTI2021

Mitigating baryonic effects with a theoretical error covariance

ABSTRACT One of the primary sources of uncertainties in modelling the cosmic-shear power spectrum on small scales is the effect of baryonic physics. Accurate cosmology for stage-IV surveys requires knowledge of the matter power spectrum deep in the non-linear regime at the per cent level. Therefore, it is important to develop reliable mitigation techniques to take into account baryonic uncertainties if information from small scales is to be considered in the cosmological analysis. In this work, we develop a new mitigation method for dealing with baryonic physics for the case of the shear angular power spectrum. The method is based on an augmented covariance matrix that incorporates baryonic uncertainties informed by hydrodynamical simulations. We use the results from 13 hydrodynamical simulations and the residual errors arising from a fit to a ΛCDM model using the extended halo model code HMCode to account for baryonic physics. These residual errors are used to model a so-called theoretical error covariance matrix that is added to the original covariance matrix. In order to assess the performance of the method, we use the 2D tomographic shear from four hydrodynamical simulations that have different extremes of baryonic parameters as mock data and run a likelihood analysis comparing the residual bias on Ωm and σ8 of our method and the HMCode for an LSST-like survey. We use different modelling of the theoretical error covariance matrix to test the robustness of the method. We show that it is possible to reduce the bias in the determination of the tested cosmological parameters at the price of a modest decrease in the precision.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Structural and Stratigraphic Controls on Methane Hydrate occurrence and distribution: Gulf of Mexico, Walker Ridge 313 and Green Canyon 955: Final Report

The goal of this project was to determine structural and stratigraphic controls on hydrate occurrence and distribution in Green Canyon (GC) 955 and Walker Ridge (WR) 313 blocks using seismic and well data. Gas hydrate was discovered in these blocks in coarse- and fine-grained sediments during the 2009 Joint Industrial project (JIP) Leg 11 drilling expedition. Although the immediate interest of the exploration community is exclusively hydrate which is present in coarse–grained sediments, factors that control hydrate and free gas distribution in the two blocks and whether coarse and fine-grained hydrate-bearing units are related in any manner, formed the core of this research. The project spanned from 10/01/2012 to 07/31/2016. In the project, in both the leased blocks, the interval spanning the gas hydrate stability zone (GHSZ) was characterized using a joint analysis of sparse Ocean Bottom Seismic (OBS) and dense, surface–towed multichannel seismic (MCS) data. The project team had the luxury of calibrating their results with two well logs. Advance processing methods such as depth migration and full-waveform inversion (FWI) were used for seismic data analysis. Hydrate quantification was achieved through interpretation of the FWI velocity field using appropriate rock physics models at both blocks. The seismic modeling/inversion methodology (common to both GC955 and WR313 blocks) was as follows. First, the MCS data were depth migrated using a P-wave velocity (VP) model constructed using inversion of reflection arrival times of a few (four in both cases) key horizons carefully picked in the OBS data to farthest possible offsets. Then, the resolution of the traveltime VP model was improved to wavelength scale by inverting OBS gathers up to the highest frequency possible (21.75 Hz for GC955 and 17.5 for WR313) using FWI. Finally, the hydrate saturation (or the volume fraction) was estimated at the well location assuming one of the other hydrate morphology (filling the primary or the secondary porosity) was extrapolated out from the wells using the FWI VP as a guide. General outcomes were as follows. First and foremost, an imaging methodology using sparse seismic data, which is easily replicable at other sites with similar datasets, has been demonstrated. The end product of this methodology at both the leased blocks is quantitative estimates of hydrate distribution. Second, at both locations there is strong evidence that the base of the GHSZ, which does not appear as a clear Bottom Simulating Reflection (BSR), manifests in the VP perturbations created by FWI, suggesting that FWI is sensitive to subtle compositional changes in shallow sediments and establishes it as a valuable tool for investigations of hydrate-bearing basins. Third, through joint interpretation of the depth migrated image and the FWI VP model, how structure and stratigraphy jointly determine hydrate and free gas distribution in both blocks could be clearly visualized. The joint interpretation also suggests that the coarse and fine grained hydrate-bearing sediments at both leased are connected. Site specific results, in addition to general results, are as follows. At GC955 the overlying fine-grained hydrate-bearing unit could have been sourced from the underlying hydrate coarse-grained channel-levee complex through a chimney feature. The channel-levee system at GC955 is compartmentalized by faults, of which only a few may be impermeable. Although compartmentalized, the channel-levee system in the GC955 as a whole might be in communication except selected zones. At WR313 the overlying fine-grained fracture-filled hydrate unit appears to be sourced from below the GHSZ. The reason that only a particular fine-grained unit has hydrate, despite having lower porosity that the bounding units, could be the presence of secondary porosity (such as those formed from clay dewatering under compaction). In conclusion, the project was a pioneering effort in in joint analysis of OBS and MCS datasets for advancing the knowledge about a hydrate and free–gas system dynamics using advanced processing methods such as FWI and depth migration. Results obtained in this project can greatly advance the tools and techniques used for delineating specific hydrate prospects. Results obtained in this project can also be seamlessly incorporated into other DOE funded project on modeling the potential productivity and commercial viability of hydrate from sand-dominated reservoirs. The OBS and MCS data in this project were acquired in 2012 (after the JIP II drilling) by the USGS and therefore the results are a posteriori. Nonetheless, the seismic inversion workflow established through this project can be used to generate various what-if quantification scenarios even in absence of logs and serve as a valuable tool for guiding drilling operations. Results from this project can augment other DOE sponsored projects on determining the commercial viability of methane production from the Gulf of Mexico.

03 NATURAL GAS↗
DOE OSTI2017

Evaluation of U.S. Building Energy Benchmarking and Transparency Programs: Attributes, Impacts, and Best Practices

In the last decade, a new policy area has emerged to boost energy efficiency in buildings that focuses on the simple action of measuring energy use as compared to buildings of similar type and size, and making that data publicly available. These efforts, referred to as benchmarking and transparency (B&T) policies, seek to unlock new energy efficiency opportunities in the country’s existing buildings by promoting data-driven decision-making and creating stronger market signals. This report focuses on the 24 state and local jurisdictions that (as of December 31, 2016) require owners of privately owned commercial buildings, multifamily buildings, or both to comply with a B&T policy. The report provides a summary of U.S. B&T policy design and implementation characteristics, reports results and impacts for jurisdictions with B&T policies, and discusses opportunities for increasing the efficacy of B&T policies, as well as suggested areas for further research. Among the findings, all but one of the B&T policy evaluation studies reviewed indicate some reduction (from 1.6% to 14%) in energy use, energy costs, or energy intensity over the two- to four-year period of the analyses. More specifically, most of the studies reviewed indicate 3% to 8% reductions in gross energy consumption or energy use intensity over a two- to four-year period of B&T policy implementation. Two additional evaluation studies indicate that there is a causal relationship between B&T policies and energy savings or energy cost savings. These documented impacts should be reviewed with some caution. While consistently showing energy savings benefits associated with B&T policies, these savings estimates should be considered preliminary because of the limited period of analyses and inconsistencies in analysis methods for the various studies. A nationally standardized method for data collection, reporting, and evaluation of B&T policies—developed with an advisory group of state and local jurisdictions, energy efficiency and evaluation experts, building owner and real estate associations, and other stakeholders—could improve the consistency and quality of B&T impact studies, providing policymakers and others with a more complete understanding of the present and future impacts of these policies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗
DOE OSTI2016

SU-G-BRB-02: An Open-Source Software Analysis Library for Linear Accelerator Quality Assurance

Purpose: Routine linac quality assurance (QA) tests have become complex enough to require automation of most test analyses. A new data analysis software library was built that allows physicists to automate routine linear accelerator quality assurance tests. The package is open source, code tested, and benchmarked. Methods: Images and data were generated on a TrueBeam linac for the following routine QA tests: VMAT, starshot, CBCT, machine logs, Winston Lutz, and picket fence. The analysis library was built using the general programming language Python. Each test was analyzed with the library algorithms and compared to manual measurements taken at the time of acquisition. Results: VMAT QA results agreed within 0.1% between the library and manual measurements. Machine logs (dynalogs & trajectory logs) were successfully parsed; mechanical axis positions were verified for accuracy and MLC fluence agreed well with EPID measurements. CBCT QA measurements were within 10 HU and 0.2mm where applicable. Winston Lutz isocenter size measurements were within 0.2mm of TrueBeam’s Machine Performance Check. Starshot analysis was within 0.2mm of the Winston Lutz results for the same conditions. Picket fence images with and without a known error showed that the library was capable of detecting MLC offsets within 0.02mm. Conclusion: A new routine QA software library has been benchmarked and is available for use by the community. The library is open-source and extensible for use in larger systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

THE INFLUENCE OF CHANGES IN NUCLEAR COVARIANCE DATA ON THE CALCULATION OF Ck FOR HIGHLY ENRICHED URANIUM SOLUTION SYSTEMS

Since their initial development nearly 20 years ago, sensitivity/uncertainty (S/U) techniques have been increasingly applied to criticality safety validation. These techniques can generally be applied quickly and for a range of purposes after the initial investment of calculating sensitivities for a safety analysis model and a range of potential benchmark experiments. This paper discusses these applications and how they have been affected by changes in covariance data libraries.Critical experiment selection is the most prominent area in which S/U methods impact validation efforts. These methods allow for rigorous calculation of similarity between safety analysis models and potential benchmarks. The most widely used of these methods is the integral parameter ck, although other integral parameters such as E and some non-integral parameters have also been explored. The ck parameter calculates the fraction of nuclear data–induced uncertainty shared between an application and an experiment; this metric is predicated on the assessment that nuclear data errors are the most likely source of computational bias. Therefore, values of ck are inherently dependent on the nuclear covariance library used to calculate them and inevitably change when these data change.Past studies have shown how much the ck values can vary, especially in difficult validation cases like burnup credit. These significant changes are the result of differences in the relative uncertainties of nuclides with large sensitivities, and they imply differences in benchmark applicability to the safety analysis model. This paper demonstrates that more common criticality safety models involving single fissile species have significantly less variability of ck between covariance libraries, thus enhancing confidence in the application of S/U methods for experiment selection in these scenarios. This paper presents case studies examining the impact of these changes on some hypothetical safety analysis systems.

Clarity, Justin↗
DOE OSTI2022

Hydrogen effects on thermal diffusivity and electrical resistivity of zircaloy cladding

Thermal diffusivity measurements on Zirconium-based cladding materials have historically been a challenge due to the difficulty to measure on specimens with curved geometries, including nuclear grade Zircaloy cladding materials. Here, in this work, we first used laser flash analysis method and four-probe configuration method to measure the thermal diffusivity and electrical resistivity of Zircaloy tubes respectively, which show good agreement with Zircaloy plates in this work, as well as previously published data. The consistent results proved the applicability of the laser flash analysis setup and four-probe configuration method for investigating thermal diffusivity and electrical resistivity of Zircaloy tubes. We further investigated the hydrogen effect on thermal diffusivity and electrical resistivity of Zircaloy. Hydrogen plays significant roles in the thermal diffusivity of Zircaloy, which depends on the hydrogen concentration. For higher hydrogen contents (1130 and 1820 wppm in this work), where phase transformation (α-Zr + δ-hydride → α-Zr + β-Zr) occurs at 567 °C, thermal diffusivity decreases as a function of temperature at the α-Zr + δ-hydride phase regime, while increase at higher temperature at the α-Zr + β-Zr phase regime. For low hydrogen concentration, where hydride dissolved into α-Zr matrix phase at higher temperature, the thermal diffusivity is lower than non-hydrided Zircaloy-4 with similar temperature-dependent trends. Such observation demonstrates the hydrogen effects on reducing thermal diffusivity of α-Zr phase. Hydrogen increases the electrical resistivity of Zircaloy. Similar to thermal diffusivity results, the phase transformation causes a reversal of temperature-dependent trends in electrical resistivity results in Zircaloy-4 with higher hydrogen contents.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Parallel ICA of FDG-PET and PiB-PET in three conditions with underlying Alzheimer's pathology

The relationships between clinical phenotype, β-amyloid (Aβ) deposition and neurodegeneration in Alzheimer's disease (AD) are incompletely understood yet have important ramifications for future therapy. The goal of this study was to utilize multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with probable AD in order to: (1) identify spatial patterns of Aβ deposition measured by ( 11 C)-labeled Pittsburgh Compound B (PiB-PET) and glucose metabolism measured by FDG-PET that correlate with specific clinical presentation and (2) explore associations between spatial patterns of Aβ deposition and glucose metabolism across the AD population. We included all patients meeting the criteria for probable AD (NIA–AA) who had undergone MRI, PiB and FDG-PET at our center (N = 46, mean age 63.0 ± 7.7, Mini-Mental State Examination 22.0 ± 4.8). Patients were subclassified based on their cognitive profiles into an amnestic/dysexecutive group (AD-memory; n = 27), a language-predominant group (AD-language; n = 10) and a visuospatial-predominant group (AD-visuospatial; n = 9). All patients were required to have evidence of amyloid deposition on PiB-PET. To capture the spatial distribution of Aβ deposition and glucose metabolism, we employed parallel independent component analysis (pICA), a method that enables joint analyses of multimodal imaging data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Updated ASME design correlations and qualification plan for powder bed fusion 316H stainless steel

This report provides an update on the Advanced Materials and Manufacturing Technologies (AMMT) program effort to qualify Laser-Powder Bed Fusion (L-PBF) 316H stainless steel for use with the ASME Boiler & Pressure Vessel Code Section III, Division 5 rules. The report summarizes progress in testing and characterizing L-PBF material at elevated temperatures by providing preliminary design data for L-PBF 316H and by comparing the elevated temperature performance of the L-PBF material to wrought and conventional fusion welded 316H. The report then updates the initial AMMT qualification plan for L-PBF 316H, originally developed in 2023, to update the accelerated qualification strategy adopted in that plan to account for the new high temperature test data. The report also explores a few methods for further accelerating the qualification process using machine learning techniques to supplement the more conventional, empirical analysis methods typically used by ASME to correlate and extrapolate time-dependent material test data.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Analytics-Driven Lossless Data Compression for Rapid In-situ Indexing, Storing, and Querying

The analysis of scientific simulations is highly data-intensive and is becoming an increasingly important challenge. Peta-scale data sets require the use of light-weight query-driven analysis methods, as opposed to heavy-weight schemes that optimize for speed at the expense of size. This paper is an attempt in the direction of query processing over losslessly compressed scientific data. We propose a co-designed double-precision compression and indexing methodology for range queries by performing unique-value-based binning on the most significant bytes of double precision data (sign, exponent, and most significant mantissa bits), and inverting the resulting metadata to produce an inverted index over a reduced data representation. Without the inverted index, our method matches or improves compression ratios over both general-purpose and floating-point compression utilities. The inverted index is light-weight, and the overall storage requirement for both reduced column and index is less than 135%, whereas existing DBMS technologies can require 200-400%. As a proof-of-concept, we evaluate univariate range queries that additionally return column values, a critical component of data analytics, against state-of-the-art bitmap indexing technology, showing multi-fold query performance improvements.

Jenkins, John↗
DOE OSTI2020

Machine Learning for Automated Extraction of Building Geometry

As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.

Touzani, Samir↗
DOE OSTI2018

Voltron Compatible Whole Building Root-Fault Detection and Diagnosis

Buildings consume more than 40% of primary energy in the U.S. Malfunctioning sensors, components, and control systems, as well as degrading components in Heating, Ventilating and Air-conditioning (HVAC) and lighting systems are main reasons for energy waste and unsatisfactory indoor environment. Field studies have shown that an energy saving of 5-30% of total building energy consumption and improved indoor air quality can be achieved by simply applying automated fault detection and diagnosis (AFDD), followed by corrections even if these are not done in real time. Extensive research has been made on the development of component level AFDD tools in the past two decades. However, in recent years, the development of method for a whole building level AFDD has received much attention. Here, a whole building fault refers to a fault happening in one subsystem but impact on more than one subsystems, or has a significant impact on building performances, such as building’s energy consumption. Existing component level AFDD tools could fail to detect or give false alarm for faults that trigger abnormalities in multiple subsystems. Isolating such whole building faults, which have impacts on multiple subsystems, is also challenging by simply using component based AFDD solutions. On the other hand, it might be more cost-effective for medium- or small-sized commercial buildings that have subsystems (e.g. primary cooling subsystem, air loop subsystem, heating subsystem, lighting subsystem, etc.) to install one whole building level AFDD tool, rather than many component level AFDD tools, to focus on significant faults. The focus of this project is firstly to provide training opportunities in building AFDD research area for undergraduate students and to develop a workforce pipeline in building sciences. The objective of the proposed research activities is to develop cost-effective and scalable AFDD solutions for whole building level faults. The developed AFDD solution is VOLTTRONTM compatible to further increase its plug-n-play capacity and the market penetration. VOLTTRONTM is an open-source innovative distributed control and sensing software platform developed by the Pacific Northwest National Laboratory. A VOLTTRONTM compatible AFDD tool brings the benefits such as low-cost deployment, and better building to grid integration, etc. The performance of the developed whole building AFDD tools is evaluated using data collected from a mixed-use medium-sized university campus building. Another significant goal of this project is to engage undergraduate students in research activities and expose them to building control and AFDD fields. A demonstration building – Nesbitt Hall at Drexel University is identified for this study. This demonstration building has a typical HVAC system configuration, which includes a water-cooled chiller system, three variable air volume (VAV) air handling unit (AHU) systems, and a hydronic heating system. Faults, that are expected to have a whole building level impact, are artificially implemented in this demonstration building for three different seasons. Building data have been collected in this project, which include fault free building automation system (BAS) data and BAS data that contain system behaviors caused by artificially implemented and naturally occurred faults. These collected data are used to evaluate the developed whole building AFDD tools. A data-driven method, which includes a weather and schedule-based pattern matching (WPM) method and Feature based Principal Component Analysis (FPCA) method, is developed for whole building level fault detection. Parameter sensitivity tests, including snapshot window size, data sample searching pool size, etc., are implemented to evaluate the impacts of various WPM-FPCA parameters. A data-driven and expert knowledge/rule-based method using Bayesian Network (BN) and WPM is developed for whole building level fault diagnosis. The developed WPM-BN method, which includes a two-layer BN structure model and BN parameters, are either learned from baseline data or developed from expert knowledge. The developed whole building AFDD tools are evaluated using BAS data collected from the demonstration building. Using the evaluation data set, the WPM-FPCA fault detection method is evaluated under two different Principle Component (PC) retention rates, i.e., 0.65 retention rate and 0.95 retention rate. It is observed that when using 0.65 PC retention rate, the WPM-FPCA fault detection method achieves a 79% fault detection rate (this rate reaches to 85% when including the six make-up fault test cases discussed in the next bullet), while when using 0.95 PC retention rate, the WPM-FPCA fault detection method achieves 100% fault detection rate (this rate remains to be 100% when including the six make-up fault test cases). Under both PC retention rates, the WPM-FPCA fault detection method demonstrates 0% false alarm rates. Using the evaluation data set, the developed WPM-BN fault diagnosis method has successfully diagnosed thirteen (13) faults out of the fourteen (14) fault test cases. The one mis-diagnosed case exhibits fewer fault symptoms in different subsystems, when compared with other fault test cases. Overall speaking, the developed whole building AFDD solutions demonstrate satisfactory fault detection/diagnosis accuracy with very low false alarm rate. Lastly, a survey and market study has been performed to understand the existing AFDD market, including its gaps and needs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Application of the Most Likely Extreme Response Method for Wave Energy Converters: Preprint

Extreme loads are often a key cost driver for wave energy converters (WECs). As an alternative to exhaustive Monte Carlo or long-term simulations, the most likely extreme response (MLER) method allows mid- and high-fidelity simulations to be used more efficiently in evaluating WEC response to events at the edges of the design envelope, and is therefore applicable to system design analysis. The study discussed in this paper applies the MLER method to investigate the maximum heave, pitch, and surge force of a point absorber WEC. Most likely extreme waves were obtained from a set of wave statistics data based on spectral analysis and the response amplitude operators (RAOs) of the floating body; the RAOs were computed from a simple radiation-and-diffraction-theory-based numerical model. A weakly nonlinear numerical method and a computational fluid dynamics (CFD) method were then applied to compute the short-term response to the MLER wave. Effects of nonlinear wave and floating body interaction on the WEC under the anticipated 100-year waves were examined by comparing the results from the linearly superimposed RAOs, the weakly nonlinear model, and CFD simulations. Overall, the MLER method was successfully applied. In particular, when coupled to a high-fidelity CFD analysis, the nonlinear fluid dynamics can be readily captured.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

ParaView Catalyst: Enabling In Situ Data Analysis and Visualization

Computer simulations are growing in sophistication and producing results of ever greater fidelity. This trend has been enabled by advances in numerical methods and increasing computing power. However these advances come with several costs including massive increases in data size, difficulties examining output data, challenges in configuring simulation runs, and difficulty debugging running codes. Interactive visualization tools, like ParaView, have been used for post-processing of simulation results. However, the increasing data sizes, and limited storage and bandwidth make high fidelity post-processing impractical. In situ analysis is recognized as one of the ways to address these challenges. In situ analysis moves some of the post-processing tasks in line with the simulation code thus short circuiting the need to communicate the data between the simulation and analysis via storage. ParaView Catalyst is a data processing and visualization library that enables in situ analysis and visualization. Built on and designed to interoperate with the standard visualization toolkit VTK and the ParaView application, Catalyst enables simulations to intelligently perform analysis, generate relevant output data, and visualize results concurrent with a running simulation. Here, we provide an overview of the Catalyst framework and some of the success stories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Investigating high-energy proton-induced reactions on spherical nuclei: Implications for the preequilibrium exciton model

A number of accelerator-based isotope production facilities utilize $100-200$ MeV proton beams due to the high production rates enabled by high-intensity beam capabilities and the greater diversity of isotope production brought on by the long range of high-energy protons. However, nuclear reaction modeling at these energies can be challenging because of the interplay between different reaction modes and a lack of existing guiding cross section data. A Tri-lab collaboration has been formed between the Lawrence Berkeley, Los Alamos, and Brookhaven National Laboratories to address these complexities by characterizing charged-particle nuclear reactions relevant to the production of established and novel radioisotopes. In the inaugural collaboration experiments, stacked-targets of niobium foils were irradiated at the Brookhaven Linac Isotope Producer ($E_p=200$ MeV) and the Los Alamos Isotope Production Facility ($E_p=100$ MeV) to measure $^{93}$Nb(p,x) cross sections between $50-200$ MeV. The results were compared with literature data as well as the default calculations of the nuclear model codes TALYS, CoH, EMPIRE, and ALICE. The default code predictions largely failed to reproduce the measurements. Therefore, we developed a standardized procedure, which determines the reaction model parameters that best reproduce the most prominent reaction channels in a physically justifiable manner. Overall, the primary focus of the procedure was to determine the best parameterization for the pre-equilibrium two-component exciton model. This modeling study revealed a trend towards a relative decrease for internal transition rates at intermediate proton energies ($E_p=20-60$ MeV) in the current exciton model as compared to the default values. The results of this work are instrumental for the planning, execution, and analysis essential to isotope production.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Temporal Analysis Of Product Computational Methods

The temporal analysis of products (TAP) reactor provides a wealth of data for transient studies of gas phase heterogeneous reactions, with the ability to provide a comprehensive picture of intrinsic reaction kinetics pertaining to complex catalytic mechanisms. A novel framework called the Y-Procedure was previously developed by Yablonsky and coworkers to extract information from this non-steady-state kinetic data. The Y-Procedure reconstructs the chemical transformation rate and gas composition in the catalytic zone with no a priori assumptions on the kinetic model and is used to relate changes in catalytic reaction rates and kinetic parameters to changes in a catalyst’s surface composition. This code provides the required steps to pre-process, analyze, and perform the Y-Procedure on experiments derived from the TAP reactor.

Kunz, Matthew R.↗