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DOE OSTI2021

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

ORIGEN-based Nuclear Fuel Inventory Module for Fuel Cycle Assessment: Final Project Report

The goal of this project, “ORIGEN-based Nuclear Fuel Depletion Module for Fuel Cycle Assessment" is to create a physics-based reactor depletion and decay module for the Cyclus nuclear fuel cycle simulator in order to assess nuclear fuel inventories over a broad space of reactor operating conditions. The overall goal of this approach is to facilitate evaluations of nuclear fuel inventories for a broad space of scenarios, including extended used nuclear fuel storage and cascading impacts on fuel cycle options such as actinide recovery in used nuclear fuel, particularly for multiple recycle scenarios. The advantages of a physics-based approach (compared to a recipe-based approach which has been typically employed for fuel cycle simulators) is in its inherent flexibility; such an approach can more readily accommodate the broad space of potential isotopic vectors that may be encountered under advanced fuel cycle options. In order to develop this flexible reactor analysis capability, we are leveraging the Origen nuclear fuel depletion and decay module from SCALE to produce a standalone “depletion engine” which will serve as the kernel of a Cyclus-based reactor analysis module. The ORIGEN depletion module is a rigorously benchmarked and extensively validated tool for nuclear fuel analysis and thus its incorporation into the Cyclus framework can bring these capabilities to bear on the problem of evaluating long-term impacts of fuel cycle option choices on relevant metrics of interest, including materials inventories and availability (for multiple recycle scenarios), long-term waste management and repository impacts, etc. Developing this Origen-based analysis capability for Cyclus requires the refinement of the Origen analysis sequence to the point where it can reasonably be compiled as a standalone sequence outside of SCALE; i.e., wherein all of the computational aspects of Origen (including reactor cross-section library processing and interpolation, input and output processing, and depletion/decay solvers) can be self-contained into a single executable sequence. Further, to embed this capability into other software environments (such as the Cyclus fuel cycle simulator) requires that Origen’s capabilities be encapsulated into a portable, self-contained library which other codes can then call directly through function calls, thereby directly accessing the solver and data processing capabilities of Origen. Additional components relevant to this work include modernization of the reactor data libraries used by Origen for conducting nuclear fuel depletion calculations. This work has included the development of new fuel assembly lattices not previously available (such as for CANDU heavy-water reactor assemblies) as well as validation of updated lattices for light-water reactors updated to employ modern nuclear data evaluations. The CyBORG reactor analysis module as-developed under this workscope is fully capable of dynamic calculation of depleted fuel compositions from all commercial U.S. reactor assembly types as well as a number of international fuel types, including MOX, VVER, MAGNOX, and PHWR CANDU fuel assemblies. In addition, the Origen-based depletion engine allows for CyBORG to evaluate novel fuel assembly and reactor design types via creation of Origen reactor data libraries via SCALE. The establishment of this new modeling capability affords fuel cycle modelers a substantially improved ability to model dynamically-changing fuel cycle and reactor conditions, including recycled fuel compositions from fuel cycle scenarios involving material recycle into thermal-spectrum systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Sensitivity Analysis of Drivers Water Shortage in the Los Angeles Region During Drought

The code and detailed step-by-step instructions for generating the model output data, processing results, and analysis and plotting are provided at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. The PyArtes model is a python adaptation of the Artes model. PyArtes uses many of the same input data and optimization model architecture as Artes. Documentation for the PyArtes model is provided in the Supplement to the paper. The primary data product are simulated monthly water shortages for indoor and outdoor demand under a large ensemble of drought scenarios (>13,000). The droughts are hypothetical and are not based on historical time series data of supply sources - though historical data did help inform ranges explored for supply parameters. Demands are informed by recent 2017-2021 water supply data. Demands used for the model can be accessed at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. Simulations resolve demand for over 90 water providers in the study region. The results report 36 months of water shortage data for each indoor and outdoor demand node. The study also developed a multilayer perceptron (MLP) neural network trained on a subset of the simulated shortage ensemble to emulate worst annual water shortage for a given set of parameter multipliers -- provided the parameter values fall within the ranges sampled in the ensemble. Emulated water shortages for synthetic ensembles are in the MLP-generated shortages folder. The MLP model was used to generate larger ensembles to support Sobol analysis that would have been extremely computationally expensive to simulate. Datasets provided in this repository*: Simulated shortages. These results are used for the analysis for Figures 5, 8, and 9 in the paper, and also to train the MLP emulator. .zip file containing outputs for the 13,312 scenario ensemble. Separate .csv files for indoor and outdoor shortage for each scenario. Rows = demand ids (~100), Columns = months (36) Units = acre-feet/month of shortage (shortage = monthly demand - supply). 1 acft = 1233.48 m^3 .csv files of aggregated shortages derived from the 13,312 ensemble Rows = scenarios (13,312), Columns = demand ids (~100) Units = acre-feet/year (either worst annual shortage or total shortage over the 3-year drought) .csv file of the parameter multipliers scenarios for the ensemble .csv file of the parameter ranges and baseline values the multipliers were applied to MLP-generated shortages. These results are used for Figures 4, 6, and 7 in the paper. mwd higher folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results Emulated shortages. Rows = scenarios, columns = demand ids, units acft Sobol results. Rows = demand ids, columns Sobol (S1, ST, or 95% confidence interval) value for each parameter mwd lower folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results same organization as mwd higher MLP performance: performance metrics (R^2, RMSE, BIAS, MAPE) for the testing subset (20% or 2,662 scenarios) and simulated vs emulated worst year shortage (acre-feet/year) for every demand node, MWD wholesale regions, and the entire study region (LAC). Supporting data for figures. Figure plotting scripts in the associated GitHub repo. These files support analysis and visualization. Geospatial Data used for plotting simulated water shortages and Sobol results. Dictionary of full names for demand nodes in the model and estimates of water supply by source type informed by Artes input files and California Urban Water Management Planning data: https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-Efficiency/Urban-Water-Management-Plans *Readme files provided for each folder.

drought↗

DOE OSTI2019

FTICR-MS data

Soils along a salinity gradient from a coastal forested floodplain (Beaver Creek in Western Washington) were analyzed for their organic compound signatures using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR). This contains 12T FTICR reports of water and chloroform fraction of soils, processed data using FREDA, data filtering steps in FREDA, data used to generate van Krevelen plots, and code for analyzing FTICR data and performing statistics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Recommendation of ruthenium source for sludge batch flowsheet studies

Included herein is a preliminary analysis of previously-generated data from sludge batches 7a, 7b, 8, and 9 sludge simulant and real-waste testing, performed to recommend a form of ruthenium for future sludge batch simulant testing under the nitric-formic flowsheet. Focus is given to reactions present in the Sludge Receipt and Adjustment Tank cycle, given that this cycle historically produces the most changes in chemical composition during Chemical Process Cell processing. Data is presented and analyzed for several runs performed under the nitric-formic flowsheet, with consideration given to effects on the production of hydrogen gas, nitrous oxide gas, consumption of formate, conversion of nitrite to nitrate, and the removal and recovery of mercury during processing. Additionally, a brief discussion is given to the effect of ruthenium source selection under the nitric-glycolic flowsheet. An analysis of data generated from scaled demonstration testing, sludge batch 9 qualification testing, and antifoam degradation testing under the nitric-glycolic flowsheet is presented. Experimental parameters of interest under the nitric-glycolic flowsheet include N2O production, glycolate destruction, conversion of glycolate to formate and oxalate, and the conversion of nitrite to nitrate. To date, the number of real-waste experiments that have been performed under the nitric-glycolic flowsheet is insufficient to provide a complete understanding of the effects of ruthenium source selection in simulant experiments with regard to fidelity to real-waste testing. Therefore, a determination of comparability between the two ruthenium sources as employed under the nitric-glycolic flowsheet is made based on available data in order to inform ruthenium source selection for future testing under the nitric-glycolic flowsheet.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Next-generation geospatial-temporal information technologies for disaster management

Traditional geographic information systems (GIS) have been disrupted by the emergence of Big Data in the form of geo-coded raster, vector, and time-series Internet-of-Things data. This article discusses the application of new scalable technologies that go far beyond relational databases and file-based storage on spinning disk or tape to incorporate both storage and processing data in the same platform. The roles of the Apache Hadoop Distributed File Systems and NoSQL key-value stores such as the Apache Hbase are discussed, along with indexing schemes that optimally support geospatial-temporal use. Here, we highlight how this new approach can rapidly search multiple GIS data layers to obtain insights in the context of early warning, impact evaluation, response, and recovery to earthquake and wildfire disasters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

3D Reality Energy Modeling Software

The team will combine Bentley Systems’ ContextCapture and extend Oak Ridge National Laboratory’s building energy modeling capabilities in order to create digital twins of buildings that allow simulation-informed improvement for energy efficiency and demand response in the design and operation of the built environment. Bentley Systems’ ContextCapture software processes data from 3D laser scanning and photographs (through photogrammetry) to create a photorealistic, 3D mesh with a scale of individual objects to building to city-sized. This capability easily provides city-level visualization models and platforms where sustainable design alternatives are readily evaluated. Oak Ridge National Laboratory (ORNL) serves the U.S. Dept. of Energy (DOE) as one of three core laboratories developing building energy modeling tools EnergyPlus and OpenStudio. ORNL’s AutoBEM software (bit.ly/AutoBEM) can process imagery (satellite, aerial, and street level), LiDAR, cartographic layers, tax assessor’s data, and other data sources to extract building footprints, height, window-to-wall ratio, building type, vintage, and other building properties. AutoBEM has created 178,368 building energy models that were empirically validated with 15-minute whole-building electrical data from a utility.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Skopi: a simulation package for diffractive imaging of noncrystalline biomolecules

X-ray free-electron lasers (XFELs) have the ability to produce ultra-bright femtosecond X-ray pulses for coherent diffraction imaging of biomolecules. While the development of methods and algorithms for macromolecular crystallography is now mature, XFEL experiments involving aerosolized or solvated biomolecular samples offer new challenges in terms of both experimental design and data processing. Skopi is a simulation package that can generate single-hit diffraction images for reconstruction algorithms, multi-hit diffraction images of aggregated particles for training machine learning classifiers using labeled data, diffraction images of randomly distributed particles for fluctuation X-ray scattering algorithms, and diffraction images of reference and target particles for holographic reconstruction algorithms. Skopi is a resource to aid feasibility studies and advance the development of algorithms for noncrystalline experiments at XFEL facilities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Fusion of Experiments and Simulations for Real-Time Identification of Pipeline Defects

In this study, we explored fusion of experiments and simulations for real time identification of pipeline defects across physical and non-physical domains. The challenges associated to data processing were addressed and a combined classification models was presented via CNN models. In addition, regression model based on XGBOOST is built to determine the defect location and defect dimension from data-driven features of guided wave signals captured by SMS fiber optic sensor.

deep learning↗

DOE OSTI2024

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics

Liquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a “primary” feature list is used as a template for matching compounds in “target” feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution’s in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Real-time software upgrade

The various technologies presented herein relate to upgrading industrial control software in real-time. During utilization of a first software to control an industrial process, a determination can be made that the first software needs to be replaced (e.g., partially or entirely) owing to a newer version of software is ready to be deployed, a vulnerability in the first software has been determined, etc. Rather than closing the industrial process down while the first software is replaced, a second software can be deployed such that the first software and the second software are operating in parallel (e.g., receiving the same operational data, process states). When a critical point (upgrade location) in the first software has been reached, control of the industrial process can be switched to the second software, thereby enabling control of the industrial process to occur without having to temporarily cease operation and/or monitoring of the industrial process.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Fusion of Experiments and Simulations for Real-Time Identification of Pipeline Defects

deep learning↗

DOE OSTI2023

Effects of error, chimera, bias, and GC content on the accuracy of amplicon sequencing

ABSTRACT Targeted amplicon sequencing is widely used in microbial ecology studies. However, sequencing artifacts and amplification biases are of great concern. To identify sources of these artifacts, a systematic analysis was performed using mock communities comprised of 16S rRNA genes from 33 bacterial strains. Our results indicated that while sequencing errors were generally isolated to low-abundance operational taxonomic units, chimeric sequences were a major source of artifacts. Singleton and doubleton sequences were primarily chimeras. Formation of chimeric sequences was significantly correlated with the GC content of the targeted sequences. Low-GC-content mock community members exhibited lower rates of chimeric sequence formation. GC content also had a large impact on sequence recovery. The quantitative capacity was notably limited, with substantial recovery variations and weak correlation between anticipated and observed strain abundances. The mock community strains with higher GC content had higher recovery rates than strains with lower GC content. Amplification bias was also observed due to the differences in primer affinity. A two-step PCR strategy reduced the number of chimeric sequences by half. In addition, comparative analyses based on the mock communities showed that several widely used sequence processing pipelines/methods, including DADA2, Deblur, UCLUST, UNOISE, and UPARSE, had different advantages and disadvantages in artifact removal and rare species detection. These results are important for improving sequencing quality and reliability and developing new algorithms to process targeted amplicon sequences. IMPORTANCE Amplicon sequencing of targeted genes is the predominant approach to estimate the membership and structure of microbial communities. However, accurate reconstruction of community composition is difficult due to sequencing errors, and other methodological biases and effective approaches to overcome these challenges are essential. Using a mock community of 33 phylogenetically diverse strains, this study evaluated the effect of GC content on sequencing results and tested different approaches to improve overall sequencing accuracy while characterizing the pros and cons of popular amplicon sequence data processing approaches. The sequencing results from this study can serve as a benchmarking data set for future algorithmic improvements. Furthermore, the new insights on sequencing error, chimera formation, and GC bias from this study will help enhance the quality of amplicon sequencing studies and support the development of new data analysis approaches.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Marine Energy Environmental Permitting and Compliance Costs

Costs to permit Marine Energy projects are poorly understood. In this paper we examine environmental compliance and permitting costs for 19 projects in the U.S., covering the last 2 decades. Guided discussions were conducted with developers over a 3-year period to obtain historical and ongoing project cost data relative to environmental studies (e.g., baseline or pre-project site characterization as well as post-installation effects monitoring), stakeholder outreach, and mitigation, as well as qualitative experience of the permitting process. Data are organized in categories of technology type, permitted capacity, pre- and post-installation, geographic location, and funding types. We also compare our findings with earlier logic models created for the Department of Energy (i.e., Reference Models). Environmental studies most commonly performed were for Fish and Fisheries, Noise, Marine Habitat/Benthic Studies and Marine Mammals. Studies for tidal projects were more expensive than those performed for wave projects and the range of reported project costs tended to be wider than ranges predicted by logic models. For eight projects reporting full project costs, from project start to FERC or USACE permit, the average amount for environmental permitting compliance was 14.6%.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Binary interaction parameters from reacting mixture data. Supercritical biodiesel process with CO 2 as cosolvent

A method is presented for the calculation of binary interaction parameters from reacting mixture data. The process studied is the supercritical methanol transesterification of triolein with CO 2 as cosolvent. The reaction proceeds through 3 reversible reactions in series, on a solid catalyst (Nafion(R) SAC-13), for which the kinetics is known. To describe the reactor performance, the kinetic constants and the binary interaction parameters for the Peng-Robinson equation of state, are necessary, and were included in the reactor model. Previously published view-cell observations and thermodynamic calculations indicate that no condensation occurs in the reactor. Kinetic and thermodynamic parameters were determined by a global optimisation procedure. From the rate constants obtained, the process exhibits some reversibility, despite the excess of methanol used. Our methodology allows to obtain the binary interaction coefficients for the intermediate reaction species (dioleins and monooleins) that were unavailable so far. The method is sensitive to the errors in the measured concentrations.

09 BIOMASS FUELS↗

DOE OSTI2021

Charged Particle Transport Libraries (Final Report FY2020 Level-2 Milestone #7127)

This document is the final report on the status and developments of the Charged-Particle Transport (CPT) Level-2 (“CP2020”) ASC/PEM Milestone work. This section gives an overview of the CP2020 work partitioned into two subsections: 1) a summary of the code tools used for generating CP (and other) data in Section 1.2; and 2) and library data and codes access information for end users in Section 1.3. Section 2 gives an overview of the status of previous work on charged-particle (CP) nuclear data, particularly focused on the work of the FY2011 CP Level-2 Milestone. New work for CP2020 is detailed in the following Section 3. Section 4 discusses testing and validation of the evaluated and processed data for the CP2020 library. Conclusions based on the experience of this Level-2 work and the outlook for future development of CPT-relevant data are given in the final Section 5. Appendices, detailed throughout the report, are provided for further information. This report, related documentation, codes, and the CPT nuclear data libraries are available via online repository.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

User Manual for MC 2 -3 Gamma Library Generation

This report provides the documentation for the revised procedure to generate the MC 2 -3 gamma library with the latest versions of PreGAMMA and GenGAMMA which have recently been updated to fix the program errors and inconsistent data processing for some isotopes identified in a recent verification and validation study of the MC 2 -3 gamma library. A few associated errors associated with gamma processing were also identified and fixed in the MC 2 -3 code. Addressing the changes in MC 2 -3, the procedure to generate the PMATRX and GAMISO datasets is discussed as well. The input data of PreGAMMA and GenGAMMA are discussed in detail using sample input data. In addition, the structure and formats of the MC 2 -3 gamma library are provided in detail.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗