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At least 1,297 records · Page 72

Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification

We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.

54 ENVIRONMENTAL SCIENCES↗

Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in HPV-related Oropharyngeal Carcinoma

Distant metastasis (DM) is the main cause of death for patients with human papillomavirus (HPV)–related oropharyngeal cancers (OPCs); yet, there are few reliable predictors of DM in this disease. The role of quantitative imaging (ie, radiomic) analysis was examined to determine whether there are primary tumor features discernible on imaging studies that are associated with a higher risk of DM developing.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Certified dual-corrected radiation patterns of phased antenna arrays by offline–online order reduction of finite-element models

This paper presents a fast numerical method for computing certified far-field patterns of phased antenna arrays over broad frequency bands as well as wide ranges of steering and look angles. The proposed scheme combines finite-element analysis, dual-corrected model-order reduction, and empirical interpolation. To assure the reliability of the results, improved a posteriori error bounds for the radiated power and directive gain are derived. Both the reduced-order model and the error-bounds algorithm feature offline–online decomposition. A real-world example is provided to demonstrate the efficiency and accuracy of the suggested approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Automated phase segmentation and quantification of high-resolution TEM image for alloy design

In the alloy design and development process, a wealth of atomically resolved structural high-resolution transmission electron microscopy (HRTEM) images are produced. Identifying the different nano-precipitate phases and tracking their evolution under various compositions and during manufacturing or post-processing requires hundreds of HRTEM images and thousands of precipitates. The nanoscopic phase information labeling and analysis purely relies on humans are prohibitively costly and time-consuming, sometimes not reliable because of the lack of authoritative knowledge. Here, in this work, we develop a novel unsupervised machine learning approach coupled with adaptive computer vision techniques with features in the Fourier space to automatically determine the number of phases and segment/quantify the phases with nanoscale resolution, allowing for quantitative correlation between nanostructure formation, processing and functional properties. To automate the phase extraction/quantification and ascertain its applicability, we have applied the developed framework to the HRTEM images from several alloy systems, processing conditions, image magnifications, and phase types and morphologies (precipitates, nano-twins, stacking faults, crystalline matrix, and amorphous structures) for verification. This study paves the road for compression, visualization, and translation of raw image structural data into physically relevant information in real-time with minimal human supervision. It shows the promise of enabling high-throughput materials characterization for the acceleration of alloy manufacturing and design.

36 MATERIALS SCIENCE↗

Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis

The recent release of the ECMWF Reanalysis Interim (ERA-I) and NCEP Climate Forecast System Reanalysis (CFSR) allows for studies of global climate and its cycles with unprecedented detail. While the developers have performed verification and validation, there is little information on their relative performance in particular related to their use in ocean modeling. This study focuses on the intercomparison of wind and wave reanalysis datasets from ERA-I and CFSR utilizing the same set of altimetry and buoy observations and error metrics to assess their consistency in time and space. Both products have good spatial homogeneity with consistent levels of errors in the Northern and Southern Hemispheres. ERA-I proves to be homogenous through time, while CFSR exhibits an abrupt decrease in the level of errors in the Southern Ocean beginning 1994. ERA-I generally underestimates the wind speed and wave height with lower standard deviations in comparison to observations, but maintains slightly better error metrics. Despite having a slightly positive bias, CFSR provides a better description of the variability of the observations and improved performance in the upper percentiles associated with extreme events. Altogether ERA-I has better homogeneity through time deeming it more reliable for modeling of long-term processes; however caution must be applied with analysis of the upper percentiles.

16 TIDAL AND WAVE POWER↗

Tensile strength of ramie yarn (spinning by machine)/HDPE thermoplastic matrix composites

Technological developments should be trooped to prevent a gap between technology and environmental sustainability, then it needs to be developed “Green technology”. In this research is making of green composites which use natural fiber ramie as reinforcement. Whereas the matrix used was HDPE (High Density Polyethylene) thermoplastic polymer which could be recycled and had a good formability and flexibility. The ramie yarns and fibers for unidirectional (0°) direction respectively were mixed with HDPE powder and processed using hot compression molding. The surface morphology was observed by SEM (Scanning Electrone Microscopy). Results showed that both tensile strength of the ramie fiber/HDPE composites increased in comparison with the ramie yarn (spinning by machine)/HDPE composites. However, the ramie yarn (spinning by machine)/HDPE composites have a good producibility for wider application. Analysis of the test results using the Weibull distribution as approaches to modeling the reliability of the specimens.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Real-time confinement regime detection in fusion plasmas with convolutional neural networks and high-bandwidth edge fluctuation measurements

Abstract A real-time detection of the plasma confinement regime can enable new advanced plasma control capabilities for both the access to and sustainment of enhanced confinement regimes in fusion devices. For example, a real-time indication of the confinement regime can facilitate transition to the high-performing wide-pedestal (WP) quiescent H-mode, or avoid unwanted transitions to lower confinement regimes that may induce plasma termination. To demonstrate real-time confinement regime detection, we use the 2D beam emission spectroscopy (BES) diagnostic system to capture localized density fluctuations of long wavelength turbulent modes in the edge region at a 1 MHz sampling rate. BES data from 330 discharges in either L-mode, H-mode, quiescent H (QH)-mode, or WP QH-mode were collected from the DIII-D tokamak and curated to develop a high-quality database to train a deep-learning classification model for real-time confinement detection. We utilize the 6×8 spatial configuration with a time window of 1024 µ s and recast the input to obtain spectral-like features via fast Fourier transform preprocessing. We employ a shallow 3D convolutional neural network for the multivariate time-series classification task and utilize a softmax in the final dense layer to retrieve a probability distribution over the different confinement regimes. Our model classifies the global confinement state on 44 unseen test discharges with an average F 1 score of 0.94, using only ∼1 ms snippets of BES data at a time. This activity demonstrates the feasibility for real-time data analysis of fluctuation diagnostics in future devices such as ITER, where the need for reliable and advanced plasma control is urgent.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Potential energy surface of monatomic liquids

In the last decade, significant progress has been made in the many-body Hamiltonian formulation of liquid dynamics theory. Earlier analysis of experimental data for a wide variety of elemental liquids had provided the reliable but qualitative description of the atomic motion: vibrations in a representative 3N -dimensional potential energy valley, plus transits, in which atoms cross the intersections between these valleys. Recent comparisons of first-principles theory with experiment for several elemental liquids at melt revealed a highly accurate and versatile theory. In the present work, we report on an extensive quench study of the entire condensed-matter structure-energy distribution for a metallic Na MD system. With these results, all that was learned from experimental data is confirmed, refined in detail, and made more accurate. Here, we show the entire structure-energy distribution, composed of widespread symmetrics and higher lying randoms, we show the increasing dominance of the randoms as N increases, until the symmetrics vanish completely, and we show the random distribution continue its spectacular narrowing as N continues to increase. This behavior certifies our early assignment of the random distribution to the liquid phase and our prediction of macroscopic uniformity of the random structures. Procedures are discussed to identify and calibrate a single random structure to represent the liquid, and the role of the structure energy in liquid thermodynamics is described. A comparison with other liquid dynamics theories is observed in the Introduction, and the relation to the equation-of-state program is noted in the Conclusion.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Individual Load Model Parameter Estimation in Distribution Systems Using Load Switching Events

There currently exists a mature literature on modeling the aggregate load of a distribution feeder by making use of measurements at its feeder-head at substation. The primary application of such feeder-aggregated load models is in sub-transmission or transmission system analysis. However, there is a growing need in practice also to model each individual load across the feeder. If available, such individual load models have applications in power distribution system analysis, e.g., to better integrate distributed energy resources or to improve power quality and reliability. Motivated by this observation, in this paper, we propose a new method for individual load modeling in power distribution systems. It works by using the measurements only at the feeder-head. It takes an innovative approach to analyzing the load switching events across the distribution feeder itself, instead of or in addition to relying on upstream voltage events that are commonly used in feeder-aggregated load modeling. By tracking the downstream load switching events, the proposed method can make a robust estimation of the ZIP load model parameters for all individual loads. The proposed method is examined on small illustrative test-feeders as well as the IEEE 33-bus test system under various operating scenarios. The adverse impact of errors in measurements and system parameters are also investigated on the performance of the developed load modeling method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Detecting Nitrous Oxide Reductase (nosZ) Genes in Soil Metagenomes: Method Development and Implications for the Nitrogen Cycle

Microbial activities in soils, such as (incomplete) denitrification, represent major sources of nitrous oxide (N 2 O), a potent greenhouse gas. The key enzyme for mitigating N 2 O emissions is NosZ, which catalyzes N 2 O reduction to N 2 . We recently described “atypical” functional NosZ proteins encoded by both denitrifiers and nondenitrifiers, which were missed in previous environmental surveys (R. A. Sanford et al., Proc. Natl. Acad. Sci. U. S. A. 109:19709–19714, 2012, doi:10.1073/pnas.1211238109). Here, we analyzed the abundance and diversity of both nosZ types in whole-genome shotgun metagenomes from sandy and silty loam agricultural soils that typify the U.S. Midwest corn belt. First, different search algorithms and parameters for detecting nosZ metagenomic reads were evaluated based on in silico-generated (mock) metagenomes. Using the derived cutoffs, 71 distinct alleles (95% amino acid identity level) encoding typical or atypical NosZ proteins were detected in both soil types. Remarkably, more than 70% of the total nosZ reads in both soils were classified as atypical, emphasizing that prior surveys underestimated nosZ abundance. Approximately 15% of the total nosZ reads were taxonomically related to Anaeromyxobacter, which was the most abundant genus encoding atypical NosZ-type proteins in both soil types. Further analyses revealed that atypical nosZ genes outnumbered typical nosZ genes in most publicly available soil metagenomes, underscoring their potential role in mediating N 2 O consumption in soils. Therefore, this study provides a bioinformatics strategy to reliably detect target genes in complex short-read metagenomes and suggests that the analysis of both typical and atypical nosZ sequences is required to understand and predict N 2 O flux in soils.

59 BASIC BIOLOGICAL SCIENCES↗

Effect of thermochemical treatments on laser-induced luminescence spectra from strontium titanate: comparison with swift ion-beam irradiation experiments

Results recently reported on the effect of thermochemical treatments on the (He-Cd) laser-excited emission spectra of strontium titanate (STO) are re-analyzed here and compared with results obtained under ion-beam irradiation. Contributing bands centered at 2.4 eV and 2.8 eV, which appear under laser excitation, present intensities dependent upon previous thermal treatments in oxidizing (O 2 ) or reducing atmosphere (H 2 ). As a key result, the emission band centered at 2.8 eV is clearly enhanced in samples exposed to a reducing atmosphere. From a comparison with the ionoluminescence data, it is concluded that the laser-excited experiments can be rationalized within a framework developed from ion-beam excitation studies. In particular, the band at 2.8 eV, sometimes attributed to oxygen vacancies, behaves as expected for optical transitions from conduction-band (CB) states to the ground state level of the self-trapped exciton center. The band at 2.0 eV reported in ion-beam irradiated STO, and attributed to oxygen vacancies, is not observed in laser-excited crystals. As a consequence of our analysis, a consistent scheme of electronic energy levels and optical transitions can now be reliably offered for strontium titanate.

74 ATOMIC AND MOLECULAR PHYSICS↗

artdaq

The artdaq toolkit is a data-acquisition framework designed for high-energy physics experiments. It provides a flexible, reliable backbone for data transfers and has several locations where users can perform custom analysis tasks using the art framework.

Flumerfelt, EricL. [Fermi National Accelerator Lab↗

Denoising Seismograms in the Time Domain Using a Deep Learning Model

Deep learning has emerged as a transformative tool for enhancing the extraction of reliable information from seismograms, addressing the increasing demand for precise and efficient seismic data analysis. We introduce an innovative encoder–decoder deep learning model, named WaveDenoiser, designed for noise reduction in the time domain, thereby eliminating the need for spectrogram computations that have been used for existing deep learning tools and significantly improving processing speed. Utilizing the benchmark dataset that is Stanford Earthquake Dataset, we developed three models of varying sizes: base, medium, and large. Notably, the large (referred to as WaveDenoiser) model demonstrated superior performance, achieving a median signal‐to‐noise ratio improvement of 8.8 dB on in‐distribution unseen data (in the same geographic region) and 7.7 dB on out‐distribution unseen data (in a new geographic region), outpacing both the base and medium models. Further evaluation of the WaveDenoiser model revealed a reduction in median arrival‐time errors by 0.02 s for P waves and 0.01 s for S waves when processing waveforms prior to phase picking using PhaseNet on in‐distribution unseen data. When tested on out‐distribution unseen data, the model also effectively reduced the P‐wave median arrival‐time error by 0.02 and 0.01 s in median arrival‐time error for S waves. Importantly, the application of WaveDenoiser resulted in a significant reduction of phase picking outliers by 1.1% to 3.6% for both P and S waves. In addition, we achieved over five times acceleration in processing speed compared with the seisBench implementation of DeepDenoiser. Our findings underscore the potential of WaveDenoiser as a powerful tool for improving seismic data analysis and processing efficiency.

P-waves↗

Initial Risk Analysis and Decision Making Framework

Commercialization of new carbon capture simulation initiative (CCSI) technology will include two key elements of risk management, namely, technical risk (will process and plant performance be effective, safe, and reliable) and enterprise risk (can project losses and costs be controlled within the constraints of market demand to maintain profitability and investor confidence). Both of these elements of risk are incorporated into the risk analysis subtask of Task 7. Thus far, this subtask has developed a prototype demonstration tool that quantifies risk based on the expected profitability of expenditures when retrofitting carbon capture technology on a stylized 650 MW pulverized coal electric power generator. The prototype is based on the selection of specific technical and financial factors believed to be important determinants of the expected profitability of carbon capture, subject to uncertainty. The uncertainty surrounding the technical performance and financial variables selected thus far is propagated in a model that calculates the expected profitability of investments in carbon capture and measures risk in terms of variability in expected net returns from these investments. Given the preliminary nature of the results of this prototype, additional work is required to expand the scope of the model to include additional risk factors, additional information on extant and proposed risk factors, the results of a qualitative risk factor elicitation process, and feedback from utilities and other interested parties involved in the carbon capture project. Additional information on proposed distributions of these risk factors will be integrated into a commercial implementation framework for the purpose of a comparative technology investment analysis.

01 COAL, LIGNITE, AND PEAT↗

Corrective Action Decision Document/Closure Report for Corrective Action Unit 567: Miscellaneous Soil Sites - Nevada National Security Site, Nevada with ROTC 1

This Corrective Action Decision Document/Closure Report presents information supporting the closure of Corrective Action Unit (CAU) 567: Miscellaneous Soil Sites, Nevada National Security Site, Nevada. The purpose of this Corrective Action Decision Document/Closure Report is to provide justification and documentation supporting the recommendation that no further corrective action is needed for CAU 567 based on the implementation of the corrective actions. The corrective actions implemented at CAU 567 were developed based on an evaluation of analytical data from the CAI, the assumed presence of COCs at specific locations, and the detailed and comparative analysis of the CAAs. The CAAs were selected on technical merit focusing on performance, reliability, feasibility, safety, and cost. The implemented corrective actions meet all requirements for the technical components evaluated. The CAAs meet all applicable federal and state regulations for closure of the site. Based on the implementation of these corrective actions, the DOE, National Nuclear Security Administration Nevada Field Office provides the following recommendations: • No further corrective actions are necessary for CAU 567. • The Nevada Division of Environmental Protection issue a Notice of Completion to the DOE, National Nuclear Security Administration Nevada Field Office for closure of CAU 567. • CAU 567 be moved from Appendix III to Appendix IV of the FFACO.

54 ENVIRONMENTAL SCIENCES↗

Preliminary Thermal-Hydraulic Analysis of the UTA–2 Subcritical Linear Accelerator–Driven System

The National Nuclear Security Administration’s mission includes establishing a reliable supply of 99 Mo without highly enriched uranium. Oak Ridge National Laboratory (ORNL) supports this objective through collaborative research and development with industrial partners. Niowave Inc., a current partner, is currently designing a subcritical linear accelerator-driven system (ADS) and preparing for the US Nuclear Regulatory Commission’s licensing process. Niowave’s system has the potential to efficiently supply medical radioisotopes. The technology includes a superconducting electron accelerator and a pile of both natural and low-enriched uranium (LEU) targets. The process fissions uranium and many valuable isotopes can then be extracted from the targets. Niowave is currently iterating through conceptual and detailed design processes for several system sizes. This report discusses UTA–2, which is at the demonstration stage. UTA–2 will validate numerical modeling results with experimental measurements before progressing to the detailed design of UTA–3, the commercial-sized ADS. The thermal-hydraulic behavior of the UTA–2 core design was numerically investigated using STAR-CCM+, as described in this report. STAR-CCM+, a state-of-the-art computational fluid dynamics (CFD) software that was commercially developed by Siemens, has an extensive user base and a set of validation studies. It is also compliant with the American Society of Mechanical Engineers’ Nuclear Quality Assurance 1 standard. Three cases are investigated in this report. Case 1 quantified the temperature field within the UTA–2 assembly and water tank using only conduction as the method of thermal energy transport. This simplified approach was overly conservative and yielded wetted cladding temperatures above the coolant saturation temperature. In this case, the maximum temperature of the wetted cladding surface of the highest power rod exceeded the saturation temperature of water by 63.8°C. Because of the overly conservative approach taken in Case 1 and its negative subcooled margin, buoyancy-driven natural circulation flow physics were implemented in Case 2. Adding coolant motion significantly distributed the thermal energy of the system through convective heat transfer. This relatively small amount of convective heat transfer significantly reduced system temperatures and increased the subcooled margin from –63.8 to 61.3°C. This margin confirmed that no boiling was expected during normal operation of UTA–2 at 230 W. Case 3 had no additional physics models but considered an overpower event during which the power of each LEU and natural uranium rod was at its respective peak values. This resulted in a study with the total assembly power equal to 176% of the nominal power of 230 W considered in Cases 1 and 2. The resulting natural circulation flows were slightly enhanced. The subcooled margin decreased slightly to 46.3°C, which is still a significant margin to local boiling of the water within the tank. This margin confirmed that no boiling was expected during an abnormal operation of UTA–2 at 406 W.

07 ISOTOPE AND RADIATION SOURCES↗

State Requirements for Electric Distribution System Planning

Utilities have conducted distribution planning since they first began building and operating electricity systems. But filing these plans for regulatory and stakeholder review is a relatively recent phenomenon. This report summarizes legislative and regulatory requirements for regulated electric utilities to file some type of distribution system plan in 20 U.S. jurisdictions. Some plans focus on expedited cost recovery for certain types of distribution system improvements; other plans focus on investments for grid modernization or distributed energy resources. Increasingly, states are adopting requirements for Integrated Distribution Plans. Such plans provide holistic grid investment strategies that address state and local policies and increasing complexity at the grid edge. The report covers the following topics for distribution system plans, highlighting advanced practices: -State goals and objectives -Procedural requirements -Forecasting loads and distributed energy resources -Hosting capacity analysis -Baseline information requirements -Grid modernization strategy -Grid needs assessment -Non-wires solutions -Reliability and resilience analyses -Stakeholder engagement -Equity -Pilots -Coordination with other planning processes The report includes links to legislation; regulatory requirements, proceedings, and orders; and filed utility plans. The U.S. Department of Energy’s Office of Electricity provided funding support.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A Comprehensive Assessment of the Viability of Small Modular Reactors in Africa: A Nuclear Security and Nonproliferation Perspective

In response to escalating global energy demands driven by industrialization and the pressing need for decarbonization, this paper explores the potential of Small Modular Reactors (SMRs) as a sustainable energy solution in Africa. Focusing on nuclear security and non-proliferation concerns, the study assesses Africa's energy landscape, emphasizing the need for diverse and reliable power sources. While highlighting the scalability and cost-effectiveness of SMRs, the analysis acknowledges potential challenges associated with their introduction, particularly concerning nuclear security and non-proliferation. Given the recommendation to use High Assay Low Enriched Uranium (HALEU) in some SMRs, it is important to critically consider the security implications of transporting nuclear materials, the proximity of the public to the plant, and the time taken to respond to planned assaults. The possibility of using HALEU makes the nuclear material more prone to adversaries such as sabotage, theft, and terrorism. Utilizing PESTLE analysis, this research seeks to outline the detailed political, economic, social, technological, environmental, and legal readiness of Africa to embrace the first-of-a-kind technology (SMR) while fulfilling its mandate to the Non-Proliferation Treaty (NPT). Examining regulatory frameworks, international cooperation, and safety protocols, the study underscores the importance of regional collaboration to prevent the misuse of nuclear technology for military and malicious intent. Drawing insights from successful case studies, the paper concludes by synthesizing key findings and proposing recommendations for policymakers and stakeholders. These recommendations encompass regulatory enhancement, capacity building, technology transfer, and diplomatic efforts to strengthen nuclear security, non-proliferation, and safeguards in Africa.

Prah, Christina↗