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At least 1,369 records · Page 76

Offshore Wind Market and Economic Analysis: Annual Market Assessment Report 2013

The U.S. offshore wind industry is transitioning from early development to demonstration of commercial viability. While there are no commercial-scale projects in operation or in the construction phase, there are eleven U.S. projects in advanced development, defined as having either been awarded a lease, conducted baseline or geophysical studies, or obtained a power purchase agreement (PPA). There are panels or task forces in place in at least 13 states to engage stakeholders to identify constraints and sites for offshore wind. U.S. policymakers are beginning to follow the examples in Europe that have proven successful in stimulating offshore wind technological advancement, project deployment, and job creation.

17 WIND ENERGY↗

Development and Implementation of a New AI-Based Tool to Support Fast Reactor Software Model Generation and Validation

This report summarizes FY26 work to develop Maggie, an artificial intelligence-based assistant designed to support software model generation and validation activities for fast reactor analysis codes. The project established a modular, code-agnostic software architecture that separates reusable agent capabilities from code-specific knowledge and tools, with initial implementation focused on the FRP-supported fast reactor safety analysis code SAS4A/SASSYS1 (SAS). A curated SAS-specific knowledge base was assembled from the code manual, training materials, historical analysis reports, and representative input files, and was integrated through retrieval-augmented generation to ground Maggie’s responses in authoritative sources. Maggie was deployed on the internal Argonne network, where it demonstrated practical user-facing capability as a chatbot for answering natural language questions about SAS and retrieving relevant technical information. Demonstration cases also showed that Maggie can generate useful snippets of SAS input for selected modeling tasks, while highlighting current limitations in reliability and consistency for more complex input generation tasks. Overall, the FY26 effort established the technical foundation for an AI-assisted capability intended to improve the efficiency, consistency, and accessibility of fast reactor software model development at Argonne and, with further improvements, to support eventual use by the broader fast reactor community, including industry users of FRP-supported analysis tools.

Thomas, Rachel [Argonne National Laboratory (ANL),↗

Machine-Learning Analysis of Moisture Carryover in Boiling Water Reactors

Moisture carryover (MCO) is modeled in the General Electric Type-4 boiling water reactor (BWR) using machine-learning methods and data from operating plants. Understanding MCO and the conditions that give rise to an elevated value is important since excessive MCO can damage critical turbine components, can result in elevated dose levels to on-site personnel, and can interfere with late-cycle power management. The analysis of MCO takes into account simplifying reactor symmetries and important geometric dependencies. The plant data are taken from several reactors and were collected over multiple years and multiple fuel cycles. A brief description of the origin of MCO in U.S. BWR plants is given. A machine-learning model is constructed from the data using applicable algorithms and data-reduction techniques. Matching model complexity with available data is one of the more challenging machine-learning tasks. Too many features and too little data will lead to overfitting. The data for each fuel cycle included over 6876 original features, 9 for each fuel bundle. Two approaches are used to reduce the data set into a manageable number of features. The first was an engineering analysis that resulted in the selection of steam quality Q and steam liquid phase velocity V L as the main features driving MCO. Using a Q and a V L for each fuel bundle gives 1528 Q and a V L feature describing the reactor behavior. An analysis of different functional forms of these two variables led to the actual inputs to the neural network model. The second approach involved the use of statistical techniques such as Pearson’s correlation and k-means analysis. The identified groupings of bundles behaved similarly. Treating each grouping as a single feature further reduced the input variable set to a manageable number. Here, a model selection criterion is proposed, and results are presented along with a discussion of related issues.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

MO-E-9A-01: Risk Based Quality Management: TG100 In Action

One of the goals of quality management in radiation therapy is to gain high confidence that patients will receive the prescribed treatment correctly. To accomplish these goals professional societies such as the American Association of Physicists in Medicine (AAPM) has published many quality assurance (QA), quality control (QC), and quality management (QM) guidance documents. In general, the recommendations provided in these documents have emphasized on performing device-specific QA at the expense of process flow and protection of the patient against catastrophic errors. Analyses of radiation therapy incidents find that they are most often caused by flaws in the overall therapy process, from initial consult through final treatment, than by isolated hardware or computer failures detectable by traditional physics QA. This challenge is shared by many intrinsically hazardous industries. Risk assessment tools and analysis techniques have been developed to define, identify, and eliminate known and/or potential failures, problems, or errors, from a system, process and/or service before they reach the customer. These include, but are not limited to, process mapping, failure modes and effects analysis (FMEA), fault tree analysis (FTA), and establishment of a quality management program that best avoids the faults and risks that have been identified in the overall process. These tools can be easily adapted to radiation therapy practices because of their simplicity and effectiveness to provide efficient ways to enhance the safety and quality of treatment processes. Task group 100 (TG100) of AAPM has developed a risk-based quality management program that uses these tools. This session will be devoted to a discussion of these tools and how these tools can be used in a given radiotherapy clinic to develop a risk based QM program. Learning Objectives: Learn how to design a process map for a radiotherapy process. Learn how to perform a FMEA analysis for a given process. Learn what Fault tree analysis is all about. Learn how to design a quality management program based upon the information obtained from process mapping, FMEA and FTA.

60 APPLIED LIFE SCIENCES↗

Dynamo: Handling Scientific Data Across Sites and Storage Media

Abstract Dynamo is a full-stack software solution for scientific data management. Dynamo’s architecture is modular, extensible, and customizable, making the software suitable for managing data in a wide range of installation scales, from a few terabytes stored at a single location to hundreds of petabytes distributed across a worldwide computing grid. This article documents the core system design of Dynamo and describes the applications that implement various data management tasks. A brief report is also given on the operational experiences of the system at the CMS experiment at the CERN Large Hadron Collider and at a small-scale analysis facility.

Iiyama, Yutaro (ORCID:0000000282975930)↗

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

building energy modeling↗

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗

Design of high-bit-rate coherent communication links

We report an analysis of the problems encountered in the design of modern high-bit-rate coherent communication links. A phenomenological communication link model is described, which is suitable for solving applied tasks of the network design with nonlinear effects taken into account. We propose an engineering approach to the design that is based on the use of fundamental nonlinearity coefficients calculated in advance for the experimental configurations of communication links. An experimental method is presented for calculating the nonlinearity coefficient of communication links. It is shown that the proposed approach allows one to successfully meet the challenges in designing communication networks. (paper)

42 ENGINEERING↗

Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast

Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at suboptical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based autosegmentation pipeline to segment and label cellular structures in hundreds of cells across three Saccharomyces cerevisiae strains. This task-based pipeline uses manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the three-dimensional (3D) whole-cell morphometric characteristics of wild-type, VPH1-GFP, and vac14 strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single-cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts.

Chen, Jianhua [Lawrence Berkeley National Laborato↗

Exponentially Reduced Circuit Depths Using Trotter Error Mitigation

Product formulas are a popular class of digital quantum simulation algorithms due to their conceptual simplicity, low overhead, and performance, which often exceeds theoretical expectations. Recently, Richardson extrapolation and polynomial interpolation have been proposed to mitigate the Trotter error incurred by the use of these formulas. This work provides a rigorous, general analysis of these techniques for computing time-evolved observables, simplifying the interpolation algorithm in the process, and shows that extrapolation generically improves the performance of product formulas for this task. We demonstrate that, to achieve error 𝜖 in a simulation of time 𝑇 using a 𝑝 ⁢th-order product formula with extrapolation, circuit depths of 𝑂⁡(𝑇 1+1/𝑝 ⁢polylog (1/𝜖)) are sufficient—an exponential improvement in the precision over product formulas alone. Furthermore, we prove that these algorithms achieve commutator scaling, and improve the 𝑇 complexity for the interpolation algorithm. By relaxing the requirement of performing exact Chebyshev interpolation, our simplified algorithm eliminates the need for fractional implementations of Trotter steps, reducing computational overhead. Finally, we show these techniques can be combined with the classical shadows method to estimate many time-evolved local observables. Taken together, our findings provide the strongest evidence yet for the utility of Trotter error-mitigation techniques in algorithmic applications.

quantum algorithms & computation↗

Generative Adversarial Network for Synthetic Time Series Data Generation in Smart Grids

The availability of fine grained time series data is a pre-requisite for research in smart-grids. While data for transmission systems is relatively easily obtainable, issues related to data collection, security and privacy hinder the widespread public availability/accessibility of such datasets at the distribution system level. This has prevented the larger research community from effectively applying sophisticated machine learning algorithms to significantly improve the distribution-level accuracy of predictions and increase the efficiency of grid operations. Synthetic dataset generation has proven to be a promising solution for addressing data availability issues in various domains such as computer vision, natural language processing and medicine. However, its exploration in the smart grid context remains unsatisfactory. Previous works have tried to generate synthetic datasets by modeling the underlying system dynamics: an approach which is difficult, time consuming, error prone and often times infeasible in many problems. In this work, we propose a novel data-driven approach to synthetic dataset generation by utilizing deep generative adversarial networks (GAN) to learn the conditional probability distribution of essential features in the real dataset and generate samples based on the learned distribution. To evaluate our synthetically generated dataset, we measure the maximum mean discrepancy (MMD) between real and synthetic datasets as probability distributions, and show that their sampling distance converges. To further validate our synthetic dataset, we perform common smart grid tasks such as k-means clustering and short-term prediction on both datasets. Experimental results show the efficacy of our synthetic dataset approach: the real and synthetic datasets are indistinguishable by solely examining the output of these tasks.

14 SOLAR ENERGY↗

Extending C++ for Heterogeneous Quantum-Classical Computing

In this report we present qcor - a language extension to C++ and compiler implementation that enables heterogeneous quantum-classical programming, compilation, and execution in a single-source context. Our work provides a first-of-its-kind C++ compiler enabling high-level quantum kernel (function) expression in a quantum-language agnostic manner, as well as a hardware-agnostic, retargetable compiler workflow targeting a number of physical and virtual quantum computing backends. qcor leverages novel Clang plugin interfaces and builds upon the XACC system-level quantum programming framework to provide a state-of-the-art integration mechanism for quantum-classical compilation that leverages the best from the community at-large. qcor translates quantum kernels ultimately to the XACC intermediate representation, and provides user-extensible hooks for quantum compilation routines like circuit optimization, analysis, and placement. This work details the overall architecture and compiler workflow for qcor, and provides a number of illuminating programming examples demonstrating its utility for near-term variational tasks, quantum algorithm expression, and feed-forward error correction schemes.

97 MATHEMATICS AND COMPUTING↗

Polaris Studio

SF-24-040 Polaris-studio is the Python entry point/front end for using the Polaris transportation simulator. The package contains the code used to create new models from open and user-provided data, an extensive range of convenience tools for data preparation and result analysis and the ability of designing, launching and tracking studies combining multiple individual Polaris simulations from a Python terminal. The Polaris Studio software contains several modules for accomplishing the above tasks, including the PolarisLib - the Polaris object model, Polaris Manager - code for setting up and running Polaris studies, QPOLARIS - an add-on for the QGis software to allow model files to be created and edited, PolarisVis - a juptyer-based postprocessing library, and Polaris Tools - a useful collection of supplemental tools for interacting with Polaris files.

Auld, Joshua↗

Structure-informed clustering for population stratification in association studies

Background: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. Results: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. Conclusions: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

59 BASIC BIOLOGICAL SCIENCES↗

Predicting Foreign Language Usage from English-Only Social Media Posts

Social media is known for its multicultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a large-scale analysis of 6 million tweets produced by 23 thousand multilingual users speaking 11 other languages besides English. We rely on this multilingual corpus to build predictive models for a novel task – inferring non- English languages that users speak exclusively from their English tweets. We contrast the predictive power of different linguistic signals and report that lexical content and syntactic structure of English tweets are the most predictive of non-English languages that users speak on Twitter. By analyzing cross-lingual transfer – the influence of non-English languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 11 languages.

code-swicthing↗

PERMEABILITY TESTING OF SIMULATED SALTSTONE CORE AND VAULT 4 CELL E SALTSTONE

The Engineering Process Development Group (EPD) of the Savannah River National Laboratory (SRNL) prepared simulated saltstone core samples to evaluate the effect of sample collection by coring on the permeability of saltstone. The Environmental Restoration Technology Section (ERTS) of the SRNL was given the task of measuring the permeability of cores of simulated saltstone. Saltstone samples collected from Vault 4 Cell E using both dry and wet coring methods were also submitted for permeability analysis. The cores from Vault 4 Cell E were in multiple pieces when they were recovered (Smith, 2008 Cheng et.al, 2009). Permeability testing was only performed on the portions of the core sample that were intact, had no visible fractures or cracks, and met the specifications for 'undisturbed specimens' identified in Method ASTM D5084-03 Standard Test Methods for Measurement of Hydraulic Conductivity of Saturated Porous Materials Using a Flexible Wall Permeameter that was used for the testing. Permeability values for cores of simulated saltstone compared with values from permeability tests conducted on molded saltstone samples by an independent laboratory using the same method. All hydraulic conductivity results for Vault 4 samples exceeded results for both molded and cored saltstone simulant samples. The average hydraulic conductivity result for Vault 4 Cell E samples of 3.9 x 10{sup -7} cm/sec is approximately two orders of magnitude greater than that of the simulated saltstone with an average of 4.1 x 10{sup -9} cm/sec. Numerical flow and transport simulations of moisture movement through saltstone performed for the performance assessment of the Saltstone Disposal Facility (SDF) used 2.0 x 10{sup -9} cm/sec for the hydraulic conductivity of saltstone (Flach et al, 2009). The results for simulated versus actual saltstone were further compared using non-parametric statistics. The results from non-parametric statistical analysis of results indicate that there is at least a 98% probability that the hydraulic conductivity of saltstone samples collected from Vault 4 Cell E saltstone is greater than that of the baseline simulant mix.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

FY2011 Status Report on SN2ND Neutronics Solver Development

A change in focus of NEAMS to a broader set of reactor types necessitated significant effort being diverted away from SN2ND development in FY2011. Nevertheless, significant gains were made with regard to building the new preconditioner and meeting the objectives of 1) parallelism in energy, 2) a spatial multigrid concept, 3) using SN vector spaces to accommodate the adjoint and time dependent functionalities needed for NEAMS work, and 4) complete manuals and documentation. While the desired goal of an exportable SN2ND solver usable within the NEAMS community for neutronics analysis was not completed, we can be confident that this year’s work puts the SN2ND solver well on its way to meeting that objective. It is important to note that SN2ND is just as capable as any other methodology for doing thermal reactor analysis, but the motivation to execute on smaller scale machines has initiated focused development on simplified treatments such as those proposed in the MOCARV tool. All of the cited coding tasks were completed but not all are fully working (i.e. debugged). The spatial multigrid technique was by far the most time consuming part of the SN2ND development process. It required multiple new data structure concepts along with identifying the necessary changes in the vectors and their associated functions. Combining the spatial multigrid as part of a GMRES preconditioner operating on the full vector space, necessary for parallelization in energy, increased the complexity of that development. Research into spatial multigrid techniques for unstructured meshes, void treatments, and code optimization and acceleration are key development needs for SN2ND. Similar to previous years, a considerable amount of effort was spent on validation work. Again we focused on fast reactor problems such as ZPR experiments and the MONJU reactor in Japan. Conventional drawer homogenized models and partially heterogeneous models were used on the ZPR calculations. The drawer homogenized models produced excellent results for both eigenvalue and foil measurements on four loadings of ZPR-6/7. The partially heterogeneous models of ZPR-6/7 also produced good results, but additional work is necessary to generate consistent effective cross section data. Calculations for MONJU were homogeneous and also gave excellent results. Finally, mock-up heterogeneous calculations were setup and attempted to scope out the performance limitations of SN2ND on whole core heterogeneous calculations. Specifically, heterogeneous models of a PWR, a VHTR, and the MONJU reactor were created using anywhere from 2 group to 33 group cross section data. Meshes with greater than 100,000,000 vertices are necessary for accuracy and a specific non-scaling memory issue in PETSc restricted the SN2ND modeling (VHTR and PWR could not be solved). All of the calculations performed this year indicate that significant computational resources beyond those available today are required to obtain a high fidelity solution possible with SN2ND. The future goals for SN2ND are to get the new version fully working, reliable in execution, and to have comparable performance to the old solver such that it can be released within the NEAMS community. After its export to RSICC, we will rely upon further preconditioner research to be done in collaboration with university partners and through the exascale hub activities. Some of that research will focus on making SN2ND applicable to thermal reactor problems, spatial multigrid, and void treatments.

Smith, M. A.↗

Using Public Participation to Improve MELs Energy Data Collection

Miscellaneous Electric Loads (MELs) have proliferated in the last decade, and comprise an increasing share of building energy consumption. Because of the diversity of MELs and our lack of understanding about how people use them, large-scale data collection is needed to inform meaningful energy reduction strategies. Traditional methods of data collection, however, usually incur high labor and metering equipment expenses. As an alternative, this paper investigates the feasibility of crowdsourcing data collection to satisfy at least part of the data collection needs with acceptable accuracy. This study assessed the reliability and accuracy of crowdsourced data, by recruiting over 20 volunteers (from the 2012 Lawrence Berkeley Lab, Open House event) to test our crowdsourcing protocol. The protocol asked volunteers to perform the following tasks for three test products with increasing complexity - record power meter and product characteristics, identify all power settings available, and report the measured power. Based on our collected data and analysis, we concluded that volunteers performed reasonably well for devices with functionalities with which they are familiar, and might not produce highly accurate field measurements for complex devices. Accuracy will likely improve when participants are measuring the power used by devices in their home which they know how to operate, by providing more specific instructions including instructional videos. When integrated with existing programs such as the Home Energy Saver tool, crowdsourcing data collection from individual homeowners has the potential to generate a substantial amount of information about MELs energy use in homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗