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DOE OSTI2023

IM3 GO WEST Parameter Search Dataset

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It is developed to address weather and water dynamics, and associated vulnerabilities in this bulk power system. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. Therefore, GO WEST can help researchers to strike a balance between model fidelity (i.e. accuracy) and computational complexity (i.e. runtime). This dataset includes model inputs and outputs from 600 model versions for each 2019, 2020, and 2021. The folder naming convention is as follows: Exp{Number of Nodes}_{Mathematical Formulation}_{Transmission Line Limit Scaling Factor in MW}_{Hurdle Rate Scaling Factor in %}_{Year}. Linear programming is designated with "simple" label whereas mixed-integer linear programming is designated with "coal" label. For example, "Exp100_simple_1000_50_2019" folder contains inputs and outputs from 2019 model version with 100 nodes, linear programming, +1000 MW transmission line limit scaling factor, and +50% hurdle rate scaling factor. GO WEST GitHub repository hosts all raw datasets, processing scripts, and model scripts. Please refer to the README file for a detailed description of the included files.

Economics↗

DOE OSTI2020

Using Deep Learning to Derive Shear-Wave Velocity Models from Surface-Wave Dispersion Data

We present a new algorithm for derivations of 1D shear-wave velocity models from surface-wave dispersion data using convolutional neural networks (CNNs). The technique is applied for continental China and the plate boundary region in southern California. Different CNNs are designed for these two regions and are trained using theoretical Rayleigh-wave phase and group velocity images computed from reference 1D VS models. The methodology is tested with 3260 phase–group images for continental China and 4160 phase–group images for southern California. The conversions of these images to velocity profiles take ~23 s for continental China and ~30 s for southern California on a personal laptop with the NVIDIA GeForce GTX 1060 core and a memory of 6 GB. The results obtained by the CNNs show high correlation with previous studies using conventional methods. The effectiveness of the CNN technique makes this fast method an important alternative for deriving shear-wave velocity models from large datasets of surface-wave dispersion data.

Geochemistry & Geophysics↗

DOE OSTI2024

Development of a rate-based ENRTL-RK process model for a water-lean solvent

Advanced water-lean solvents (WLS) for post-combustion CO2 capture offer several advantages over the aqueous amine solvents . WLS have lower parasitic energy penalty, lower corrosion, lower temperature and high-pressure CO2 regeneration leading to lower cost of CO2 capture. RTI International, with funding from the US Department of Energy, has been developing its novel water-lean solvent, that has shown specific reboiler duty of 2.3 GJ/t-CO2 at the 60-kWe pilot testing unit (Tiller Plant, SINTEF, Norway) and 2.6 GJ/t-CO2 at the engineering scale testing system (12 MWe) at the Technology Centre Mongstad (TCM) in Norway. All heat duties, including the one from TCM testing, were consistent with Aspen Plus modeling of the specific configuration of each test plant. This work focuses on the development of a detailed process model using in-house laboratory measurements and process data at pilot scale. The eNTRL-RK model used in this work is based on an unsymmetric activity coefficient model with the reference states chosen to be pure liquids for solvents and ideal dilute solution at unit solute molality (resulting in activity coefficient of unity at infinite dilution) for electrolytes. It uses the Redlich-Kwong equation of state for vapor phase properties and Henry’s law for solubility of supercritical gases. The model was validated using process data from the pilot-scale campaign at the Tiller plant, and the engineering scale test campaign at TCM. Data on CO2 capture rate, absorber, and regenerator temperature profiles and specific reboiler duties from two different test campaigns at Tiller and TCM, were used to further refine and validate the model and the model compares favorably to experimental data. The validation results against TCM campaign will be presented in this work.

CO2 capture↗

DOE OSTI2021

A two-equation soot-in-flamelet modeling approach applied under Spray A conditions

Soot production (including formation and oxidation) is studied in the transient, high-pressure and turbulent n-dodecane Spray A flames from the Engine Combustion Network (ECN) using computational fluid dynamics (CFD) simulations. A two-equation soot-in-flamelet modeling approach is applied within the framework of the Unsteady Flamelet Progress Variable (UFPV) model and results are validated against experimental data. Equations for soot mass fraction and soot number density derived in the mixture fraction space are solved in the context of detailed flamelet calculations. Source terms for the different steps in the soot chemistry are tabulated and incorporated in the flamelet manifold. For the reference condition, the modeling approach based on the tabulated flamelet manifold reduces the computational cost of a CFD calculation by approximately 40 times compared to a non-tabulated well-mixed (WM) modeling approach. The soot-in-flamelet approach is then extended to study the effect of ambient oxygen concentration, ambient mixture composition and ambient temperature on soot production. Results show that the modeling approach is able to capture the experimental trends for the soot volume fraction (SVF) with good quantitative agreement, especially in the soot ramp-up region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Kinetic modeling of [ 18 F]V AT , a novel radioligand for positron emission tomography imaging vesicular acetylcholine transporter in non‐human primate brain

Abstract Molecular imaging of vesicular acetylcholine transporter ( VACh T) in the brain provides an important cholinergic biomarker for the pathophysiology and treatment of dementias including Alzheimer's disease. In this study, kinetics modeling methods were applied and compared for quantifying regional brain uptake of the VACh T‐specific positron emission tomography radiotracer, ((−)‐(1‐(‐8‐(2‐fluoroethoxy)‐3‐hydroxy‐1,2,3,4‐tetrahydronaphthalen‐2‐yl)piperidin‐4‐yl)(4‐fluorophenyl)‐methanone) ([ 18 F]V AT ) in macaques. Total volume distribution ( V T ) estimates were compared for one‐tissue compartment model (1 TCM ), two‐tissue compartment model (2 TCM ), Logan graphic analysis (LoganA IF ) and multiple linear analysis (M A 1) with arterial blood input function using data from three macaques. Using the cerebellum‐hemispheres as the reference region with data from seven macaques, three additional models were compared: reference tissue model ( RTM ), simplified RTM ( SRTM ), and Logan graphic analysis (Logan REF ). Model selection criterion indicated that a) 2 TCM and SRTM were the most appropriate kinetics models for [ 18 F]V AT ; and b) S RTM was strongly correlated with 2 TCM (Pearson's coefficients r > 0.93, p < 0.05). Test–retest studies demonstrated that [ 18 F]V AT has good reproducibility and reliability ( TRV < 10%, ICC > 0.72). These studies demonstrate [ 18 F]V AT is a promising VACh T positron emission tomography tracer for quantitative assessment of VACh T levels in the brain of living subjects. image

Jin, Hongjun↗

DOE OSTI2022

Impacts of wind field characteristics and non-steady deterministic wind events on time-varying main-bearing loads

Abstract. This work considers the characteristics and drivers of the loads experienced by wind turbine main bearings. Simplified load response models of two different hub and main-bearing configurations are presented, representative of both inverting direct-drive and four-point-mounted geared drivetrains. The influences of deterministic wind field characteristics, such as wind speed, shear, yaw offset, and veer, on the bearing load patterns are then investigated for similarity scaled 5, 7.5, and 10 MW reference wind turbine models. Main-bearing load response in cases of deterministic gusts and extreme changes in wind direction are also considered for the 5 MW model. Perhaps surprisingly, veer is identified as an important driver of main-bearing load fluctuations. Upscaling results indicate that similar behaviour holds as turbines become larger, but with mean loads and load fluctuation levels increasing at least cubically with the turbine rotor radius. Strong links between turbine control and main-bearing load response are also observed.

17 WIND ENERGY↗

DOE OSTI2022

Demand Response Analysis for Different Residential Personas in a Comfort-Driven Behavioral Context

Low demand response (DR) participation and high program drop-out rates continue to impede DR goals that could save up to $13 billion in annual grid expansion and electricity demand costs. Yet, the literature lacks a thorough understanding of how different residential customer segments enrolled in DR programs respond to utility signals in view of occupant comfort considerations. The objective of this study is to gain a clear understanding of the effects of four different customer personas on residential DR. Given current data limitations, this work developed an array of hypothetical personas with varied priorities, activity levels, and comfort thresholds based on demographic variables that have been found in previous studies to influence energy consumption. A BEopt DR model for a reference residential single-family building located in Colorado was built to isolate the effect of differences in buildings or climate. The results provide useful evidence on how persona-comfort differences lead to significant deviations in DR goals (especially peak demand reduction), ranging from 0.1% to 20%. This work presents a novel framework representing comfort preferences in DR models. The data generated, albeit synthetic, and the results could inform DR program design considerations of how different people respond to different comfort priorities.

BEopt↗

DOE OSTI2022

WaterTAP3 Model Results for NAWI's Baseline Analyses

Description: This folder contains the results for the WaterTAP3 model that was used for the eight NAWI (National Alliance for Water Innovation) baseline studies published in the Environmental Science and Technology special issue: Technology Baselines and Innovation Priorities for Water Treatment and Supply. The data structure and content are described in a README.txt file. For more details on how to use the data and interpret the results please refer to the model documentation and GitHub site linked in the submission.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Wind Turbine Aeroelastic Stability in OpenFAST

Wind turbines are growing in size and increasingly suffer from aeroelastic instabilities. Unfortunately, numerical models often show inconsistent results during verification studies. We address this gap by first introducing novel linearization capabilities within the open-source aero-hydro-servo-elastic framework OpenFAST. Next, a code-to-code benchmark study is presented that compares modal parameters between OpenFAST and HAWCStab2 for a land-based version of the International Energy Agency 15-MW reference wind turbine modeled with quasi-steady aerodynamics. The two solvers are in strong agreement except for discrepancies in the second rotor flapwise modes. The differences are attributed to the torsional flexibility of the tower, which is assumed torsionally stiff in the OpenFAST model. Work is ongoing to close this modeling gap. The aeroelastic stability of a low-specific-power land-based wind turbine is also investigated. The impact of design choices is discussed, high-lighting how narrow the margins are between a stable design and an unstable design.

17 WIND ENERGY↗

DOE OSTI2020

SMART Mobility. Multi-Modal Freight Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Multi-Modal Freight Pillar. The Multi Modal Freight Pillar’s objective is to assess the effectiveness of emerging freight movement technologies and understand the impacts of the growing trends in consumer spending and e-commerce on parcel movement considering mobility, energy, and productivity. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

A consensus-based ensemble approach to improve transcriptome assembly

Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble. Without using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

SU-F-T-124: Radiation Biological Equivalent Presentations OfLEM-1 and MKM Approaches in the Carbon-Ion Radiotherapy

Purpose: To study the correlations of the radiation biological equivalent doses (BED) along depth and lateral distance between LEM-1 and MKM approaches. Methods: In NIRS-MKM (Microdosimetric Kinetic Model) approach, the prescribed BED, referred as C-Eq, doses aims to present the relative biological effectiveness (RBE) for different energies of carbon-ions on a fixed 10% survival value of HCG cell with respect to convention X-ray. Instead of a fixed 10% survival, the BED doses of LEM-1 (Local Effect Model) approach, referred as X-Eq, aims to present the RBE over the whole survival curve of chordoma-like cell with alpha/beta ratio of 2.0. The relationship of physical doses as a function of C-Eq and X-Eq doses were investigated along depth and lateral distance for various sizes of cubic targets in water irradiated by carbon-ions. Results: At the center of each cubic target, the trends between physical and C-Eq or X-Eq doses can be described by a linear and 2nd order polynomial functions, respectively. Using fit functions can then calculate a scaling factor between C-Eq and X-Eq doses to have similar physical doses. With equalized C-Eq and X-Eq doses at the depth of target center, over- and under-estimated X-Eq to C-Eq are seen for depths before and after the target center, respectively. Near the distal edge along depth, sharp rising of RBE value is observed for X-Eq, but sharp dropping of RBE value is observed for C-Eq. For lateral locations near and just outside 50% dose level, sharp raising of RBE value is also seen for X-Eq, while only minor increasing with fast dropping for C-Eq. Conclusion: An analytical function to model the differences between the CEq and X-Eq doses along depth and lateral distance need to further investigated to explain varied clinic outcome of specific cancers using two different approaches to calculated BED doses.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2015

Designing Pay-As-You-Throw schemes in municipal waste management services: A holistic approach

Highlights: • Pay-As-You-Throw (PAYT) schemes are becoming widespread in several countries. • Economic, organizational and technological issues have to be integrated in an efficient PAYT model design. • Efficiency refers to a PAYT system which support high citizen participation rates as well as economic sustainability. • Different steps and constraints have to be evaluated from collection services to type technologies. • An holistic approach is discussed to support PAYT systems diffusion. - Abstract: Pay-As-You-Throw (PAYT) strategies are becoming widely applied in solid waste management systems; the main purpose is to support a more sustainable – from economic, environmental and social points of view – management of waste flows. Adopting PAYT charging models increases the complexity level of the waste management service as new organizational issues have to be evaluated compared to flat charging models. In addition, innovative technological solutions could also be adopted to increase the overall efficiency of the service. Unit pricing, user identification and waste measurement represent the three most important processes to be defined in a PAYT system. The paper proposes a holistic framework to support an effective design and management process. The framework defines most critical processes and effective organizational and technological solutions for supporting waste managers as well as researchers.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Bundling measures for food systems transformation: a global, multimodel assessment

Background Current food systems leave one in ten individuals at risk of hunger while driving unsustainable environmental impacts. Inaction risks further exacerbating negative impacts on both human and planetary health. These challenges emerge from complex system interactions, requiring approaches that engage with this complexity and consider how transformation measures interact across food systems. We aimed to quantify the magnitude and uncertainty of the impacts of key food systems transformation measures both individually and in a bundle using an ensemble of global economic models. Methods In this global multimodel assessment, we applied an ensemble of ten state-of-the-art global economic models to evaluate the potential of four key measures in transforming food systems: increasing agricultural productivity, halving food loss and waste, shifting towards healthier diets, and economy-wide climate mitigation policies aligned with limiting warming to 1·5°C. The scenarios used a middle-of-the-road shared socioeconomic pathway for population and gross domestic product growth, climate impact data from Jägermeyr and colleagues, Thornton and colleagues, and Nelson and colleagues, and dietary targets based on the EAT–Lancet healthy reference diet, with model simulations conducted from 2020 to 2050. We then assessed the effect of these measures in isolation and in combination in a bundled scenario. To further understand the interactions between these measures, we conducted a decomposition analysis that distinguishes between the individual effects of a measure (effect when implemented alone), total effects (its contribution within the bundle), and interaction effects (the difference between total and individual effects). This approach aimed to show complementarities and trade-offs that emerge when multiple measures are implemented simultaneously. Findings Our analysis showed that individual measures in isolation are insufficient to achieve high-level environmental objectives and might generate unintended consequences. In contrast, bundling measures produces co-benefits: avoiding 50% of projected agricultural greenhouse gas emissions by 2050 and almost 20% of anticipated land conversion, while moderating food price increases associated with ambitious climate change mitigation policies. Our decomposition analysis further shows that measures can have varying effects across different dimensions. Although dietary shifts and climate mitigation policies are the largest drivers of environmental benefits (each contributing to a median decline of >10 percentage points in non-CO 2 emissions and 5 percentage points in agricultural land use globally), productivity improvements and reducing food loss and waste play essential roles in moderating price increases (each contributing to a median decline of >5 percentage points in average prices). Interpretation This study highlights the importance of implementing coordinated approaches to food system transformation and climate change mitigation rather than relying on isolated interventions. Comprehensive transformation requires understanding how supply-side and demand-side changes can interact with climate mitigation policies, enabling policy makers to design intervention packages that maximise benefits while minimising trade-offs across environmental, economic, and social dimensions.

Sundiang, Marina [Cornell Univ., Ithaca, NY (Unite↗

DOE OSTI2024

A model for trapping and re-solution regarding intra-granular bubbles in UO 2 , linked to atomic-scale simulations

In the literature, a clear definition of the irradiation re-solution frequency of gas from bubbles in the UO 2 fuel is absent. Moreover, for intra-granular bubbles, a detailed calculation of the cumulated displaced gas quantities in function of the distance from the radius of the bubble after a re-solution event has never been published. The assessment of these two elements is very useful if we want to increase the adherence of fission gas release codes to our present knowledge of the behavior of fission gases. Hence, we suggest to link the definition of the re-solution frequency to atomic-scale simulations. Furthermore, we present the cumulated displaced gas quantities obtained from Molecular Dynamics calculations, from which we have derived a re-solution profile that can be exploited to better consider the irradiation re-solution phenomenon inside Fission Gas Release codes. On top of that, we have built a new trapping/re-solution model for intra-granular bubbles linked to Molecular Dynamics simulations that can be easily incorporated into Fission Gas Release codes. In conclusion, we also check that the model is properly built through the comparison of the new model against a reference.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

How Well Can We Represent the Spectrum of Convective Clouds in a Climate Model? Comparisons between Internal Parameterization Variables and Radar Observations

Current climate models cannot resolve individual convective clouds, and hence parameterizations are needed. The primary goal of convective parameterization is to represent the bulk impact of convection on the gridbox-scale variables. Spectral convective parameterizations also aim to represent the key features of the subgrid-scale convective cloud field such as cloud-top-height distribution and in-cloud vertical velocities in addition to precipitation rates. Ground-based radar retrievals of these quantities have been made available at Darwin, Australia, permitting direct comparisons of internal parameterization variables and providing new observational references for further model development. A spectral convective parameterization [the convective cloud field model (CCFM)] is discussed, and its internal equation of motion is improved. Results from the ECHAM–HAM model in single-column mode using the CCFM and the bulk mass flux Tiedtke–Nordeng scheme are compared with the radar retrievals at Darwin. The CCFM is found to outperform the Tiedtke–Nordeng scheme for cloud-top-height and precipitation-rate distributions. Radar observations are further used to propose a modified CCFM configuration with an aerodynamic drag and reduced entrainment parameter, further improving both the convective cloud-top-height distribution (important for large-scale impact of convection) and the in-cloud vertical velocities (important for aerosol activation). This study provides a new development in the CCFM, improving the representation of convective cloud spectrum characteristics observed in Darwin. This is a step toward an improved representation of convection and ultimately of aerosol effects on convection. It also shows how long-term radar observations of convective cloud properties can help constrain parameters of convective parameterization schemes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Mulayim, Ozan Baris↗