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At least 1,135 records · Page 63

Carbon dioxide pipeline network transportation cost model: evaluating economic and geographic factors for efficient carbon capture, storage, and utilization

This study presents a comprehensive pipeline network modeling framework to estimate the CO 2 delivery cost for CO 2 utilization and geologic CO 2 storage across the United States. We developed a Python-based CO 2 pipeline transportation cost model leveraging Argonne National Laboratory’s pipeline engineering expertise and detailed natural gas transmission pipeline cost data across U.S. regions. Using existing road corridors as practical routing guides, the model designs pipeline networks that aggregate CO 2 from one or multiple sources and deliver it to selected destinations. It then minimizes the total transportation cost by optimizing pipeline diameters and incorporating booster pumps. A key contribution is the incorporation of up-to-date, region-specific cost factors with itemized components for materials, labor, miscellaneous construction expenses, and right-of-way acquisition. Results emphasize that regional variation and economies of scale associated with CO 2 pipeline costs are significant and should be explicitly accounted for in screening and planning studies. By combining realistic routing constraints with regionalized cost inputs, the model provides transparent design methodology and location-specific insights into source–destination delivery costs, including the effects of routing complexity along existing road networks. We demonstrate the model with two illustrative case studies – one for CO 2 storage and one for CO 2 utilization – in which the model designs pipeline networks spanning hundreds of miles across the states, collecting CO 2 from multiple sources and delivering it to designated endpoints while minimizing levelized cost of delivery via diameter and compression optimization. The model offers a practical, scalable approach for alternative design option screening and early-stage CO 2 transportation planning.

CCS↗

Molecular Modeling and Molecular Dynamics Simulation of a Packed and Intact Bacterial Microcompartment

Bacterial microcompartments (BMCs) are protein-bound organelles found in some bacteria which encapsulate enzymes for enhanced catalytic activity. These compartments spatially sequester enzymes within semipermeable shell proteins and are packed full of enzyme cargoes and metabolites as they fulfill their function. Coupling together recent SAXS and proteomics work, it is possible to develop molecular models for these microcompartments and interrogate enzyme and metabolite dynamics within. Our primary goal of this study is to quantify the permeability of metabolite glyceraldehyde-3-phosphate (G3P) and dihydroxyacetone phosphate (DHAP) across the BMC shell through classical molecular dynamics simulation. The Haliangium ochraceum model of BMC shell (PDB: 6MZX) was used to model an intact BMC of approximately 10 million atoms. Working at this scale presented its own challenges in managing large data sets, with multiple challenges and hardware advances discussed that facilitated this work. Over approximately 750 ns of aggregate simulation, we see multiple permeation events for these metabolites that were added at high concentration through the pores present within BMC shell tiles. When compared to independent permeability estimates for the same metabolites determined through replica exchange umbrella sampling simulations, the permeabilities varied by approximately 3 orders of magnitude. Regardless, the permeability coefficients for both G3P and DHAP are highly similar and very high, such that only very small concentration gradients can be maintained across the BMC shell between the cytosol and BMC interior. The large simulation systems also facilitated comparisons for molecular diffusivity in the crowded environment within the BMC shell. By our estimates, the viscosity within a packed BMC shell is at least 10-fold higher than it would be in neat solution and is the real driver for varying permeability estimates we obtained through simulation. These findings will be used as design inputs for future bioengineering efforts to make products from BMCs, highlighting how permeable BMC shells can be.

Diffusion↗

Structure of Lung-Mimetic Multilamellar Bodies with Lipid Compositions Relevant in Pneumonia

The hierarchical assembly of lipids, as modulated by composition and environment, plays a significant role in the function of biological membranes and a myriad of diseases. Elevated concentrations of calcium ions and cardiolipin (CL), an anionic tetra-alkyl lipid found in mitochondria and some bacterial cell membranes, have been implicated in pneumonia recently. However, their impact on the physicochemical properties of lipid assemblies in lungs and how it impairs alveoli function is still unknown. We use small- and wide-angle X-ray scattering (S/WAXS) and solid-state nuclear magnetic resonance (ssNMR) to probe the structure and dynamics of lung-mimetic multilamellar bodies (MLBs) in the presence of Ca 2+ and CL. We conjecture that CL overexpressed in the hypophase of alveoli strongly affects the structure of lung-lipid bilayers and their stacking in the MLBs. Specifically, S/WAXS data revealed that CL induces significant shrinkage of the water-layer separating the concentric bilayers in multilamellar aggregates. ssNMR measurements indicate that this interbilayer tightening is due to undulation repulsion damping as CL renders the glycerol backbone of the membranes significantly more static. In addition to MLB dehydration, CL promotes intrabilayer phase separation into saturated-rich and unsaturated-rich lipid domains that couple across multiple layers. Expectedly, addition of Ca 2+ screens the electrostatic repulsion between negatively charged lung membranes. Furthermore, when CL is present, addition of Ca 2+ results in an apparent interbilayer expansion likely due to local structural defects. Combining S/WAXS and ssNMR on systems with compositions pertinent to healthy and unhealthy lung membranes, we propose how alteration of the physiochemical properties of MLBs can critically impact the breathing cycle.

59 BASIC BIOLOGICAL SCIENCES↗

Basin-Scale Structural Features Database

The Basin-Scale Structural Features database provides spatial datasets of faults, fractures, folds, and earthquakes compiled from public, authoritative sources (e.g., U.S. Geological Survey and State Geological Surveys) and aggregated into derivative forms to support subsurface assessments. Recognizing that characterizing basin-scale structural features requires interpreting data that are often ambiguous or lack key information, the source data were evaluated using a knowledge-data framework and geospatial fuzzy logic method (Justman et al., 2020) to represent both measured (observed) and predicted (inferred or potential) structural features as derivative datasets. This workflow employs conceptual models for known structural features and predicted structural features, incorporating geospatial data to estimate potential, even with limited data. The aim is to aid and support an understanding of basin-scale features and identify potential gaps in data and knowledge. As of 4/30/2025, the database includes resources for nine sedimentary basins: Appalachian, Denver, U.S. Gulf Coast, Illinois, Michigan, Permian, Sacramento, San Joquin and Williston. The database is organized by basin and then data category: 1) Faults, fractures, folds, 2) Earthquakes, 3) Topographic, 4) Structural contours and isopachs, 5) Geophysical, and 6) Structural feature density assessment maps.

basin scale↗

Factors Influencing Building Demand Flexibility

The U.S. Department of Energy’s National Roadmap for Grid-interactive Efficient Buildings (GEB) acknowledged that building demand flexibility (DF) is both an important strategy to decarbonizing the buildings sector and an important resource for meeting the changing needs of the electrical grid such as improving grid reliability. However, understanding the complexity and uncertainties in real building field performance of DF strategies is a large gap hindering stakeholders on both grid and buildings side to make investments on deploying such strategies. The research work in this report intended to advance understanding of the variability and influential factors in building demand flexibility. Adding such knowledge based on lab testing results and measured performance data from real buildings is an important contribution. The report uses standardized metrics and methods to quantify DF performance from field-measured DF datasets of two significant building groups of big-box retail and medium office buildings to present the challenge of building DF variability in multiple dimensions. The report presents findings related to how several key factors influence building demand flexibility from implementing a common, cost-effective DF control strategy (i.e., adjusting zone temperatures). The findings are supported by full-scale lab testing, field data analysis and simulation research. The authors also provided application-oriented recommendations to stakeholders such as building aggregators, utility program design professionals, sophisticated building portfolio owners, and more.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DE-SC0004913: The role of microstructural neighborhoods in forming and evolving slip localization in polycrystalline metals during cyclic plasticity (Final Report)

During this grant we processed and analyzed data from High Energy X-ray Diffraction (HEXD). We conducted a study of the evolution of an aggregate of commercially pure copper crystals subjected to cyclic loading conditions consistent with low cycle fatigue (LCF); each fatigue cycle contains macroscopically elastic-plastic deformation. The uniaxial test specimen initially loads elastically and yields in tension, then unloads elastically and re– yields in compression. The macroscopic cyclic stress-strain curve forms characteristic hysteresis loops that change in size (typically increase) with each cycle reflecting the plasticity-induced material evolution that is taking place within each crystal. It has been hypothesized that at some point, the to-and-fro plastic slip within one crystal or a group of crystals initiates a fatigue crack. Using HE x-ray diffraction we studied the cyclic plasticity within every deforming crystal by measuring how the lattice misorientation changes as the sample is loaded around the hysteresis loop.

36 MATERIALS SCIENCE↗

Early results of Pioneer 10 particulate experiments.

On March 2, 1972 the Pioneer 10 spacecraft was launched on an interplanetary trajectory to encounter Jupiter late in 1973. The spacecraft carries four experiments designed to measure interplanetary particulates in a wide range of sizes from submicron to millimeter diameters. One experiment measures individual particulates by reflected sunlight. Two measure the aggregate particulates by scattered sunlight, while the fourth measures penetrations caused by individual particles. All have been collecting data from one to beyond four astronomical units. The data includes the first in situ measurements of small particulates in the Asteroid Belt. The results of the early analyses will be presented with particular emphasis on the distribution in the Asteroid Belt and its comparison to the interplanetary particulate distribution between the orbits of Earth and Mars.

Soberman, R. K.↗

Estimation of multidimensional precipitation parameters by areal estimates of oceanic rainfall

The parameters of the multidimensional precipitation model proposed by Waymire et al. (1984) are estimated using the areal-averaged radar measurements of precipitation of the Global Atlantic Tropical Experiment (GATE) data set. The procedure followed was the fitting of the first- and second-order moments at different aggregation scales by nonlinear regression techniques. The numerical estimates of the parameters using different subsets of GATE information were reasonably stable, i.e., they were not affected by changes of the area-averaging size, temporal length of the records, and percentage of areal coverage of rainfall. This suggests that the estimation procedure is relatively robust and suitable to estimate the parameters of the multidimensional model in areas of sparse density of rain gages. The use of the space-time spectrum of rainfall to help in the determination of sampling errors due to intermittent visits of future space-borne low-altitude sensors of precipitation is also discussed.

Valdes, J. B.↗

Implementation of a 622 Mbps Digital Modem

This paper presents the implementation and initial test results of an Orthogonal Frequency Division Multiplexing (OFDM) digital modem (modulator and demodulator) with an aggregate information throughput of 622 megabits per second (Mbps). The OFDM waveform is constructed by dividing an incoming data stream into four channels, each channel using either a 16-ary Quadrature Amplitude Modulation (16QAM) scheme or an 8-Phase Shift Keying (8PSK) scheme. The generation and detection of the composite waveform are performed using Discrete Fourier Transform (DFT) and polyphase filtering, to digitally stack and band-limit the individual carriers respectively. The four-channel OFDM approach enables the implementation of a modem that can be both power and bandwidth efficient, with sufficient parallelism to meet higher data rate goals. As a result, the OFDM modem requires only a 240 MHz bandwidth to transmit 622 Mbps. Hardware and simulation results in the form of spectrum diagrams and bit-error-rate (BER) curves are also presented in this paper.

Kifle, Muli↗

Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation

This paper presents a data-driven distribution system load modeling methodology targeting quasi-static time-series (QSTS) simulation. The proposed methodology is appropriate for modeling down to the level of the customer transformer, and it has three main features: 1) both load pattern diversity and intra-second load variability are considered, 2) the load profiles can be populated for multiple nodes on a circuit in such a way that the diversity factor of the feeder can be defined and tuned, and 3) the load aggregation method can be used to populate the profiles for different nodes at various load aggregation levels. Furthermore, as the foundation of the modeling methodology, variability and diversity libraries have been established based on high-resolution load data collected on customer transformers from real utility feeders. The proposed modeling methodology has been used to build load data sets for both the IEEE 123-bus feeder model and a realistic utility feeder model. The QSTS simulation results on the two evaluation feeders have demonstrated that the load data sets established from the proposed modeling methodology can effectively capture the load impact on feeder operations. For the realistic utility feeder, the effectiveness of the proposed methodology has also been validated by comparing the voltage characteristics of the feeder with modeled loads and the voltage characteristics of realistic voltage data from the same feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

Data Analytics↗

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Ocpp 2.0.1. Interim Kpi Calculator

The project is split into four pieces. The first is a raw OCPP log parser. The second is a file splitter. The third is a message parser. The final piece is the Interim KPI calculator. The OCPP log parser was created from two different formats of raw OCPP 2.0.1 data. Its intended purpose is to extract device IDs and OCPP event messages from nontabular text logs. The parser looks for specific substrings in the logs to identify which of the two "standards" it should select from. The KPI generator does not perform any of its calculations in parallel. Instead, we opt for a naive batching approach. The splitter takes the file generated from the parser and creates many smaller files for each of the device IDs in the dataset. This allows the pandas queries in the log formatter to be iterate over a significantly smaller slice of data, increasing performance significantly. The message parser step takes messages from each of the files (containing distinct device IDs) and breaks the message out into pieces. The final result is a file with different columns specifying different attributes of the JSON message. The file is an aggregation of all different devices. This is the most complex portion of the code. The KPI calculator takes the parsed messages, as a single file, and calculates the KPI from that data. An excel file is produced with four sheets. These contain the metrics for Session Success, Charge Start Success, Charge End Success, and Charge Start Time. It includes the metrics for the different equations in the Interim KPI Implementation Guide as well as a weighted sum of the different equations for each KPI (excluding Charge End Success and Charge Start Time).

Quinn, Casey↗

What is the effect of LiDAR-derived DEM resolution on large-scale watershed model results?

This paper examines the effect of raster cell size on hydrographic feature extraction and hydrological modeling using LiDAR derived DEMs. LiDAR datasets for three experimental watersheds were converted to DEMs at various cell sizes. Watershed boundaries and stream networks were delineated from each DEM and were compared to reference data. Hydrological simulations were conducted and the outputs were compared. Smaller cell size DEMs consistently resulted in less difference between DEM-delineated features and reference data. However, minor differences been found between streamflow simulations resulted for a lumped watershed model run at daily simulations aggregated at an annual average. These findings indicate that while higher resolution DEM grids may result in more accurate representation of terrain characteristics, such variations do not necessarily improve watershed scale simulation modeling. Hence the additional expense of generating high resolution DEM's for the purpose of watershed modeling at daily or longer time steps may not be warranted.

99 GENERAL AND MISCELLANEOUS↗

Electricity consumption variation versus economic structure during COVID-19 on metropolitan statistical areas in the US

The outbreak of novel coronavirus disease (COVID-19) has resulted in changes in productivity and daily life patterns, and as a result electricity consumption (EC) has also shifted. In this paper, we construct estimates of EC changes at the metropolitan level across the continental U.S., including total EC and residential EC during the initial two months of the pandemic. The total and residential data on the state level were broken down into the county level, and then metropolitan level EC estimates were aggregated from the counties included in each metropolitan statistical area (MSA). This work shows that the reduction in total EC is related to the shares of certain industries in an MSA, whereas regardless of the incidence level or economic structure, the residential sector shows a trend of increasing EC across the continental U.S. Since the MSAs account for 86% of the total population and 87% of the total EC of the continental U.S., the analytical result in this paper can provide important guidelines for future social-economic crises.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

36 MATERIALS SCIENCE↗

Multi Time-scale Imputation aided State Estimation in Distribution System

With the transition to a smart grid, we are witnessing a significant growth in sensor deployments and smart metering infrastructure in the distribution system. However, information from these sensors and meters are typically unevenly sampled at different time-scales and are incomplete. It is critical to effectively aggregate these information sources for situational awareness. In order to reconcile the heterogeneous multi-scale time-series data, we present a multi-task Gaussian process framework. This framework exploits the spatio-temporal correlation across the time-series data to impute data at any desired timescale while providing confidence bounds on the imputations. The value of the imputed data for distribution system operation is illustrated via a matrix completion based state estimation strategy. Results on the IEEE 37 bus distribution system reveals the superior performance of the proposed approach relative to linear interpolation approaches.

Dahale, Shweta↗

Is Nematicity in Cuprates Real?

In La 2-x Sr x CuO 4 (LSCO), a prototype high-temperature superconductor (HTS) cuprate, a nonzero transverse voltage is observed in zero magnetic fields. This is important since it points to the breaking of the rotational symmetry in the electron fluid, the so-called electronic nematicity, presumably intrinsic to LSCO (and other cuprates). An alternative explanation is that it arises from extrinsic factors such as the film’s inhomogeneity or some experimental artifacts. We confront this hypothesis with published and new experimental data, focusing on the most direct and sensitive probe—the angle-resolved measurements of transverse resistivity (ARTR). The aggregate experimental evidence overwhelmingly refutes the extrinsic scenarios and points to an exciting new effect—intrinsic electronic nematicity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗