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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Diallo, Ousmane H.↗
NASA NTRSDate not supplied

Understanding Atmospheric Absorption Effects on UV Spectra from Sounding Rockets using a Spherical-Shells Model

Our team is working on building and calibrating the FURST sounding rocket, with an expected launch in mid-2023. The goal is to image the most complete and highest resolution UV spectra to date. To do this, precise radiometric and wavelength calibration techniques have been developed. We describe below our model of O2 atmospheric absorption and couple that with simulated FURST images. With a high-enough SNR, we can estimate our ability to use absorption peaks for calibration, or for back-calculating atmospheric properties. If data is available, this method could be applied to older sounding rocket data to find hidden science.

MSFC, NASA, UAH, CSPAR, FURST, MSU, SHINE, Aeronom↗
DOE OSTI2025

Physics-Driven Construction of Compact Primitive Gaussian Density Fitting Basis Sets

We present a model-assisted density fitting (MADF) basis set generator, an algorithm for generating primitive atomic Gaussian density fitting (DF) basis sets (DFBSs) from a contracted Gaussian orbital basis set (OBS). The MADF algorithm produces DFBSs suitable for accurate robust DF approximation of 2-particle interactions in mean-field and correlated electronic structures. The algorithm is designed to (a) saturate the OBS product space by a large regularized set of primitive solid-harmonic Gaussian shells with nonuniform distribution of exponents, followed by (b) pruning of the shells according to their contributions to the 2- body energy of a correlated atomic ensemble. Building the DFBS generator model almost exclusively on mathematical and physical principles allows one to limit the number of parameters that control the density fitting error to three, with a single set of parameters sufficient for computations with all basis cardinal numbers, with and without correlation of core electrons, with and without scalar and spin-dependent relativistic effects, spanning almost all of the Periodic Table. Performance assessment included basis sets up to quadruple-ζ quality from several major basis set families, using molecules composed of main-group, d-block, and f-block elements. The resulting DF errors in Hartree−Fock and second-order MP2 energies (with relativistic all-electron treatments, when appropriate) were on the order of 20 and 10 μE h per electron, respectively.

Approximation↗
DOE OSTI2026

Spatial analysis of cell patterning to aid genetic and phenotypic understanding of grass stomatal density: A case study in maize

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Optimal Control of an Oscillating Surge Wave Energy Converter

During this project, we experimentally investigated the hydrodynamics and performance of a laboratory-scale oscillating surge wave energy converter (OSWEC).We looked at how flap buoyancy and driveline losses (primarily in the form of stiction) affected the dynamics and performance of the device. In addition, we assessed the influence of flap profile (rounded vs. square edges) on OSWEC hydrodynamics. Through this, we were able to develop a deeper understanding of OSWEC performance and provide guidance on strategies to counteract artifacts that may be present in laboratory models, but are absent in field-scale devices. To do this, we tested a laboratory-scale OSWEC in the Sea Wave Environmental Lab (SWEL) wave tank at the National Renewable Energy Laboratory (NREL). We ran several types of experiments to investigate the hydrodynamics and performance of the device. Overall, we achieved the overall goal of experimentally investigating the hydrodynamics and performance of this device. We discovered important and unexpected trends in performance, and collected time-resolved data to help us further investigate the underlying hydrodynamics responsible for these trends. In addition, we are currently using the time-resolved data from these experiments to build data-driven models of the dynamics, which can in turn be used to inform data-driven model predictive control of this device and address this objective in the future.

16 TIDAL AND WAVE POWER↗
NASA NTRS2009

Active Learning for Directed Exploration of Complex Systems

Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.

oracles↗
NASA NTRS1995

Spacecraft Cabin Air Quality Control and Its Application to Tight Buildings

Air quality is an important consideration not only for the external environment, but also for the indoor environment. Most people spend a majority of their lives indoors and the air that they breathe is important to their physical and emotional well being. Since most modern building designs have focused on energy efficiency, less fresh air is brought from the outside. As a result, pollutants from building materials, furniture, cleaning, and cooking have no place to go. To make matters worse, most ventilation systems do not include any means for removing pollutants from the recycled air. Unfortunately, pollution at even a small level can result in eye, throat, and lung irritation in addition to chronic headaches, nausea, and fatigue. A spacecraft cabin, which represents the worst case in tight building design, requires special consideration of air quality since any effects pollutants may have on a crewmember can potentially place a mission or other crewmembers at risk. A detailed approach has been developed by the National Aeronautics and Space Administration (NASA) to minimize cabin atmosphere pollution and provide the crew with an environment which is as free of pollutants as possible. This approach is a combination of passive and active contamination control concepts involving the evaluation and selection of materials to be used onboard the spacecraft, the establishment of air quality standards to ensure crew health, and the use of active control means onboard the spacecraft to further ensure an acceptable atmosphere. This approach has allowed NASA to prevent illness by providing crewmembers with a cabin atmosphere which contains pollutant concentrations up to 100 times lower than those specified for terrestrial indoor environments. Standard building construction, however, does not take into account the potentially harmful effects of materials used in the construction process on the health of future occupants and relies primarily on remedial rather than preventive techniques when addressing contamination. This approach results in a building that theoretically has low operating costs, but may actually have high costs associated with lost work days, increased medical claims, decreased productivity, and problem remediation. A similar approach to NASA's may be adopted by the building construction community which can tap the extensive database of material offgassing properties that has been collected to support the space program. Many materials used by NASA are commercially available and are frequently used in building construction. In addition, computer models which have been developed for assessing various methods of active contamination control can be applied to building ventilation system design and the analysis of their economics. Through using NASA' s experience, the expense associated with the current remedial approach can be avoided.

Perry, J. L.↗
NASA NTRSDate not supplied

Wind Tunnel to Full Scale Mapping of Winds and Loads for Launch-Vehicle Ground Wind Loads

A launch vehicle ground-wind-loads program was conducted at the NASA Langley Transonic Dynamics Tunnel. The objectives were to quantify key aerodynamic and structural characteristics that impact the occurrence of large wind-induced oscillations of a launch vehicle when exposed to ground winds prior to launch. Of particular interest is the dynamic response of a launch vehicle when a von Kármán vortex street forms in the wake of the vehicle resulting in quasiperiodic lift and drag forces.Vehicle response to these quasiperiodic forces can become quite large when the frequency of vortex shedding nears that of a lowly-damped structural mode thereby exciting a resonant response. The study of ground-wind-loads presents unique challenges to quantify significant characteristics of the approaching wind and to relate the wind-tunnel acquired static, dynamic, and gravitational loads to full-scale vehicles. This paper will explain the correlation process between model-scale and full-scale wind characteristics and resulting structural loads. Characterization of the wind at a launch site requires knowledge of the vehicle dynamics and the anemometer performance. Vehicle dynamics dictate the frequency range of interest in characterization of the atmospheric turbulence, and in defining the required data window-length to model dynamic oscillation build-up during wind-gust analysis. Knowledge of the anemometer performance is required to infer peak wind magnitudes since most anemometers cannot directly measure instantaneous peak values. Following proper wind-speed correlation, static and dynamic wind-tunnel loads must be converted to full-scale equivalent values to derive design and operational guidance. In addition to the direct computation of full-scale equivalent loads using relevant scaling parameters, the corrections applied to dynamic loads accounting for differences in structural damping will also be discussed. Furthermore, a phenomenon that manifests in many slender and flexible launch vehicles is a load magnification due to gravity resulting from mass offset under deflection. A method for scaling this contribution from model-scale to full-scale is presented. Finally, a comparison of wind-tunnel derived loads using the described methodologies to launch vehicle measured loads is presented.

ground wind loads↗
NASA NTRSDate not supplied

Wind-Tunnel to Full-Scale Mapping of Winds and Loads for Launch-Vehicle Ground Wind Loads

A launch vehicle ground-wind-loads program was conducted at the NASA Langley Transonic Dynamics Tunnel. The objectives were to quantify key aerodynamic and structural characteristics that impact the occurrence of large wind-induced oscillations of a launch vehicle when exposed to ground winds prior to launch. Of particular interest is the dynamic response of a launch vehicle when a von Kármán vortex street forms in the wake of the vehicle resulting in quasiperiodic lift and drag forces. Vehicle response to these quasiperiodic forces can become quite large when the frequency of vortex shedding nears that of a lowly-damped structural mode thereby exciting a resonant response. The study of ground-wind-loads presents unique challenges to quantify significant characteristics of the approaching wind and to relate the wind-tunnel acquired static, dynamic, and gravitational loads to full-scale vehicles. This paper will explain the correlation process between model-scale and full-scale wind characteristics and resulting structural loads. Characterization of the wind at a launch site requires knowledge of the vehicle dynamics and the anemometer performance. Vehicle dynamics dictate the frequency range of interest in characterization of the atmospheric turbulence, and in defining the required data window-length to model dynamic oscillation build-up during wind-gust analysis. Knowledge of the anemometer performance is required to infer peak wind magnitudes since most anemometers cannot directly measure instantaneous peak values. Following proper wind-speed correlation, static and dynamic wind-tunnel loads must be converted to full-scale equivalent values to derive design and operational guidance. In addition to the direct computation of full-scale equivalent loads using relevant scaling parameters, the corrections applied to dynamic loads accounting for differences in structural damping will also be discussed. Furthermore, a phenomenon that manifests in many slender and flexible launch vehicles is a load magnification due to gravity resulting from mass offset under deflection. A method for scaling this contribution from model-scale to full-scale is presented. Finally, a comparison of wind-tunnel derived loads using the described methodologies to launch vehicle measured loads is presented.

Ground wind loads↗
NASA NTRSDate not supplied

Multi-Class Anomaly Detection in Flight Data using Semi-Supervised Explainable Deep Learning Model

Identifying precursor for safety incidents in aviation data is a crucial task, yet extremely challenging. The main approach, in practice, leverages domain expertise to define expected tolerances in system’s behavior and alarm exceedance from such safety margins. However, this approach is incapable of identifying unknown risk and vulnerabilities. Machine learning has been long studied and deployed to identify precursors for such anomalies, with the great challenge of the need for sufficient labelled set of data to achieve a reliable and accurate performance. In this article, we develop an explainable deep semi-supervised model for anomaly detection in aviation, building upon recent advancements in the machine learning literature. The proposed model combines feature engineering and classification in the feature space, while leveraging all available data (labelled and unlabeled). Validating on two case studies of anomaly detection in take-off and landing phases of commercial aircraft, we show that our model is able to outperform state-of-the-art supervised anomaly detection model and reach significantly high accuracy and low false alarm with minimum amount of available labelled data.

Anomaly Detection↗
DOE OSTI2025

Online Dynamic Mode Decomposition Based System Identification of Multi-Zone Building HVAC Systems

Many works have recently been conducted to reduce the electricity consumption of smart buildings and allow them to support various grid services. Most of these works require accurate system models for the various appliances in the building including heating, ventilation, and air conditioning (HVAC) units. In this paper, we investigate a recursive data-driven system identification strategy to construct the thermal model for a time-varying building with a multi-zone HVAC unit. The online dynamic mode decomposition (DMD)-based strategy is employed to identify the multi-zone thermal building dynamics, where a simple information update (rank-1) is selected to avoid computational complexity. The DMD-based identification strategy is validated using a real gymnasium building equipped with a 4-zone HVAC unit, and its performance is compared with that of the traditional nuclear-norm subspace identification (N2SID) strategy.

Wu, Tumin [University of Tennessee, Knoxville (UTK↗
NASA NTRS2004

Electromagnetic Propagation Prediction Inside Aircraft Cabins

Electromagnetic propagation models for signal strength prediction within aircraft cabins are essential for evaluating and designing a wireless communication system to be implemented onboard aircraft. A model was developed using Wireless Valley's SitePlanner; which is commercial grade software intended for predictions within office buildings. The performance of the model was evaluated through a comparison with test data measurements taken on several aircraft. The comparison concluded that the model can accurately predict power propagation within the cabin. This model can enhance researchers understanding of power propagation within aircraft cabins and will aid in future research.

Hankins, Genevieve↗
DOE OSTI2025

Monitoring installation of partially occluded subassemblies in modular construction factories using BIM, ray tracing, and computer vision

Modular and offsite construction methods are being increasingly adopted due to the advantages they offer in terms of project completion time, quality, and energy-efficiency. Despite these advantages, the current state of monitoring systems in modular construction factories highly relies on labor-intensive, subjective, and error-prone observational methods. A large body of research has aimed to automate the monitoring process using an array of sensors, such as IMUs and RFIDs, during the past two decades. Recently, computer vision-based methods have gained increasing interest as a non-intrusive technology to monitor the process inside modular construction factories. However, partial occlusion challenges have impeded their practical application on a large scale. This challenge is specifically important for monitoring the installation of subassemblies since they can obstruct the view of the monitoring camera, especially those that enable long-term monitoring like closed-circuit television (CCTV) fixed-view surveillance cameras. Here, this paper aims to address this challenge by proposing a novel computer vision-based method to monitor the installation of new subassemblies inside modular factories in highly occluded scenes. The proposed methodology identifies the subassemblies in the CCTV video footage using computer vision, analyzes the occlusions using BIM and ray casting techniques, and estimates the progress of assembly by comparing the BIM model with the detected subassemblies in the video. The proposed methodology was successfully validated on surveillance videos captured from a volumetric modular construction factory in the U.S., achieving 93% accuracy in identifying the installation of subassemblies. The results from this research show that the integration of BIM and computer vision is a promising method for monitoring the installation processes inside modular factories under severe occlusion.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Propel: Tools and Methods for Practical Source Code Model Checking

The work reported here is an overview and snapshot of a project to develop practical model checking tools for in-the-loop verification of NASA s mission-critical, multithreaded programs in Java and C++. Our strategy is to develop and evaluate both a design concept that enables the application of model checking technology to C++ and Java, and a model checking toolset for C++ and Java. The design concept and the associated model checking toolset is called Propel. It builds upon the Java PathFinder (JPF) tool, an explicit state model checker for Java applications developed by the Automated Software Engineering group at NASA Ames Research Center. The design concept that we are developing is Design for Verification (D4V). This is an adaption of existing best design practices that has the desired side-effect of enhancing verifiability by improving modularity and decreasing accidental complexity. D4V, we believe, enhances the applicability of a variety of V&V approaches; we are developing the concept in the context of model checking. The model checking toolset, Propel, is based on extending JPF to handle C++. Our principal tasks in developing the toolset are to build a translator from C++ to Java, productize JPF, and evaluate the toolset in the context of D4V. Through all these tasks we are testing Propel capabilities on customer applications.

Mansouri-Samani, Massoud↗
DOE OSTI2025

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2025

Boosting efficiency and reducing graph reliance: Basis adaptation integration in Bayesian multi-fidelity networks

The computational cost of high-fidelity numerical models makes outer-loop analysis, which requires repeated interrogation of the model such as uncertainty quantification, computationally demanding. Multi-fidelity methods, which construct a surrogate model using data from an ensemble of models of varying cost and accuracy, can substantially reduce the cost of outer-loop analysis. However, these methods can be difficult to apply when the model ensemble does not admit a clear hierarchy a priori and the correlations between models are low. Consequently, in this paper, we present a multi-fidelity method that leverages dimension reduction to enhance the correlation between models, thereby reducing the amount of data needed to train a surrogate from an unordered ensemble of models. Our method utilizes basis adaptation to build low-dimensional polynomial chaos expansions of each model and employs Multi-fidelity Networks to encode the relationships among models. We show that the resulting method exhibit two notable advantages over its counterpart: (1) enhanced accuracy (both reduced bias and variance); and (2) reduced dependency on the graph structure encoding relationships among models. We demonstrate the approach on an analytical test problem and a challenging finite element model for a spent nuclear fuel. Our method produces a surrogate model that is significantly more accurate than either a single-fidelity surrogate or a multi-fidelity surrogate constructed without basis adaptation.

42 ENGINEERING↗
NASA NTRS2000

Load Diffusion in Composite Structures

This research has been concerned with load diffusion in composite structures. Fundamental solid mechanics studies were carried out to provide a basis for assessing the complicated modeling necessary for large scale structures used by NASA. An understanding of the fundamental mechanisms of load diffusion in composite subcomponents is essential in developing primary composite structures. Analytical models of load diffusion behavior are extremely valuable in building an intuitive base for developing refined modeling strategies and assessing results from finite element analyses. The decay behavior of stresses and other field quantities provides a significant aid towards this process. The results are also amendable to parameter study with a large parameter space and should be useful in structural tailoring studies.

Horgan, Cornelius O.↗