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DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing

We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Turbulent drifts of impurity ions as an explanation for anomalous radial transport in the far-SOL of DIII-D

Abstract Successful fusion reactor operation relies on minimal core contamination by impurities, otherwise too much power may be radiated and harm performance. This requires reliable predictions of impurity transport from the scrape-off layer (SOL) into the core, beyond the traditional ‘anomalous’ diffusion approach. We report a set of far-SOL tungsten transport simulations that demonstrate the role of turbulent drifts on radial impurity transport. A turbulent plasma background is simulated using the gyrokinetic SOL code Gkeyll. Tungsten ions are followed within the plasma background using only their drifts. We find that tungsten tends to travel radially outwards with velocities between v r = 300–1200 m s −1 primarily due to polarization drift. We also extract an anomalous radial diffusion coefficient that varies from D r anom = 5–20 m 2 s −1 . These results are compared to and agree with previous interpretive modeling results. We also show how the turbulent polarization drift can transport some tungsten ions from the wall inwards with effective pinch velocities up to 10 000 m s −1 . We conclude that turbulent drifts are a likely explanation for historically anomalous radial impurity transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Progress in pedestal and edge physics: Chapter 3 of the special issue: on the path to tokamak burning plasma operation

This paper describes the extensive progress that has been made in the understanding of tokamak pedestal physics since the 2007 publication of ‘Progress in the ITER Physics Basis’ (Ikeda 2007 Nucl. Fusion 47 E01–S500). It serves as Chapter 3 of the 2025 Nuclear Fusion Special Issue titled ‘On the Path to Tokamak Burning Plasma Operation’ (Campbell et al 2025 Nucl. Fusion ). This review was compiled by the pedestal and edge physics (PEP) community affiliated with the International Tokamak Physics Activity organization. It attempts to collect in one place citations to the majority of published literature on the pedestal physics topics that will be most important for the operation of a future power producing burning plasma tokamak. These include citations to publications describing the physics of the pedestal plasmas in many operating tokamaks worldwide and the pedestal physics projections for several near-term future devices including ITER. Descriptions of experimental results, interpretive modeling and predictive extrapolations are integrated together and comprehensive references are provided. This review is organized around four primary technical sections, viz.: pedestal structure, edge localized mode (ELM) characteristics, ELM control and regimes without large ELMs. Key results from many of the references are described briefly and set into the tokamak burning plasma power plant context. In addition, different perspectives on pedestal physics topics that are currently under debate within the community are also described, to provide guidance on needs for future research. Finally, attempts are made to describe conclusions from all of this progress consistent with discussions by the pedestal physics community at this time. The goal of this review is to provide a useful reference document for pedestal physics researchers going forward toward operation of a burning tokamak fusion plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Identification of Prompt Proton Emission in $N = Z - 1$ 61 Ga: Isospin Symmetry at the Limit of Nuclear Binding

Excited states in the proton drip line nucleus 61 Ga were populated via the fusion-evaporation reaction 24 Mg ⁢( 40 Ca, 𝑝⁢2⁢𝑛)⁢ 61 Ga. The experimental setup at Argonne National Laboratory comprised a novel combination of the Gammasphere array with two CD-shaped double-sided Si-strip detectors inside the Microball CsI(Tl) charged-particle detection array, as well as the Neutron-Shell liquid scintillators and the Fragment Mass Analyzer. Owing to the setup’s unprecedented in-beam proton spectroscopy and tracking capabilities, a coincidence between a 957.6(5)-keV 𝛾 ray and a 1.876(24)-MeV proton line was observed, which identifies the quasibound proton 𝜋⁢𝑔 9/2 single-particle state in 61 Ga at 𝐸 𝑥 = 2150⁢(34) keV. This probes isospin symmetry at the limit of nuclear binding by providing a unique challenge for the shell-model interpretation of mirror nuclei beyond doubly magic 56 Ni .

Hrabar, Yuliia [Lund Univ. (Sweden)] (ORCID:000000↗

DOE OSTI2024

From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range

This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.

Bolme, David↗

DOE OSTI2026

Machine learning of factors for improving oyster hatchery production

Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis offered crucial insights into model interpretations, highlighting week number, Normalized Difference Vegetation Index, salinity, turbidity, and fecundity as primary drivers of yield variability. For low-yield cases, salinity-related variables were particularly important. Our findings provide an early warning system for potential production downturns, empowering hatchery operators to make data-driven decisions for optimizing water conditions, feeding schedules, and broodstock management. By boosting predictability and efficiency, this research directly supports economic stability of the oyster industry and ecological health of the Chesapeake Bay.

Vishwakarma, Srishti [Oak Ridge National Laborator↗

DOE OSTI2026

Linear viscoelastic measurements of commerical therapy putties for design and intuition

This dataset complements the publication Mapping Linear Viscoelasticity for Design and Tactile Intuition (Corman & Ewoldt, 2019). It contains rigorous shear rheometry measurements for six commercial silicone putties, including creep recovery, SAOS, LAOS, and stress relaxation. Additional, unpublished data was generated as part of a graduate rheology course taught by Randy Ewoldt at University of Illinois Urbana-Champaign. Together, these datasets provide a comprehensive example of linear viscoelasticity of a commercial material. The collection is intended both as a pedagogical resource for developing physical intuition in linear viscoelasticity and as a reference dataset for data interpretation, model fitting, and comparative rheological analysis.

C. Marsh, Maxwell↗

DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗

NASA NTRS1971

Analysis of solar ultraviolet lines

The formation of the strongest ultra-violet emission lines of Mg II, O I, C II, and C III in the solar atmosphere is studied in detail. The equations of statistical equilibrium and radiative transfer for each ion are solved using a general computer program that is capable of solving non-LTE line-formation problems for arbitrary atmospheric and atomic models. Interpreting the results in terms of the structure of the solar atmosphere, it is concluded that the HSRA atmosphere has a temperature too low by about 500 K near h = 1100 km and that a temperature plateau with T sub e approximately = 18,000 K and width close to 60 km exists in the upper chromosphere. The structure of the solar atmosphere in the range 20,000 to 100,000 K and the effects of microturbulence on the formation of lines are also investigated. Approximate analytic line-formation problems are solved, and more exact solutions are derived later. An attempt is made to make the best possible fit to the Ca II K line center-to-limb profiles with a one-component atmosphere, with an assumed source function and microturbulent velocity.

Chipman, E.↗

NASA NTRS1974

The X-ray spectra of the Vela and Puppis supernova remnants and the shock-wave model of supernova remnants

The structure and spectrum of the Vela and Puppis supernova remnants (SNRs) were observed in soft (0.1 to 1.5 keV) X rays from a rocket. The spectral data of both objects below 1.5 keV are consistent with thermal radiation from a hot plasma and are not consistent with a simple power-law function. Fitting the data to a Tucker-Koren model results in values of T = (4.3 plus or minus 0.3) x 1,000,000 K for Vela and T = (7 plus or minus 1) x 1,000,000 K for Puppis. The present data, earlier data from the Cygnus Loop, and results on Cas A and Tycho reported by others are considered within the context of a simple model in which X rays are thermally produced as a result of a shock wave expanding into the interstellar medium. X-ray data on the temperature, intrinsic diameter, and intrinsic luminosity of the five SNRs are used to compute the age, energy release, and initial interstellar density for each one. From measurements of X-ray absorption along the line of sight, and also from shock-wave model interpretations of temperature-diameter-luminosity relations, it is concluded that the average density of interstellar material is about 0.1 per cu cm between the sun and the nearest X-ray emitting SNR, Vela.

Gorenstein, P.↗

NASA NTRS1985

Subionospheric VLF/LF phase perturbations produced by lightning-whistler induced particle precipitation

Rapid phase perturbations of subionospherically propagating VLF/LF signals are associated with lightning-generated whistlers which propagate in the magnetosphere. The perturbations, called 'Trimpi events' or 'Trimpi effects' after their discoverer, are attributed to alterations in the earth-ionosphere waveguide caused by localized enhancements in the ionospheric D region. These enhancements are produced by bursts of high-energy electrons which are precipitated out of the earth's radiation belts by the whistler waves. The present paper has the objective to present and discuss new evidence of whistler-associated phase perturbations of subionospheric signals. Events of this type, called phase Trimpi's were first reported by Lohrey and Kaiser (1979). Attention is given to occurrence statistics in terms of the seasonal distribution and hourly rate of events, possible effects on global VLF/LF navigation, and an interpretive model.

Inan, U. S.↗

NASA NTRS1991

Estimation of porphyrin concentration in the kerogen fraction of shales using high-resolution reflectance spectroscopy

An interpretive model for estimating porphyrin concentration in bitumen and kerogen from spectral reaflectance data in the visible and near-ultraviolet region of the spectrum is derived and calibrated. Preliminary results obtained using the model are consistent with concentrations determined from the bitumen extract and suggest that 40 to 60 percent of the total porphyrin concentration remains in the kerogen after extraction of bitumen from thermally immature samples. The reflectance technique will contribute to porphyrin and kerogen studies and can be applied at its present level of development to several areas of geologic and paleo-oceanographic research.

Holden, Peter N.↗

NASA NTRS1992

The narrow emission lines from SN 1987 A

Observations of the narrow lines from SN 1987 A have been reviewed, along with model interpretations of these observations. In addition, a discussion is provided on characteristics of the radiation from the supernova outburst, and how this affects the thermal structure of the circumstellar gas. Preliminary results from 2D hydrodynamical calculations for the circumstellar structure are also presented.

Lundqvist, Peter↗

NASA NTRS1993

A simple model of clastic sediments on Mars

In preparation for the start of Mars Observer operations at Mars later this year, Viking Infrared Thermal Mapper (IRTM) observations were synthesized into a simple but geologically coherent conceptual model for use in establishing targets for coordinated Thermal Emission Spectrometer (TES) and Mars Observer Camera (MOC) observations. The model is based on three assumptions that are, at best, only partly true: that albedo is a measure of the presence or absence of dust; that thermal inertia is a measure of the weighted average particle size; and that rock abundance is a measure of the statistical thickness of fine sediment (i.e., that the observed areal abundance of rock reflects the whole or partial burial of rocks). Using this model, it is possible to show that, on the scale of 30 km, mantles of wind-transportable sediment (dust and sand) are at most about 1 m thick, and that on a global average such mantles are about 35 cm thick. It is shown that 3.8 x 10(exp 19) cu cm of such sediment covers Mars equatorward of +/- 60 deg latitude. Using the model, interpretation of digital maps of IRTM data focus attention not only on areas where dust is the primary sediment (e.g., Arabia Terra), but also on areas where sand is the primary sediment (e.g., eastern Valles Marineris) and where rocks and other coarse materials are abundant (e.g., eastern Kasei Vallis). Three IRTM data sets are used in the analysis: Viking 1 and 2 pre-dawn observations mosaiced into a global map of single-point thermal inertias at 0.5 deg/bin resolution; Pleskot and Miner's global albedo map using the best available, clear-period IRTM observations (1x/bin); and Christensen's 1 deg/bin rock abundance map. Uncertainty analyses indicate these data sets to be accurate to 5 percent, 2 percent, and 20 percent, respectively.

Christensen, Philip R.↗

NASA NTRS2004

Electrocomposite of Alumina in Nickel Matrix

Nickel/aluminum oxide composite was electroformed in a sulfamate bath with 50 g/L of 0.05-micron aluminum oxide powder. Different plating methods including direct current plating, periodic pulse plating, and periodic reverse pulse plating were used. With conventional direct current plating, the maximum particle inclusion in the nickel matrix remains about 2% (wt). However, much higher percentile particle inclusions were achieved when a specific pulse reversal plating technique was applied. The particle incorporation approaches theoretical maximum when the deposit thickness per cycle approaches the particle diameter size at lower duty cycle. The highest particle incorporation achieved is 23% (by weight). Conceptual models interpreting the dramatic differences in the results of these plating methods were also proposed.

Xiong-Skiba, Pei↗

NASA NTRS2011

Graphical Language for Data Processing

A graphical language for processing data allows processing elements to be connected with virtual wires that represent data flows between processing modules. The processing of complex data, such as lidar data, requires many different algorithms to be applied. The purpose of this innovation is to automate the processing of complex data, such as LIDAR, without the need for complex scripting and programming languages. The system consists of a set of user-interface components that allow the user to drag and drop various algorithmic and processing components onto a process graph. By working graphically, the user can completely visualize the process flow and create complex diagrams. This innovation supports the nesting of graphs, such that a graph can be included in another graph as a single step for processing. In addition to the user interface components, the system includes a set of .NET classes that represent the graph internally. These classes provide the internal system representation of the graphical user interface. The system includes a graph execution component that reads the internal representation of the graph (as described above) and executes that graph. The execution of the graph follows the interpreted model of execution in that each node is traversed and executed from the original internal representation. In addition, there are components that allow external code elements, such as algorithms, to be easily integrated into the system, thus making the system infinitely expandable.

Alphonso, Keith↗