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DOE OSTI2021

Validation of numerical models for seismic fluid-structure-interaction analysis of nuclear, safety-related equipment

Seismic design, qualification, and risk assessment of nuclear safety-related vessels filled with liquid will have to consider the interaction between the vessel (tank), the contained liquid, and submerged components, if any. Seismic fluid-structure-interaction (FSI) analysis of nuclear vessels will rely on numerical models, which are required to be verified and validated. This study validates previously-verified numerical models using test data generated from earthquake-simulator experiments involving a cylindrical tank. Two solvers in LS-DYNA for FSI simulations are used for the numerical analysis: Arbitrary-Lagrangian-Eulerian (ALE) and Incompressible Computational Fluid Dynamics (ICFD). Numerical and test results are compared for responses critical to seismic design of advanced reactor vessels: hydrodynamic pressures on the tank wall, reactions at the support, and wave heights of the contained liquid. Analysis is performed for one-, two-, and three-directional seismic inputs with a range of intensity, and rocking motions. The accuracy of the numerical results and different methods for outputting wave heights are discussed. Recommendations for validation of seismic FSI numerical models of advanced reactors are provided. As a result, the validation exercise presented in this study is broadly applicable to cylindrical tanks, regardless of industry sector.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Laboratory infrared spectroscopy of gaseous negatively charged polyaromatic hydrocarbons

Based largely on infrared spectroscopic evidence, polycyclic aromatic hydrocarbon (PAH) molecules are now widely accepted to occur abundantly in the interstellar medium. Laboratory infrared spectra have been obtained for a large variety of neutral and cationic PAHs, but data for anionic PAHs are scarce. Nonetheless, in regions with relatively high electron densities and low UV photon fluxes, PAHs have been suggested to occur predominantly as negatively charged ions (anions), having substantial influence on cloud chemistry. While some matrix spectra have been reported for radical anion PAHs, no data is available for even-electron anions, which are more stable against electron detachment. Here we present the first laboratory infrared spectra of deprotonated PAHs ([PAH-H]{sup –}) in the wavelength ranges between 6 and 16 μm and around 3 μm. Wavelength-dependent infrared multiple-photon electron detachment is employed to obtain spectra for deprotonated naphthalene, anthracene, and pyrene in the gas phase. Spectra are compared with theoretical spectra computed at the density functional theory level. We show that the relative band intensities in different ranges of the IR spectrum deviate significantly from those of neutral and positively charged PAHs, and moreover from those of radical anion PAHs. These relative band intensities are, however, well reproduced by theory. An analysis of the frontier molecular orbitals of the even- and odd-electron anions reveals a high degree of charge localization in the deprotonated systems, qualitatively explaining the observed differences and suggesting unusually high electric dipole moments for this class of PAH molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2013

Effect of different breathing patterns in the same patient on stereotactic ablative body radiotherapy dosimetry for primary renal cell carcinoma: A case study

Stereotactic ablative body radiotherapy (SABR) for primary renal cell carcinoma (RCC) targets requires motion management strategies to verify dose delivery. This case study highlights the effect of a change in patient breathing amplitude on the dosimetry to organs at risk and target structures. A 73-year-old male patient was planned for receiving 26 Gy of radiation in 1 fraction of SABR for a left primary RCC. The patient was simulated with four-dimensional computed tomography (4DCT) and the tumor internal target volume (ITV) was delineated using the 4DCT maximum intensity projection. However, the initially planned treatment was abandoned at the radiation oncologist's discretion after pretreatment cone-beam CT (CBCT) motion verification identified a greater than 50% reduction in superior to inferior diaphragm motion as compared with the planning 4DCT. This patient was resimulated with respiratory coaching instructions. To assess the effect of the change in breathing on the dosimetry to the target, each plan was recalculated on the data set representing the change in breathing condition. A change from smaller to larger breathing showed a 46% loss in planning target volume (PTV) coverage, whereas a change from larger breathing to smaller breathing resulted in an 8% decrease in PTV coverage. ITV coverage was similarly reduced by 8% in both scenarios. This case study highlights the importance of tools to verify breathing motion prior to treatment delivery. 4D image guided radiation therapy verification strategies should focus on not only verifying ITV margin coverage but also the effect on the surrounding organs at risk.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2023

Observation of laser ablation of silicon as a function of pulse length at constant fluence via time-resolved x-ray spectroscopy

We investigate the ablation of silicon as a function of laser pulse length at a constant fluence using time-resolved x-ray spectroscopy data obtained from OMEGA EP experiments at the University of Rochester's Laboratory for Laser Energetics. Our targets consisted of three-layer planar structures composed of Si (50 μm), Cu (25 μm), and SiO 2 (500 μm) layers. The Si layer was irradiated by a 351-nm laser with varying pulse widths of 250 ps, 500 ps, 1 ns, and 10 ns while maintaining a constant fluence of ~27.9 kJ/cm 2 . Electron temperatures and densities of the ablated plasma were determined by analyzing the time-resolved x-ray spectroscopy data through a comparison of experimental measurements with synthetic results obtained from Si atomic calculations in a steady state and non-local thermodynamic equilibrium. These calculations were computed using PrismSPECT. Additionally, radiation-hydrodynamics simulations with FLASH are used to generate simulated plasma-density and plasma-temperature profiles, which are then compared with the experimental measurements. Our analyses reveal that increasing the laser pulse length at a constant fluence results in a decrease in electron temperatures and densities. Furthermore, the longer pulses with lower intensities lead to deeper ablation regions before reaching the peak ablation but lower ionization balances in the silicon layer. Furthermore, these findings emphasize the critical role of laser pulse length in plasma ablation and shock generation for laser-impulse studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

A Ku band pulsed electron paramagnetic resonance spectrometer using an arbitrary waveform generator for quantum control experiments at millikelvin temperatures

We present a 17 GHz (Ku band) arbitrary waveform pulsed electron paramagnetic resonance spectrometer for experiments down to millikelvin temperatures. The spectrometer is located at room temperature, while the resonator is placed either in a room temperature magnet or inside a cryogen-free dilution refrigerator; the operating temperature range of the dilution unit is from ca. 10 mK to 8 K. This combination provides the opportunity to perform quantum control experiments on electron spins in the pure-state regime. At 0.6 T, spin echo experiments were carried out using γ-irradiated quartz glass from 1 K to 12.3 mK. With decreasing temperatures, we observed an increase in spin echo signal intensities due to increasing spin polarizations, in accordance with theoretical predictions. Through experimental data fitting, thermal spin polarization at 100 mK was estimated to be at least 99%, which was almost pure state. Next, to demonstrate the ability to create arbitrary waveform pulses, we generate a shaped pulse by superposing three Gaussian pulses of different frequencies. The resulting pulse was able to selectively and coherently excite three different spin packets simultaneously—a useful ability for analyzing multi-spin system and for controlling a multi-qubit quantum computer. By applying this pulse to the inhomogeneously broadened sample, we obtain three well-resolved excitations at 8 K, 1 K, and 14 mK.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Analysis and Benchmarking of feature reduction for classification under computational constraints

Abstract Machine learning is most often expensive in terms of computational and memory costs due to training with large volumes of data. Current computational limitations of many computing systems motivate us to investigate practical approaches, such as feature selection and reduction, to reduce the time and memory costs while not sacrificing the accuracy of classification algorithms. In this work, we carefully review, analyze, and identify the feature reduction methods that have low costs/overheads in terms of time and memory. Then, we evaluate the identified reduction methods in terms of their impact on the accuracy, precision, time, and memory costs of traditional classification algorithms. Specifically, we focus on the least resource intensive feature reduction methods that are available in Scikit-Learn library. Since our goal is to identify the best performing low-cost reduction methods, we do not consider complex expensive reduction algorithms in this study. In our evaluation, we find that at quadratic-scale feature reduction, the classification algorithms achieve the best trade-off among competitive performance metrics. Results show that the overall training times are reduced 61%, the model sizes are reduced 6×, and accuracy scores increase 25% compared to the baselines on average with quadratic scale reduction.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Tropical Cyclone Super Resolution using conditional diffusion denoising probabilistic model from mesoscale simulation to LES

Accurate modeling of tropical cyclone wind fields is essential for the design, risk assessment, and operational planning of offshore energy infrastructure. While mesoscale simulations are widely used thanks to their computational efficiency, they lack the necessary resolution to capture key features such as wind shear and veer profiles as well as the distribution turbulent kinetic energy (TKE). High-fidelity large-eddy simulation (LES) models on the other hand, can resolve turbulent structures and provide a more accurate representation of the complex wind field, albeit at a higher computational cost. To address this modeling gap, we introduce a two-part generative framework to enhance the resolution and physics-capturing ability of mesoscale simulations. First, a reduced-order model based on Karhunen–Loève (KL) decomposition is used to extract dominant spatial modes from one-dimensional mean wind profiles. A multilayer perceptron (MLP) is trained to map mesoscale mode weights to their LES counterparts, enabling accurate reconstruction of vertical velocity profiles. Second, a conditional Diffusion Denoising Probabilistic Model (DDPM) is developed to super-resolve coarse and low-fidelity mesoscale velocity fields, recovering fine-scale turbulence structures and stress distributions. The framework is evaluated across different tropical cyclone intensity categories defined by the Saffir–Simpson scale and demonstrates strong performance in both interpolation and extrapolation tasks. The generated fields accurately reproduce spatial coherence, stress distributions, and spectral energy characteristics observed in LES data. By bridging the fidelity gap between mesoscale and LES outputs, this approach offers a scalable, data-driven solution for enhancing the representation of tropical cyclone wind fields, enabling more robust offshore energy infrastructure systems design in tropical-cyclone-prone areas.

17 WIND ENERGY↗

DOE OSTI2022

Quantifying Dynamic Signal Spread in Real-Time High-Energy X-ray Diffraction

Abstract Measured intensity in high-energy monochromatic X-ray diffraction (HEXD) experiments provides information regarding the microstructure of the crystalline material under study. The location of intensity on an areal detector is determined by the lattice spacing and orientation of crystals so that changes in the heterogeneity of these quantities are reflected in the spreading of diffraction peaks over time. High temporal resolution of such dynamics can now be experimentally observed using technologies such as the mixed-mode pixel array detector (MM-PAD) which facilitates in situ dynamic HEXD experiments to study plasticity and its underlying mechanisms. In this paper, we define and demonstrate a feature computed directly from such diffraction time series data quantifying signal spread in a manner that is correlated with plastic deformation of the sample. A distinguishing characteristic of the analysis is the capability to describe the evolution from the distinct diffraction peaks of an undeformed alloy sample through to the non-uniform Debye–Scherrer rings developed upon significant plastic deformation. We build on our previous work modeling data using an overcomplete dictionary by treating temporal measurements jointly to improve signal spread recovery. We demonstrate our approach in simulations and on experimental HEXD measurements captured using the MM-PAD. Our method for characterizing the temporal evolution of signal spread is shown to provide an informative means of data analysis that adds to the capabilities of existing methods. Our work draws on ideas from convolutional sparse coding and requires solving a coupled convex optimization problem based on the alternating direction method of multipliers.

Engineering↗

DOE OSTI2017

PyDREAM: high-dimensional parameter inference for biological models in python

Biological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration against experimental data. Markov chain Monte Carlo (MCMC) methods are suitable to estimate multivariate posterior model parameter distributions, but these methods may exhibit slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM, a Python implementation of the (Multiple-Try) Differential Evolution Adaptive Metropolis [DREAM(ZS)] algorithm developed by Vrugt and ter Braak (2008) and Laloy and Vrugt (2012). PyDREAM achieves excellent performance for complex, parameter-rich models and takes full advantage of distributed computing resources, facilitating parameter inference and uncertainty estimation of CPU-intensive biological models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Effective Missing Value Imputation Methods for Building Monitoring Data

To understand behaviors of natural and man-made events, such as energy consumption of buildings, which accounts for 40% of energy uses in the US, we deploy automated monitoring devices to record periodic observations. However, such experimental and observation data often contains problems and irregularities that have to be cleaned up before analyses. Due to various conditions affecting sensor operations, the communication channels, recording steps, or the recording media, the recorded data might have missing values, errors, or anomalous values. An effective way to clean up these problems is to replace these missing values, errors and anomalous values with expected values, a process generally known as imputation. In this work, we survey commonly used missing value imputation techniques and compare their performance on a set of building monitoring data. To compare the different types of sensor measurements with widely varying characteristics, we use normalized root mean squared error (NRMSE) as the key metric for the effectiveness of the imputation methods. We additionally consider periodicity and run time when considering comparing methods. Through extensive testing, we find that for small gap sizes, up to 8 consecutive missing values, linear interpolation performs the best; for larger gaps stretching up to 48 consecutive missing values, K-nearest neighbors provides the most accurate imputations; for even larger gaps, more computational intensive methods, such as matrix factorization, achieve the smallest NRMSE. Additionally, we observe that these computationally intensive algorithms not only provide accurate imputations for large gaps, but are also more robust across all types of sensors.

Cho, B↗

DOE OSTI2016

Direct comparison of elastic incoherent neutron scattering experiments with molecular dynamics simulations of DMPC phase transitions

Neutron scattering techniques have been employed to investigate 1,2-dimyristoyl-sn-glycero-3- phosphocholine (DMPC) membranes in the form of multilamellar vesicles (MLVs) and deposited, stacked multilamellar-bilayers (MLBs), covering transitions from the gel to the liquid phase. Neutron diffraction was used to characterise the samples in terms of transition temperatures, whereas elastic incoherent neutron scattering (EINS) demonstrates that the dynamics on the sub-macromolecular length-scale and pico- to nano-second time-scale are correlated with the structural transitions through a discontinuity in the observed elastic intensities and the derived mean square displacements. Molecular dynamics simulations have been performed in parallel focussing on the length-, time- and temperature-scales of the neutron experiments. They correctly reproduce the structural features of the main gel-liquid phase transition. Particular emphasis is placed on the dynamical amplitudes derived from experiment and simulations. Two methods are used to analyse the experimental data and mean square displacements. They agree within a factor of 2 irrespective of the probed time-scale, i.e. the instrument utilized. Mean square displacements computed from simulations show a comparable level of agreement with the experimental values, albeit, the best match with the two methods varies for the two instruments. Consequently, experiments and simulations together give a consistent picture of the structural and dynamical aspects of the main lipid transition and provide a basis for future, theoretical modelling of dynamics and phase behaviour in membranes. The need for more detailed analytical models is pointed out by the remaining variation of the dynamical amplitudes derived in two different ways from experiments on the one hand and simulations on the other.

Aoun, Bachir↗

DOE OSTI2018

Do energy costs really affect commercial mortgage default risk? New results and implications for energy efficiency investments [PowerPoint]

This paper presents new results on the link between energy factors and commercial mortgage default. First, we summarize results from an empirical analysis of the impact of source energy use intensity (EUI) and electricity prices on mortgage default. We used a unique data set that merges building-level energy use data from city benchmarking ordinances and financial data for commercial mortgages on the same buildings. We found that building source EUI and electricity price are statistically and economically associated with commercial mortgage defaults. Next, we present five case studies on the impact of energy use and price variations on default risk: three office buildings, a hotel, and a multi-family residential building. We use the empirical model coefficients to compute the default risk impacts due to variations in source EUI and electricity price over the course of the mortgage term. We found that variations in source EUI could raise or lower the default rates in these properties by between 5% and 40%, depending on the property type and geography. Electricity pricing has an even greater effect – roughly 60% change in default rate in the Denver area and nearly 90% in northern California. Finally, we propose an energy risk score that lenders can use to assess energy risk and inform mortgage terms. The score is being developed and piloted in collaboration with three mortgage lenders. We conclude with implications of this score as a market signal and mechanism for incentivizing energy efficiency investments through the commercial mortgage channel.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation

Purpose: To predict microsatellite instability (MSI) status of colon cancer on preoperative CT imaging using radiomic analysis. Methods: This retrospective study involved radiomic analysis of preoperative CT imaging of patients who underwent resection of stage II–III colon cancer from 2004 to 2012. A radiologist blinded to MSI status manually segmented the tumor region on CT images. 254 Intensity-based radiomic features were extracted from the tumor region. Three prediction models were developed with (1) only clinical features, (2) only radiomic features, and (3) “combined” clinical and radiomic features. Patients were randomly separated into training (n = 139) and test (n = 59) sets. The model was constructed from training data only; the test set was reserved for validation only. Model performance was evaluated using AUC, sensitivity, specificity, PPV, and NPV. Results: Of the total 198 patients, 134 (68%) patients had microsatellite stable tumors and 64 (32%) patients had MSI tumors. The combined model performed slightly better than the other models, predicting MSI with an AUC of 0.80 for the training set and 0.79 for the test set (specificity = 96.8% and 92.5%, respectively), whereas the model with only clinical features achieved an AUC of 0.74 and the model with only radiomic features achieved an AUC of 0.76. The model with clinical features alone had the lowest specificity (70%) compared with the model with radiomic features alone (95%) and the combined model (92.5%). Conclusions: Preoperative prediction of MSI status via radiomic analysis of preoperative CT adds specificity to clinical assessment and could contribute to personalized treatment selection.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

Predicting Atomistic Transitions with Transformers

Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.

36 MATERIALS SCIENCE↗

DOE OSTI2015

MO-G-304-02: Knowledge Based DVH Prediction Using a Geometric Dose Transform

Purpose: To demonstrate a novel method for predicting patient dose-volume histograms (DVHs) using a prior database of optimized radiotherapy treatment plans. Such predicted DVHs could be useful for automating treatment planning. Methods: Our initial demonstration utilized a database of 100 prostate intensity-modulated radiotherapy (IMRT) data-sets. Each data-set contained a CT image with contours of the planning target volume (PTV), rectum, and bladder, the parameters of a clinically approved IMRT plan, and a corresponding simulated dose distribution. We applied a novel geometric transformation to remove the influence of the PTV size, shape, and location on the dose distribution. We termed the transformed distribution the geometrically normalized dose distribution (GNDD). This normalization transform was applied to 80 data-sets randomly selected from the database, and a population GNDD was computed as the average. Next, the population GNDD was mapped onto each of the remaining 20 patient datasets using the reverse of the geometric normalization transform, and predicted DVHs were calculated from the reverse transformed dose distributions (GNDD-DVHs). In addition, a state of the art machine learning based method from the literature was tested for comparison. Results: DVH prediction accuracy was quantified by calculating the relative root mean squared error (rRMSE) on predicted DVHs for the 20 test patients using their known DVHs. For bladder, rectum, and PTV average rRMSEs for the GNDD method were 9.7 ± 4.2%, 13.9 ± 6.0%, and 2.3 ± 0.5% respectively. Prediction results using GNDD were roughly equivalent to that from the machine learning method. Conclusion: We developed a new method for predicting DVH curves from a database of prior patient plans. We demonstrated that our simple approach achieves accuracy comparable to a method using a complicated machine learning based approach.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

The antibody mining toolbox: An open source tool for the rapid analysis of antibody repertoires

In vitro selection has been an essential tool in the development of recombinant antibodies against various antigen targets. Deep sequencing has recently been gaining ground as an alternative and valuable method to analyze such antibody selections. The analysis provides a novel and extremely detailed view of selected antibody populations, and allows the identification of specific antibodies using only sequencing data, potentially eliminating the need for expensive and laborious low-throughput screening methods such as enzyme-linked immunosorbant assay. The high cost and the need for bioinformatics experts and powerful computer clusters, however, have limited the general use of deep sequencing in antibody selections. Here, we describe the AbMining ToolBox, an open source software package for the straightforward analysis of antibody libraries sequenced by the three main next generation sequencing platforms (454, Ion Torrent, MiSeq). The ToolBox is able to identify heavy chain CDR3s as effectively as more computationally intense software, and can be easily adapted to analyze other portions of antibody variable genes, as well as the selection outputs of libraries based on different scaffolds. The software runs on all common operating systems (Microsoft Windows, Mac OS X, Linux), on standard personal computers, and sequence analysis of 1–2 million reads can be accomplished in 10–15 min, a fraction of the time of competing software. Use of the ToolBox will allow the average researcher to incorporate deep sequence analysis into routine selections from antibody display libraries.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Discrete Green’s functions and spectral graph theory for computationally efficient thermal modeling

Here, this work concerns solutions of the heat equation with the spectral graph method, for which the temperature is defined at discrete points in the domain and the spatial relationship among the points is described by a graph. The heat equation on the graph is solved using matrix techniques involving the eigenvectors and eigenvalues of the Laplacian matrix. The spectral graph approach precludes the computationally intensive meshing and numerous time-integration steps of the finite element method. In the present work, the spectral graph method is extended to include heat loss at the boundaries with a generalized boundary condition, and physics-based edge weights are introduced which simplify the calibration process. From this approach a discrete Green’s function is defined which allows for solutions under a variety of heating conditions including: space-varying initial conditions; time-and-space varying internal heating; and, time-and-space-varying heating at boundaries of type 1 (Dirichlet), type 2 (Neumann) and type 3 (Robin). Results are provided for benchmark heat transfer problems in one spatial dimension and in three spatial dimensions, and verification is provided by comparison with exact analytical solutions and finite difference solutions. The spectral graph method converges within 0.4% error of the analytical solution. The practical utility of the approach is demonstrated by thermal simulation of a multilayer additive manufacturing process. The spectral graph results are compared to experimentally-obtained temperature data for two metal parts, with error less than 5% of the experimental measurements, with computation time less than one minute on a desktop computer.

36 MATERIALS SCIENCE↗