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DOE OSTI2020

Influence of Irradiation-induced Microstructural Defects on Thermal Transport in Single Crystal Thorium Dioxide

Microstructural defects formed as a result of fission fragment damage are known to drastically alter thermal conductivity, a fuel property that governs the efficiency of a nuclear reactor. A fundamental understanding of radiation-induced effects on thermal transport is critical for the development of advanced fuels. Ion-irradiation has widely been used to simulate the effects of neutron-irradiation by seeding atomic-to-nanoscale defects of controllable size and density. In this work, we investigate the influence of microstructural defects, induced by ion-irradiation, on the thermal properties of single crystal thorium dioxide samples grown using the hydrothermal technique. The samples were irradiated using 2 MeV protons at room temperature. A laser-based modulated thermoreflectance technique was used to measure the thermal diffusivity and conductivity within the damaged ThO2 region. The experimentally measured thermal properties were compared with Boltzmann Transport Equation predictions, to determine the impact of various defect types on the thermal properties of ThO2.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2014

TH-E-18A-01: Developments in Monte Carlo Methods for Medical Imaging

Monte Carlo simulation methods are widely used in medical physics research and are starting to be implemented in clinical applications such as radiation therapy planning systems. Monte Carlo simulations offer the capability to accurately estimate quantities of interest that are challenging to measure experimentally while taking into account the realistic anatomy of an individual patient. Traditionally, practical application of Monte Carlo simulation codes in diagnostic imaging was limited by the need for large computational resources or long execution times. However, recent advancements in high-performance computing hardware, combined with a new generation of Monte Carlo simulation algorithms and novel postprocessing methods, are allowing for the computation of relevant imaging parameters of interest such as patient organ doses and scatter-to-primaryratios in radiographic projections in just a few seconds using affordable computational resources. Programmable Graphics Processing Units (GPUs), for example, provide a convenient, affordable platform for parallelized Monte Carlo executions that yield simulation times on the order of 10{sup 7} xray/ s. Even with GPU acceleration, however, Monte Carlo simulation times can be prohibitive for routine clinical practice. To reduce simulation times further, variance reduction techniques can be used to alter the probabilistic models underlying the x-ray tracking process, resulting in lower variance in the results without biasing the estimates. Other complementary strategies for further reductions in computation time are denoising of the Monte Carlo estimates and estimating (scoring) the quantity of interest at a sparse set of sampling locations (e.g. at a small number of detector pixels in a scatter simulation) followed by interpolation. Beyond reduction of the computational resources required for performing Monte Carlo simulations in medical imaging, the use of accurate representations of patient anatomy is crucial to the virtual generation of medical images and accurate estimation of radiation dose and other imaging parameters. For this, detailed computational phantoms of the patient anatomy must be utilized and implemented within the radiation transport code. Computational phantoms presently come in one of three format types, and in one of four morphometric categories. Format types include stylized (mathematical equation-based), voxel (segmented CT/MR images), and hybrid (NURBS and polygon mesh surfaces). Morphometric categories include reference (small library of phantoms by age at 50th height/weight percentile), patient-dependent (larger library of phantoms at various combinations of height/weight percentiles), patient-sculpted (phantoms altered to match the patient's unique outer body contour), and finally, patient-specific (an exact representation of the patient with respect to both body contour and internal anatomy). The existence and availability of these phantoms represents a very important advance for the simulation of realistic medical imaging applications using Monte Carlo methods. New Monte Carlo simulation codes need to be thoroughly validated before they can be used to perform novel research. Ideally, the validation process would involve comparison of results with those of an experimental measurement, but accurate replication of experimental conditions can be very challenging. It is very common to validate new Monte Carlo simulations by replicating previously published simulation results of similar experiments. This process, however, is commonly problematic due to the lack of sufficient information in the published reports of previous work so as to be able to replicate the simulation in detail. To aid in this process, the AAPM Task Group 195 prepared a report in which six different imaging research experiments commonly performed using Monte Carlo simulations are described and their results provided. The simulation conditions of all six cases are provided in full detail, with all necessary data on material composition, source, geometry, scoring and other parameters provided. The results of these simulations when performed with the four most common publicly available Monte Carlo packages are also provided in tabular form. The Task Group 195 Report will be useful for researchers needing to validate their Monte Carlo work, and for trainees needing to learn Monte Carlo simulation methods. In this symposium we will review the recent advancements in highperformance computing hardware enabling the reduction in computational resources needed for Monte Carlo simulations in medical imaging. We will review variance reduction techniques commonly applied in Monte Carlo simulations of medical imaging systems and present implementation strategies for efficient combination of these techniques with GPU acceleration. Trade-offs involved in Monte Carlo acceleration by means of denoising and “sparse sampling” will be discussed. A method for rapid scatter correction in cone-beam CT (<5 min/scan) will be presented as an illustration of the simulation speeds achievable with optimized Monte Carlo simulations. We will also discuss the development, availability, and capability of the various combinations of computational phantoms for Monte Carlo simulation of medical imaging systems. Finally, we will review some examples of experimental validation of Monte Carlo simulations and will present the AAPM Task Group 195 Report. Learning Objectives: Describe the advances in hardware available for performing Monte Carlo simulations in high performance computing environments. Explain variance reduction, denoising and sparse sampling techniques available for reduction of computational time needed for Monte Carlo simulations of medical imaging. List and compare the computational anthropomorphic phantoms currently available for more accurate assessment of medical imaging parameters in Monte Carlo simulations. Describe experimental methods used for validation of Monte Carlo simulations in medical imaging. Describe the AAPM Task Group 195 Report and its use for validation and teaching of Monte Carlo simulations in medical imaging.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

The effect of concentration on the structure and crystallinity of a cementitious waste form for caustic wastes

Cement-based waste forms have long been considered economical technologies for disposal of various types of waste. A solidified cementitious waste form, Cast Stone, was developed to immobilize the radioactive secondary waste from vitrification processes. In this work, Cast Stone was considered for a Na-based caustic liquid waste, and its physical properties were analyzed as a function of liquid waste loading up to 2 M Na. Differences in crystallinity (phase composition), microstructure, mesostructure (pore size distribution, surface area), and macrostructure (density, compressive strength) were investigated using various analytical techniques, in order to assess the suitability of Cast Stone as a chemically durable waste. It was found that the concentration of secondary waste simulant (caustic waste) had little effect on the relevant engineering properties of Cast Stone, showing that Cast Stone could be an effective and tolerant waste form for a wide range of concentrations of high sodium waste.

Cast Stone↗

DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗

DOE OSTI2013

A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

We investigate machine learning (ML) techniques for predicting the number of galaxies (N{sub gal}) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N{sub gal}. In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N{sub gal} by training our algorithms on the following six halo properties: number of particles, M{sub 200}, {sigma}{sub v}, v{sub max}, half-mass radius, and spin. For Millennium, our predicted N{sub gal} values have a mean-squared error (MSE) of {approx}0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to {approx}5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N{sub gal}. Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high M{sub star}, low M{sub star}). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

ON THE ROBUSTNESS OF z = 0-1 GALAXY SIZE MEASUREMENTS THROUGH MODEL AND NON-PARAMETRIC FITS

We present the size-stellar mass relations of nearby (z = 0.01-0.02) Sloan Digital Sky Survey galaxies, for samples selected by color, morphology, Sérsic index n, and specific star formation rate. Several commonly employed size measurement techniques are used, including single Sérsic fits, two-component Sérsic models, and a non-parametric method. Through simple simulations, we show that the non-parametric and two-component Sérsic methods provide the most robust effective radius measurements, while those based on single Sérsic profiles are often overestimates, especially for massive red/early-type galaxies. Using our robust sizes, we show for all sub-samples that the mass-size relations are shallow at low stellar masses and steepen above ∼3-4 × 10{sup 10} M{sub ☉}. The mass-size relations for galaxies classified as late-type, low-n, and star-forming are consistent with each other, while blue galaxies follow a somewhat steeper relation. The mass-size relations of early-type, high-n, red, and quiescent galaxies all agree with each other but are somewhat steeper at the high-mass end than previous results. To test potential systematics at high redshift, we artificially redshifted our sample (including surface brightness dimming and degraded resolution) to z = 1 and re-fit the galaxies using single Sérsic profiles. The sizes of these galaxies before and after redshifting are consistent and we conclude that systematic effects in sizes and the size-mass relation at z ∼ 1 are negligible. Interestingly, since the poorer physical resolution at high redshift washes out bright galaxy substructures, single Sérsic fitting appears to provide more reliable and unbiased effective radius measurements at high z than for nearby, well-resolved galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1989

Using passive, thermal remote sensing techniques for detecting subsurface gravel accumulations in vegetated, unconsolidated sedimentary terrains

Multiband radiometric data from an airborne imaging thermal scanner are being studied for use in finding buried gravel deposits. The techniques are based on measuring relative differences in the thermal properties between gravel-laden targets and the surrounding gravelless background. These properties are determined from modeling the spectral radiant emittance recorded over both types of surfaces in conjunction with ground measurements of the most significant heat flows above and below the surface. Thermodynamic properties of sampled materials from control sites are determined, and diurnal and annual subsurface heat waves are recorded. Thermal models that account for heat exchange at the surface, as well as varying levels of soil moisture, humidity, and vegetation, are needed for adaptation and modification to simulate the physical and radiative environments of this region.

Burns, Gregory S.↗

NASA NTRS2008

A Globally Optimal Particle Tracking Technique for Stereo Imaging Velocimetry Experiments

An important phase of any Stereo Imaging Velocimetry experiment is particle tracking. Particle tracking seeks to identify and characterize the motion of individual particles entrained in a fluid or air experiment. We analyze a cylindrical chamber filled with water and seeded with density-matched particles. In every four-frame sequence, we identify a particle track by assigning a unique track label for each camera image. The conventional approach to particle tracking is to use an exhaustive tree-search method utilizing greedy algorithms to reduce search times. However, these types of algorithms are not optimal due to a cascade effect of incorrect decisions upon adjacent tracks. We examine the use of a guided evolutionary neural net with simulated annealing to arrive at a globally optimal assignment of tracks. The net is guided both by the minimization of the search space through the use of prior limiting assumptions about valid tracks and by a strategy which seeks to avoid high-energy intermediate states which can trap the net in a local minimum. A stochastic search algorithm is used in place of back-propagation of error to further reduce the chance of being trapped in an energy well. Global optimization is achieved by minimizing an objective function, which includes both track smoothness and particle-image utilization parameters. In this paper we describe our model and present our experimental results. We compare our results with a nonoptimizing, predictive tracker and obtain an average increase in valid track yield of 27 percent

McDowell, Mark↗

DOE OSTI2024

Impulse-driven release of gas-encapsulated drops

Gas-encapsulated drops, much like antibubbles, are drops enclosed in a bubble within a liquid. They show potential as payload carriers in fluid transport and mixing techniques where sound waves can be leveraged to induce the collapse of the gas core and the subsequent release of the drop. Here, the interaction of millimetre-sized gas-encapsulated drops with impulsive laser-induced shock waves is investigated to gain fundamental insights into the release process. Experimental synchrotron X-ray phase contrast imaging, which allows the drop dynamics to be visualised inside the encapsulating bubble, is complemented by numerical simulations to study the intricate physics at play. Three drop dynamical release regimes are discovered, namely the drop impact , partial deposition and jet impact regimes. The regime type is mainly dependent on the shape of the bubble interface impacting the drop and the associated Weber and Reynolds numbers. The drop dynamics of the drop impact and partial deposition regimes show similarities with the canonical configuration of drops impacting flat liquid surfaces, whereas the jet impact regime resembles binary drop collisions, which allows existing scaling laws to be applied to describe the underlying processes. The release of the drop is investigated numerically. The time evolution of the drop dissemination within the surrounding liquid discloses enhanced mixing for dynamics involving high Weber and Reynolds numbers such as the drop impact and jet impact regimes.

42 ENGINEERING↗

DOE OSTI2015

Picosecond dissociation of amyloid fibrils with infrared laser: A nonequilibrium simulation study

Recently, mid-infrared free-electron laser technology has been developed to dissociate amyloid fibrils. Here, we present a theoretical framework for this type of experiment based on laser-induced nonequilibrium all-atom molecular dynamics simulations. We show that the fibril is destroyed due to the strong resonance between its amide I vibrational modes and the laser field. The effects of laser irradiation are determined by a balance between fibril formation and dissociation. While the overall rearrangements of the fibril finish over short time scales, the interaction between the peptides and the solvent continues over much longer times indicating that the waters play an important role in the dissociation process. Our results thus provide new insights into amyloid fibril dissociation by laser techniques and open up new venues to investigate the complex phenomena associated with amyloidogenesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

The Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation-based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theory with the classical CKF, which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviate from the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.

42 ENGINEERING↗

NASA NTRS1991

A simulation evaluation of a four-engine jet transport using engine thrust modulation for flightpath control

The use of throttle control laws to provide adequate flying qualities for flightpath control in the event of a total loss of conventional flight control surface use was evaluated. The results are based on a simulation evaluation by transport research pilots of a B-720 transport with visual display. Throttle augmentation control laws can provide flightpath control capable of landing a transport-type aircraft with up to moderate levels of turbulence. The throttle augmentation mode dramatically improves the pilots' ability to control flightpath for the approach and landing flight condition using only throttle modulation. For light turbulence, the average Cooper-Harper pilot rating improved from unacceptable to acceptable (a pilot rating improvement of 4.5) in going from manual to augmented control. The low frequency response characteristics of the engines require a considerably different piloting technique. The various techniques used by the pilots resulted in considerable scatter in the data. Many pilots readily adapted to a good piloting technique while some has difficulty. The research demonstrates a new and viable approach to providing an independent means of redundancy or increasing the redundancy capability of transport aircraft flightpath control.

Gilyard, Glenn B.↗

DOE OSTI2015

Asymptotic Expansion Homogenization for Multiscale Nuclear Fuel Analysis

Engineering scale nuclear fuel performance simulations can benefit by utilizing high-fidelity models running at a lower length scale. Lower length-scale models provide a detailed view of the material behavior that is used to determine the average material response at the macroscale. These lower length-scale calculations may provide insight into material behavior where experimental data is sparse or nonexistent. This multiscale approach is especially useful in the nuclear field, since irradiation experiments are difficult and expensive to conduct. The lower length-scale models complement the experiments by influencing the types of experiments required and by reducing the total number of experiments needed. This multiscale modeling approach is a central motivation in the development of the BISON-MARMOT fuel performance codes at Idaho National Laboratory. These codes seek to provide more accurate and predictive solutions for nuclear fuel behavior. One critical aspect of multiscale modeling is the ability to extract the relevant information from the lower length-scale sim- ulations. One approach, the asymptotic expansion homogenization (AEH) technique, has proven to be an effective method for determining homogenized material parameters. The AEH technique prescribes a system of equations to solve at the microscale that are used to compute homogenized material constants for use at the engineering scale. In this work, we employ AEH to explore the effect of evolving microstructural thermal conductivity and elastic constants on nuclear fuel performance. We show that the AEH approach fits cleanly into the BISON and MARMOT codes and provides a natural, multidimensional homogenization capability.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

End-to-end microgrid protection using distributed data-driven methods

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Phase-field modeling of the interactions between an edge dislocation and an array of obstacles

Obstacles, such as voids and precipitates, are prevalent in crystalline materials. They strengthen crystals by serving as barriers to dislocation glide. Here in this work, we develop a phase-field dislocation dynamics (PFDD) technique for investigating the interactions between dislocations and second-phase obstacles, which can be either voids or precipitates. The PFDD technique is constructed to account for elastic heterogeneity, elastic anisotropy, dissociation of the dislocation, and dislocation transmission across bicrystalline interfaces. Within the framework, we present a model for “pseudo-voids”, which are voids shearable by dislocations, in contrast to unphysical, unshearable voids in conventional phase-field dislocation formulations. We employ the PFDD technique to investigate the in-plane interactions between an edge dislocation and an array of nano-scale obstacles with different spacings. In this application, the interactions take place in glide planes of either a face-centered cubic (FCC) Cu or a body-centered cubic (BCC) Nb matrix, while the precipitates have a Cu 1-x Nb x composition, with x varying from 0.1 to 0.9. Our atomistic simulations find that the alloy precipitates can have an FCC, an amorphous, or a BCC phase, depending on the compositional ratio between Cu and Nb, i.e., value of x. Among all types of obstacles, the critical stresses for dislocation bypass are the highest for unshearable amorphous precipitates, followed by shearable crystalline precipitates, and then the pseudo-voids.

42 ENGINEERING↗

DOE OSTI2020

Biaxial steel plated concrete constitutive models for composite structures: Implementation and validation

The Steel-plated Concrete (SC) technique is an alternative construction technique with faster onsite construction speed, reduced construction time, and increased structural performance. Aiming to predict the force transfer mechanism of SC elements, an innovative constitutive model package is proposed by implementing experimental-based biaxial steel plate concrete models into the nonlinear finite element (FE) model “Membrane Model of SC (MM-CS).” First, the formulation and implementation of the MM-SC is illustrated in detail, including the equilibrium and compatibility equations and the implementation of constitutive models. Next, various experimental data of SC members are selected and simulated using the proposed MM-SC model. In this research, two types of structures, SC panels and framed SC shear walls, and three types of loading conditions, including uniaxial compression, pure shear, and combined axial-flexural-shear tests, are analyzed respectively. Good agreements were obtained between the reported results and the FE simulation results in terms of yield capacity and ultimate capacity, proving the reliability of the implemented analytical constitutive material models and the biaxial membrane model formulations.

42 ENGINEERING↗

DOE OSTI2022

Optimal, centralized dynamic curbside parking space zoning

In this paper we formulate a dynamic mixed integer program for optimally zoning curbside parking spaces subject to transportation policy-inspired constraints and regularization terms. First, we illustrate how given some objective of curb zoning valuation as a function of zone type (paid parking, bus stop, etc.), dynamically rezoning involves unrolling this optimization program over a fixed time horizon. Second, we implement two different solution methods given an example curb zoning valuation. In the first method, we solve long horizon dynamic zoning problems via approximate dynamic programming. In the second method, we employ Dantzig-Wolfe decomposition to break-up the mixed-integer program into a master problem and several sub-problems that can be solved in parallel. This speeds up the computational solve-time of the MIP considerably. We present simulation results and comparisons of the different employed techniques on vehicle arrival-rate data obtained for a neighborhood in downtown Seattle, Washington, USA.

Nazir, Mohammad Nawaf↗

NASA NTRS1995

Improved CDMA Performance Using Parallel Interference Cancellation

This report considers a general parallel interference cancellation scheme that significantly reduces the degradation effect of user interference but with a lesser implementation complexity than the maximum-likelihood technique. The scheme operates on the fact that parallel processing simultaneously removes from each user the interference produced by the remaining users accessing the channel in an amount proportional to their reliability. The parallel processing can be done in multiple stages. The proposed scheme uses tentative decision devices with different optimum thresholds at the multiple stages to produce the most reliably received data for generation and cancellation of user interference. The 1-stage interference cancellation is analyzed for three types of tentative decision devices, namely, hard, null zone, and soft decision, and two types of user power distribution, namely, equal and unequal powers. Simulation results are given for a multitude of different situations, in particular, those cases for which the analysis is too complex.

Simon, Marvin↗