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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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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

In Situ Study of Particle Precipitation in Metal-Doped CeO2 during Thermal Treatment and Ion Irradiation for Emulation of Irradiating Fuels

Metallic particles formed in oxide fuels (e.g., UO 2 ) during neutron irradiation have an adverse impact on fuel performance. A fundamental investigation of particle precipitation is needed to predict the fuel performance and potentially improve fuel designs and operations. In this study, we report on the precipitation of Mo-dominant β-phase particles in polycrystalline CeO 2 (surrogate for UO 2 ) films doped with Mo, Pd, Rh, Ru, and Re (surrogate for Tc). In situ heating scanning transmission electron microscopy indicates that particle precipitation starts at ~1073 K with a limited particle growth to ~10 nm. While particle concentration increases with increasing temperature, particle size remains largely unchanged up to 1273 K. There is a dramatic change in the microstructure following vacuum annealing at 1373 K, probably due to phase transition of reduced cerium oxide. At the high temperature, particles grow up to 75 nm or larger with distinctive facets. The particles are predominantly composed of Mo with a body-centered cubic structure (β phase). An oxide layer was observed after storage at ambient conditions. In situ heating X-ray photoelectron spectroscopy reveals an increasing reduction of Ce charge state from 4+ to 3+ in the doped CeO 2 film at temperatures from 673 to 1273 K. In situ ion irradiation transmission electron microscopy with 2 MeV Al 2+ ions up to a dose of ~20 displacements per atom at nominally room temperature does not lead to precipitation of visible particles. However, irradiation with 1.7 MeV Au 3+ ions to ~10 dpa at 973 K produces ~2 nm sized pure Pd particles; Au 3+ irradiation at 1173 K appears to result in precipitates of ~6 nm in size. Some of the defects produced by ion irradiation could be nucleation sites for precipitation, leading to generation of smaller particles with a higher concentration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗
DOE OSTI2024

An active learning framework for the rapid assessment of galvanic corrosion

The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately predict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procuring the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic couple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maximum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗
DOE OSTI2015

In-flight observations of low-mode ρR asymmetries in NIF implosions

Charged-particle spectroscopy is used to assess implosion symmetry in ignition-scale indirect-drive implosions for the first time. Surrogate D{sup 3}He gas-filled implosions at the National Ignition Facility produce energetic protons via D+{sup 3}He fusion that are used to measure the implosion areal density (ρR) at the shock-bang time. By using protons produced several hundred ps before the main compression bang, the implosion is diagnosed in-flight at a convergence ratio of 3–5 just prior to peak velocity. This isolates acceleration-phase asymmetry growth. For many surrogate implosions, proton spectrometers placed at the north pole and equator reveal significant asymmetries with amplitudes routinely ≳10%, which are interpreted as ℓ=2 Legendre modes. With significant expected growth by stagnation, it is likely that these asymmetries would degrade the final implosion performance. X-ray self-emission images at stagnation show asymmetries that are positively correlated with the observed in-flight asymmetries and comparable in magnitude, contradicting growth models; this suggests that the hot-spot shape does not reflect the stagnated shell shape or that significant residual kinetic energy exists at stagnation. More prolate implosions are observed when the laser drive is sustained (“no-coast”), implying a significant time-dependent asymmetry in peak drive.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

3D printer generated thorax phantom with mobile tumor for radiation dosimetry

This article describes the design, construction, and properties of an anthropomorphic thorax phantom with a moving surrogate tumor. This novel phantom permits detection of dose both inside and outside a moving tumor and within the substitute lung tissue material. A 3D printer generated the thorax shell composed of a chest wall, spinal column, and posterior regions of the phantom. Images of a computed tomography scan of the thorax from a patient with lung cancer provided the template for the 3D printing. The plastic phantom is segmented into two materials representing the muscle and bones, and its geometry closely matches a patient. A surrogate spherical plastic tumor controlled by a 3D linear stage simulates a lung tumor’s trajectory during normal breathing. Sawdust emulates the lung tissue in terms of average and distribution in Hounsfield numbers. The sawdust also provides a forgiving medium that permits tumor motion and sandwiching of radiochromic film inside the mobile surrogate plastic tumor for dosimetry. A custom cork casing shields the film and tumor and eliminates film bending during extended scans. The phantom, lung tissue surrogate, and radiochromic film are exposed to a seven field plan based on an ECLIPSE plan for 6 MV photons from a Trilogy machine delivering 230 cGy to the isocenter. The dose collected in a sagittal plane is compared to the calculated plan. Gamma analysis finds 8.8% and 5.5% gamma failure rates for measurements of large amplitude trajectory and static measurements relative to the large amplitude plan, respectively. These particular gamma analysis results were achieved using parameters of 3% dose and 3 mm, for regions receiving doses >150 cGy. The plan assumes a stationary detection grid unlike the moving radiochromic film and tissues. This difference was experimentally observed and motivated calculated dose distributions that incorporated the phase of the tumor periodic motion. These calculations modestly improve agreement between the measured and intended doses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Examining astrophysical gas cloud collapse using an optical depth-scaled, x-ray-irradiated, carbon-foam sphere

When stellar radiation interacts with a molecular cloud, the cloud's fate depends on the strength of the incident radiation and the radiation's mean-free-path within the cloud [F. Bertoldi, Astrophys. J. 346, 735–755 (1989)]. Under the right conditions, the radiation compresses the cloud and a star formation may occur. Where and when the stellar formation occurs in the cloud's collapse are open questions. Direct observation of the complete star–cloud lifecycle is nearly impossible due to the immense timescales and distances over which the interaction occurs. Laboratory astrophysics offers a way to investigate such a system by scaling the important astrophysical parameters to the laboratory. This work describes laboratory experiments to study the radiation-driven implosion of clouds, using x rays from a laser-irradiated, thin, gold foil as a surrogate star and a carbon-foam sphere as a surrogate cloud. An optically thick system, theoretically corresponding to a star-forming regime, was selected by choice of the foam density. Gold foil and sphere motions were imaged by x-ray radiography. Radiographic images show the formation of an interface between rarefied gold and carbon plasmas, a shock moving into the sphere, and a blunting of the initial sphere's shape. Measurements show that the shock moved linearly around 64 μm/ns into the sphere, and the gold–carbon interface formed by 2 ns at the sphere edge remained stationary. The deformation of the sphere was driven by the incident radiation and not by mechanical pressures applied by gold plasma. The blunting of the sphere was likely due to the geometric reduction of flux near the sphere's poles. Higher x-ray flux near the sphere's equator caused high compression and a faster shock, which flattened the sphere. We will discuss the results and implications of our observations.

VanDervort, R. W. [University of Michigan 1 , Ann ↗
DOE OSTI2022

Neural network acceleration of large-scale structure theory calculations

Here, we make use of neural networks to accelerate the calculation of power spectra required for the analysis of galaxy clustering and weak gravitational lensing data. For modern perturbation theory codes, evaluation time for a single cosmology and redshift can take on the order of two seconds. In combination with the comparable time required to compute linear predictions using a Boltzmann solver, these calculations are the bottleneck for many contemporary large-scale structure analyses. Here, we construct neural network-based surrogate models for Lagrangian perturbation theory (LPT) predictions of matter power spectra, real and redshift space galaxy power spectra, and galaxy-matter cross power spectra that attain ~ 0.1% (at one sigma) accuracy over a broad range of scales in a ωCDM parameter space. The neural network surrogates can be evaluated in approximately one millisecond, a factor of 1000 times faster than the full Boltzmann code and LPT computations. In a simulated full-shape redshift space galaxy power spectrum analysis, we demonstrate that the posteriors obtained using our surrogates are accurate compared to those obtained using the full LPT model. We make our surrogate models public at https://github.com/sfschen/EmulateLSS, so that others may take advantage of the speed gains they provide to enable rapid iteration on analysis settings, something that is essential in complex contemporary large-scale structure analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The case for digital twins in metal additive manufacturing

The digital twin (DT) is a relatively new concept that is finding increased acceptance in industry. A DT is generally considered as comprising a physical entity, its virtual replica, and two-way digital data communications in-between. Its primary purpose is to leverage the process intelligence captured within digital models—or usually their faster-solving surrogates—towards generating increased value from the physical entities. The surrogate models are created using machine learning based on data obtained from the field, experiments and digital models, which may be physics-based or statistics-based. Anomaly detection and correction, and diagnostic closed-loop process control are examples of how a process DT can be deployed. In the manufacturing industry, its use can achieve improvements in product quality and process productivity. Metal additive manufacturing (AM) stands to gain tremendously from the use of DTs. This is because the AM process is inherently chaotic, resulting in poor repeatability. However, a DT acting in a supervisory role can inject certainty into the process by actively keeping it within bounds through real-time control commands. Closed-loop feedforward control is achieved by observing the process through sensors that monitor critical parameters and, if there are any deviations from their respective optimal ranges, suitable corrective actions are triggered. The type of corrective action (e.g. a change in laser power or a modification to the scanning speed) and its magnitude are determined by interrogating the surrogate models. Because of their artificial intelligence (AI)-endowed predictive capabilities, which allow them to foresee a future state of the physical twin (e.g. the AM process), DTs proactively take context-sensitive preventative steps, whereas traditional closed-loop feedback control is usually reactive. Apart from assisting a build process in real-time, a DT can help with planning the build of a part by pinpointing the optimum processing window relevant to the desired outcome. Again, the surrogate models are consulted to obtain the required information. In this article, we explain how the application of DTs to the metal AM process can significantly widen its application space by making the process more repeatable (through quality assurance) and cheaper (by getting builds right the first time).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Methods to Account for CAP-88 PC-Omitted Nuclides in Radioactive Air Emissions From DOE Facilities

Routine research and development activities at US Department of Energy facilities can result in the release of radioactive emissions, potentially exposing the public and the environment. Such emissions are subject to certain Clean Air Act regulations, specifically those outlined in 40 CFR Part 61, Subpart H, which are enforced by the US Environmental Protection Agency. Compliance is determined in part with a dose standard. Doses to members of the public from most US Department of Energy facility airborne emissions are modeled using CAP-88 PC software, the latest version of which contains more than 1,200 radionuclides. Radioactive effluents from US Department of Energy facilities may contain radioisotopes that are not available in CAP-88 PC, and those radioisotopes must still be evaluated to determine whether doses to members of the public are below established limits. CAP-88 PC-omitted radionuclides can be accounted for using alternate methods, including the use of surrogate radionuclides. Here, this paper elucidates the process of accounting for CAP-88 PC-omitted radionuclides by providing criteria and rationale for surrogate selection, a compilation of surrogate radionuclides used in the past by US Department of Energy facilities, a discussion of alternate methods used to account for CAP-88 PC-omitted radionuclides and a comparison of methods and impacts on receptor doses. Ultimately, this paper aims to aid in the process of surrogate selection and consequently to simplify and expedite compliance with Clean Air Act regulations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Decay branching ratios of excited Mg 24

Here, the nuclear reactions 12 C( 12 C, α) 20 Ne, 12 C( 12 C, p) 23 Na, and 12 C( 12 C, n) 23 Mg are the primary reactions in carbon burning, which occurs as part of several stellar processes. The Gamow window, which describes the energy range where most of these reactions take place, is typically around 1.5 MeV in the center-of-mass frame. Direct measurements of the cross sections at this energy are difficult due to the large Coulomb barrier present between the carbon nuclei; however, a successful surrogate measurement can provide the branching ratios between these reactions while avoiding the 12 C + 12 C Coulomb barrier. Finally, an experiment was performed using inelastic scattering of 40 MeV α particles on 24 Mg as a possible surrogate for the 12 C + 12 C compound nucleus.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

False-negative rate, limit of detection and recovery efficiency performance of a validated macrofoam-swab sampling method for low surface concentrations of Bacillus anthracis Sterne and Bacillus atrophaeus spores

The performance of a macrofoam-swab sampling method was evaluated using Bacillus anthracis Sterne (BAS) and Bacillus atrophaeus Nakamura (BG) spores applied at nine low target amounts (2-500 spores) to positive-control plates and test coupons (2 in × 2 in) of four surface materials (glass, stainless steel, vinyl tile, and plastic). Test results from cultured samples were used to evaluate the effects of surrogate, surface concentration, and surface material on recovery efficiency (RE), false negative rate (FNR), and limit of detection. For RE, surrogate and surface material had statistically significant effects, but concentration did not. Mean REs were the lowest for vinyl tile (50.8% with BAS and 40.2% with BG) and the highest for glass (92.8% with BAS and 71.4% with BG). FNR values ranged from 0 to 0.833 for BAS and 0 to 0.806 for BG; values increased as concentration decreased in the range tested (0.078 to 19.375 CFU/cm2). Surface material also had a statistically significant effect. A FNR-concentration curve was fit for each combination of surrogate and surface material. For both surrogates, the FNR curves tended to be the lowest for glass and highest for vinyl title. The FNR curves for BG tended to be higher than for BAS at lower concentrations, especially for glass. Results using a modified Rapid Viability-Polymerase Chain Reaction (mRV-PCR) analysis method were also obtained. The mRV-PCR results and comparisons to the culture results will be discussed in a subsequent article.

Bacillus anthracis↗
DOE OSTI2024

A Bayesian Multi-fidelity Neural Network to Predict Nonlinear Frequency Backbone Curves

The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. Here, this paper presents a multi-fidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics Research Challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model's uncertainty. Once trained, the multi-fidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.

42 ENGINEERING↗
DOE OSTI2015

SU-E-J-234: Application of a Breathing Motion Model to ViewRay Cine MR Images

Purpose: A respiratory motion model previously used to generate breathing-gated CT images was used with cine MR images. Accuracy and predictive ability of the in-plane models were evaluated. Methods: Sagittalplane cine MR images of a patient undergoing treatment on a ViewRay MRI/radiotherapy system were acquired before and during treatment. Images were acquired at 4 frames/second with 3.5 × 3.5 mm resolution and a slice thickness of 5 mm. The first cine frame was deformably registered to following frames. Superior/inferior component of the tumor centroid position was used as a breathing surrogate. Deformation vectors and surrogate measurements were used to determine motion model parameters. Model error was evaluated and subsequent treatment cines were predicted from breathing surrogate data. A simulated CT cine was created by generating breathing-gated volumetric images at 0.25 second intervals along the measured breathing trace, selecting a sagittal slice and downsampling to the resolution of the MR cines. A motion model was built using the first half of the simulated cine data. Model accuracy and error in predicting the remaining frames of the cine were evaluated. Results: Mean difference between model predicted and deformably registered lung tissue positions for the 28 second preview MR cine acquired before treatment was 0.81 +/− 0.30 mm. The model was used to predict two minutes of the subsequent treatment cine with a mean accuracy of 1.59 +/− 0.63 mm. Conclusion: Inplane motion models were built using MR cine images and evaluated for accuracy and ability to predict future respiratory motion from breathing surrogate measurements. Examination of long term predictive ability is ongoing. The technique was applied to simulated CT cines for further validation, and the authors are currently investigating use of in-plane models to update pre-existing volumetric motion models used for generation of breathing-gated CT planning images.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-AB-303-06: Does Online Adaptive Radiation Therapy Mean Zero Margin for Intermediate-Risk Prostate Cancer? An Intra-Fractional Seminal Vesicles Motion Analysis

Purpose: To provide benchmark for seminal vesicles (SVs) margin selection to account for intra-fractional motion; and to investigate the effectiveness of two motion surrogates in predicting intra-fractional SV underdosage. Methods: 9 prostate SBRT patients were studied; each has five pairs of pre-treatment and post-treatment cone-beam CTs (CBCTs). Each pair of CBCTs was registered based on fiducial markers in the prostate. To provide “ground truth” for coverage evaluation, all pre-treatment SVs were expanded with isotropic margin of 1,2,3,5 and 8mm, and their overlap with post-treatment SVs were used to quantify intra-fractional coverage. Two commonly used motion surrogates, the center-of-mass (COM) and the border of contour (the most distal points in SI/AP/LR directions) were evaluated using Receiver-Operating Characteristic (ROC) analyses for predicting SV underdosage due to intra-fractional motion. Action threshold of determining underdosage for each surrogate was calculated by selecting the optimal balancing between sensitivity and specificity. For comparison, margin for each surrogate was also calculated based on traditional margin recipe. Results: 90% post-treatment SV coverage can be achieved in 47%, 82%, 91%, 98% and 98% fractions for 1,2,3,5 and 8mm margins. 3mm margin ensured the 90% intra-fractional SV coverage in 90% fractions when prostate was aligned. The ROC analysis indicated the AUC for COM and border were 0.88 and 0.72. The underdosage threshold was 2.9mm for COM and 4.1mm for border. The Van Herk’s margin recipe recommended 0.5, 0 and 1.8mm margin in LR, AP and SI direction based on COM and for border, the corresponding margin was 2.1, 4.5 and 3mm. Conclusion: 3mm isotropic margin is the minimum required to mitigate the intra-fractional SV motion when prostate is aligned. ROC analysis reveals that both COM and border are acceptable predictors for SV underdosage with 2.9mm and 4.1mm action threshold. Traditional margin calculation is less reliable for this application. This work is partially supported a master research grant from Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Accelerated iterative beam angle selection in IMRT

Purpose: Iterative methods for beam angle selection (BAS) for intensity-modulated radiation therapy (IMRT) planning sequentially construct a beneficial ensemble of beam directions. In a naïve implementation, the nth beam is selected by adding beam orientations one-by-one from a discrete set of candidates to an existing ensemble of (n − 1) beams. The best beam orientation is identified in a time consuming process by solving the fluence map optimization (FMO) problem for every candidate beam and selecting the beam that yields the largest improvement to the objective function value. This paper evaluates two alternative methods to accelerate iterative BAS based on surrogates for the FMO objective function value. Methods: We suggest to select candidate beams not based on the FMO objective function value after convergence but (1) based on the objective function value after five FMO iterations of a gradient based algorithm and (2) based on a projected gradient of the FMO problem in the first iteration. The performance of the objective function surrogates is evaluated based on the resulting objective function values and dose statistics in a treatment planning study comprising three intracranial, three pancreas, and three prostate cases. Furthermore, iterative BAS is evaluated for an application in which a small number of noncoplanar beams complement a set of coplanar beam orientations. This scenario is of practical interest as noncoplanar setups may require additional attention of the treatment personnel for every couch rotation. Results: Iterative BAS relying on objective function surrogates yields similar results compared to naïve BAS with regard to the objective function values and dose statistics. At the same time, early stopping of the FMO and using the projected gradient during the first iteration enable reductions in computation time by approximately one to two orders of magnitude. With regard to the clinical delivery of noncoplanar IMRT treatments, we could show that optimized beam ensembles using only a few noncoplanar beam orientations often approach the plan quality of fully noncoplanar ensembles. Conclusions: We conclude that iterative BAS in combination with objective function surrogates can be a viable option to implement automated BAS at clinically acceptable computation times.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Preliminary results on the feasibility of using ultrasound to monitor intrafractional motion during radiation therapy for pancreatic cancer

Purpose: Substantial intrafraction organ motion during radiation therapy (RT) for pancreatic cancer is well recognized as a major limiting factor for accurate delivery of RT. The aim of this work is to determine the feasibility of monitoring the intrafractional motion of the pancreas or surrounding structures using ultrasound for RT delivery. Methods: Transabdominal ultrasound (TAUS) and 4DCT data were acquired on ten pancreatic cancer patients during radiation therapy process in a prospective study. In addition, TAUS and MRI were collected for five healthy volunteers. The portal vein (PV) and the head of the pancreas (HP) along with other structures were contoured on these images. Volume changes, distance between the HP and PV, and motion difference between the HP and PV were measured to examine whether PV can be used as a motion surrogate for HP. TAUS images were acquired and processed using a research version of the Clarity autoscan ultrasound system (CAUS). Motion monitoring was performed with the ultrasound probe mounted on an arm fixed to the couch. Video segments of the monitoring sessions were captured. Results: On TAUS, PV is better visualized than HP. The measured mean volume deviation for all patients for the HP and PV was 1.4 and 0.6 ml, respectively. The distance between the HP and PV was close to a constant with 0.22 mm mean deviation throughout the ten breathing phases. The mean of the absolute motion difference for all patients was 1.7 ± 0.8 mm in LR, 1.5 ± 0.5 mm in AP, and 2.3 ± 0.7 mm in SI, suggesting that the PV is a good surrogate for HP motion estimation. By using this surrogate, the HP motion tracking using TAUS was demonstrated. Conclusions: Large intrafractional organ motion due to respiratory and/or bowel motion is a limiting factor in administering curative radiation doses to pancreatic tumors. The authors investigate the use of real-time ultrasound to track pancreas motion. Due to the poor visibility of the pancreas head on an ultrasound image, the portal vein is identified as a surrogate. The authors have demonstrated the feasibility of tracking HP motion through the localization of the PV using TAUS. This will potentially allow real-time tracking of intrafractional motion to justify small PTV-margins and to account for unusual motions, thus, improving normal tissue sparing.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Tencoder: tensor-product encoder-decoder architecture for predicting solutions of PDEs with variable boundary data

It is widely hoped that artificial intelligence will boost data-driven surrogate models in science and engineering. However, fundamental spatial aspects of AI surrogate models remain under-studied. We investigate the ability of neural-network surrogate models to predict solutions to PDEs under variable boundary values. We do not wish to retrain the model when the boundary values change but to make them inputs to the model and infer the solution of the PDE under those boundary conditions. Such a capability is essential to making AI-based surrogate models practically useful. While simple feedforward networks are used for one-dimensional (1D) Poisson equation, an encoder-decoder architecture with a tensor-product layer is developed for the two-dimensional Poisson equation posed on a rectangular domain. We show that it is indeed possible to infer solutions to PDEs from variable boundary data using neural networks in this relatively simple setting, and point to future directions.

Kashi, Aditya↗