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DOE OSTI2025

Polarization-agnostic continuous-variable quantum key distribution

Here, we introduce a polarization-agnostic method for Gaussian-modulated coherent-state (GCMS) continuous-variable quantum key distribution (CVQKD). Due to the random and continuous nature of the GCMS protocol, Alice, the transmitter, can encode two distinct quadratures in each of two orthogonal polarization modes, such that Bob, the receiver, measures valid GCMS quadratures in a single polarization mode even when polarization changes occur during transmission. This method does not require polarization correction in the optical domain, does not require monitoring both polarization modes, reduces loss by eliminating optical components, and avoids the noise injected by polarization correction algorithms.

Williams, Brian P. [Oak Ridge National Laboratory ↗

DOE OSTI2024

Low-energy enhancement in the magnetic dipole γ-ray strength functions of heavy nuclei

A low-energy enhancement (LEE), which was observed experimentally in the gamma-ray strength function (γSF) describing the decay of compound nuclei, would have profound effects on r-process nucleosynthesis if it persists in heavy neutron-rich nuclei. The LEE was shown to be a feature of the magnetic dipole (M1) strength function in configuration-interaction shell-model calculations in medium-mass nuclei. However, its existence in heavy open-shell nuclei remains an open question. Here, using a combination of many-body methods, we identify a LEE in the M1 γSFs of heavy samarium nuclei. In particular, we use the static-path plus random-phase approximation (SPA+RPA), which includes static and small-amplitude quantal fluctuations beyond the mean field. Using the SPA+RPA strength as a prior, we apply the maximum-entropy method (MEM) to obtain finite-temperature M1 γSFs from exact imaginary-time response functions calculated with the shell model Monte Carlo (SMMC) method. We find that the slope of the LEE in samarium isotopes is roughly independent of the average initial energy over a wide range below the neutron separation energy. As the neutron number increases, strength transfers to a low-energy excitation, which we interpret as the scissors mode built on top of excited states.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Investigation of countercurrent flow profile and liquid holdup in random packed column with local CFD data

Liquid holdup and mass transfer area are critical parameters used in packed column design and CO 2 capture efficiency prediction. Compared to experimental methods, 3D computational fluid dynamics (CFD) can provide high-resolution data for studying countercurrent flow hydrodynamics in a packed column. In this paper, a framework was established for modeling the liquid–gas countercurrent flow in a random packed column with pall rings. Besides the column-averaged information, the radial pall ring distribution, velocity, and liquid holdup profiles are obtained in the simulations. The radial two-phase velocity and liquid holdup profiles show significant fluctuations caused by the non-uniform pall ring distribution. The entrance effect and wall influence are also observed in the packed column. By using local countercurrent flow information, the packing specific area $a_p$ and liquid velocity $u_L$ range are significantly expanded for liquid holdup hL prediction. This enables us to develop the liquid holdup correlation with one packing size and six CFD runs only. The proposed power law of the liquid holdup correlation $h_L~u_L^{0.44}$ indicates the random packed column falls in a viscous to turbulent transition regime with a Reynolds Number range of [6.7–40.2]. The derived liquid holdup correlation is in good agreement with existing correlations that are developed using the column-averaged experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Fuel performance analysis of fully-resolved TRISO compact

The TRi-structural ISOtropic (TRISO) fuel multilayered coating structure offers multiple barriers to fission product release, enhancing safety and performance. The heterogeneous nature of TRISO fuel compacts, comprising thousands of randomly distributed coated fuel particles embedded in a graphite matrix, creates intricate stress fields and thermal gradients that cannot be accurately modeled using simplified one-dimensional or homogenized approaches. Consequently, three-dimensional modeling enables the prediction of fuel compact dimensional changes, internal pressure buildup, and fission product transport pathways under diverse irradiation and thermal conditions. This capability facilitates detailed analysis of particle-to-particle interactions, matrix cracking mechanisms, and the statistical distribution of coating failures, which directly impact fuel performance and safety margins. This capability is particularly critical for advanced reactors, such as high-temperature gas-cooled reactors and other Generation IV reactor designs where TRISO fuel operates at elevated temperatures and burn-up levels. This work introduces a novel method to generate an optimized packing of TRISO compacts and a complete 3D mesh with random distribution of TRISO particles, which are discretized into each coating component layer.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Stand validation of lidar forest inventory modeling for a managed southern pine forest

We evaluated area-based approaches (ABAs) to light detection and ranging (lidar) predictions of plot- and stand-level forest attributes (tree count, height, basal area, volume, aboveground biomass, broadleaf/conifer, and diameter at breast height — “diameter”). ABA methods included post-stratification (PS), ordinary least squares (OLSs) regression, k nearest neighbors ( kNN), and random forest (RF). This study was conducted on the Savannah River Site in South Carolina, USA. Plot- and stand-level predictions were validated against fixed-radius 0.04 ha (0.1 acre) plots in 49 ≈2.0 ha (5 acre) stands. Our findings demonstrate that lidar can be incorporated operationally into forest inventory systems to provide stand-level inferences for a wide range of forest attributes. Volume predictions for specific diameter classes, however, often fared poorly (root mean squared error (RMSE) > 100%) for the methods we explored, especially for larger (less common) diameter trees. Stand-level results were consistently better than pixel-level results (10–200+ percentage points). kNN and RF performed similarly and better than OLS and PS, but RF was the most robust to model configurations, while kNN has practical advantages such as simultaneous predictions of many attributes.

Forestry↗

DOE OSTI2016

An integrated model-driven method for in-treatment upper airway motion tracking using cine MRI in head and neck radiation therapy

Purpose: For the first time, MRI-guided radiation therapy systems can acquire cine images to dynamically monitor in-treatment internal organ motion. However, the complex head and neck (H&N) structures and low-contrast/resolution of on-board cine MRI images make automatic motion tracking a very challenging task. In this study, the authors proposed an integrated model-driven method to automatically track the in-treatment motion of the H&N upper airway, a complex and highly deformable region wherein internal motion often occurs in an either voluntary or involuntary manner, from cine MRI images for the analysis of H&N motion patterns. Methods: Considering the complex H&N structures and ensuring automatic and robust upper airway motion tracking, the authors firstly built a set of linked statistical shapes (including face, face-jaw, and face-jaw-palate) using principal component analysis from clinically approved contours delineated on a set of training data. The linked statistical shapes integrate explicit landmarks and implicit shape representation. Then, a hierarchical model-fitting algorithm was developed to align the linked shapes on the first image frame of a to-be-tracked cine sequence and to localize the upper airway region. Finally, a multifeature level set contour propagation scheme was performed to identify the upper airway shape change, frame-by-frame, on the entire image sequence. The multifeature fitting energy, including the information of intensity variations, edge saliency, curve geometry, and temporal shape continuity, was minimized to capture the details of moving airway boundaries. Sagittal cine MR image sequences acquired from three H&N cancer patients were utilized to demonstrate the performance of the proposed motion tracking method. Results: The tracking accuracy was validated by comparing the results to the average of two manual delineations in 50 randomly selected cine image frames from each patient. The resulting average dice similarity coefficient (93.28% ± 1.46%) and margin error (0.49 ± 0.12 mm) showed good agreement between the automatic and manual results. The comparison with three other deformable model-based segmentation methods illustrated the superior shape tracking performance of the proposed method. Large interpatient variations of swallowing frequency, swallowing duration, and upper airway cross-sectional area were observed from the testing cine image sequences. Conclusions: The proposed motion tracking method can provide accurate upper airway motion tracking results, and enable automatic and quantitative identification and analysis of in-treatment H&N upper airway motion. By integrating explicit and implicit linked-shape representations within a hierarchical model-fitting process, the proposed tracking method can process complex H&N structures and low-contrast/resolution cine MRI images. Future research will focus on the improvement of method reliability, patient motion pattern analysis for providing more information on patient-specific prediction of structure displacements, and motion effects on dosimetry for better H&N motion management in radiation therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

From stochastic processes to numerical methods: A new scheme for solving reaction subdiffusion fractional partial differential equations

We have introduced a new explicit numerical method, based on a discrete stochastic process, for solving a class of fractional partial differential equations that model reaction subdiffusion. The scheme is derived from the master equations for the evolution of the probability density of a sum of discrete time random walks. We show that the diffusion limit of the master equations recovers the fractional partial differential equation of interest. This limiting procedure guarantees the consistency of the numerical scheme. The positivity of the solution and stability results are simply obtained, provided that the underlying process is well posed. We also show that the method can be applied to standard reaction–diffusion equations. This work highlights the broader applicability of using discrete stochastic processes to provide numerical schemes for partial differential equations, including fractional partial differential equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Improving Solar and Solar+Storage Screening Techniques to Reduce Utility Interconnection Time and Costs (Final Technical Report)

Residential PV installations have increased rapidly over the last decade, and the increased application volume has caused permitting delays and lower overall adoption rates. In this project, we developed and evaluated whether data-driven secondary modeling and screening techniques can help utilities assess customer applications more accurately than traditional screening shortcuts. Secondary topologies are predicted using decision trees and commonly available information, such as service transformer, customer, and street locations. Conductors were predicted using a logistic regression method based on real world object (RWO) types, service transformer ratings, conductor length, and distance to transformer. After developing the combined primary and secondary distribution network model, hosting capacity results were used to train a random forest model to predict the pass/fail likelihood of a customer application. Powerflow based models with predicted secondaries and data-driven methods both increased the screening success rate, relative to common utility heuristics, by as much as 55 percentage points. Data-driven screening techniques were described by one utility as a "right-sized" approach for residential customers given the low-risk of small errors and the high-cost of accurate modeling.

14 SOLAR ENERGY↗

DOE OSTI2024

Quantification of Native Lignin Structural Features with Gel–Phase 2D–HSQC 0 Reveals Lignin Structural Changes During Extraction

Our ability to study and valorize the lignin fraction of biomass is hampered by the fundamental and still unmet challenge of precisely quantifying native lignin's structural features. Here, we developed a rapid elevated-temperature 1 H– 13 C Heteronuclear Single-Quantum Coherence Zero (HSQC 0 ) NMR method that enables this precise quantification of native lignin structural characteristics even with whole plant cell wall (WPCW) NMR spectroscopy, overcoming fast spin relaxation in the gel phase. We also formulated a Gaussian fitting algorithm to perform automatic and reliable spectral integration. By combining HSQC 0 measurements with yield measurements following depolymerisation, we can confirm the combinatorial nature of radical coupling reactions during biosynthesis leading to a random sequential organization of linkages within a largely linear lignin chain. Such analyses illustrate how this analytical method can greatly facilitate the study of native lignin structure, which can then be used for fundamental studies or to understand lignin depolymerization methods like reductive catalytic fractionation or aldehyde-assisted fractionation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Quantification of Native Lignin Structural Features with Gel–Phase 2D–HSQC 0 Reveals Lignin Structural Changes During Extraction

09 BIOMASS FUELS↗

DOE OSTI2023

Fast truncated SVD of sparse and dense matrices on graphics processors

We investigate the solution of low-rank matrix approximation problems using the truncated singular value decomposition (SVD). For this purpose, we develop and optimize graphics processing unit (GPU) implementations for the randomized SVD and a blocked variant of the Lanczos approach. Our work takes advantage of the fact that the two methods are composed of very similar linear algebra building blocks, which can be assembled using numerical kernels from existing high-performance linear algebra libraries. Furthermore, the experiments with several sparse matrices arising in representative real-world applications and synthetic dense test matrices reveal a performance advantage of the block Lanczos algorithm when targeting the same approximation accuracy.

Computer Science↗

DOE OSTI2017

Filters for Improvement of Multiscale Data from Atomistic Simulations

Multiscale computational models strive to produce accurate and efficient numerical simulations of systems involving interactions across multiple spatial and temporal scales that typically differ by several orders of magnitude. Some such models utilize a hybrid continuum-atomistic approach combining continuum approximations with first-principles-based atomistic models to capture multiscale behavior. By following the heterogeneous multiscale method framework for developing multiscale computational models, unknown continuum scale data can be computed from an atomistic model. Concurrently coupling the two models requires performing numerous atomistic simulations which can dominate the computational cost of the method. Furthermore, when the resulting continuum data is noisy due to sampling error, stochasticity in the model, or randomness in the initial conditions, filtering can result in significant accuracy gains in the computed multiscale data without increasing the size or duration of the atomistic simulations. In this work, we demonstrate the effectiveness of spectral filtering for increasing the accuracy of noisy multiscale data obtained from atomistic simulations. Moreover, we present a robust and automatic method for closely approximating the optimum level of filtering in the case of additive white noise. By improving the accuracy of this filtered simulation data, it leads to a dramatic computational savings by allowing for shorter and smaller atomistic simulations to achieve the same desired multiscale simulation precision.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Cluster Variation Method Analysis of Correlations and Entropy in BCC Solid Solutions

Solid solutions occur when multiple chemical species share sites of a common crystal lattice. Although the single site occupation is random, chemical interaction preferences bias the occupation probabilities of neighboring sites, and this bias reduced the entropy of mixing below its ideal value. Sufficiently strong bias leads to symmetry-breaking phase transitions. Here, we apply the cluster variation method to explore solid solutions on body centered cubic lattices in the context of two specific compounds that exhibit opposite ordering trends. Employing density functional theory to model the energetics, we show that CuZn exhibits an order-disorder transition to the CsCl prototype structure, while AlLi instead takes the NaTl prototype structure, and we evaluate their temperature-dependent order parameters, correlations and entropies.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Quantitative Assessment of Breast Cosmetic Outcome After Whole-Breast Irradiation

Purpose: To measure, by quantitative analysis of digital photographs, breast cosmetic outcome within the setting of a randomized trial of conventionally fractionated (CF) and hypofractionated (HF) whole-breast irradiation (WBI), to identify how quantitative cosmesis metrics were associated with patient- and physician-reported cosmesis and whether they differed by treatment arm. Methods and Materials: From 2011 to 2014, 287 women aged ≥40 with ductal carcinoma in situ or early invasive breast cancer were randomized to HF-WBI (42.56 Gy/16 fractions [fx] + 10-12.5 Gy/4-5 fx boost) or CF-WBI (50 Gy/25 fx + 10-14 Gy/5-7 fx). At 1 year after treatment we collected digital photographs, patient-reported cosmesis using the Breast Cancer Treatment and Outcomes Scale, and physician-reported cosmesis using the Radiation Therapy Oncology Group scale. Six quantitative measures of breast symmetry, labeled M1-M6, were calculated from anteroposterior digital photographs. For each measure, values closer to 1 imply greater symmetry, and values closer to 0 imply greater asymmetry. Associations between M1-M6 and patient- and physician-reported cosmesis and treatment arm were evaluated using the Kruskal-Wallis test. Results: Among 245 evaluable patients, patient-reported cosmesis was strongly associated with M1 (vertical symmetry measure) (P<.01). Physician-reported cosmesis was similarly correlated with M1 (P<.01) and also with M2 (vertical symmetry, P=.01) and M4 (horizontal symmetry, P=.03). At 1 year after treatment, HF-WBI resulted in better values of M2 (P=.02) and M3 (P<.01) than CF-WBI; treatment arm was not significantly associated with M1, M4, M5, or M6 (P≥.12). Conclusions: Quantitative assessment of breast photographs reveals similar to improved cosmetic outcome with HF-WBI compared with CF-WBI 1 year after treatment. Assessing cosmetic outcome using these measures could be useful for future comparative effectiveness studies and outcome reporting.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids

We present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In the traditional data-driven GPR method, the form of the Gaussian Process covariance matrix is assumed and its parameters are found from measurements. In the physics-informed GPR, we treat unknown variables (including wind speed and mechanical power) as a random process and compute the covariance matrix from the resulting stochastic power grid equations. We demonstrate that the physics-informed GPR method is significantly more accurate than the standard data-driven one for immediate forecasting of generators' angular velocity and phase angle. We also show that the physics-informed GPR provides accurate predictions of the unobserved wind mechanical power, phase angle, or angular velocity when measurements from only one of these variables are available. The immediate forecast of observed variables and predictions of unobserved variables can be used for effectively managing power grids (electricity market clearing, regulation actions) and early detection of abnormal behavior and faults. The physics-based GPR forecast time horizon depends on the combination of input (wind power, load, etc.) correlation time and characteristic (relaxation) time of the power grid and can be extended to short and medium-range times.

machine learning, power grid, forecasting, Gaussia↗

DOE OSTI2021

Optimal design of acoustic metamaterial cloaks under uncertainty

In this work, we consider the problem of optimal design of an acoustic cloak under uncertainty and develop scalable approximation and optimization methods to solve this problem. The design variable is taken as an infinite-dimensional spatially-varying field that represents the material property, while an additive infinite-dimensional random field represents the variability of the material property or the manufacturing error. Discretization of this optimal design problem results in high-dimensional design variables and uncertain parameters. To solve this problem, we develop a computational approach based on a Taylor approximation and an approximate Newton method for optimization, which is based on a Hessian derived at the mean of the random field. We show our approach is scalable with respect to the dimension of both the design variables and uncertain parameters, in the sense that the necessary number of acoustic wave propagations is essentially independent of these dimensions, for numerical experiments with up to one million design variables and half a million uncertain parameters. Additionally, we demonstrate that, using our computational approach, an optimal design of the acoustic cloak that is robust to material uncertainty is achieved in a tractable manner. The optimal design under uncertainty problem is posed and solved for the classical circular obstacle surrounded by a ring-shaped cloaking region, subjected to both a single-direction single-frequency incident wave and multiple-direction multiple-frequency incident waves. Finally, we apply the method to a deterministic large-scale optimal cloaking problem with complex geometry, to demonstrate that the approximate Newton method’s Hessian computation is viable for large, complex problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

A Phase 3 Trial of 2 Years of Androgen Suppression and Radiation Therapy With or Without Adjuvant Chemotherapy for High-Risk Prostate Cancer: Final Results of Radiation Therapy Oncology Group Phase 3 Randomized Trial NRG Oncology RTOG 9902

Purpose: Long-term (LT) androgen suppression (AS) with radiation therapy (RT) is a standard treatment of high-risk, localized prostate cancer (PCa). Radiation Therapy Oncology Group 9902 was a randomized trial testing the hypothesis that adjuvant combination chemotherapy (CT) with paclitaxel, estramustine, and oral etoposide plus LT AS plus RT would improve overall survival (OS). Methods and Materials: Patients with high-risk PCa (prostate-specific antigen 20-100 ng/mL and Gleason score [GS] ≥7 or clinical stage ≥T2 and GS ≥8) were randomized to RT and AS (AS + RT) alone or with adjuvant CT (AS + RT + CT). CT was given as four 21-day cycles, delivered beginning 28 days after 70.2 Gy of RT. AS was given as luteinizing hormone-releasing hormone for 24 months, beginning 2 months before RT plus an oral antiandrogen for 4 months before and during RT. The study was designed based on a 6% improvement in OS from 79% to 85% at 5 years, with 90% power and a 2-sided alpha of 0.05. Results: A total of 397 patients (380 eligible) were randomized. The patients had high-risk PCa, 68% with GS 8 to 10 and 34% T3 to T4 tumors, and median prostate-specific antigen of 22.6 ng/mL. The median follow-up period was 9.2 years. The trial closed early because of excess thromboembolic toxicity in the CT arm. The 10-year results for all randomized patients revealed no significant difference between the AS + RT and AS + RT + CT arms in OS (65% vs 63%; P=.81), biochemical failure (58% vs 54%; P=.82), local progression (11% vs 7%; P=.09), distant metastases (16% vs 14%; P=.42), or disease-free survival (22% vs 26%; P=.61). Conclusions: NRG Oncology RTOG 9902 showed no significant differences in OS, biochemical failure, local progression, distant metastases, or disease-free survival with the addition of adjuvant CT to LT AS + RT. The trial results provide valuable data regarding the natural history of high-risk PCa treated with LT AS + RT and have implications for the feasibility of clinical trial accrual and tolerability using CT for PCa.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2017

Slate – A new record for crystal preferred orientation

This report describes microstructures and preferred orientation of a Devonian slate from the Belgian Ardennes (La Fortelle quarry). The sample, composed largely of muscovite, chlorite, and quartz, displays extraordinary texture strength of muscovite, exceeding 100 multiples of random distribution (m.r.d.) for (001) pole figures and thus setting a new record for preferred orientation in rocks. Quantitative texture analysis was performed with the Rietveld method on synchrotron X-ray diffraction images. Interestingly, quartz also displays strong shape preferred orientation (SPO) as revealed by X-ray microtomography and SEM imaging but largely random crystallographic preferred orientation (CPO). In conclusion, microstructural evidence suggests that this fine-grained rock formed by recrystallization of shale under stress, with growth of phyllosilicates and dissolution and growth of quartz.

58 GEOSCIENCES↗