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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Automatic Differentiation in MetaPhysicL and Its Applications in MOOSE

Efficient solution via Newton’s method of nonlinear systems of equations requires an accurate representation of the Jacobian, corresponding to the derivatives of the component residual equations with respect to the degrees of freedom. In practice these systems of equations often arise from spatial discretization of partial differential equations used to model physical phenomena. These equations may involve domain motion or material equations that are complex functions of the systems’ degrees of freedom. Computing the Jacobian by hand in these situations is arduous and prone to error. Finite difference approximations of the Jacobian or its action are prone to truncation error, especially in multiphysics settings. Symbolic differentiation packages may be used, but often result in an excessive number of terms in realistic model scenarios. An alternative to symbolic and numerical differentiation is automatic differentiation (AD), which propagates derivatives with every elementary operation of a computer program, corresponding to continual application of the chain rule. Automatic differentiation offers the guarantee of an exact Jacobian at a relatively small overhead cost. In this work, we outline the adoption of AD in the Multiphysics Object Oriented Simulation Environment (MOOSE) via the MetaPhysicL package. We describe the application of MOOSE’s AD capability to several sets of physics that were previously infeasible to model via hand-coded or Jacobian-free simulation techniques, including arbitrary Lagrangian-Eulerian and level-set simulations of laser melt pools, phase-field simulations with free energies provided through neural networks, and metallic nuclear fuel simulations that require inner Newton loop calculation of nonlinear material properties.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Two-dimensional inverse planning and delivery with a preclinical image guided microirradiator

Purpose: Recent advances in preclinical radiotherapy systems have provided the foundation for scaling many of the elements of clinical radiation therapy practice to the dimensions and energy demanded in small animal studies. Such systems support the technical capabilities to accurately deliver highly complex dose distributions, but methods to optimize and deliver such distributions remain in their infancy. This study developed an optimization method based on empirically measured two-dimensional dose kernel measurements to deliver arbitrary planar dose distributions on a recently developed small animal radiotherapy platform.Methods: A two-dimensional dose kernel was measured with repeated radiochromic film measurements for the circular 1 mm diameter fixed collimator of the small animal radiotherapy system at 1 cm depth in a solid water phantom. This kernel was utilized in a sequential quadratic programming optimization framework to determine optimal beam positions and weights to deliver an arbitrary desired dose distribution. The positions and weights were then translated to a set of stage motions to automatically deliver the optimized dose distribution. End-to-end efficacy of the framework was quantified through five repeated deliveries of two dosimetric challenges: (1) a 5 mm radius bullseye distribution, and (2) a “sock” distribution contained within a 9 × 13 mm bounding box incorporating rectangular, semicircular, and exponentially decaying geometric constructs and a rectangular linear dose gradient region. These two challenges were designed to gauge targeting, geometric, and dosimetric fidelity.Results: Optimization of the bullseye and sock distributions required 2.1 and 5.9 min and utilized 50 and 77 individual beams for delivery, respectively. Automated delivery of the resulting optimized distributions, validated using radiochromic film measurements, revealed an average targeting accuracy of 0.32 mm, and a dosimetric delivery error along four line profiles taken through the sock distribution of 3.9%. Mean absolute delivery error across the 0–1 Gy linear dose gradient over 7.5 mm was 0.01 Gy.Conclusions: The work presented here demonstrates the potential for complex dose distributions to be planned and automatically delivered with millimeter scale heterogeneity at submillimeter accuracy. This capability establishes the technical foundation for preclinical validation of biologically guided radiotherapy investigations and development of unique radiobiological experiments.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Role of Solar Water Heating in Multifamily Zero Energy Homes

Solar domestic hot water (SDHW) systems have been installed on buildings for decades, but because of relatively high costs they have not achieved significant market penetration in most of the country. As more buildings move towards zero net energy consumption, however, many designers and developers are looking more closely at SDHW. In multifamily buildings especially, SDHW may be more practical for several reasons: 1) When designing for zero net energy consumption, solar water heating may be part of the lowest cost approach to meet water heating loads; 2) Because of better scale, SDHW systems in multifamily buildings cost significantly less per dwelling than in single-family homes; 3) Many low-load buildings are moving away from fossil fuels entirely. SDHW savings are substantially greater when displacing electric resistance water heating; and 4) In addition to federal tax incentives, some states have substantial financial incentives that dramatically reduce the costs (or increase the benefits) of SDHW systems in multifamily buildings. With support from the U.S. DOE Building America program, the Consortium for Advanced Residential Buildings (CARB) worked with a developer in western Massachusetts to evaluate a SDHW system on a 12-unit apartment building. Olive Street Development completed construction in spring of 2014, and CARB has been monitoring performance of the water heating systems since May 2014.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Radiation biology workforce in the United States

In recent decades, the principal goals of participants in the field of radiation biologists have included defining dose thresholds for cancer and non-cancer endpoints to be used by regulators, clinicians and industry, as well as informing on best practice radiation utilization and protection applications. Importantly, much of this work has required an intimate relationship between “bench” radiation biology scientists and their target audiences (such as physicists, medical practitioners and epidemiologists) in order to ensure that the requisite gaps in knowledge are adequately addressed. However, despite the growing risk for public exposure to higher-than-background levels of radiation, e.g. from long-distance travel, the increasing use of ionizing radiation during medical procedures, the threat from geopolitical instability, and so forth, there has been a dramatic decline in the number of qualified radiation biologists in the U.S. Contributing factors are thought to include the loss of applicable training programs, loss of jobs, and declining opportunities for advancement. This report was undertaken in order to begin addressing this situation since inaction may threaten the viability of radiation biology as a scientific discipline.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Data-Enabled Building Energy Savings (D-E BES)

Building sector energy consumption represents a significant fraction of the overall energy consumption in urban communities. While there has been increasing focus on the development of smart environments to support energy savings in urban commercial buildings, the development of smart environments for the residential sector has been less common. Instead, urban residential energy-saving efforts have focused on improving a building's physical infrastructure, introducing energy-saving appliances, and motivating residents to adopt energy conservation practices. Herein this paper illustrates that creating an affinity between a building resident's thermal preferences and a building apartment's unregulated thermal environment represents an alternative means of generating an energy-efficient environment for multi-family, residential buildings. Two years of 15-min interval summer data, obtained from smart cyber-physical systems installed in 310 apartments across two New York City buildings, is used to develop data-enabled (D-E) models of resident thermal preference and unregulated apartment temperatures. Both of these models use a linear mixed-effects approach. The alignment of optimal resident-apartment pairs is then formulated as an integer-programming problem to explore the building energy saving (BES) potential associated with minimizing the difference between unregulated apartment temperature and the residents' thermal preference based on summer cooling loads. The work shows that the energy saving potential vary with a building's physical characteristics as well as the variation in residents' thermal preferences. The work further demonstrates that both the unregulated temperature and the resident's preferred temperature in a given apartment are not only affected by outside temperature and external relative humidity, but also strongly affected by the apartment's geographic orientation. For the specific buildings considered, up to 28% cooling energy saving is possible with full alignment. The paper provides all building monitoring data as an open source data set.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Terry Turbopump Expanded Operating Band Modeling and Simulation Efforts in Fiscal Year 2020 - Progress Report

The Terry Turbine Expanded Operating Band Project is currently conducting testing at Texas A&M University as part of a revised experimental program meant to supplant previous full-scale testing plans under the headings of Milestone 5 and Milestone 6. In consultation with Sandia National Laboratories technical staff and with modeling and simulation support from the same, the hybrid Milestone 5&6 plan is moving forward with experiments aimed at addressing knowledge gaps regarding scale, working fluid, and turbopump self-regulation. Modeling and simulation efforts at Sandia National Laboratories in FY20 fell under the broad umbrella of Milestone 7 and consisted exclusively of MELCOR-related tasks aimed at: 1) Constructing/improving input models of Texas A&M University experiments, 2) Constructing a generic boiling water reactor input model according to best practices with systems-level Teny turbine capabilities, and 3) Adding code capability in order to leverage experimental data/findings, address bugs, and improve general code robustness Project impacts of the Covid-19 pandemic have fortunately been minimal thus far but are mentioned as necessary when discussing the hybrid Milestone 5&6 progress as well as the corresponding Milestone 7 modeling and simulation progress.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Robotic Assembly of SRF Cavity Pair

Superconducting Radio Frequency (SRF) cavities for particle accelerators require tight tolerances, ultrahigh vacuum, and strict cleanliness during assembly. As in the semiconductor industry, defects such as particles and residues are deleterious to performance, possibly rendering a cavity unfit for use. This problem is addressed primarily through cleanroom assembly during sensitive steps and rigorous chemical processing to prevent and remove such defects. Human workers are often the largest source of contamination, even with proper gowning and practices. The semiconductor industry has long integrated robots in cleanroom operation, but this has not occurred for SRF cavity production; SRF cavities, unlike wafers, are complex shapes, require more hands-on mechanical assembly, and are low-volume production items. At Jefferson Laboratory, a co-operative robot (cobot) has been setup to overcome these problems. Cobots are safe for use alongside human workers and can integrate new tools such as a 3D camera part detection and gripper for item manipulation. Therefore, cobots represent a promising avenue for reducing particulate generated during a variety of assembly tasks. A mockup of a cavity pair and coupler was setup and the cobot programmed to automatically pick up the coupler and place on the mating cavity flange. Particle counting methods were setup to measure human vs cobot assembly particulate generation inside a cavity mockup. Other potential uses will be discussed for further improving SRF cavity assembly steps, where a cobot can replace or assist a human operator, and what potential gains are expected.

Duzik, Adam [Thomas Jefferson National Accelerator↗
DOE OSTI2023

Reactivity Coefficient Measurements to Aid in Reducing Compensating Errors in Plutonium Nuclear Data

Compensating errors between several nuclear data observables in a nuclear data library can adversely impact application simulations. The primary goal of the EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) is to reduce compensating errors between fast (0.1–5 MeV) 239Pu nuclear data for prompt fission neutron spectra (PFNS), average prompt fission neutron multiplicities, and neutron induced fission, capture, elastic, and inelastic cross sections. This work will focus on the design and execution of void reactivity coefficient measurements in the EUCLID experiment, performed on the Planet vertical lift critical assembly machine at the National Criticality Experiments Research Center (NCERC). Two different base configurations were designed and measured, one with high neutron leakage, and one with low neutron leakage. Both were primarily made up of plutonium metal (Zero Power Physics Reactor plates) without interstitial moderators and reflected by half-inch aluminum. Design optimization showed that void reactivity coefficient measurements in three locations per configuration was most impactful to reduce nuclear data uncertainties due to the varying impacts from elastic and inelastic scattering, as well as fission and capture. The locations for measurements were chosen based on preliminary studies which balanced measurement uncertainty and measurement practicality. The measurements were also selected to have sensitivities maximally complementary to previous arrangements. Comparisons across nuclear data libraries highlight the potential impact.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

Application of stochastic optimization to nuclear power plant asset management decisions

We describe the development and application of stochastic optimization models and algorithms to address an issue of critical importance in the strategic allocation of resources; namely, the selection of a portfolio of capital investment projects under the constraints of a limited and uncertain budget. This issue is significant and one that faces decision-makers across all industries. The objective of this strategic decision process is generally self evident - to maximize the value obtained from the portfolio of selected projects (with value usually measured in terms of the portfolio's net present value). However, heretofore, many organizations have developed processes to make these investment decisions using simplistic rule-based rank-ordering schemes. This approach has the significant limitation of not accounting for the (often large) uncertainties in the costs or economic benefits associated with the candidate projects or in the uncertainties in the actual funds available to be expended over the projected period of time. As a result, the simple heuristic approaches that typically are employed in industrial practice generate outcomes that are non-optimal and do not achieve the level of benefits intended. In this paper we describe the results of research performed to utilize stochastic optimization models and algorithms to address this limitation by explicitly incorporating the evaluation of uncertainties in the analysis and decision making process. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Innovative Patient Room Lighting System with Integrated Spectrally Adaptive Control

In December of 2013, the U.S. Department of Energy’s SSL R&D Program released a Funding Opportunity Announcement (FOA), that for the first time, contained opportunities for comprehensive application-specific system development. The FOA included opportunities for two applications, one of which was a Patient Room. Philips Lighting Research North America, submitted a proposal for the Patient Room application, and was selected for the complete project award. The award amount was for $497,127, with a Philips Research co-funding commitment 165,709 dollars. The total project value was 662,836 dollars. This project sought to redefine lighting for the patient room application. The goal was to deliver an innovative LED patient suite (patient room and bathroom) lighting system solution that was 40% more energy-efficient than traditional fluorescent incumbent technologies, and would meet all the visual and non-visual needs of patients, caregivers and visitors, and improve the patient experience. State-of-the-art multichannel LED platforms and control technologies that would provide spectral tuning and become part of an intelligent, connected lighting system drove the solution. The project was scoped into four main task areas that included a) System Concept Creation, b) Identification of the Luminaire Portfolio, c) Development of the Connected Lighting Infrastructure, and d) System Performance Validation. Each of the four main tasks were completed and validated extensively over the course the 2 ½ year project. The system concept was created by first developing a lighting design that demonstrated best practices for patient room lighting – illuminance and uniformity for task performance, reduced glare, and convenient controls, in addition to giving patients control over the lighting in their environment. A framework was defined to deliver circadian support via software behaviors. Through that process luminaires were identified from the Philips portfolio that were adaptable – by their form, dimensions, and optical materials – to mix multicolor LED platforms uniformly and deliver target design lumen levels. The Blue Sky luminaire was selected for the patient bed area to give the illusion of skylight while providing white light on the patient bed. Luminaires used existing 2-channel tunable white LED boards, and newly developed 4-channel LED boards. Red-Orange, Blue, Green, and Blue-shifted Yellow LED chips were selected based on spectral characteristics and their ability to produce high quality white light. 4-channel Power over Ethernet (PoE) drivers were developed and firmware written so they would communicate with both 2- and 4-channel boards. These components formed the backbone of the connected lighting infrastructure. Software, flexible and nuanced in its complexity, was written to set behaviors for myriad lighting scenes in the room throughout the 24 hour day – and all could be overridden by manual controls. This included a dynamic tunable white program, three color changing automatic programs that simulated degrees of sunrise to sunset palettes, and an amber night lighting system that offered visual cues for postural stability to minimize the risk of falls. All programs were carefully designed to provide visual comfort for all occupants, support critical task performance for staff, and to support the patient’s 24hr rhythms. A full scale mockup room was constructed in the Philips Cambridge Lab. The lighting system was installed, tested and functionality demonstrated to ensure smooth operation of system components – luminaires, drivers, PoE switches, wall controls, patient remote, and daylight and occupancy sensors. How did the system perform? It met visual criteria, confirmed by calculations, simulations and measurements in the field. It met non-visual criteria, confirmed by setting circadian stimulus (CS) targets and performing calculations using the calculator developed by the Lighting Research Center. Finally, human factors validation studies were conducted to gain insight from real end users in the healthcare profession; surveys were administered, data analyzed, and audio comments captured. The general consensus was positive, with requests to pilot the system in their hospitals. The importance of the research completed under this grant is that it allowed the exploration and development of a unique lighting system, one that would deliver a blend of visual and non-visual criteria in patient room design for today’s healthcare environment. The research investigated the area of multichannel LED technology, multichannel Power over Ethernet (PoE) drivers and their integration with automatic and manual controls as a system – uncovering and meeting challenges along the way. It married visual needs of patients and staff with support for 24 hour rhythms, placing value on the wellbeing of the patient – while successfully saving energy over incumbent technologies. Indications are that the market is ready and willing to invest – multiple healthcare facilities are in line to pilot this system, recognizing its value beyond energy to patient and staff well-being. Its value to the public can best be expressed by a patient support coordinator who, after spending several hours in the room being immersed in the lighting, analyzing all its features, commented: “This re-writes lighting for healthcare”.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Recent advances in automotive catalysis for NOx emission control by small-pore microporous materials

The ever increasing demand to develop highly fuel efficient engines coincides with the need to minimize air pollution originating from the exhaust gases of internal combustion engines. Dramatically improved fuel efficiency can be achieved at air-to-fuel ratios much higher than stoichiometric. In the presence of oxygen in large excess, however, traditional three-way catalysts are unable to reduce NOx. Among the number of lean-NOx reduction technologies, selective catalytic reduction (SCR) of NOx by NH3 over Cu- and Fe-ion exchanged zeolite catalysts has been extensively studied over the past 30+ years. Despite the significant advances in developing a viable practical zeolite-based catalyst for lean NOx reduction, the insufficient hydrothermal stabilities of the zeolite structures considered cast doubts about their real-world applicability. During the past decade a renewed interest in zeolite-based lean NOx reduction was spurred by the discovery of the very high activity of Cu-SSZ-13 (and the isostructural Cu-SAPO-34) in the NH3 SCR of NOx. These new, small-pore zeolite-based catalysts not only exhibited very high NOx conversion and N2 selectivity, but also exhibited exceptional high hydrothermal stability at high temperatures. In this review we summarize the key discoveries of the past ~5 years that lead to the introduction of these catalysts into practical application. The review first briefly discusses the structure and preparation of the CHA structure-based zeolite catalysts, and then summarizes the key learnings of the rather extensive (but not complete) characterisation work. Then we summarize the key findings of reaction kinetics studies, and provide some mechanistic details emerging from these investigations. At the end of the review we highlight some of the issues that are still need to be addressed in automotive exhaust control catalysis. Funding A.M.B. and I.L.G. would like to thank EPSRC for funding. F.G., C.H.F.P. and J.Sz. gratefully acknowledge financial support from the US Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Program.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2013

Advanced Test Reactor Core Modeling Update Project Annual Report for Fiscal Year 2013

Legacy computational reactor physics software tools and protocols currently used for support of Advanced Test Reactor (ATR) core fuel management and safety assurance, and to some extent, experiment management, are inconsistent with the state of modern nuclear engineering practice, and are difficult, if not impossible, to verify and validate (V&V) according to modern standards. Furthermore, the legacy staff knowledge required for effective application of these tools and protocols from the 1960s and 1970s is rapidly being lost due to staff turnover and retirements. In late 2009, the Idaho National Laboratory (INL) initiated a focused effort, the ATR Core Modeling Update Project, to address this situation through the introduction of modern high-fidelity computational software and protocols. This aggressive computational and experimental campaign will have a broad strategic impact on the operation of the ATR, both in terms of improved computational efficiency and accuracy for support of ongoing DOE programs as well as in terms of national and international recognition of the ATR National Scientific User Facility (NSUF).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Approximating a linear multiplicative objective in watershed management optimization

Implementing management practices in a cost-efficient manner is critical for regional efforts to reduce the amount of pollutants entering the Chesapeake Bay. We study the problem of selecting a subset of practices that minimizes pollutant load—subject to budgetary and environmental constraints—as simulated in a widely used regulatory watershed model. Mimicking the computation of pollutant load in the regulatory model, we formulate this problem as a continuous optimization model with a linear multiplicative objective function and linear constraints. To lay the groundwork for incorporating additional stakeholder requirements in the future, especially those that would require integer variables, we present and study a continuous linear optimization model that approximates the nonlinear model. The linear model, which requires an exponential number of variables, arises naturally as an alternative model for the same underlying physical process. We examine the theoretical behavior of these optimization models and investigate restrictions of the linear model to handle its large number of variables. Through extensive computational tests on real and randomly generated instances, we demonstrate that the linear model and its restrictions provide optimal solutions close to those of the nonlinear model in practice, despite poor approximation properties in the worst case. We conclude that the linear model—together with our approach to handling its large number of variables—provides a viable framework from which to extend the optimization model to better meet the needs of the Chesapeake Bay watershed management stakeholders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-T-51: Bayesian Network Models for Radiotherapy Error Detection

Purpose: To develop a probabilistic model of radiotherapy plans using Bayesian networks that will detect potential errors in radiation delivery. Methods: Semi-structured interviews with medical physicists and other domain experts were employed to generate a set of layered nodes and arcs forming a Bayesian Network (BN) which encapsulates relevant radiotherapy concepts and their associated interdependencies. Concepts in the final network were limited to those whose parameters are represented in the institutional database at a level significant enough to develop mathematical distributions. The concept-relation knowledge base was constructed using the Web Ontology Language (OWL) and translated into Hugin Expert Bayes Network files via the the RHugin package in the R statistical programming language. A subset of de-identified data derived from a Mosaiq relational database representing 1937 unique prescription cases was processed and pre-screened for errors and then used by the Hugin implementation of the Estimation-Maximization (EM) algorithm for machine learning all parameter distributions. Individual networks were generated for each of several commonly treated anatomic regions identified by ICD-9 neoplasm categories including lung, brain, lymphoma, and female breast. Results: The resulting Bayesian networks represent a large part of the probabilistic knowledge inherent in treatment planning. By populating the networks entirely with data captured from a clinical oncology information management system over the course of several years of normal practice, we were able to create accurate probability tables with no additional time spent by experts or clinicians. These probabilistic descriptions of the treatment planning allow one to check if a treatment plan is within the normal scope of practice, given some initial set of clinical evidence and thereby detect for potential outliers to be flagged for further investigation. Conclusion: The networks developed here support the use of probabilistic models into clinical chart checking for improved detection of potential errors in RT plans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Safety Requirement Management System (SRMS) - Improved Documented Safety Analyses Implementation - 18476

Savannah River Remediation (SRR) developed a new software application, Safety Requirement Management System (SRMS), to improve Documented Safety Analysis (DSA) safety requirement implementation in the Savannah River Site's Liquid Waste facilities. Technical Safety Requirement (TSR) Limited Conditions of Operations (LCOs) and related surveillances were previously tracked using four different applications. Due to their differences and their age, the applications introduced a number of operational challenges and vulnerabilities, including outdated platforms, usage demands beyond original design, and different organizational work processes. SRMS was developed to resolve these issues by using a single web-based approach that provides consistency, wider accessibility, simpler maintenance, and a platform for additional functionality. Other SRS facilities are evaluating the application for its applicability as a standard practice across the site. The new SRMS application decreased the administrative overhead for application maintenance, provided the needed TSR change management functionality, and reduced error precursors with the following advantages: - Consistent process/application across all SRR facilities - More efficient way to track and communicate compliance with DSA time-related requirements (i.e., TSR LCOs and surveillances). - Web-based search capability for LCO entries/exits and a record of surveillance performances. - TSR change management and independent reviews through electronic approvals. For example, the application provides the ability to bundle and stage LCO and surveillance changes associated with a TSR revision. The approval process involves multiple roles from the facility to ensure validity. - Formal configuration management program, fully supported by software life cycle documents. SRMS was implemented at Defense Waste Processing Facility (DWPF) in June 2017, with implementation in all Liquid Waste facilities expected by the end of 2017. Next, SRR plans to expand SRMS functionality to include a linking document database (LDD) to support the DSA requirements. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Implementation of CMFD Acceleration Scheme in PROTEUS-MOC

The PROTEUS-MOC code is a three-dimensional (3D) transport code, which is being developed at Argonne National Laboratory under the DOE-NE's Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. It provides a fully consistent 3D discretization of the transport equation by combining the two-dimensional (2D) method of characteristics (MOC) for the planar direction with the discontinuous Galerkin method for the treatment of the axial variable. PROTEUS-MOC uses the unstructured finite element method (FEM) and thus allows the explicit representation of any extruded geometries. It also has a capability of massively parallel computation in energy group, angle, plane, and space using a top-level GMRES linear algebra solver. For parallel computation in space, it uses an unstructured domain decomposition by adapting METIS. The computational burden for direct heterogeneous core calculations based on unstructured geometries is excessive for practical reactor problems, and hence it is essential to implement an efficient acceleration scheme in PROTEUS-MOC. Recently, the coarse mesh finite difference (CMFD) method, which was originally applied to the nodal diffusion methods, has been successfully applied to the MOC transport solvers, resulting in a remarkable reduction in the number of fission source iterations. In addition, a modified CMFD method has been developed to overcome the limitation of the conventional CMFD that is applicable only to neutronics solvers with a conservative discretization. This CMFD method was derived to be consistent with the FEM, which is based on a nonconservative discretization. In this method, the neutron balance in each coarse mesh is enforced by introducing a pseudo absorption cross section (PAXS), and the well-established alternating solution process of CMFD and transport calculations is employed to accelerate source convergence. This modified CMFD acceleration scheme was adopted in PROTEUS-MOC to accelerate the fission source iteration. Pin-cell and assembly based CMFD schemes have been implemented by defining individual pin cells or assemblies as a coarse mesh. For this, an algorithm to map the finite elements to coarse meshes was developed, and the routines were developed to determine the coupling coefficients and PAXS of the CMFD equations from the transport solution. Preliminary performance tests were carried out using the C5G7 benchmark problem. This paper presents the main features of this CMFD acceleration scheme and the preliminary test results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Classical combinatorial optimization scaling for random Ising models on 2D heavy-hex graphs

Motivated by near term quantum computing hardware limitations, combinatorial optimization problems that can be addressed by current quantum algorithms and noisy hardware with little or no overhead are used to probe capabilities of quantum algorithms such as the quantum approximate optimization algorithm. In this study, a specific class of near term quantum computing hardware defined combinatorial optimization problems, Ising models on heavy-hex graphs both with and without geometrically local cubic terms, are examined for their classical computational hardness via empirical computation time scaling quantification. Specifically the time-to-solution (TTS) metric using the classical heuristic simulated annealing is measured for finding optimal variable assignments (ground states), as well as the time required for the optimization software Gurobi to find an optimal variable assignment. Because of the sparsity of these Ising models, the classical algorithms are able to find optimal solutions efficiently even for large instances (i.e. 100 000 spin variables). The Ising models both with and without geometrically local cubic terms exhibit average-case linear-time or weakly quadratic scaling when solved exactly using Gurobi, and the Ising models with no cubic terms show evidence of exponential-time TTS scaling when sampled using simulated annealing. These findings point to the necessity of developing and testing more complex, namely more densely connected, optimization problems in order for quantum computing to ever have a practical advantage over classical computing. Our results are another illustration that different classical algorithms can indeed have exponentially different running times, thus making the identification of the best practical classical technique important in any quantum computing vs. classical computing comparison.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

SU-F-T-340: Direct Editing of Dose Volume Histograms: Algorithms and a Unified Convex Formulation for Treatment Planning with Dose Constraints

Purpose: To develop a procedure for including dose constraints in convex programming-based approaches to treatment planning, and to support dynamic modification of such constraints during planning. Methods: We present a mathematical approach that allows mean dose, maximum dose, minimum dose and dose volume (i.e., percentile) constraints to be appended to any convex formulation of an inverse planning problem. The first three constraint types are convex and readily incorporated. Dose volume constraints are not convex, however, so we introduce a convex restriction that is related to CVaR-based approaches previously proposed in the literature. To compensate for the conservatism of this restriction, we propose a new two-pass algorithm that solves the restricted problem on a first pass and uses this solution to form exact constraints on a second pass. In another variant, we introduce slack variables for each dose constraint to prevent the problem from becoming infeasible when the user specifies an incompatible set of constraints. We implement the proposed methods in Python using the convex programming package cvxpy in conjunction with the open source convex solvers SCS and ECOS. Results: We show, for several cases taken from the clinic, that our proposed method meets specified constraints (often with margin) when they are feasible. Constraints are met exactly when we use the two-pass method, and infeasible constraints are replaced with the nearest feasible constraint when slacks are used. Finally, we introduce ConRad, a Python-embedded free software package for convex radiation therapy planning. ConRad implements the methods described above and offers a simple interface for specifying prescriptions and dose constraints. Conclusion: This work demonstrates the feasibility of using modifiable dose constraints in a convex formulation, making it practical to guide the treatment planning process with interactively specified dose constraints. This work was supported by the Stanford BioX Graduate Fellowship and NIH Grant 5R01CA176553.

60 APPLIED LIFE SCIENCES↗