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DOE OSTI2022

Chemoselective, osmium-free, dihydroxylation/oxidative cleavage of heteroaryl isoprenes by a contemporary Malaprade reaction

The methyl ketone is a central synthetic building block for the construction of advanced heteroaryl scaffolds and systems. Reactions, including oxidative cyclization strategies, are often predicated on efficient access to this ubiquitous moiety. In the context of arenes, standard approaches leveraging Markovnikov hydration/oxidation or oxidative cleavage of the C–C π bond often afford satisfactory performance. However, when the substrate contains an electron-deficient heteroaryl core, the traditional Malaprade reaction, and related oxidative-cleavage strategies, frequently result in diminished performance over carbon-based arenes. In this work we present the development and application of an oxidative cleavage reaction of various pyridinyl isoprenes towards accessing the downstream methyl ketone for utilization in advanced cyclizations for the preparation of soft-N-donor complexant scaffolds. Further, this efficient protocol parallels the principles of Green chemistry by exchanging KMnO 4 for the toxic OsO 4 and offers the end-user an efficient, more environmentally friendly option for accessing heteroaryl methyl ketones in one hour of reaction time using potassium permanganate and sodium paraperiodate as a synergistically potent oxidative cleavage system. The wide substrate scope defined access to simple, as well as advanced heteroaryl methyl ketones. Method development, optimization, substrate scope, preliminary mechanistic observations, and a scale up reaction are delineated herein.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Deer Vigilance and Movement Behavior Are Affected by Edge Density and Connectivity

ABSTRACT Animal behavior is an important component of individual, population, and community responses to anthropogenic habitat alteration. For example, antipredator behavior (e.g., vigilance) and animal movement behavior may both be important behavioral responses to the increased density of habitat edges and changes in patch connectivity that characterize highly modified habitats. Importantly, edge density and connectivity might interact, and this interaction is likely to mediate animal behavior: linear, edge‐rich landscape features often provide structural connectivity between patches, but the functional connectedness of patches for animal use could depend upon how edge density modifies animal vigilance and movement. Using remote cameras in large‐scale experimental landscapes that manipulate edge density (high‐ vs. low‐density edges) and patch connectivity (isolated or connected patches), we examined the effects of edge density and connectivity on the antipredator behavior and movement behavior of white‐tailed deer ( Odocoileus virginianus ). Deer vigilance was 1.38 times greater near high‐density edges compared to low‐density edges, regardless of whether patches were connected or isolated. Deer were also more likely to move parallel to connected high‐density edges than all other edge types, suggesting that connectivity promotes movement along high‐density edges. These results suggest that increases in edge density that accompany human fragmentation of existing habitats may give rise to large‐scale changes in the antipredator behavior of deer. These results also suggest that conservation strategies that simultaneously manipulate edge density and connectivity (i.e., habitat corridors) may have multiple effects on different aspects of deer behavior: linear habitat corridors were areas of high vigilance, but also areas where deer movement behavior implied increased movement along the habitat edge.

Bartel, Savannah L. [University of Wisconsin‐Madis↗

NASA NTRS1993

Next generation initiation techniques

Four-dimensional data assimilation strategies can generally be classified as either current or next generation, depending upon whether they are used operationally or not. Current-generation data-assimilation techniques are those that are presently used routinely in operational-forecasting or research applications. They can be classified into the following categories: intermittent assimilation, Newtonian relaxation, and physical initialization. It should be noted that these techniques are the subject of continued research, and their improvement will parallel the development of next generation techniques described by the other speakers. Next generation assimilation techniques are those that are under development but are not yet used operationally. Most of these procedures are derived from control theory or variational methods and primarily represent continuous assimilation approaches, in which the data and model dynamics are 'fitted' to each other in an optimal way. Another 'next generation' category is the initialization of convective-scale models. Intermittent assimilation systems use an objective analysis to combine all observations within a time window that is centered on the analysis time. Continuous first-generation assimilation systems are usually based on the Newtonian-relaxation or 'nudging' techniques. Physical initialization procedures generally involve the use of standard or nonstandard data to force some physical process in the model during an assimilation period. Under the topic of next-generation assimilation techniques, variational approaches are currently being actively developed. Variational approaches seek to minimize a cost or penalty function which measures a model's fit to observations, background fields and other imposed constraints. Alternatively, the Kalman filter technique, which is also under investigation as a data assimilation procedure for numerical weather prediction, can yield acceptable initial conditions for mesoscale models. The third kind of next-generation technique involves strategies to initialize convective scale (non-hydrostatic) models.

Warner, Tom↗

DOE OSTI2020

ELSI — An open infrastructure for electronic structure solvers

Routine applications of electronic structure theory to molecules and periodic systems need to compute the electron density from given Hamiltonian and, in case of non-orthogonal basis sets, overlap matrices. System sizes can range from few to thousands or, in some examples, millions of atoms. Different discretization schemes (basis sets) and different system geometries (finite non-periodic vs. infinite periodic boundary conditions) yield matrices with different structures. The ELectronic Structure Infrastructure (ELSI) project provides an open-source software interface to facilitate the implementation and optimal use of high-performance solver libraries covering cubic scaling eigensolvers, linear scaling density-matrix-based algorithms, and other reduced scaling methods in between. In this paper, we present recent improvements and developments inside ELSI, mainly covering (1) new solvers connected to the interface, (2) matrix layout and communication adapted for parallel calculations of periodic and/or spin-polarized systems, (3) routines for density matrix extrapolation in geometry optimization and molecular dynamics calculations, and (4) general utilities such as parallel matrix I/O and JSON output. The ELSI interface has been integrated into four electronic structure code projects (DFTB+, DGDFT, FHI-aims, SIESTA), allowing us to rigorously benchmark the performance of the solvers on an equal footing. Based on results of a systematic set of large-scale benchmarks performed with Kohn–Sham density-functional theory and density-functional tight-binding theory, we identify factors that strongly affect the efficiency of the solvers, and propose a decision layer that assists with the solver selection process. As a result, we describe a reverse communication interface encoding matrix-free iterative solver strategies that are amenable, e.g., for use with planewave basis sets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Optimizing transmit field inhomogeneity of parallel RF transmit design in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a higher signal-to-noise ratio and, thereby, higher spatial resolution. However, UHF MRI introduces challenges such as transmit radiofrequency (RF) field (B+1) inhomogeneities, leading to uneven flip angles and image intensity anomalies. These issues can significantly degrade imaging quality and its medical applications. This study addresses B+1 field homogeneity through a novel deep learning-based strategy. Traditional methods like Magnitude Least Squares (MLS) optimization have been effective but are time-consuming and dependent on the patient’s presence. Recent machine learning approaches, such as RF Shim Prediction by Iteratively Projected Ridge Regression and deep learning frameworks, have shown promise but face limitations like extensive training times and oversimplified architectures. We propose a two-step deep learning strategy. First, we obtain the desired reference RF shimming weights from multi-channel B+1 fields using random-initialized Adaptive Moment Estimation. Then, we employ Residual Networks (ResNets) to train a model that maps B+1 fields to target RF shimming outputs. Our approach does not rely on pre-calculated reference optimizations for the testing process and efficiently learns residual functions. Comparative studies with traditional MLS optimization demonstrate our method’s advantages in terms of speed and accuracy. The proposed strategy achieves a faster and more efficient RF shimming design, significantly improving imaging quality at UHF. This advancement holds potential for broader applications in medical imaging and diagnostics.

Lu, Zhengyi [Vanderbilt University]↗

NASA NTRS1998

New Parallel Algorithms for Structural Analysis and Design of Aerospace Structures

Subspace and Lanczos iterations have been developed, well documented, and widely accepted as efficient methods for obtaining p-lowest eigen-pair solutions of large-scale, practical engineering problems. The focus of this paper is to incorporate recent developments in vectorized sparse technologies in conjunction with Subspace and Lanczos iterative algorithms for computational enhancements. Numerical performance, in terms of accuracy and efficiency of the proposed sparse strategies for Subspace and Lanczos algorithm, is demonstrated by solving for the lowest frequencies and mode shapes of structural problems on the IBM-R6000/590 and SunSparc 20 workstations.

Nguyen, Duc T.↗

NASA NTRS1996

Some Experiences with Nonoverlapping Schur Complement Parallel Preconditioning for CFD Calculations

In this work we consider solving matrices which arise from the discretization of advection-diffusion field equations on arbitrary triangulated domains using stabilized numerical methods. The talk will discuss several candidate matrix preconditioning algorithms based on the 2 x 2 block factorization induced by an apriori partitioning of the triangulated domain. Application of the 2 x 2 block preconditioner requires the formation and inversion of the Schur complement submatrix. We consider several strategies for simplifying this task: incomplete Schur complement factorizations, drop tolerance element filling, Schur complement probing, and localized Schur complement inversion. Numerical results will be shown comparing performance and efficiency of these approximations. The matrix preconditioner has also been embedded into a Newton algorithm for solving the nonlinear Euler and Navier-Stokes equations governing compressible flow. The remainder of the talk will show numerous examples in CFD to demonstrate the efficiency and robustness of the techniques.

Barth, Timothy J.↗

DOE OSTI2019

A Comparison of Classical and Aggregation-Based Algebraic Multigrid Preconditioners for High-Fidelity Simulation of Wind Turbine Incompressible Flows

This paper presents a comparison of parallel strong scaling performance of classical and aggregation algebraic multigrid (AMG) preconditioners in the context of wind turbine simulations. Fluid motion is governed by the incompressible Navier--Stokes equations, discretized in space with control-volume finite elements and in time with an inexact projection scheme using an implicit integrator. A discontinuous-Galerkin sliding-mesh algorithm captures rotor motion. The momentum equations are solved with iterative Krylov methods, preconditioned by symmetric Gauss--Seidel (SGS) in Trilinos and $\ell_1$ SGS in hypre. The mass-continuity equation is solved with GMRES preconditioned by AMG and can account for the majority of simulation time. Reducing this continuity solve time is crucial. Wind turbine simulations present two unique challenges for AMG preconditioned solvers: the computational meshes include strongly anisotropic elements, and mesh motion requires matrix reinitialization and computation of preconditioners at each time step. Detailed timing profiles are presented and analyzed, and best practices are discussed for both classical and aggregation-based AMG. Results are presented for simulations of two different wind turbines with up to 6 billion grid points on two different computer architectures. For moving mesh problems that require linear-system reinitialization, the well-established strategy of amortizing preconditioner setup costs over a large number of time steps to reduce the solve time is no longer valid. Instead, results show that faster time to solution is achieved by reducing preconditioner setup costs at the expense of linear-system solve costs. Standard smoothed aggregation with Chebyshev relaxation was found to perform poorly when compared with classical AMG in terms of solve time and robustness. However, plain aggregation was comparable to classical AMG.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Polynomial Preconditioned Arnoldi with Stability Control

Polynomial preconditioning can improve the convergence of the Arnoldi method for computing eigenvalues. Such preconditioning significantly reduces the cost of orthogonalization; for difficult problems, it can also reduce the number of matrix-vector products. Parallel computations can particularly benefit from the reduction of communication-intensive operations. Additoinally, the GMRES algorithm provides a simple and effective way of generating the preconditioning polynomial. For some problems high degree polynomials are especially effective, but they can lead to stability problems that must be mitigated. A two-level “double polynomial preconditioning” strategy provides an effective way to generate high-degree preconditioners.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

ELECTRONIC STRUCTURE METHODS AND PROTOCOLS WITH APPLICATION TO DYNAMICS, KINETICS AND THERMOCHEMISTRY

Hydrocarbon combustion involves the reaction dynamics of a tremendous number of species beginning with many-component fuel mixtures and proceeding via a complex system of intermediates to form primary and secondary products. Combustion conditions corresponding to new advanced engines and/or alternative fuels rely increasingly on autoignition and low-temperature-combustion chemistry. In these regimes various transient radical species such as HO2, ROO·, ·QOOH, HCO, NO2, HOCO, and Criegee intermediates play important roles in determining the detailed as well as more general dynamics. A clear understanding and accurate representation of these processes is needed for effective modeling. Given the difficulties associated with making reliable experimental measurements of these systems, computation can play an important role in developing these energy technologies. Accurate calculations have their own challenges since even within the simplest dynamical approximations such as transition state theory, the rates depend exponentially on critical barrier heights and these may be sensitive to the level of quantum chemistry. Moreover, it is well-known that in many cases it is necessary to go beyond statistical theories and consider the dynamics. Quantum tunneling, resonances, radiative transitions, and non-adiabatic effects governed by spin-orbit or derivative coupling can be determining factors in those dynamics. Building upon progress made during a period of prior support through the DOE Early Career Program, this project combines developments in the areas of potential energy surface (PES) fitting and multistate multireference quantum chemistry to allow spectroscopically and dynamically/kinetically accurate investigations of key molecular systems (such as those mentioned above), many of which are radicals with strong multireference character and have the possibility of multiple electronic states contributing to the observed dynamics. An ongoing area of investigation is to develop general strategies for robustly convergent electronic structure theory for global multichannel reactive surfaces including diabatization of energy and other relevant surfaces such as dipole transition. Combining advances in ab initio methods with automated interpolative PES fitting allows the construction of high-quality PESs (incorporating thousands of high-level data) to be done rapidly through parallel processing on high-performance computing (HPC) clusters. In addition, new methods and approaches to electronic structure theory will be developed and tested through applications. This project will explore limitations in traditional multireference calculations (e.g., MRCI) such as those imposed by internal contraction, lack of high-order correlation treatment and poor scaling. Methods such as DMRG-based extended active-space CASSCF and various Quantum Monte Carlo (QMC) methods will be applied (including VMC/DMC and FCIQMC). Insight into the relative significance of different orbital spaces and the robustness of application of these approaches on leadership class computing architectures will be gained. Synergy with other components of this research program such as automated PES fitting and multireference quantum chemistry will be used to address challenges encountered by the standard approaches to computational thermochemistry (those being single-reference quantum chemistry and perturbative treatments of the anharmonic vibrational energy, which break down for some cases of electronic structure or floppy strongly coupled vibrational modes).

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2020

Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Sirius: Simulation and Evaluation of Tactical Conflict Management in an Urban Environment

This presentation provides an overview of the SIRIUS framework, updates to the ICAROUS core modules, and highlights the results of recent simulation and evaluation of tactical deconfliction in an urban environment. The SIRIUS simulation framework provides a fast time method for simulation a large number of aircraft in parallel. Updates to the ICAROUS core modules highlighted in the presentation include the evaluation of the encounter type geometry and changes to the resolution type selection based on the encounter type and following right of way (ROW) rules. The main objective of the simulation was to evaluate the use of strategic and tactical conflict management strategies using a set of simulated UAM scenarios.

Andrew Peters↗

DOE OSTI2023

Towards a Circular Economy for PET Bottles in the US

The United States generates the most plastic waste of any country. Along with that GHG emissions from the global plastic economy are expected to increase to 15% of the global carbon budget by 2050. It is imperative that plastic recycling is made a reality to reduce both plastic pollution in the environment and GHG emissions. A portfolio of end-of-life strategies must be implemented to minimize environmental impacts and retain valuable plastic material, but it is challenging to compare options that generate products with different utility and lifetime. Plastic use reduction, reuse and recycling are thus increasingly important, but making informed policy and research decisions within this space can be challenging given the diverse range of available solutions. The novel analysis framework, Plastic Parallel Pathways Platform (4P) has been equipped with consequential life cycle assessment, techno-economic analysis, and a plastic circularity indicator to estimate the greenhouse gas (GHG) emissions, circularity, and cost of polyethylene terephthalate (PET) down-cycling to lower-quality resin, closed-loop recycling to food-grade PET bottles, up-cycling to fiber-reinforced plastic (FRP), and conversion to non-plastic products (electricity, oil) on a United States economy-wide basis. Integrating system dynamics into this robust plastics model that already incorporates techno-economics, circularity, and environmental impacts will enable identification of key bottlenecks between manufacturers, waste sorters, and reclaimers that currently prevent rapid decarbonization of the plastics economy. System dynamics (SD) explore the evolution of activities and technologies based on changed macro parameters such as plastic demand and supply, market shifts, and cross-sectoral interactions. This project particularly aims to explore the interplay between, waste collection, plastic waste sorting, recycling, and manufacturing, as well as the effect of plastic bale quality and plastic reuse initiatives on the surrounding process stages. This functionality will facilitate combinatory analysis in which a portfolio of end-of-life pathways are assessed simultaneously, with the exact makeup of that portfolio affected by parameters such as technology scales, resource constraints, and waste mitigation efforts. Integrating SD with the 4P framework enables analyzing the effect of increased revenue and reinvestment into improving process efficiencies, sorting and collection quantities. Through that, market effects of increased recycled resin availability can be studied for the plastics systems model for the US. The results will help identify technical or economic bottlenecks that currently limit efforts to decarbonize the U.S. plastics economy.

circular economy↗

DOE OSTI2013

Application of High Performance Computing for Simulating the Unstable Dynamics of Dilute Spark-Ignited Combustion

In collaboration with a major automotive manufacturer, we are using computational simulations of in-cylinder combustion to understand the multi-scale nonlinear physics of the dilute stability limit. Because some key features of dilute combustion can take thousands of successive cycles to develop, the computation time involved in using complex models to simulate these effects has limited industrys ability to exploit simulations in optimizing advanced engines. We describe a novel approach for utilizing parallel computations to reveal long-timescale features of dilute combustion without the need to simulate many successive engine cycles in series. Our approach relies on carefully guided, concurrent, single-cycle simulations to create metamodels that preserve the long-timescale features of interest. We use a simplified combustion model to develop and demonstrate our strategy for adaptively guiding the concurrent simulations to generate metamodels. We next will implement this strategy with higher-fidelity, multi-scale combustion models on large computing facilities to generate more refined metamodels. The refined metamodels can then be used to accelerate engine development because of their efficiency. Similar approaches might also be used for rapidly exploring the dynamics of other complex multi-scale systems that evolve with serial dependency on time.

Finney, Charles↗

DOE OSTI2018

Orthogonal potency analysis of mesenchymal stromal cell function during ex vivo expansion

Highlights: • MSC potencies for their different functions are not identically affected by age. • In vitro age can strongly predict MSC adipogenic and hematopoietic-supporting potencies. • MSC shape can also strongly predict for hematopoietic-supporting potency. Adult bone marrow mesenchymal stromal cells (MSCs) have cross-functional, intrinsic potency that is of therapeutic interest. Their ability to regenerate bone, fat, and cartilage, modulate the immune system, and nurture the growth and function of other bone marrow hematopoietic stem/progenitor cells have all been evaluated by transplant applications of MSCs. These applications require the isolation and expansion scaled cell production. To investigate biophysical properties of MSCs that can be feasibly utilized as predictors of bioactivity during biomanufacturing, we used a low-density seeding model to drive MSCs into proliferative stress and exhibit the hallmark characteristics of in vitro aging. A low-density seeding method was used to generate MSCs from passages 1–7 to simulate serial expansion of these cells to maximize yield from a single donor. MSCs were subjected to three bioactivity assays in parallel to ascertain whether patterns in MSC age, size, and shape were associated with the outcomes of the potency assays. MSC age was found to be a predictor of adipogenesis, while cell and nuclear shape was strongly associated to hematopoietic-supportive potency. Together, these data evaluate morphological changes associated with cell potency and highlight new strategies for purification or alternatives to assessing MSC quality.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

The need for carbon-emissions-driven climate projections in CMIP7

Abstract. Previous phases of the Coupled Model Intercomparison Project (CMIP) have primarily focused on simulations driven by atmospheric concentrations of greenhouse gases (GHGs), for both idealized model experiments and climate projections of different emissions scenarios. We argue that although this approach was practical to allow parallel development of Earth system model simulations and detailed socioeconomic futures, carbon cycle uncertainty as represented by diverse, process-resolving Earth system models (ESMs) is not manifested in the scenario outcomes, thus omitting a dominant source of uncertainty in meeting the Paris Agreement. Mitigation policy is defined in terms of human activity (including emissions), with strategies varying in their timing of net-zero emissions, the balance of mitigation effort between short-lived and long-lived climate forcers, their reliance on land use strategy, and the extent and timing of carbon removals. To explore the response to these drivers, ESMs need to explicitly represent complete cycles of major GHGs, including natural processes and anthropogenic influences. Carbon removal and sequestration strategies, which rely on proposed human management of natural systems, are currently calculated in integrated assessment models (IAMs) during scenario development with only the net carbon emissions passed to the ESM. However, proper accounting of the coupled system impacts of and feedback on such interventions requires explicit process representation in ESMs to build self-consistent physical representations of their potential effectiveness and risks under climate change. We propose that CMIP7 efforts prioritize simulations driven by CO2 emissions from fossil fuel use and projected deployment of carbon dioxide removal technologies, as well as land use and management, using the process resolution allowed by state-of-the-art ESMs to resolve carbon–climate feedbacks. Post-CMIP7 ambitions should aim to incorporate modeling of non-CO2 GHGs (in particular, sources and sinks of methane and nitrous oxide) and process-based representation of carbon removal options. These developments will allow three primary benefits: (1) resources to be allocated to policy-relevant climate projections and better real-time information related to the detectability and verification of emissions reductions and their relationship to expected near-term climate impacts, (2) scenario modeling of the range of possible future climate states including Earth system processes and feedbacks that are increasingly well-represented in ESMs, and (3) optimal utilization of the strengths of ESMs in the wider context of climate modeling infrastructure (which includes simple climate models, machine learning approaches and kilometer-scale climate models).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2006

The Use of Dynamic Visual Acuity as a Functional Test of Gaze Stabilization Following Space Flight

After prolonged exposure to a given gravitational environment the transition to another is accompanied by adaptations in the sensorimotor subsystems, including the vestibular system. Variation in the adaptation time course of these subsystems, and the functional redundancies that exist between them make it difficult to accurately assess the functional capacity and physical limitations of astro/cosmonauts using tests on individual subsystems. While isolated tests of subsystem performance may be the only means to address where interventions are required, direct measures of performance may be more suitable for assessing the operational consequences of incomplete adaptation to changes in the gravitational environment. A test of dynamic visual acuity (DVA) is currently being used in the JSC Neurosciences Laboratory as part of a series of measures to assess the efficacy of a countermeasure to mitigate postflight locomotor dysfunction. In the current protocol, subjects visual acuity is determined using Landolt ring optotypes presented sequentially on a computer display. Visual acuity assessments are made both while standing and while walking at 1.8 m/s on a motorized treadmill. The use of a psychophysical threshold detection algorithm reduces the required number of optotype presentations and the results can be presented immediately after the test. The difference between the walking and standing acuity measures provides a metric of the change in the subject s ability to maintain gaze fixation on the visual target while walking. This functional consequence is observable regardless of the underlying subsystem most responsible for the change. Data from 15 cosmo/astronauts have been collected following long-duration (approx. 6 months) stays in space using a visual target viewing distance of 4.0 meters. An investigation of the group mean shows a change in DVA soon after the flight that asymptotes back to baseline approximately one week following their return to earth. The performance of some subjects nicely parallels the stereotypical recovery curve observed in the group mean data. Others show dramatic changes in DVA from one test day to another. These changes may be indicative of a re-adaptation process that is not characterized by a steady improvement with the passage of time, but is instead a dynamic search for appropriate coordinative strategy to achieve the desired gaze stabilization goal. Ground-based data have been collected in our lab using DVA with one of the goals being to improve the DVA test itself. In one of these studies, the DVA test was repeated using a visual target viewing distance of 0.5 meters. While walking, the relative contributions of the otoliths and semi-circular canals that are required to stabilize gaze are affected by visual target viewing distance. It may be possible to exploit this using the current treadmill DVA test to differentially assess changes in these vestibular subsystems. The postflight DVA evaluations currently used have been augmented to include the near target version of the test. Preliminary results from these assessments, as well as the results from the ground-based tests will also be reported. DVA provides a direct measure of a subject's ability to see clearly in the presence of self-motion. The use of the current tests for providing a functionally relevant metric is evident. However, it is possible to expand the scope of DVA testing to include scenarios other than walking. A facility for measuring DVA in the presence of passive movements is being created. Using a mechanized platform to provide the perturbation, it should be possible to simulate aircraft and automobile vibration profiles. Used in conjunction with the far and near visual displays this facility should be able to assess a subject s ability to clearly see distant objects as well as those that appear on the dashboard or instrument control panel during functionally relevant situations.

Peters, B. T.↗

DOE OSTI2025

A strategy for automated core design to increase economic viability and minimize fuel fragmentation, relocation, and dispersal susceptibility in high-burnup cores

The nuclear industry aims to increase the cycle length of pressurized water reactors from 18 to 24 months to increase power plant capacity factors and economic viability. These cycle length extensions will inherently require fuel rods to exceed the current peak rod average burnup limit of 62 GWd/MTU. A chief concern of operating beyond the current burnup limit is the fuel fragmentation, relocation, and dispersal (FFRD) phenomenon in which pulverized fuel fragments can axially relocate and escape through a burst in the cladding formed during a loss-of-coolant accident. In this work, we demonstrate an approach for automating core design employing an optimization tool based on a penalty-free, parallel simulated annealing algorithm to produce pressurized water reactor core designs with two different optimization objectives. The two objectives were to produce core designs with (1) mitigated FFRD susceptibility while achieving 24-month cycle lengths (2) maximum cycle length with no regard for the likelihood of FFRD. Batch size was considered in tandem with both cases to maximize economic viability. The PARCS nodal model was the primary reactor physics tool used in the optimizations and used nuclear cross sections calculated with 2D Polaris lattice physics models. Reactor performance and safety characteristics of the optimized cores were verified using high-fidelity Virtual Environment for Reactor Applications models. The core designs produced by the optimization tool are compared with each other and to a high-burnup core design produced and analyzed in previous works to highlight the fuel management strategies that may enhance high-burnup reactor safety and economic viability. The optimized cores satisfied their respective objective functions, producing a maximum cycle length of 720 effective full-power days in one core design and one that may reduce FFRD susceptibility by up to 50% based on the first-order approximation to FFRD risk formulated in this work. The optimized cores met most constraints but exceeded the hot channel factor limit, especially in FFRD cases where fresh fuel carried more power. Furthermore, this highlights the need for future lattice-level optimizations and broader assembly options.

Cycle length↗