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At least 451 records · Page 25

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DOE OSTI2023

Shutdown dose rate analysis with the Shift Monte Carlo radiation transport code and modular verification workflow

Calculation of the shutdown dose rate is crucial for safe fusion reactor operations. The Rigorous-two-step (R2S) method is a method that requires connected neutron transport, activation, and gamma transport. Shift has integrated variance reduction with a deterministic solver Denovo, supports multiple geometry formats, and is scalable. These features make it an attractive transport solver choice for an R2S workflow. An R2S workflow for the Shift Monte Carlo code is developed and compared to the existing Oak Ridge National Laboratory Shutdown Dose Rate Code Suite (ORCS) workflow. Also, a Python framework for integrating two R2S workflows is developed to mix and match each step in the R2S workflow for improved collaboration and verification experience. In this study, results show that the Shift-Denovo R2S workflow and the ORCS workflow calculate the shutdown dose rate of the ITER Shutdown Dose Rate benchmark problem with an average relative error of 2.285%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Facile fabrication of 2D material multilayers and vdW heterostructures with multimodal microscopy and AFM characterization

Reliable transfer processes that enable manipulation of two-dimensional (2D) materials, e.g., transition metal dichalcogenides (TMDCs) and MXenes, from one substrate to another has been a necessity for successful device fabrication. Here, with both mechanical exfoliation and chemical vapor deposition (CVD) widely used, a versatile, clean, deterministic, and yet simple transfer technique is highly needed. To address such need, we developed a transfer method that takes advantage of wettability contrast between interfaces without the use of sacrificial layers or chemical processes. More importantly, a setup was developed to carry out this transfer method with high sample selectivity and fine control of the position and orientation of transferred TMDC crystals, a feature required for fabrication of the devices based on vertical 2D heterostructures. Using both exfoliated and CVD grown materials and subsequent atomic force microscopy (AFM), photoluminescence (PL), confocal Raman and tip enhanced Raman spectroscopy (TERS) characterization, we ascertained the quality of interfaces resulting from the transfer process while preserving excellent 2D material integrity. PL and TERS maps revealed nanometer-scale heterogeneities in the interfaces of fabricated heterostructures, which should enable further perfection of the transfer technique. TERS/TEPL information were employed to identify areas suitable for nanodevice fabrication, making the reported transfer and characterization methods ideal for making high quality assembly of 2D heterostructure more accessible, which should facilitate exploration of vertical 2D heterostructures for applications in electronics, batteries, solar cells, and twistronics.

2D material transfer↗

DOE OSTI2018

A multistage stochastic programming model for a multi-period strategic expansion of biofuel supply chain under evolving uncertainties

Here, we develop a multistage, stochastic mixed-integer model to support biofuel supply chain expansion under evolving uncertainties. By utilizing the block-separable recourse property, we reformulate the multistage program in an equivalent two-stage program and solve it using an enhanced nested decomposition method with maximal non-dominated cuts. We conduct extensive numerical experiments and demonstrate the application of the model and algorithm in a case study based on the South Carolina settings. The value of multistage stochastic programming method is also explored by comparing the model solution with the counterparts of an expected value based deterministic model and a two-stage stochastic model.

09 BIOMASS FUELS↗

NASA NTRS1986

Two-phase flow

An experimental program to characterize the spray from candidate nozzles for icing-cloud simulation is discussed. One canidate nozzle, which is currently used for icing research, has been characterized for flow and drop size. The median-volume diameter (MVD) from this air-assist nozzle is compared with correlations in the literature. The new experimental spray facility is discussed, and the drop-size instruments are discussed in detail. Since there is no absolute standard for drop-size measurements and there are other limitations, such as drop -size range and velocity range, several instruments are used and results are compared. A two-phase model was developed at Pennsylvania State University. The model uses the k-epsilon model of turbulence in the continous phase. Three methods for treating the discrete phase are used: (1) a locally homogeneous flow (LHF) model, (2) a deterministic separated flow (DSF) model, and (3) a stochastic separated flow (SSF) model. In the LHF model both phases have the same velocity and temperature at each point. The DSF model provides interphase transport but ignores the effects of turbulent fluctuations. In the SSF model the drops interact with turbulent eddies whose properties are determined by the k-epsilon turbulence model. The two-phase flow model has been extended to include the effects of evaporation and combustion.

Tacina, Robert R.↗

NASA NTRS2015

Piloted Simulator Evaluation of Maneuvering Envelope Information for Flight Crew Awareness

The implementation and evaluation of an efficient method for estimating safe aircraft maneuvering envelopes are discussed. A Bayesian approach is used to produce a deterministic algorithm for estimating aerodynamic system parameters from existing noisy sensor measurements, which are then used to estimate the trim envelope through efficient high- fidelity model-based computations of attainable equilibrium sets. The safe maneuverability limitations are extended beyond the trim envelope through a robust reachability analysis derived from an optimal control formulation. The trim and maneuvering envelope limits are then conveyed to pilots through three axes on the primary flight display. To evaluate the new display features, commercial airline crews flew multiple challenging approach and landing scenarios in the full motion Advanced Concepts Flight Simulator at NASA Ames Research Center, as part of a larger research initiative to investigate the impact on the energy state awareness of the crew. Results show that the additional display features have the potential to significantly improve situational awareness of the flight crew.

maneuvers↗

DOE OSTI2019

Coupled Deterministic and Monte Carlo Neutronics for Vessel Fluence Calculations

Throughout core life, the reactor vessel is continually bombarded with neutron flux embrittling the vessel and limiting its useful life. As a result, it is imperative to adequately predict how much flux has passed through the vessel during all stages of its life, as a means of ensuring it has adequate ductility to preclude a brittle fracture even under design accident conditions. At present many of these predictions are made either by purely deterministic or purely stochastic means, each offering their own advantages and disadvantages. This paper introduces a coupled method for calculating fluence through the VERA framework, which enables modeling incremental accumulations of fluence while simultaneously accounting for several factors as they evolve through core life. The intention of this method is to simplify the calculation process and provide better resource utilization in obtaining the required accuracy.

Henderson, Shane↗

DOE OSTI2026

Faster Randomized Dynamical Decoupling

We present a randomized dynamical decoupling (DD) protocol that can substantially improve the performance of any given deterministic DD scheme for suppressing coherent noise by using no more than two additional pulses. Our construction is implemented by probabilistically applying sequences of pulses, which, when combined, effectively eliminate the error terms that scale linearly with the system-environment coupling strength. As a result, we show that a randomized protocol using a few pulses can outperform deterministic DD protocols that require considerably more pulses. Furthermore, we prove that the randomized protocol provides an improvement compared to deterministic DD sequences that aim to reduce the error in the system’s Hilbert space, such as Uhrig DD, which had been previously regarded to be optimal. To rigorously evaluate the performance, we introduce new analytical methods suitable for analyzing higher-order DD protocols that might be of independent interest. Here, we also present numerical simulations confirming the significant advantage of using randomized protocols compared to widely used deterministic protocols.

Quantum algorithms & computation↗

DOE OSTI2018

RISK-INFORMED EXTERNAL HAZARDS ANALYSIS BY EEVE TOOLKIT

Introducing risk-informed safety analyses for external events can bring sensible benefits to the nuclear power industry easing the regulatory burden introduced in the aftermath of the Fukushima accident. In the framework of the US-DOE Light-Water Reactor Sustainability program, Risk-Informed Margin Characterization project (LWRS-RISMC), research activities were conducted at INL for developing and applying a toolkit for performing risk-informed external hazards analysis. The “EEVE” (External EVEnts) toolkit, a chain of INL and state-of-the-art codes, was set-up and applied for simulating earthquake-induced transients with internal flooding in a PWR. This was done using methodology set-up for evaluating new data and determining critical areas that would benefit most from advanced analysis methods. The methodology is agnostic and it can be applied to other external hazards than just seismic and flooding (e.g., high winds, intense precipitation, etc.). The first step was the deterministic calculations for evaluating the propagation of a set of earthquakes and for determining the effects on a fire suppression system in an auxiliary building. These deterministic calculations were performed using the LS-DYNA and the OPENSEE structural mechanics codes. Probability failures and event trees of a generic 3-loops Westinghouse PWR were derived from a generic SAPHIRE model. Seismically induced Loss of Offsite Power, Station Blackout, and fire suppression piping failure events were considered. SAPHIRE code allowed a pre-screening of all possible branches of the event tree and a selection of those with the largest Core Damage Frequency increase for further analysis using dynamic PRA methods. INL's EMRALD code performed dynamic PRA calculations of the identified sequences, integrating risk analysis with on-line deterministic safety analysis results (seismically-induced internal flooding calculations by NEUTRINO code and system analysis by RELAP5-3D code). RELAP5-3D Best-Estimate calculations coupled with RAVEN statistical code were used for deriving limit surfaces as function of relevant transient parameters. This data was used by EMRALD for determining the final core status (core damage/core safe). Finally, the coupling of seismic, flooding, system thermal-hydraulic, uncertainty quantification and probabilistic risk analysis for an advanced risk-informed external hazards analysis was demonstrated. Results showed the sensible decrease in the level of conservatism achieved using the developed risk-informed methodology, which allowed also improving the evaluation of the failure modes and failure probabilities of different components.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Hierarchical Composites Patterned via 3D Printed Cellular Fluidics

Additive manufacturing of freeform structures containing multiple materials with deterministic spatial arrangement and interactions remains a challenge for most 3D printing processes, due to complex fabrication tool requirements and limitations in printability of some material classes. Here, in this paper, a versatile method is reported to produce architected composites using the concept of cellular fluidics, in which lattices of unit cells are used as templating scaffolds to guide flowable infill materials in a programmed spatial pattern, upon which they are cured in place to produce a deterministically ordered multimaterial solid. The lattice design relies on the unit cell size, type, strut diameter, surface wetting, and distribution of cellular structures to control liquid flow and retention. Individual unit cells are tuned to achieve reliable infilling and combined into higher-order architectures to achieve multiscale composite materials with disparate mechanical properties, including those considered non-printable. Lattice design considerations for leveraging capillary phenomena and demonstrate several methods of patterning polymers in 3D-printed cellular fluidic structures are presented. The concept of tuning the compressive response of an architected composite using a flexible-elastomer as the lattice and a stiff-epoxy as the infill material is illustrated.

36 MATERIALS SCIENCE↗

NASA NTRS2003

Structural Reliability Using Probability Density Estimation Methods Within NESSUS

A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been proposed by the Society of Automotive Engineers (SAE). The test cases compare different probabilistic methods within NESSUS because it is important that a user can have confidence that estimates of stochastic parameters of a response will be within an acceptable error limit. For each response, the mean, standard deviation, and 0.99 percentile, are repeatedly estimated which allows confidence statements to be made for each parameter estimated, and for each method. Thus, the ability of several stochastic methods to efficiently and accurately estimate density parameters is compared using four valid test cases. While all of the reliability methods used performed quite well, for the new LHS module within NESSUS it was found that it had a lower estimation error than MC when they were used to estimate the mean, standard deviation, and 0.99 percentile of the four different stochastic responses. Also, LHS required a smaller amount of calculations to obtain low error answers with a high amount of confidence than MC. It can therefore be stated that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ and the newest LHS module is a valuable new enhancement of the program.

Chamis, Chrisos C.↗

DOE OSTI2025

Convergence analysis for a nonlocal gradient descent method via directional Gaussian smoothing

We analyze the convergence of a nonlocal gradient descent method for minimizing a class of high-dimensional non-convex functions, where a directional Gaussian smoothing (DGS) is proposed to define the nonlocal gradient (also referred to as the DGS gradient). The method was first proposed in [Zhang et al., Enabling long-range exploration in minimization of multimodal functions, UAI 2021], in which multiple numerical experiments showed that replacing the traditional local gradient with the DGS gradient can help the optimizers escape local minima more easily and significantly improve their performance. However, a rigorous theory for the efficiency of the method on nonconvex landscape is lacking. In this work, we investigate the scenario where the objective function is composed of a convex function, perturbed by deterministic oscillating noise. We provide a convergence theory under which the iterates exponentially converge to a tightened neighborhood of the solution, whose size is characterized by the noise wavelength. Here, we also establish a correlation between the optimal values of the Gaussian smoothing radius and the noise wavelength, thus justifying the advantage of using moderate or large smoothing radii with the method. Furthermore, if the noise level decays to zero when approaching the global minimum, we prove that DGS-based optimization converges to the exact global minimum with linear rates, similarly to standard gradient-based methods in optimizing convex functions. Several numerical experiments are provided to confirm our theory and illustrate the superiority of the approach over those based on the local gradient.

Tran, Hoang [Oak Ridge National Laboratory (ORNL),↗

DOE OSTI2019

Shape optimization under uncertainty for rotor blades of horizontal axis wind turbines

We present a computational framework for the shape optimization of a Horizontal-Axis Wind Turbine (HAWT) rotor blade under uncertainty. Our framework integrates aerodynamic simulations based on the blade element method which utilizes reduced order models of the blade structure and wind load with design sensitivity analysis and nonlinear programming. The wind velocity is modeled as a stochastic process to account for variations in time and space. An additional stochastic process accounts for uncertainties in the structural material properties. The uncertainty propagation is based on a non-intrusive polynomial chaos expansion that allows accurate estimation of stochastic performance metrics such as generated power and structural compliance. Sensitivities of cost and constraint functions with respect to shape parameters namely twist angles are computed with an efficient scheme to enable gradient based optimization. To demonstrate the effect of uncertainty, designs obtained from optimization under uncertainty are compared to those obtained from deterministic optimization.

42 ENGINEERING↗

DOE OSTI2019

Development of a Novel Accelerator for Neutron Transport Solution Using the Galerkin Spectral Element Methods (Final Report)

This report summarizes the results of a three-year research project sponsored by the U.S. Department of Energy (DOE) Nuclear Energy University Program (NEUP) to develop and implement advanced acceleration schemes for the DOE NEAMS neutronics code PROTEUSSN. The project team included the University of Massachusetts Lowell, The Ohio State University, University of Michigan, and Argonne National Laboratory. The PROTEUS code is a high-fidelity capable deterministic neutron transport code based on unstructured finite element meshes, which solves the steady-state and transient neutron transport problem using the 2nd-order discrete ordinate method (SN2ND), the method of characteristics (MOC), and the advanced nodal transport method (NODAL). The DSA scheme has been implemented in PROTEUS to speed up SN transport calculations. The existing DSA scheme employs a consistent finite element formulation on the same fine mesh structure as the SN solution While the use of consistent discretizations makes the DSA method effective, it does not necessarily make it efficient because the numerical solution of a discretized elliptic diffusion problem itself can be costly, particularly when the problem size becomes large. We developed a new discontinuous Galerkin (DG) discretization of the diffusion equation, called DG-DSA, which can effectively and efficiently accelerate the SN transport iterations. As compared with the previous work, the novelty of our method is that the diffusion equation is solved on a coarse-mesh grid using the DG methods, and the DG diffusion discretization incorporates local hp adaptation, i.e., local adaptation of mesh size and/or polynomial degree, based on local total cross section (or optical thickness). Therefore, the resulting number of degrees of freedom (DOF) of the DG discretization is much less than the conventional consistent DSA discretizations, and thus DG-DSA can achieve significant improvement in computational efficiency. We implemented this scheme in PROTEUS-SN. In addition, we developed a new stabilization scheme named linear prolongation CMFD (lpCMFD). A novel feature of this scheme is that the conventional flat flux ratio–based scaling approach is replaced with a linear interpolation of the scalar flux differences at the coarse-mesh cell edges between the neutron transport and CMFD calculations. Fourier convergence analysis and numerical results show that lpCMFD is unconditionally stable and more effective than other CMFD based on acceleration schemes such as pCMFD, odCMFD. We also developed a new nonlinear diffusion acceleration scheme for solving neutron transport equations. This scheme, called LR-NDA, employs a local refinement approach on the framework of CMFD by solving a local boundary value problem of the scalar flux on the coarse-mesh structure to replace the piecewise constant scalar flux obtained by CMFD. The refined flux is then used to update the scalar flux in the neutron transport source iteration. It has been demonstrated that that LR-NDA is much more effective and stable than CMFD for a wide range of optical thicknesses. LR-NDA is a local adaptive method, which means LR-NDA does not necessarily require local refinement for all the coarse-mesh cells on the problem domain, i.e., it can be used only for relatively optically thick regions where the standard CMFD scheme would encounter the convergence problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis

Hypothesis: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. Experiments and Simulations: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. Findings: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. In conclusion, this research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Autonomous Inversion of In Situ Deformation Measurement Data for Injection-Induced Stress Change

Geologic carbon storage (GCS) is likely to play a key part of the global effort to dramatically reduce CO2 emissions and perhaps even reduce atmospheric CO2 concentrations through carbon negative operations. A critical part of effort to commercialize and widely deploy this technology is developing the capability to rapidly assimilate real-time monitoring data into a form that will enable site operators to make decisions to manage the safe and efficient operations. Two of the risks associate with GCS are the risk of inducing fractures in the sealing formations that can create leakage pathways and the risk of inducing earthquakes of sufficient magnitude to cause public concern, property damage, or safety risks. To properly manage these risks the site operator needs to know the initial state of stress, the change in stress induced by injection, and the relationship between operational parameters such as injection rate and pressure and the change in stress. Current methods of estimating the change in stress require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. These characterization methods interrogate the geologic formations using length scales, loading rates or magnitudes that are quite different from those encountered by the actual storage system. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method that uses automatic differentiation and a finite-element based geomechanical model perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. Furthermore, only isotropic linear elasticity is considered in this paper, the approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.

Burghardt, Jeffrey A.↗

NASA NTRS1979

Pricing of NASA Space Shuttle transportation system cargo

A two-part pricing policy is investigated as the most feasible method of pricing the transportation services to be provided by NASA's SSTS. Engineering cost estimates and a deterministic operating cost model generate a data base and develop a procedure for pricing the services of the SSTS. It is expected that the SSTS will have a monopoly on space material processing in areas of crystal growth, glass processing, metallurgical space applications, and biomedical processes using electrophoresis which will require efficient pricing. Pricing problems, the SSTS operating costs based on orbit elevation, number of launch sites, and number of flights, capital costs of the SSTS, research and development costs, allocation of joint transportation costs of the SSTS to a particular space processing activity, and rates for the SSTS are discussed. It is concluded that joint costs for commercial cargoes carried in the SSTS can be most usefully handled by making cost allocations based on proportionate capacity utilization.

Hale, C. W.↗

NASA NTRS1984

Development and evaluation of a generic active helicopter vibration controller

A computerized generic active controller is developed, which alleviates helicopter vibration by closed-loop implementation of higher harmonic control (HHC). In the system, the higher harmonic blade pitch is input through a standard helicopter swashplate; for a four-blade helicopter rotor the 4/rev vibration in the rotorcraft is minimized by inducing cyclic pitch motions at 3, 4, and 5/rev in the rotating system. The controller employs the deterministic, cautious, and dual control approaches and two linear system models (local and global), as well as several methods of limiting control. Based on model testing, performed at moderate to high values of forward velocity and rotor thrust, reductions in the rotor test apparatus vibration from 75 to 95 percent are predicted, with HHC pitch amplitudes of less than one degree. Good performance is also noted for short-duration maneuvers.

Davis, M. W.↗

NASA NTRS1985

Structure of nonevaporating sprays. II - Drop and turbulence properties

This is the second part of a study reporting structure measurements in the dilute portion of axisymmetric nonevaporating sprays. Measurements are compared with predictions of three typical methods for analyzing sprays: (1) locally homogeneous flow (LHF) analysis, where slip between the phases is neglected; (2) deterministic separated flow (DSF) analysis, where slip is considered but effects of drop interactions with turbulence are ignored; and (3) stochastic separated flow (SSF) analysis, where both slip and effects of drop interactions with turbulence are considered. This part of the study reports measurements of mean and fluctuating drop velocities, the variation of Sauter mean diameter, and gas-phase turbulence properties in the dilute portion of the sprays. Best agreement between predictions and measurements was obtained with the SSF analysis. For present measurements in the dilute region (void fraction greater than 99.1 percent), effects of drops on gas-phase turbulence properties (turbulence modulation) were small. However, as the dense spray regions near the injector were approached, the measurements indicated modification of turbulence properties by drop motion.

Solomon, A. S. P.↗