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At least 1,351 records · Page 75

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NASA NTRS2003

Unsteady Flowfield in a High-Pressure Turbine Modeled by TURBO

Forced response, or resonant vibrations, in turbomachinery components can cause blades to crack or fail because of the large vibratory blade stresses and subsequent high-cycle fatigue. Forced-response vibrations occur when turbomachinery blades are subjected to periodic excitation at a frequency close to their natural frequency. Rotor blades in a turbine are constantly subjected to periodic excitations when they pass through the spatially nonuniform flowfield created by upstream vanes. Accurate numerical prediction of the unsteady aerodynamics phenomena that cause forced-response vibrations can lead to an improved understanding of the problem and offer potential approaches to reduce or eliminate specific forced-response problems. The objective of the current work was to validate an unsteady aerodynamics code (named TURBO) for the modeling of the unsteady blade row interactions that can cause forced response vibrations. The three-dimensional, unsteady, multi-blade-row, Reynolds-averaged Navier-Stokes turbomachinery code named TURBO was used to model a high-pressure turbine stage for which benchmark data were recently acquired under a NASA contract by researchers at the Ohio State University. The test article was an initial design for a high-pressure turbine stage that experienced forced-response vibrations which were eliminated by increasing the axial gap. The data, acquired in a short duration or shock tunnel test facility, included unsteady blade surface pressures and vibratory strains.

Bakhle, Milind A.↗

DOE OSTI2020

Quantification of silicon carbide grain structure in TRISO fuel by BSE image analysis

The grain structure of the silicon carbide (SiC) layer in tristructural-isotropic (TRISO) fuels has commonly been qualified in fuel specifications based on a qualitative comparison to visual standards to ensure undesirable microstructure are not produced. This approach is inherently dependent on subjective judgement and provides limited data for analysis or comparison. An alternative method to rapidly quantify (i.e., provide a numeric measure of) the grain structure of the SiC layer in TRISO fuels has been developed. This method relies on the identification of silicon carbide grain boundaries based on brightness variation in backscattered electron (BSE) images through the application of automated image processing algorithms. The speed with which the requisite BSE imaging and analysis may be completed enables the practical use of this method on an industrial scale as a part of qualifying TRISO fuel to meet a given specification on silicon carbide grain structure, which is an important parameter for TRISO fuel performance. The method is benchmarked by comparison to prior Electron-Backscattered Diffraction (EBSD) results for TRISO particles from the Advanced Gas Reactor (AGR) program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT

Purpose: In x-ray computed tomography (CT), a violation of the Tuy data sufficiency condition leads to limited-view artifacts. In some applications, it is desirable to use data corresponding to a narrow temporal window to reconstruct images with reduced temporal-average artifacts. However, the need to reduce temporal-average artifacts in practice may result in a violation of the Tuy condition and thus undesirable limited-view artifacts. In this paper, the authors present a new iterative reconstruction method, synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON), to eliminate limited-view artifacts using data acquired within an ultranarrow temporal window that severely violates the Tuy condition. Methods: In time-resolved contrast enhanced CT acquisitions, image contrast dynamically changes during data acquisition. Each image reconstructed from data acquired in a given temporal window represents one time frame and can be denoted as an image vector. Conventionally, each individual time frame is reconstructed independently. In this paper, all image frames are grouped into a spatial–temporal image matrix and are reconstructed together. Rather than the spatial and/or temporal smoothing regularizers commonly used in iterative image reconstruction, the nuclear norm of the spatial–temporal image matrix is used in SMART-RECON to regularize the reconstruction of all image time frames. This regularizer exploits the low-dimensional structure of the spatial–temporal image matrix to mitigate limited-view artifacts when an ultranarrow temporal window is desired in some applications to reduce temporal-average artifacts. Both numerical simulations in two dimensional image slices with known ground truth and in vivo human subject data acquired in a contrast enhanced cone beam CT exam have been used to validate the proposed SMART-RECON algorithm and to demonstrate the initial performance of the algorithm. Reconstruction errors and temporal fidelity of the reconstructed images were quantified using the relative root mean square error (rRMSE) and the universal quality index (UQI) in numerical simulations. The performance of the SMART-RECON algorithm was compared with that of the prior image constrained compressed sensing (PICCS) reconstruction quantitatively in simulations and qualitatively in human subject exam. Results: In numerical simulations, the 240{sup ∘} short scan angular span was divided into four consecutive 60{sup ∘} angular subsectors. SMART-RECON enables four high temporal fidelity images without limited-view artifacts. The average rRMSE is 16% and UQIs are 0.96 and 0.95 for the two local regions of interest, respectively. In contrast, the corresponding average rRMSE and UQIs are 25%, 0.78, and 0.81, respectively, for the PICCS reconstruction. Note that only one filtered backprojection image can be reconstructed from the same data set with an average rRMSE and UQIs are 45%, 0.71, and 0.79, respectively, to benchmark reconstruction accuracies. For in vivo contrast enhanced cone beam CT data acquired from a short scan angular span of 200{sup ∘}, three 66{sup ∘} angular subsectors were used in SMART-RECON. The results demonstrated clear contrast difference in three SMART-RECON reconstructed image volumes without limited-view artifacts. In contrast, for the same angular sectors, PICCS cannot reconstruct images without limited-view artifacts and with clear contrast difference in three reconstructed image volumes. Conclusions: In time-resolved CT, the proposed SMART-RECON method provides a new method to eliminate limited-view artifacts using data acquired in an ultranarrow temporal window, which corresponds to approximately 60{sup ∘} angular subsectors.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1971

Experimental physics characteristics of a heavy-metal-reflected fast-spectrum critical assembly

A zero-power critical assembly was designed, constructed, and operated for the purpose of conducting a series of benchmark experiments dealing with the physics characteristics of a UN-fueled, Li-7 cooled, Mo-reflected, drum-controlled compact fast reactor for use with a space-power electric conversion system. The experimental program consisted basically of measuring the differential neutron spectra and the changes in critical mass that accompanied the stepwise addition of (Li-7)3N, Hf, Ta, and W to a basic core fueled with U metal in a pin-type Ta honeycomb structure. In addition, experimental results were obtained on power distributions, control characteristics, neutron lifetime, and reactivity worths of numerous absorber, structural, and scattering materials.

Heneveld, W. H.↗

NASA NTRS2014

EVA Suit R and D for Performance Optimization

Designing a planetary suit is very complex and often requires difficult trade‐offs between performance, cost, mass, and system complexity. To verify that new suit designs meet requirements, full prototypes must be built and tested with human subjects. However, numerous design iterations will occur before the hardware meets those requirements. Traditional draw‐prototype‐test paradigms for R&D are prohibitively expensive with today's shrinking Government budgets. Personnel at NASA are developing modern simulation techniques which focus on human‐centric designs by creating virtual prototype simulations and fully adjustable physical prototypes of suit hardware. During the R&D design phase, these easily modifiable representations of an EVA suit's hard components will allow designers to think creatively and exhaust design possibilities before they build and test working prototypes with human subjects. It allows scientists to comprehensively benchmark current suit capabilities and limitations for existing suit sizes and sizes that do not exist. This is extremely advantageous and enables comprehensive design down‐selections to be made early in the design process, enables the use of human performance as design criteria, and enables designs to target specific populations

Cowley, Matthew S.↗

DOE OSTI2015

First Industrial Tests of a Drum Monitor Matrix Correction for the Fissile Mass Measurement in Large Volume Historic Metallic Residues with the Differential Die-away Technique

The fissile mass in radioactive waste drums filled with compacted metallic residues (spent fuel hulls and nozzles) produced at AREVA La Hague reprocessing plant is measured by neutron interrogation with the Differential Die-away measurement Technique (DDT. In the next years, old hulls and nozzles mixed with Ion-Exchange Resins will be measured. The ion-exchange resins increase neutron moderation in the matrix, compared to the waste measured in the current process. In this context, the Nuclear Measurement Laboratory (NML) of CEA Cadarache has studied a matrix effect correction method, based on a drum monitor ({sup 3}He proportional counter inside the measurement cavity). A previous study performed with the NML R and D measurement cell PROMETHEE 6 has shown the feasibility of method, and the capability of MCNP simulations to correctly reproduce experimental data and to assess the performances of the proposed correction. A next step of the study has focused on the performance assessment of the method on the industrial station using numerical simulation. A correlation between the prompt calibration coefficient of the {sup 239}Pu signal and the drum monitor signal was established using the MCNPX computer code and a fractional factorial experimental design composed of matrix parameters representative of the variation range of historical waste. Calculations have showed that the method allows the assay of the fissile mass with an uncertainty within a factor of 2, while the matrix effect without correction ranges on 2 decades. In this paper, we present and discuss the first experimental tests on the industrial ACC measurement system. A calculation vs. experiment benchmark has been achieved by performing dedicated calibration measurement with a representative drum and {sup 235}U samples. The preliminary comparison between calculation and experiment shows a satisfactory agreement for the drum monitor. The final objective of this work is to confirm the reliability of the modeling approach and the industrial feasibility of the method, which will be implemented on the industrial station for the measurement of historical wastes. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. Furthermore, to facilitate Bayesian inference with intractable likelihoods, we propose to utilize optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. We demonstrate the effectiveness of our Bayesian optimization algorithms in nonconvex benchmark objective functions, in a machine learning hyperparameter tuning problem, and in a black-box engineering design problem. The effectiveness of our posterior approximation approach is demonstrated in two Bayesian inference problems for parameters of dynamical systems.

Bayesian inference↗

DOE OSTI2024

Three-dimensional continuum point cloud method for large deformation and its verification

This study presents a strong form based meshfree collocation method, which is named Continuum Point Cloud Method, to solve nonlinear field equations derived from classical mechanics for deformed bodies in three-dimensional Euclidean space. The method and its implementation are benchmarked against a nonlinear vector field using manufactured solutions. The analysis of mechanical fields firstly focuses on the study of St. Venant Kirchhoff and compressible neo-Hookean materials. Results for various initial boundary value problems are presented, including benchmark cases involving unidirectional tension and simple shear. Subsequently, the study concludes with an analysis of a displacement-controlled simulation of a compressible neo-Hookean material, specifically a bar that is pulled to 50% of its original length and rotated 90°. The pure tension case yields a 1.5% error in displacement between computed and expected values and a combined tension and torsion loading case provides further insight into material behavior under complex loading conditions. The resulting normal axial and transverse stress-strain curves are also presented. Lastly, the consistency and robustness of the proposed nonlinear numerical schemes are successfully demonstrated through various numerical experiments.

Compressible neo-Hookean materials↗

DOE OSTI2023

DeePMD-kit v2: A software package for deep potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Eigenvector Continuation with Subspace Learning

A common challenge faced in quantum physics is finding the extremal eigenvalues and eigenvectors of a Hamiltonian matrix in a vector space so large that linear algebra operations on general vectors are not possible. There are numerous efficient methods developed for this task, but they generally fail when some control parameter in the Hamiltonian matrix exceeds some threshold value. In this Letter we present a new technique called eigenvector continuation that can extend the reach of these methods. The key insight is that while an eigenvector resides in a linear space with enormous dimensions, the eigenvector trajectory generated by smooth changes of the Hamiltonian matrix is well approximated by a very low-dimensional manifold. We prove this statement using analytic function theory and propose an algorithm to solve for the extremal eigenvectors. We benchmark the method using several examples from quantum many-body theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes.

16S rRNA↗

DOE OSTI2013

Extended Forward Sensitivity Analysis for Uncertainty Quantification

This paper presents the extended forward sensitivity analysis as a method to help uncertainty qualification. By including time step and potentially spatial step as special sensitivity parameters, the forward sensitivity method is extended as one method to quantify numerical errors. Note that by integrating local truncation errors over the whole system through the forward sensitivity analysis process, the generated time step and spatial step sensitivity information reflect global numerical errors. The discretization errors can be systematically compared against uncertainties due to other physical parameters. This extension makes the forward sensitivity method a much more powerful tool to help uncertainty qualification. By knowing the relative sensitivity of time and space steps with other interested physical parameters, the simulation is allowed to run at optimized time and space steps without affecting the confidence of the physical parameter sensitivity results. The time and space steps forward sensitivity analysis method can also replace the traditional time step and grid convergence study with much less computational cost. Two well-defined benchmark problems with manufactured solutions are utilized to demonstrate the method.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2013

Challenges in the development of high-fidelity LWR core neutronics tools

Modern computing has made possible the solution of extremely large-scale reactor simulations, and the literature has numerous examples of high-resolution methods (often Monte Carlo) applied to full-core reactor problems. However, there are currently no examples in the literature of application of such 'High-Fidelity' or 'First Principles' methods to operating Light Water Reactor (LWR) analysis. This paper seeks to remind code developers, project managers, and analysts of the many important aspects of LWR simulation that must be incorporated to produce truly high-fidelity analysis tools. The authors offer a monetary prize to the first person (or group) that successfully solves a new two-cycle operational PWR depletion benchmark problem using high-fidelity tools and demonstrates acceptable accuracy by comparison with measured operational plant data (open source) provided to the reactor analysis community. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1986

Efficient solutions of two-dimensional incompressible steady viscous flows

A simple, efficient, and robust numerical technique is provided for solving two dimensional incompressible steady viscous flows at moderate to high Reynolds numbers. The proposed approach employs an incremental multigrid method and an extrapolation procedure based on minimum residual concepts to accelerate the convergence rate of a robust block-line-Gauss-Seidel solver for the vorticity-stream function Navier-Stokes equations. Results are presented for the driven cavity flow problem using uniform and nonuniform grids and for the flow past a backward facing step in a channel. For this second problem, mesh refinement and Richardson extrapolation are used to obtain useful benchmark solutions in the full range of Reynolds numbers at which steady laminar flow is established.

Morrison, J. H.↗

NASA NTRS1988

Efficient solutions of two-dimensional incompressible steady viscous flows

Morrison, J. H.↗

DOE OSTI2020

Deep learning of free boundary and Stefan problems

Free boundary problems appear naturally in numerous areas of mathematics, science and engineering. These problems present a great computational challenge because they necessitate numerical methods that can yield an accurate approximation of free boundaries and complex dynamic interfaces. In this work, we propose a multi-network model based on physics-informed neural networks to tackle a general class of forward and inverse free boundary problems called Stefan problems. Specifically, we approximate the unknown solution as well as any moving boundaries by two deep neural networks. Besides, we formulate a new type of inverse Stefan problems that aim to reconstruct the solution and free boundaries directly from sparse and noisy measurements. We demonstrate the effectiveness of our approach in a series of benchmarks spanning different types of Stefan problems, and illustrate how the proposed framework can accurately recover solutions of partial differential equations with moving boundaries and dynamic interfaces. All code and data accompanying this manuscript are publicly available at https://github.com/PredictiveIntelligenceLab/DeepStefan.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Influence of non-solvent chemistry on polybenzimidazole hollow fiber membrane preparation

Polybenzimidazole membrane materials have attractive H 2 /CO 2 separation characteristics and high thermo-chemical stability for elevated temperature synthesis (syn) gas separations. The development of PBI membranes with a thin defect-free selective layer and porous support morphology is vital to achieving industrially attractive separation performance. This work is focused on developing a fundamental understanding of the liquid-liquid demixing-based phase inversion process for asymmetric PBI hollow fiber membrane (HFM) formation. The development of industrially attractive HFMs is a challenging process due to the complex interplay between phase equilibria, phase inversion kinetics, and interfacial mass transfer that exist during the liquid-liquid demixing process. Numerous parameters including the dope, bore, and outer coagulant chemistries and compositions significantly influence the HFM morphologies produced. Here, a systematic study is conducted to investigate the phase inversion process parameters including the roles of the non-solvent solubility and diffusivity parameters with respect to the solvent and PBI on the phase inversion process. Furthermore, the influence of dope, bore and coagulant chemistries and compositions on PBI HFM morphology are investigated. As a result, the fabricated PBI HFMs are evaluated for their ideal H 2 and CO 2 permeance and H 2 /CO 2 selectivity at 250 °C to benchmark their separation performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Exploring the hidden valley at MATHUSLA

Hidden valley models naturally predict numerous long-lived particles, the distinctive signatures of which would be compelling evidence for a hidden valley scenario. As these are typically low-energy particles, they pose a challenge in terms of passing energy triggers in traditional searches at the Large Hadron Collider. The MATHUSLA (MAssive Timing Hodoscope for Ultra-Stable neutraL pArticles) experiment is specifically designed for the purpose of detecting long-lived particles. It also has the capability of detecting lower energy particles relative to ATLAS and CMS. In this paper, we assess MATHUSLA's potential for effectively probing hidden valley models. As a benchmark, we assume the hidden valley sector communicates with Standard Model sectors via a heavy vector propagator that couples to Standard Model quarks as well as hidden valley quarks. We model the showering and hadronization in the hidden valley sector using PYTHIA and study the detector acceptance as a function of the hidden valley meson's lifetime. We find that MATHUSLA possesses significant capabilities to explore previously uncharted parameter space within hidden valley models.

Hidden sector↗