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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A direct numerical simulation of Jet A flame kernel quenching

The safe operation of aeronautical engines requires an understanding of flame ignition, propagation and extinction. In this study, direct numerical simulations are performed using a 29 species reduced chemical mechanism for jet fuel surrogate Jet A to understand the flame quenching process. Here, initially laminar spherical flames of varying sizes and equivalence ratios are subject to an identical periodic domain of decaying and isotropic high intensity turbulence with a turbulent Reynolds number of 2400. All cases become quenched, except for the larger kernel with lower Karlovitz number. An analysis of the flame structure shows broadened preheat zone, flame shortening on the product side, differential species diffusion and partial fuel pyrolysis in the fresh mixture. Two extinction mechanisms are identified arising from flame shortening and high flame stretch. Flame shortening occurs due to turbulence-chemistry interactions that resemble the flame–flame interaction in a laminar counterflow reactant-to-reactant configuration, which contorts and breaks up the ignition kernel. Flame stretch is a local effect that attenuates the heat release rate and causes the flame to retreat towards the product mixtures, similar to what has been observed for reactant-to-product laminar counterflow flames. Chemical explosive mode analysis was also performed to quantify the flame structure and local combustion mode. The diffusion–reaction balance in pinched-off flame islands favors extinction of these smaller structures, while auto-ignition modes are observed within the flame kernel after fresh mixture is engulfed and preheated in the product kernel. Statistics of the density-weighted displacement speed conditional on local combustion mode indicates strong correlation between the local extinction mode and negative displacement speed. The local balance between diffusion and reaction ultimately determines the propensity for local extinction in both laminar and turbulent flames, the extent of which has an impact on global flame propagation.

42 ENGINEERING↗
DOE OSTI2023

New machine learning techniques for simulation-based inference: InferoStatic nets, kernel score estimation, and kernel likelihood ratio estimation

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential \varphi φ . In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

Kong, Kyoungchul↗
DOE OSTI2022

New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

In situ multi-tier auto-ignition detection applied to dual-fuel combustion simulations

Here we use an anomaly detection methodology that is centered on analyzing fourth-order joint moments (co-kurtosis), particularly focusing on its application in auto-ignition of combustion problems with large numbers of species. Unsupervised anomaly detection is challenging to generalize across problem types and domains. A recent technique, centered on analyzing information in the fourth-order joint moment co-kurtosis, has shown promise, especially for high-dimensional scientific data. In this work we present developments to the co-kurtosis based anomaly detection method needed to make it effective and scalable for large-scale distributed scientific data, such as those generated by massively parallel simulations. An in situ co-kurtosis algorithm is employed as the anomaly detection method for identifying ignition kernels in simulations of turbulent combustion. Here, we extend an existing methodology which identifies regions of the domain where anomalies are present, and add another tier of anomaly detection where the individual samples contributing to the anomaly are identified. We apply this algorithm on-the-fly to a variety of turbulent reacting flow problems and compare it to the widely used (but significantly more expensive) chemical explosive mode analysis (CEMA). We demonstrate the ability of the method to detect and identify the onset of low and high temperature ignition which can be used for computational steering, as chemical and combustion anomalies occur intermittently at spatio-temporal locations unknown a priori. Finally, we apply our lightweight in situ algorithm to an exascale high-fidelity simulation with a total of 2.4 Trillion degrees of freedom, performed using an adaptive mesh refinement solver. Furthermore, through a scalability analysis, we show that the relative computational cost of this in-situ anomaly detection algorithm compared to an iteration of the reacting flow solver is negligible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Kokkos Kernels v. 0.9

Linear Algebra Kernels and Graph Kernels are foundational technologies for scientific simulations. These foundational kernels need to run on several computer platforms that are popular for scientific simulations. KokkosKernels provides an implementation of different kernels that is portable to different architectures.

ECP↗
DOE OSTI2015

Reproducing kernel potential energy surfaces in biomolecular simulations: Nitric oxide binding to myoglobin

Multidimensional potential energy surfaces based on reproducing kernel-interpolation are employed to explore the energetics and dynamics of free and bound nitric oxide in myoglobin (Mb). Combining a force field description for the majority of degrees of freedom and the higher-accuracy representation for the NO ligand and the Fe out-of-plane motion allows for a simulation approach akin to a mixed quantum mechanics/molecular mechanics treatment. However, the kernel-representation can be evaluated at conventional force-field speed. With the explicit inclusion of the Fe-out-of-plane (Fe-oop) coordinate, the dynamics and structural equilibrium after photodissociation of the ligand are correctly described compared to experiment. Experimentally, the Fe-oop coordinate plays an important role for the ligand dynamics. This is also found here where the isomerization dynamics between the Fe–ON and Fe–NO state is significantly affected whether or not this co-ordinate is explicitly included. Although the Fe–ON conformation is metastable when considering only the bound {sup 2}A state, it may disappear once the {sup 4}A state is included. This explains the absence of the Fe–ON state in previous experimental investigations of MbNO.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Test Plan for the Reirradiation Heating Test of Fuel Particles from AGR 2 Compact 5-4-2

Post irradiation examination (PIE) and high-temperature heating tests of tristructural isotropic (TRISO)-coated fuel particles and compacts from the second Advanced Gas Reactor (AGR) irradiation experiment, AGR 2, are in progress. This test plan describes the reirradiation and subsequent high-temperature testing of loose fuel particles obtained from AGR 2 Compact 5-4-2. To simulate exposed kernels, four of these particles have been cracked at Oak Ridge National Laboratory (ORNL) to produce localized defects that extend through the TRISO layers (outer-pyrolytic carbon [OPyC], silicon carbide [SiC], and inner-pyrolytic carbon [IPyC]) while keeping these layers in-place and maintaining the shape and integrity of the kernel. To generate short-lived fission products, these particles will be reirradiated in the Neutron Radiography (NRAD) reactor prior to heating testing in the Fuel Accident Condition Simulator (FACS) furnace. FACS testing will be at 1600°C, and fission products released from the reirradiated particles will be measured. The intention of the test is to assess the retention of short-lived fission products I-131 (t1/2 = 8.02 days) and Xe-133 (t1/2 = 5.2 days) in the fuel kernels at accident temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Influence of neutrino-electron scattering and neutrino-pair annihilation on hypermassive neutron star

Here, we investigate the influence of inelastic neutrino microphysics in general-relativistic magnetohydrodynamics simulations of a hypermassive neutron star. In particular, we include species/energy groups coupled neutrino-matter interactions, such as inelastic neutrino-electron scattering and electron-positron annihilation kernels, into simulations up to 50 ms. Neutrino-electron inelastic scattering is known to have effective neutrino-matter energy exchange. We show that, with neutrino-electron inelastic scattering, simulations predict 75% higher disc mass with slightly different mass-averaged compositions, and 18% more ejected mass with similar distributions. The enhancement of the mass of the disc and the ejecta results in stronger baryon pollution, leading to less favorable jet launching environments. Furthermore, neutrino luminosities are about 50%, 40%, and 30% higher for electron neutrino, electron antineutrino, and heavy-lepton neutrinos. In contrast, we do not see any significant impacts due to electron-positron annihilation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Test Plan for the Third Reirradiation Heating Test of Fuel Particles from AGR 2 Compact 5-4-2

Post irradiation examination (PIE) and high-temperature heating tests of tristructural isotropic (TRISO)-coated fuel particles and compacts from the second Advanced Gas Reactor (AGR) irradiation experiment, AGR 2, are in progress. This test plan describes the reirradiation and subsequent high-temperature testing of loose fuel particles obtained from AGR 2 Compact 5-4-2. To simulate exposed kernels, four of these particles have been cracked at Oak Ridge National Laboratory (ORNL) to produce localized defects that extend through the TRISO layers (outer pyrolytic carbon, silicon carbide [SiC], and inner pyrolytic carbon) while keeping these layers in place and maintaining the shape and integrity of the kernel. To generate short-lived fission products, these particles will be reirradiated in the Neutron Radiography Reactor (NRAD) at the Hot Fuel Examination Facility (HFEF) at the Idaho National Laboratory (INL) Materials and Fuels Complex prior to heating testing in the Fuel Accident Condition Simulator (FACS) furnace at HFEF. FACS testing will be at 1200°C, and fission products released from the reirradiated particles will be measured. The intention of the test is to assess the retention of short-lived fission products I-131 (t1/2 = 8.02 days) and Xe-133 (t1/2 = 5.2 days) in the fuel kernels at high temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

SU-E-T-378: Evaluation of An Analytical Model for the Inter-Seed Attenuation Effect in 103-Pd Multi-Seed Implant Brachytherapy

Purpose: Brachytherapy treatment planning systems based on TG-43 protocol calculate the dose in water and neglects the heterogeneity effect of seeds in multi-seed implant brachytherapy. In this research, the accuracy of a novel analytical model that we propose for the inter-seed attenuation effect (ISA) for 103-Pd seed model is evaluated. Methods: In the analytical model, dose perturbation due to the ISA effect for each seed in an LDR multi-seed implant for 103-Pd is calculated by assuming that the seed of interest is active and the other surrounding seeds are inactive. The cumulative dosimetric effect of all seeds is then summed using the superposition principle. The model is based on pre Monte Carlo (MC) simulated 3D kernels of the dose perturbations caused by the ISA effect. The cumulative ISA effect due to multiple surrounding seeds is obtained by a simple multiplication of the individual ISA effect by each seed, the effect of which is determined by the distance from the seed of interest. This novel algorithm is then compared with full MC water-based simulations (FMCW). Results: The results show that the dose perturbation model we propose is in excellent agreement with the FMCW values for a case with three seeds separated by 1 cm. The average difference of the model and the FMCW simulations was less than 8%±2%. Conclusion: Using the proposed novel analytical ISA effect model, one could expedite the corrections due to the ISA dose perturbation effects during permanent seed 103-Pd brachytherapy planning with minimal increase in time since the model is based on multiplications and superposition. This model can be applied, in principle, to any other brachytherapy seeds. Further work is necessary to validate this model on a more complicated geometry as well.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-EF-303-09: Proton-Acoustic Range Verification in Proton Therapy

Purpose: We investigated proton-acoustic signals detection for range verification with current ultrasound instruments in typical clinical scenarios. Using simulations that included a realistic noise model, we determined the theoretical minimum dose required to generate detectable proton-acoustic signals. Methods: An analytical model was used to calculate the dose distributions and local pressure rise (per proton) for beams of different energy (100 and 160 MeV) and spot widths (1, 5, and 10 mm) in a water phantom. The acoustic waves propagating from the Bragg peak were modeled by the general 3D pressure wave equation and convolved with Gaussian kernels to simulate various proton pulse widths (0.1 – 10 ms). A realistic PZT ultrasound transducer (5 cm diameter) was simulated with a Butterworth band-pass filter, and ii) randomly generated noise based on a model of thermal noise in the transducer. The signal-to-noise ratio was calculated, determining the minimum number of protons and dose required per pulse. The maximum spatial resolution was also estimated from the signal spectrum. Results: The calculated noise in the transducer was 12–28 mPa, depending on the transducer center frequency (70–380 kHz). The minimum number of protons were on the order of 0.6–6 million per pulse, leading to 3–110 mGy dose per pulse at the Bragg peak, depending on the spot size. The acoustic signal consisted of lower frequencies for wider pulses, leading to lower noise levels, but also worse spatial resolution. The resolution was 1-mm for a 0.1-µs pulse width, but increased to 5-mm for a 10-µs pulse width. Conclusion: We have established minimum dose detection limits for proton-acoustic range validation. These limits correspond to a best case scenario with a large detector with no losses and only detector thermal noise. Feasible proton-acoustic range detection will require at least 10{sup 7} protons per pulse and pulse widths ≤ 1-µs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Development of a transient signal validation technique via a modified kernel regression model - 365

The demand for robust and resilient performance has led to the use of online-monitoring and signal validation techniques to monitor the process parameters and equipment conditions using several empirical models. One of these models is kernel regression (KR). Although KR is being used and performed excellently when applied to steady state, it has limitation in time-varying data that has several repetition of the same data point of the signals, especially if those signals are used to infer the other signals. In addition, many situations are related to variation and fluctuation during normal states, such as transient - start-up and shutdown mode. However, accurate estimation of the process signal can lead to the proper understanding of the equipment behaviours as well as enhance the online monitoring applications. In the sight of foregoing, this paper proposed a modified KR model, especially for transient signal validation to resolve the setback of conventional KR by improving the measure of similarities in the conventional KR. The performance of the proposed model is verified using both experimental data and the plant simulation data, and its results are compared with that of conventional KR. The results show that the proposed model has a greater robustness in particular situations compared to conventional KR, and it can be used to improve process and equipment monitoring applications. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

A probabilistic graphical model based stochastic input model construction

Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

HPCReact

HPCReact is a library that contains functions to assist in the creation of portable computational kernels to simulate the evolution of chemical processes.

Settgast, RandolphR [Lawrence Livermore National L↗
NASA NTRS2009

Active Learning for Directed Exploration of Complex Systems

Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.

oracles↗
NASA NTRSDate not supplied

An Expanded Role for Python in Expediting System Simulation Development

The Python programming language has traditionally been used as a “scripting” language and is not generally recognized as a language for building system simulations, where C++, Java, and other compiled languages are typically used. While very powerful and gaining favor for a large set of programming tasks, Python is generally regarded as not having the speed to directly do the extensive numeric computations required in large-scale simulation. This paper aims to present an approach, supported by case studies, to re-think this assertion. Directly using Python in this new role has the potential to expedite an agile, spiral cycle of development of design-test-modify for systems simulation.

programming language↗
DOE OSTI2024

Kernel fusion in atomistic spin dynamics simulations on Nvidia GPUs using tensor core

In atomistic spin dynamics simulations, the time cost of constructing the space- and time-displaced pair correlation function in real space increases quadratically as the number of spins N, leading to significant computational effort. The GEMM subroutine can be adopted to accelerate the calculation of the dynamical spin-spin correlation function, but the computational cost of simulating large spin systems (>40000 spins) on CPUs remains expensive. In this work, we perform the simulation on the graphics processing unit (GPU), a hardware solution widely used as an accelerator for scientific computing and deep learning. Here we show that GPUs can accelerate the simulation up to 25-fold compared to multi-core CPUs when using the GEMM subroutine on both. To hide memory latency, we fuse the element-wise operation into the GEMM kernel using CUTLASS that can improve the performance by 26% ~ 33% compared to implementation based on cuBLAS. Furthermore, we perform the on-the-fly calculation in the epilogue of the GEMM subroutine to avoid saving intermediate results on global memory, which makes the large-scale atomistic spin dynamics simulation feasible and affordable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Realistic simulation of reduced-dose CT with noise modeling and sinogram synthesis using DICOM CT images

Purpose: Reducing the patient dose while maintaining the diagnostic image quality during CT exams is the subject of a growing number of studies, in which simulations of reduced-dose CT with patient data have been used as an effective technique when exploring the potential of various dose reduction techniques. Difficulties in accessing raw sinogram data, however, have restricted the use of this technique to a limited number of institutions. Here, we present a novel reduced-dose CT simulation technique which provides realistic low-dose images without the requirement of raw sinogram data. Methods: Two key characteristics of CT systems, the noise equivalent quanta (NEQ) and the algorithmic modulation transfer function (MTF), were measured for various combinations of object attenuation and tube currents by analyzing the noise power spectrum (NPS) of CT images obtained with a set of phantoms. Those measurements were used to develop a comprehensive CT noise model covering the reduced x-ray photon flux, object attenuation, system noise, and bow-tie filter, which was then employed to generate a simulated noise sinogram for the reduced-dose condition with the use of a synthetic sinogram generated from a reference CT image. The simulated noise sinogram was filtered with the algorithmic MTF and back-projected to create a noise CT image, which was then added to the reference CT image, finally providing a simulated reduced-dose CT image. The simulation performance was evaluated in terms of the degree of NPS similarity, the noise magnitude, the bow-tie filter effect, and the streak noise pattern at photon starvation sites with the set of phantom images. Results: The simulation results showed good agreement with actual low-dose CT images in terms of their visual appearance and in a quantitative evaluation test. The magnitude and shape of the NPS curves of the simulated low-dose images agreed well with those of real low-dose images, showing discrepancies of less than +/−3.2% in terms of the noise power at the peak height and +/−1.2% in terms of the spatial frequency at the peak height. The magnitudes of the noise measured for 12 different combinations the phantom size, tube current, and reconstruction kernel for the simulated and real low-dose images were very similar, with differences of 0.1 to 4.7%. Thep value for a statistical testing of the difference in the noise magnitude ranged from 0.99 to 0.11, showing that there was no difference statistically between the noise magnitudes of the real and simulated low-dose images using our method. The strength and pattern of the streak noise in an anthropomorphic phantom was also consistent with expectations. Conclusions: A novel reduced-dose CT simulation technique was developed which uses only CT images while not requiring raw sinogram data. Our method can provide realistic simulation results under reduced-dose conditions both in terms of the noise magnitude and the textual appearance. This technique has the potential to promote clinical research for patient dose reductions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗