Publications

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We present a comprehensive study of transport coefficients including DC electrical conductivity and related optical properties, electrical contribution to the thermal conductivity, and the shear viscosity via ab initio molecular dynamics and density functional theory calculations on the “priority 1” cases from the “Second Charged-Particle Transport Coefficient Workshop” [Stanek et al., Phys. Plasmas (to be published 2024)]. The purpose of this work is to carefully document the entire workflow used to generate our reported transport coefficients, up to and including our definitions of finite size and statistical convergence, extrapolation techniques, and choice of thermodynamic ensembles. In pursuit of accurate optical properties, we also present a novel, simple, and highly accurate algorithm for evaluating the Kramers-Kronig relations. These heuristics are often not discussed in the literature, and it is hoped that this work will facilitate the reproducibility of our data.

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Sandia National Laboratories (SNL) requested a measure of possible human error for each state verification method for a safety mechanism performed at partnering production agencies. A team of three human factors individuals were tasked with conducting observations during site visits of both production agencies in order to complete a Human Reliability Analysis (HRA). A HRA will be used because it provides both qualitative and quantitative reports of human error. This report is the first phase of that effort, which will describe the methods which occur at one of the production agencies.

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This article describes the implementation of a new numerical model of the power take-off system installed in the Monterey Bay Aquarium Research Institute wave energy converter, a device developed to provide power to various oceanic research missions. The simultaneous presence of hydraulic, pneumatic, and electrical subsystems in the power take-off system represents a significant challenge in forging an accurate model able to replicate the main dynamic characteristics of the system. The validation of the new numerical model is addressed by comparing simulations with the measurements obtained during a series of bench tests. Data from the bench tests show good agreement with the numerical model. The validated model provides deeper insights into the complex nonlinear dynamics of the power take-off system and will support further performance improvements in the future.

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The overall goal of this investigation was to develop an innovative high-temperature chloride molten salt flow control valve capable of operation up to 750 °C. The team developed an integrated active and passive thermal management system to ensure robust design for freeze-thaw cycles, with either a bellows-sealed configuration, a high-temperature stuffing box, or combination of the two. The STM system is unique in the industry.

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Computational singular perturbation (CSP) is a method to analyze dynamical systems. It targets the decoupling of fast and slow dynamics using an alternate linear expansion of the right-hand side of the governing equations based on eigenanalysis of the associated Jacobian matrix. This representation facilitates diagnostic analysis, detection and control of stiffness, and the development of simplified models. We have implemented CSP in a C++ open-source library CSPlib1 using the Kokkos2 parallel programming model to address portability across diverse heterogeneous computing platforms, i.e., multi/many-core CPUs and GPUs. We describe the CSPlib implementation and present its computational performance across different computing platforms using several test problems. Specifically, we test the CSPlib performance for a constant pressure ignition reactor model on different architectures, including IBM Power 9, Intel Xeon Skylake, and NVIDIA V100 GPU. The size of the chemical kinetic mechanism is varied in these tests. As expected, the Jacobian matrix evaluation, the eigensolution of the Jacobian matrix, and matrix inversion are the most expensive computational tasks. When considering the higher throughput characteristic of GPUs, GPUs performs better for small matrices with higher occupancy rate. CPUs gain more advantages from the higher performance of well-tuned and optimized linear algebra libraries such as OpenBLAS. Program summary: Program Title: CSPlib CPC Library link to program files: https://doi.org/10.17632/p9gb7z54sp.1 Developer's repository link: https://github.com/sandialabs/csplib Licensing provisions: BSD 2-clause Programming language: C++ Nature of problem: Dynamical systems can involve coupled processes with a wide range of time scales. The computational singular perturbation (CSP) method offers a reformulation of these systems which enables the use of dynamically-based diagnostic tools to better comprehend the dynamics by decoupling fast and slow processes. CSPlib is an open-source software library for analyzing general ordinary differential equation (ODE) and differential algebraic equation (DAE) systems, with specialized implementations for detailed chemical kinetic ODE/DAE systems. It relies on CSP for the analysis of these systems. CSPlib has been used in gas kinetic and heterogeneous catalytic kinetic models. Solution method: CSP analysis seeks a set of basis vectors to linearly decompose the right-hand side (RHS) of a dynamical system in a manner that decouples fast and slow processes. The CSP basis vectors are often well approximated with the right eigenvectors of the RHS Jacobian. And the left basis vectors are found by the inversion of the matrix, whose columns are the CSP basis vectors. Accordingly, the right and left CSP basis vectors are orthonormal. CSP defines mode amplitudes as the projections of the left basis vectors on the RHS; the time scales as the reciprocals of the RHS Jacobian eigenvalue magnitudes; and the CSP pointers, which are the element-wise multiplication of the transpose of the right CSP basis vectors with the left CSP basis vectors. For kinetic models that can be cast as the product of a generalized stoichiometric matrix and a rate of progress vector, CSP defines the participation index, which represents the contribution of a chemical reaction to each mode. Further, it defines the slow and fast importance indices, which describe the contribution of a chemical reaction to the slow and fast dynamics of a state variable, respectively. These indices are useful in diagnostic studies of dynamical systems and the construction of simplified models. Additional comments including restrictions and unusual features: CSPlib is a portable library that carries out many CSP analyses in parallel and can be used in modern high-performance platforms.

As modern gasoline direct injection (GDI) engines utilize sophisticated injection strategies, a detailed understanding of the air-fuel mixing process is crucial to further improvements in engine emission and fuel economy. In this study, a comprehensive evaluation of the spray process of single-component iso-octane (IC8) and multi-component gasoline surrogate E00 (36 % n-pentane, 46 % iso-octane, and 18 % n-undecane, by volume) fuels was conducted using an Engine Combustion Network (ECN) Spray G injector. High-speed extinction, schlieren, and microscopy imaging campaigns were carried out under engine-like ambient conditions in a spray vessel. Experimental results including liquid/vapor penetration, local liquid volume fraction, droplet size, and projected liquid film on the nozzle tip were compared under ECN G1 (573 K, 3.5 kg/m3), G2 (333 K, 0.5 kg/m3), and G3 (333 K, 1.01 kg/m3) conditions. In addition to the experiments, preferential evaporation process of the E00 fuel was elucidated by Large–Eddy Simulations (LES). The three-dimensional liquid volume fraction measurement enabled by the computed tomographic reconstruction showed substantial plume collapse for E00 under the G2 and G3 conditions having wider plume growth and plume-to-plume interaction due to the fuel high vapor pressure. The CFD simulation of E00 showed an inhomogeneity in the way fuel components vaporized, with more volatile components carried downstream in the spray after the end of injection. The high vapor pressure of E00 also results in ∼4 μm smaller average droplet diameter than IC8, reflecting a higher rate of initial vaporization even though the final boiling point temperature is higher. Consistent with high vapor pressure, E00 had a wider plume cone angle and enhanced interaction with the wall to cover the entire surface of the nozzle tip in a film. However, the liquid fuel underwent faster evaporation, so the final projected tip wetting area was smaller than the IC8 under the flash-boiling condition.

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A clear definition of system dynamics modeling can provide shared understanding and clarify the impact of the field. We introduce a set of characteristics that define quantitative system dynamics, selected to capture core philosophy, describe theoretical and practical principles, and apply to historical work but be flexible enough to remain relevant as the field progresses. The defining characteristics are: (1) models are based on causal feedback structure, (2) accumulations and delays are foundational, (3) models are equation-based, (4) concept of time is continuous, and (5) analysis focuses on feedback dynamics. We discuss the implications of these principles and use them to identify research opportunities in which the system dynamics field can advance. These research opportunities include causality, disaggregation, data science and AI, and contributing to scientific advancement. Progress in these areas has the potential to improve both the science and practice of system dynamics. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

We present a new optimization-based property-preserving algorithm for passive tracer transport. The algorithm utilizes a semi-Lagrangian approach based on incremental remapping of the mass and the total tracer. However, unlike traditional semi-Lagrangian schemes, which remap the density and the tracer mixing ratio through monotone reconstruction or flux correction, we utilize an optimization-based remapping that enforces conservation and local bounds as optimization constraints. In so doing we separate accuracy considerations from preservation of physical properties to obtain a conservative, second-order accurate transport scheme that also has a notion of optimality. Moreover, we prove that the optimization-based algorithm preserves linear relationships between tracer mixing ratios. We illustrate the properties of the new algorithm using a series of standard tracer transport test problems in a plane and on a sphere.

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The Redmond Salt Mine (RSM) Monitoring Experiment in Utah was designed to record seis-moacoustic data at distances less than 50 km for algorithm testing and development. During the experiment from October 2017 to July 2019, six broadband seismic stations were operating at a time, with three of them having fixed locations for the duration, whereas the three other stations were moved to different locations every one-and-half to two-and-half months. RSM operations consist of nighttime underground blasting several times per week. The RSM is located in proximity to a belt of active seismicity, allowing direct comparison of natural and anthropogenic sources. Using the recorded data set, we built 1373 events with local magnitude (ML) of −2.4 and lower to 3.3. For 75 blasts (RMEs) from the Redmond Salt Mine and 206 tectonic earthquakes (EQs), both ML and the coda duration magnitude (MC) are well constrained. We used these events to test and design discriminants that separate the RMEs from the EQs and are effective at local distances. The discriminants consist of ML −MC, low-frequency Sg to high-frequency Sg, Pg/Sg phase-amplitude ratios, and Rg/Sg spectral amplitude ratios, as well as different combinations of two or more of these classifiers. The areas under the receiver operating characteristic curves (AUCs) of 0.92–1.0 for ML −MC, low-frequency Sg to high-frequency Sg, and Rg/Sg indicate that these discriminants are very effective. Conversely, the AUC of only 0.57 for Pg/Sg suggests that this discriminant is only slightly better than a random classifier. Among the effective classifiers, Rg/Sg, shows the lowest likelihood of misclassification (4.3%) for the populations. Results of joint discriminant analyses suggest that even the arguably inef-fective single classifier, like Pg/Sg in this case, can provide some value when used in combi-nation with others.

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Additive manufacturing (AM) maintains a wide process window that enables complex designs otherwise unattainable via conventional production technologies. However, the lack of confidence in qualifying AM parts that leverage AM process–structure–property–performance (PSPP) relationships stymies design optimization and adoption of AM. While continuing efforts to map fundamental PSPP relationships that cover the potential design space, we first need pragmatic and then long-term solutions that overcome challenges associated with qualifying AM-designed parts. Two pragmatic solutions include: (1) AM material specifications to substantiate process reproducibility, and (2) component risk categorization to associate system risk relative to part performance and required part quality. A novel qualification paradigm under development involves efficient prediction of part performance over wide-ranging PSPP relationships through targeted testing and computational simulation. This paper describes projects at Sandia National Laboratories on PSPP relationship discovery, these pragmatic approaches, and the novel qualification approach.

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