Numerical Analysis of Transient Teflon Ablation with a Domain Decomposition Finite Volume Implicit Method on Unstructured Grids

This work investigates numerically the process of Teflon ablation using a finite-volume discretization, implicit time integration and a domain decomposition method in three-dimensions. The interest in Teflon stems from its use in Pulsed Plasma Thrusters and in thermal protection systems for reentry vehicles. The ablation of Teflon is a complex process that involves phase transition, a receding external boundary where the heat flux is applied, an interface between a crystalline and amorphous (gel) phase and a depolymerization reaction which happens on and beneath the ablating surface. The mathematical model used in this work is based on a two-phase model that accounts for the amorphous and crystalline phases as well as the depolymerization of Teflon in the form of an Arrhenius reaction equation. The model accounts also for temperature-dependent material properties, for unsteady heat inputs and boundary conditions in 3D. The model is implemented in 3D domains of arbitrary geometry with a finite volume discretization on unstructured grids. The numerical solution of the transient reaction-diffusion equation coupled with the Arrhenius-based ablation model advances in time using implicit Crank-Nicolson scheme. For each time step the implicit time advancing is decomposed into multiple sub-problems by a domain decomposition method. Each of the sub-problems is solved in parallel by Newton-Krylov non-linear solver. After each implicit time-advancing step, the rate of ablation and the fraction of depolymerized material are updated explicitly with the Arrhenius-based ablation model. After the computation, the surface of ablation front and the melting surface are recovered from the scalar field of fraction of depolymerized material and the fraction of melted material by post-processing. The code is verified against analytical solutions for the heat diffusion problem and the Stefan problem. The code is validated against experimental data of Teflon ablation. The verification and validation demonstrates the ability of the numerical method in simulating three dimensional ablation of Teflon

Statistical Performance Analysis of Sparse Linear Arrays

Direction-of-arrival (DOA) estimation remains an important topic in array signal processing. With uniform linear arrays (ULAs), traditional subspace-based methods can resolve only up to M-1 sources using M sensors. On the other hand, by exploiting their so-called difference coarray model, sparse linear arrays, such as co-prime and nested arrays, can resolve up to O(M^2) sources using only O(M) sensors. Various new sparse linear array geometries were proposed and many direction-finding algorithms were developed based on sparse linear arrays. However, the statistical performance of such arrays has not been analytically conducted. In this dissertation, we (i) study the asymptotic performance of the MUtiple SIgnal Classification (MUSIC) algorithm utilizing sparse linear arrays, (ii) derive and analyze performance bounds for sparse linear arrays, and (iii) investigate the robustness of sparse linear arrays in the presence of array imperfections. Based on our analytical results, we also propose robust direction-finding algorithms for use when data are missing. We begin by analyzing the performance of two commonly used coarray-based MUSIC direction estimators. Because the coarray model is used, classical derivations no longer apply. By using an alternative eigenvector perturbation analysis approach, we derive a closed-form expression of the asymptotic mean-squared error (MSE) of both estimators. Our expression is computationally efficient compared with the alternative of Monte Carlo simulations. Using this expression, we show that when the source number exceeds the sensor number, the MSE remains strictly positive as the signal-to-noise ratio (SNR) approaches infinity. This finding theoretically explains the unusual saturation behavior of coarray-based MUSIC estimators that had been observed in previous studies. We next derive and analyze the Cramér-Rao bound (CRB) for general sparse linear arrays under the assumption that the sources are uncorrelated. We show that, unlike the classical stochastic CRB, our CRB is applicable even if there are more sources than the number of sensors. We also show that, in such a case, this CRB remains strictly positive definite as the SNR approaches infinity. This unusual behavior imposes a strict lower bound on the variance of unbiased DOA estimators in the underdetermined case. We establish the connection between our CRB and the classical stochastic CRB and show that they are asymptotically equal when the sources are uncorrelated and the SNR is sufficiently high. We investigate the behavior of our CRB for co-prime and nested arrays with a large number of sensors, characterizing the trade-off between the number of spatial samples and the number of temporal samples. Our analytical results on the CRB will benefit future research on optimal sparse array designs. We further analyze the performance of sparse linear arrays by considering sensor location errors. We first introduce the deterministic error model. Based on this model, we derive a closed-form expression of the asymptotic MSE of a commonly used coarray-based MUSIC estimator, the spatial-smoothing based MUSIC (SS-MUSIC). We show that deterministic sensor location errors introduce a constant estimation bias that cannot be mitigated by only increasing the SNR. Our analytical expression also provides a sensitivity measure against sensor location errors for sparse linear arrays. We next extend our derivations to the stochastic error model and analyze the Gaussian case. We also derive the CRB for joint estimation of DOA parameters and deterministic sensor location errors. We show that this CRB is applicable even if there are more sources than the number of sensors. Lastly, we develop robust DOA estimators for cases with missing data. By exploiting the difference coarray structure, we introduce three algorithms to construct an augmented covariance matrix with enhanced degrees of freedom. By applying MUSIC to this augmented covariance matrix, we are able to resolve more sources than sensors. Our method utilizes information from all snapshots and shows improved estimation performance over traditional DOA estimators

Metagenomic Insights Into the Contribution of Phages to Antibiotic Resistance in Water Samples Related to Swine Feedlot Wastewater Treatment

In this study, we examined the types of antibiotic resistance genes (ARGs) possessed by bacteria and bacteriophages in swine feedlot wastewater before and after treatment using a metagenomics approach. We found that the relative abundance of ARGs in bacterial DNA in all water samples was significantly higher than that in phages DNA (>10.6-fold), and wastewater treatment did not significantly change the relative abundance of bacterial- or phage-associated ARGs. We further detected the distribution and diversity of the different types of ARGs according to the class of antibiotics to which they confer resistance, the tetracycline resistance genes were the most abundant resistance genes and phages were more likely to harbor ATP-binding cassette transporter family and ribosomal protection genes. Moreover, the colistin resistance gene mcr-1 was also detected in the phage population. When assessing the contribution of phages in spreading different groups of ARGs, β-lactamase resistance genes had a relatively high spreading ability even though the abundance was low. These findings possibly indicated that phages not only could serve as important reservoir of ARG but also carry particular ARGs in swine feedlot wastewater, and this phenomenon is independent of the environment

Genetic Diversity Analysis of Sapindus in China and Extraction of a Core Germplasm Collection Using EST-SSR Markers

Sapindus is an important forest tree genus with utilization in biodiesel, biomedicine, and it harbors great potential for biochemical engineering applications. For advanced breeding of Sapindus, it is necessary to evaluate the genetic diversity and construct a rationally designed core germplasm collection. In this study, the genetic diversity and population structure of Sapindus were conducted with 18 expressed sequence tag-simple sequence repeat (EST-SSR) markers in order to establish a core germplasm collection from 161 Sapindus accessions. The population of Sapindus showed high genetic diversity and significant population structure. Interspecific genetic variation was significantly higher than intraspecific variation in the Sapindus mukorossi, Sapindus delavayi, and combined Sapindus rarak plus Sapindus rarak var. velutinus populations. S. mukorossi had abundant genetic variation and showed a specific pattern of geographical variation, whereas S. delavayi, S. rarak, and S. rarak var. velutinus showed less intraspecific variation. A core germplasm collection was created that contained 40% of genetic variation in the initial population, comprising 53 S. mukorossi and nine S. delavayi lineages, as well as single representatives of S. rarak and S. rarak var. velutinus. These results provide a germplasm basis and theoretical rationale for the efficient management, conservation, and utilization of Sapindus, as well as genetic resources for joint genomics research in the future.Peer reviewe

Modeling of droplet evaporation, flash-boiling, and mixture preparation in internal combustion engines

The evolving regulation on internal combustion engine emissions as well as the rising expectation on the engine efficiency and performance pose challenges in the development of the future engine technology. Computational methods are needed to understand the mechanism of the advanced engine combustion concepts and to facilitate the development of the future clean and efficient engine technology. This study examines numerical models essential to the simulation of mixture preparation, a vital process that determines the combustion outcome. Combustion models are also developed for compression-ignition and spark-ignition engines, and are used to simulate the advanced engine operation concepts. In this study, a modular multi-component droplet evaporation model is developed based on the existing model by the author’s lab. The updated model is capable of estimating the thermal and transport properties of real mixture of known compositions. Also added is a vapor-liquid equilibrium solver based on the fugacity coefficient of the Peng-Robinson equation of state. The modular droplet evaporation model is integrated into a customized engine CFD software, KIVA, to simulate the droplet evaporation in fuel spray. Flash-boiling of spray generates ultra-fine fuel mist and is potentially beneficial to the mixture preparation. The author examines the existing model for the droplet flashing breakup. The existing model is reformulated and merged with the Taylor analogy breakup model, an aerodynamic droplet breakup model. The unified droplet breakup model is capable of simulating the droplet breakup under the combined effects of aerodynamic excitation and internal flashing bubble growth. Flashing spray simulations are conducted with the unified droplet breakup model. For the compression-ignition engine operation, the chemical kinetics translates the result of mixture preparation into combustion outcome. An efficient chemical kinetics solver is developed for the chemical reaction calculation in engine CFD. The solver exploits an estimated Jacobian matrix of the chemical kinetics problem and reduces the computational time without compromising the accuracy of the solution. Studies of various advanced compression-ignition engine concepts are conducted with the efficient chemical kinetics solver. Spark-ignition engine simulation requires accurate flame propagation prediction. In this study, an efficient G-equation model is developed to model the flame propagation without resolving the flame structure. An advantage over the existing G-equation models is the addition of a knock prediction method inspired by the Livengood-Wu integral. The combination of the G-equation model and the spontaneous ignition calculation results in an unified combustion model for the simulation of spark-ignition engine operation with end-gas ignition, a.k.a., knocking. The model is tested by simulating the operation of a swirl-dominant gasoline direct-injection engine. As the concluding remark, this study develops not only mixture preparation models that predict the mixture preparation outcome, but also combustion models that assess the combustion’s dependency on the mixture preparation. CFD simulations demonstrate the usefulness of these models in the discovery of the future engine technologies.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste

Modeling of droplet evaporation, flash-boiling, and mixture preparation in internal combustion engines

The evolving regulation on internal combustion engine emissions as well as the rising expectation on the engine efficiency and performance pose challenges in the development of the future engine technology. Computational methods are needed to understand the mechanism of the advanced engine combustion concepts and to facilitate the development of the future clean and efficient engine technology. This study examines numerical models essential to the simulation of mixture preparation, a vital process that determines the combustion outcome. Combustion models are also developed for compression-ignition and spark-ignition engines, and are used to simulate the advanced engine operation concepts. In this study, a modular multi-component droplet evaporation model is developed based on the existing model by the author’s lab. The updated model is capable of estimating the thermal and transport properties of real mixture of known compositions. Also added is a vapor-liquid equilibrium solver based on the fugacity coefficient of the Peng-Robinson equation of state. The modular droplet evaporation model is integrated into a customized engine CFD software, KIVA, to simulate the droplet evaporation in fuel spray. Flash-boiling of spray generates ultra-fine fuel mist and is potentially beneficial to the mixture preparation. The author examines the existing model for the droplet flashing breakup. The existing model is reformulated and merged with the Taylor analogy breakup model, an aerodynamic droplet breakup model. The unified droplet breakup model is capable of simulating the droplet breakup under the combined effects of aerodynamic excitation and internal flashing bubble growth. Flashing spray simulations are conducted with the unified droplet breakup model. For the compression-ignition engine operation, the chemical kinetics translates the result of mixture preparation into combustion outcome. An efficient chemical kinetics solver is developed for the chemical reaction calculation in engine CFD. The solver exploits an estimated Jacobian matrix of the chemical kinetics problem and reduces the computational time without compromising the accuracy of the solution. Studies of various advanced compression-ignition engine concepts are conducted with the efficient chemical kinetics solver. Spark-ignition engine simulation requires accurate flame propagation prediction. In this study, an efficient G-equation model is developed to model the flame propagation without resolving the flame structure. An advantage over the existing G-equation models is the addition of a knock prediction method inspired by the Livengood-Wu integral. The combination of the G-equation model and the spontaneous ignition calculation results in an unified combustion model for the simulation of spark-ignition engine operation with end-gas ignition, a.k.a., knocking. The model is tested by simulating the operation of a swirl-dominant gasoline direct-injection engine. As the concluding remark, this study develops not only mixture preparation models that predict the mixture preparation outcome, but also combustion models that assess the combustion’s dependency on the mixture preparation. CFD simulations demonstrate the usefulness of these models in the discovery of the future engine technologies