Development of a Quasi-Dimension GCI Combustion Model Aided by CFD

Advanced combustion strategies have been proposed to improve fuel efficiency while minimizing exhaust emissions. Gasoline compression ignition (GCI) combustion featuring partially premixed compression ignition (PPCI) and diffusion combustion has been recognized as an attractive, viable combustion strategy for its potential and advantages over conventional diesel and gasoline engines. The optimization of the GCI engine system requires the development of a quasi-dimensional GCI combustion model capable of simulating GCI combustion while requesting less computational burden than CFD simulation, which is very critical in engine system simulation. This study developed a quasi-dimension, phenomenological combustion model for PPCI and diffusion combustion to facilitate the early development of GCI combustion strategy. Due to the limited GCI engine test results, additional parametric CFD studies were conducted and served as a reference to develop the GCI combustion model and investigate the effect on GCI combustion of thermal conditions typically considered during early strategy development. A reduced toluene primary reference fuel and ethanol (TPRFE) mechanism with 65 species and 283 reactions was used to simulate GCI combustion in CFD and quasi-dimension models. Additionally, the behavior of high-pressure gasoline spray was investigated using CFD to support the development of the phenomenological spray dynamics model. The traditional phenomenological SI and CI combustion model frameworks were improved to simulate gasoline PPCI-diffusion combustion accurately with the spray dynamics, air entrainment, ignition delay, and heat release sub-models. The traditional spray model was improved and validated using CFD simulation results as a reference. The CFD result identified a high level of fuel concentration at the spray tip due to the drag and pushing momentum by the following fuel packets. This observation was accounted for in the development of the spray model. The ignition delay was calculated by solving the chemistry kinetics and curve fitting using the identical chemistry mechanism employed in CFD analysis. This research demonstrated that the phenomenological combustion model developed in this study could simulate fuel spray, fuel atomization, ignition delay, and heat release process. The GCI model has been integrated into GT-Suite and successfully applied to improve the combustion process with the valvetrain system. Various variable valve actuation (VVA) strategies were investigated at low-load operating conditions, including early exhaust valve open (EEVO), late exhaust valve open (LEVO), negative valve overlap (NVO), positive valve overlap (PVO), and exhaust gas rebreathing (RB). The RB strategies were identified as the most effective in promoting in-cylinder gas temperature by increasing the hot internal residual gas fraction. This research also numerically investigated the potential of a close coupled-selective catalytic reduction (CC-SCR) system in further NOx emissions of a heavy-duty diesel engine using GT-suite. Diesel engine transient test results were utilized to evaluate CC-SCR instead of GCI results due to limited GCI testing data available. The effects of volume and geometry of the CC-SCR on NOx reduction were numerically investigated under the HD FTP transient cycle. The simulation results revealed that CC-SCR was a very effective strategy, showing that nearly 80 % of the total reduction was realized at the CC-SCR under the transient cycle. This study examined the necessity of accounting for the non-uniform distribution of exhaust gas and urea in the SCR model based on the observation of inhomogeneity at the inlet of CC-SCR in CFD simulation

A Bootstrap Metropolis-Hastings algorithm for Bayesian Analysis of Big Data

Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their compute-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this thesis, we propose the so-called bootstrap Metropolis-Hastings (BMH) algorithm, which provides a general framework for how to tame powerful MCMC methods to be used for big data analysis; that is to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-conquer method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis-Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively

Searching for Flavored Gauge Bosons

Standard Model may allow an extended gauge sector with anomaly-free flavored gauge symmetries, such as $L_{i} - L_{j}$, $B_{i} - L_{j}$, and $B - 3L_{i}$, where $i,j=1,2,3$ are flavor indices. We investigate phenomenological implications of the new flavored gauge boson $Z^{\prime}$ in the above three classes of gauge symmetries. Focusing on the gauge boson mass above 5 GeV, we use the lepton universality test in the $Z$ and $\tau/\mu$ decays, LEP searches, LHC searches, neutrino trident production bound, and LHC $Z\rightarrow 4\mu$ searches to put constraints on the $g^{\prime}-M_{Z^{\prime}}$ plane. When $L_1$ is involved, the LEP bounds on the $e^{-}e^{+} \rightarrow \ell^{-}\ell^{+}$ processes give the most stringent bounds, while the LHC bound becomes the strongest constraints in the large $M_{Z^{\prime}}$ region when $B_{i}$ is involved. The bound from $Z\rightarrow 4\mu$ productions, which is applicable for $L_2$-involved scenarios, provides stringent bounds in the small $M_{Z^{\prime}}$ region. One exception is the $B-3L_2$ scenario, in which case only a small region is favored due to the lepton universality.Comment: v3: updated LHC bounds for B-3L_i model

Primordial black holes and gravitational waves from nonminimally coupled supergravity inflation

We study formation of primordial black holes and generation of gravitational waves in a class of cosmological models that are direct supersymmetric analogues of the observationally favored nonminimally coupled Higgs inflation model. It is known that this type of model naturally includes multiple scalar fields which may be regarded as the inflaton. For the sake of simplicity we focus on the case where the inflaton field space is two dimensional. We analyze the multifield dynamics and find the region of parameters that gives copious production of primordial black holes that may comprise a significant part of the present dark matter abundance. We also compute the spectrum of the gravitational waves and discuss their detectability by means of future ground-based and space-borne gravitational wave observatories.Comment: 13 page

A Bootstrap Metropolis-Hastings algorithm for Bayesian Analysis of Big Data

Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their compute-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this thesis, we propose the so-called bootstrap Metropolis-Hastings (BMH) algorithm, which provides a general framework for how to tame powerful MCMC methods to be used for big data analysis; that is to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-conquer method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis-Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively