Numerical Prediction of Turbulent Non-Premixed Forced Ignition in Altitude Relight

Fast and reliable altitude relight performance is one of the end goals of aircraft engine design. Relight involves the use of an external heat source (a kernel, for instance), to ignite a cold mixture of fuel and air in a turbulent flow environment. Due to the variabilities in spark kernel formation, its transport in a turbulent flow environment, and the mixing and chemical reactions that are influenced by turbulent mixing, ignition is described statistically in terms of a probability of success. Currently, full-engine tests remain the most direct approach to evaluating relight probability at relevant conditions. However, this approach is expensive both in terms of time and monetary cost. Computational models that can accurate predict ignition processes in a statistical sense can vastly accelerate engine design, and significantly reduce cost. The objective of the dissertation is to develop a predictive computational framework that addresses this key need. Forced ignition, due to the nature of turbulent flow, exhibits complex flame structure. To describe the combustion processes, a novel hybrid tabulation approach is formulated. This method combined a conventional flamelet-progress variable tabulation with a homogeneous reaction model to capture the spark transition from a homogeneous volumetric reaction process to a diffusion-controlled flame. Since the success of kernel ignition or failure has to be described statistically, a procedure for introducing uncertainties from spark discharge and the turbulent flow is developed. The resulting computational model involves an ensemble approach, where a large set of realizations of a detailed large eddy simulation (LES) based description of the flow along with the hybrid tabulation model is used to determine ignition probabilities The proposed framework is thoroughly validated using a stratified forced ignition experiment designed to replicate high altitude relight. The model is found to successfully reproduce the fundamental physics, including the evolution of the spark kernel, and the entrainment of the fuel-air mixture into the hot kernel discharge. A particular experiment using methane as fuel is used to calibrate the spark discharge model, which is then used without modification in the study of alternative jet fuels. It is shown that the prediction framework capture the ignition probability for different fuels and operating conditions. This new computational framework provides the first rigorous approach to modeling high altitude relight.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168027/1/yhtang_1.pd

Toward Understanding the Influence of Individual Clients in Federated Learning

Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guarantee of the global model, however, it is still unclear how each individual client influences the collaborative training process. In this work, we defined a new notion, called {\em Fed-Influence}, to quantify this influence over the model parameters, and proposed an effective and efficient algorithm to estimate this metric. In particular, our design satisfies several desirable properties: (1) it requires neither retraining nor retracing, adding only linear computational overhead to clients and the server; (2) it strictly maintains the tenets of federated learning, without revealing any client's local private data; and (3) it works well on both convex and non-convex loss functions, and does not require the final model to be optimal. Empirical results on a synthetic dataset and the FEMNIST dataset demonstrate that our estimation method can approximate Fed-Influence with small bias. Further, we show an application of Fed-Influence in model debugging.Comment: Accepted at AAAI 202

An analytical modeling for high-velocity impacts on woven Kevlar composite laminates

In this paper, an analytical model, which based on energy balance, is built to study the process of high velocity impacts on woven Kevlar composite laminates by a cylindrical projectile. Four different mechanisms, such as laminate crushing, linear momentum transfer and tensile fiber failure, and shear plugging, is absorbed by the laminate while impacting. Then, simplification of the model is done to obtain the residual velocity and ballistic limit. The analytical results are validated with the results of experiment, and the perturbation analysis is done to analyze the reason of error

Turbulent Mixing and Combustion of Supercritical Jets

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143049/1/6.2017-0141.pd