Variational Asymptotic Method for Unit Cell Homogenization of Thermomechanical Behavior of Composite Materials

To seek better material behaviors, the research of material properties has been massively carried out in both industrial and academic fields throughout the twentieth century. Composite materials are known for their abilities of combining constituent materials in or- der to fulfill the desirable overall material performance. One of the advantages of composite materials is the adjustment between stiffness and lightness of materials in order to meet the needs of various engineering designs. Even though the finite element analysis is mature, composites are heterogeneous in nature and can present difficulties at the structural level with the acceptable computational time. A way of simplifying such problems is to find a way to connect structural analysis with corresponding analysis of representative microstructure of the material, which is normally called micromechanics modeling or homogenization.Generally speaking, the goal of homogenization is to predict a precise material behavior by taking into account the information stored in both microscopic and macroscopic levels of the composites. Of special concern to researchers and engineers is the thermomechanical behavior of composite materials since thermal effect is almost everywhere in real practical cases of engineering. In aerospace engineering, the thermomechanical behaviors of composites are even more important since flight under high speed usually produces a large amount of heat which will cause very high thermal-related deformation and stress.In this dissertation, the thermomechanical behavior of composites will be studied based on the variational asymptotic method for unit cell homogenization (VAMUCH) which was recently developed as an efficient and accurate micromechanics modeling tool. The theories and equations within the code are based on the variational asymptotic method invented by Prof. Berdichevsky. For problems involving small parameters, the traditional asymptotic method is often applied by solving a system of differential equations while the variational asymptotic method is using a variational statement that only solves one functional of such problems where the traditional asymptotic method may apply.First, we relax the assumption made by traditional linear thermoelasticity that not only a small overall strain is assumed to be small but also the temperature variation. Of course, in this case we need to add temperature dependent material properties to VAMUCH so that the secant material properties can be calculated. Then, we consider the temperature field to be point-wise different within the microstructure; a micromechanics model with nonuniformly distributed temperature field will be addressed. Finally, the internal and external loads induced energies are considered in order to handle real engineering structures under their working conditions

Modeling of Turbulent Sooting Flames

Modeling multiphase particles in turbulent fluid environment is a challenging task. To accurately describe the size distribution, a large number of scalars need to be transported at each time-step. Add to that the heat release and species mass fraction changes from nonlinear combustion chemistry reactions, and you have a tightly coupled set of equations that describe the (i) turbulence, (ii) chemistry, and (iii) soot particle interactions (physical agglomeration and surface chemistry reactions). Uncertainty in any one of these models will inadvertently introduce errors of up to a few orders of magnitude in predicted soot quantities. The objective of this thesis is to investigate the effect of turbulence and chemistry on soot evolution with respect to different soot aerosol models and to develop accurate models for simulating soot evolution in aircraft combustors. To investigate the effect of small scale turbulence time-scales on soot evolution, a partially-stirred reactor (PaSR) configuration is used and coupled with soot models from semi-empirical to detailed statistical models. Differences in soot property predictions including soot particle diameter and number density among the soot models are highlighted. The soot models will then be used to simulate the turbulent sooting flame in an aircraft swirl combustor to determine the large scale soot-turbulence-chemistry interactions. Highlights of this study include the differences in location of bulk soot mass production in the combustor using different soot models. A realistic aircraft combustor operating condition is simulated using a state-of-the-art minimally dissipative turbulent combustion solver and soot method of moments to investigate pressure scaling and soot evolution in different operating conditions. A separate hydrodynamic scaling is introduced to the pressure scaling, in addition to thermochemical scaling from previous studies. Finally, a Fourier analysis of soot evolution in the combustor will be discussed. A lower sooting frequency mode is found in the combustor, separate from the dominant fluid flow frequency mode that could affect statistical data collection for soot properties in turbulent sooting flame simulations.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147513/1/stchong_1.pd

Bright light therapy as part of a multicomponent management program improves sleep and functional outcomes in delirious older hospitalized adults.

ObjectiveDelirium is associated with poor outcomes following acute hospitalization. A specialized delirium management unit, the Geriatric Monitoring Unit (GMU), was established. Evening bright light therapy (2000-3000 lux; 6-10 pm daily) was added as adjunctive treatment, to consolidate circadian activity rhythms and improve sleep. This study examined whether the GMU program improved sleep, cognitive, and functional outcomes in delirious patients.MethodA total of 228 patients (mean age = 84.2 years) were studied. The clinical characteristics, delirium duration, delirium subtype, Delirium Rating Score (DRS), cognitive status (Chinese Mini-Mental State Examination), functional status (modified Barthel Index [MBI]), and chemical restraint use during the initial and predischarge phase of the patient's GMU admission were obtained. Nurses completed hourly 24-hour patient sleep logs, and from these, the mean total sleep time, number of awakenings, and sleep bouts (SB) were computed.ResultsThe mean delirium duration was 6.7 ± 4.6 days. Analysis of the delirium subtypes showed that 18.4% had hypoactive delirium, 30.2% mixed delirium, and 51.3% had hyperactive delirium. There were significant improvements in MBI scores, especially for the hyperactive and mixed delirium subtypes (P < 0.05). Significant improvements were noted on the DRS sleep-wake disturbance subscore, for all delirium-subtypes. The mean total sleep time (7.7 from 6.4 hours) (P < 0.05) and length of first SB (6.0 compared with 5.3 hours) (P < 0.05) improved, with decreased mean number of SBs and awakenings. The sleep improvements were mainly seen in the hyperactive delirium subtype.ConclusionThis study shows initial evidence for the clinical benefits (longer total sleep time, increased first SB length, and functional gains) of incorporating bright light therapy as part of a multicomponent delirium management program. The benefits appear to have occurred mainly in patients with hyperactive delirium, which merits further in-depth, randomized controlled studies

Finding Answers to Definition Questions Using Web Knowledge Bases

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Positioning control of XY table using 2-DOF PID controller

A two-degree-of-freedom (2-DOF) PID controller is designed for an AC servo ball screw driven XY table. XY table is widely used in manufacturing industry especially in CNC machineries. The most commonly used controller in industries is conventional PID controller. This controller has satisfactory performance, simple structure, and is one-degree-of-freedom (1DOF). Nonetheless, PID controller can only achieve either good set-point response or good disturbance response. This leads to introduction of 2-DOF PID controller which can achieve both good set-point response and disturbance response. In this project, 2-DOF PID is used for accurate tracking purpose. 2-DOF PID controller is designed using two-steps-tuning-method. Disturbance response is optimized by tuning parameters of 〖 K〗_P,T_i,〖and T〗_D using Ziegler-Nichols 2nd method, followed by optimization of set-point response by tuning of 2-DOF parameters, α and β. Tracking performance of 2-DOF PID controller is compared with conventional PI and 1-DOF PID. Maximum absolute error, sum of absolute error, and mean square error are analyzed for all tracking performance of compensated system. Result shows that tracking error compensation (set-point response) of 1-DOF PID controller is better than 2-DOF PID controller. However, this is due to tuning of α and β parameters in simulation in this project. α and β values should be tuned experimentally. Disturbance response of 1-DOF PID and 2-DOF PID are almost similar due to same 〖 K〗_P,T_i,〖and T〗_D values are used in both controllers

A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition

The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously. However, current methods encode the dialog context in only one direction, which limits their ability to thoroughly comprehend the context. Moreover, these methods overlook the explicit correlations between sentiment and act labels, which leads to an insufficient ability to capture rich sentiment and act clues and hinders effective and accurate reasoning. To address these issues, we propose a Bi-directional Multi-hop Inference Model (BMIM) that leverages a feature selection network and a bi-directional multi-hop inference network to iteratively extract and integrate rich sentiment and act clues in a bi-directional manner. We also employ contrastive learning and dual learning to explicitly model the correlations of sentiment and act labels. Our experiments on two widely-used datasets show that BMIM outperforms state-of-the-art baselines by at least 2.6% on F1 score in DAR and 1.4% on F1 score in DSC. Additionally, Our proposed model not only improves the performance but also enhances the interpretability of the joint sentiment and act prediction task.Comment: Accepted by NLPCC 202