Browsing by Author "K. Ito, Member"

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Analysis of Thermal Conductivity in Composite Adhesives
(2001-08-08) Bihari, Kathleen L.; H. T. Banks, Chair; K. Ito, Member; H. T. Tran, Member; J.-P. Fouque, Member
Thermally conductive composite adhesives are desirable in many industrial applications, including computers, microelectronics, machinery and appliances. These composite adhesives are formed when a filler particle of high conductivity is added to a base adhesive. Typically, adhesives are poor thermal conductors. Experimentally only small improvements in the thermal properties of the composite adhesives over the base adhesives have been observed. A thorough understanding of heat transfer through a composite adhesive would aid in the design of a thermally conductive composite adhesive that has the desired thermal properties.In this work, we study design methodologies for thermally conductive composite adhesives. We present a three dimensional model for heat transfer through a composite adhesive based on its composition and on the experimental method for measuring its thermal properties. For proof of concept, we reduce our model to a two dimensional model. We present numerical solutions to our two dimensional model based on a composite silicone and investigate the effect of the particle geometry on the heat flow through this composite. We also present homogenization theory as a tool for computing the "effective thermal conductivity" of a composite material.We prove existence, uniqueness and continuous dependence theorems for our two dimensional model. We formulate a parameter estimation problem for the two dimensional model and present numerical results. We first estimate the thermal conductivity parameters as constants, and then use a probability based approach to estimate the parameters as realizations of random variables. A theoretical framework for the probability based approach is outlined.Based on the results of the parameter estimation problem, we are led to formally derive sensitivity equations for our system. We investigate the sensitivity of our composite silicone with respect to the thermal conductivity of both the base silicone polymer and the filler particles. Numerical results of this investigation are also presented.
A Biologically-Based Controlled Growth and Differentiation Model Using Delay Differential Equations: Development, Applications and Stability Analysis.
(2000-11-21) Whitaker, Shree Yvonne; H. T. Tran, Chair; H. T. Banks, Member; K. Ito, Member; C. J. Portier, Member
This work investigates the development, applications and stability analysis of a biologically-based dose-response model for developmental toxicology. The biologically-based controlled growth and differentiation model is based on a model originally developed by Leroux et al. (1996). The original model had two basic states; precursor cells and differentiated cells with both states subject to a linear birth-death process. The research discussed in this dissertation describes the development of a mathematical model that is both biologically- and statistically-based. The model is developed with a highly controlled birth and death process for precursor cells. This model limits the number of replications allowed in the development of a tissue or organ and more closely reflects the presence of a true stem cell population. The mathematical formulation of the Leroux et al. (1996) model was derived from a partial differential equation for the generating function that limits further expansion into more realistic models of mammalian development. The same formulae for the probability of a defect (a system of ordinary differential equations) can be derived through the Kolmogorov forward equations due to the nature of this Markov process. This modified approach is easily amenable to the expansion of more complicated models of the developmental process. Comparisons between the Leroux et al. (1996) model and the controlled growth and differentiation (CGD) model are also discussed.The versatility of the CGD model is highlighted through a discussion of two general applications. The normal developmental process of spermatocytogenesis is investigated as the first application. Time delays are introduced into the system to more accurately mimic the development of male germ cells. As the second application, the spermatocytogenesis model is then altered to demonstrate a modeling strategy for hormesis. Asymptotic stability is investigated using the system of delay differential equations for spermatocytogenesis. The direct Lyapunov method for linear differential equations without delay is modified to establish delay-dependent stability conditions for delay differential equations with multiple delays. The stability conditions are expressed in terms of the existence of a positive definite solution to the Riccati matrix equations. Numerical simulations further verify the stability conditions.
Computational Methods for Feedback Control in Structural Systems
(1998-11-05) Rosario, Ricardo C.H.; H.T. Banks, Chair; R.C. Smith, Member; K. Ito, Member; H.T. Tran, Member
Numerical methods, LQR control, an abstract formulation andreduced basis techniques for a system consisting of a thin cylindrical shellwith surface-mounted piezoceramic actuators are investigated.Donnell-Mushtari equations,modified to include Kelvin-Voigt damping and passive patch contributions,are used to model the system dynamics. The voltage-induced piezoceramicpatch contributions, used as input inthe control regime, enter the equations as externalforces and moments. Existence and uniqueness of solutions are demonstratedthrough variational and semigroup formulations of the system equations.The semigroup formulation is also used to establish theoretical controlresults and illustrate convergence of the finite dimensional controlsand Riccati operators.The spatial components of the state arediscretized using a Galerkin expansion resulting in an ordinarydifferential equation that can be readily marched in time by existingordinary differential equationsolvers.Full order approximation methods which employ standard basiselements such as cubic or linear splines result in large matrixdimensions rendering the system computationally expensive for real-timesimulations. To lessen on-line computational requirements, reducedbasis methods employing snapshots of the full order model as basisfunctions are investigated. As a first step in validating the model, a shell with obtainable analyticnatural frequencies and modes was considered. The derived frequenciesand modeswere then compared with numerical approximations using full order basisfunctions. Further testing on the static and dynamic performance of the fullorder model was carried out through the following steps:(i) choose true state solutions, (ii) solve for the forces in theequations that would lead to these known solutions, and (iii) comparenumerical results excited by the derived forces with the true solutions.Reduced order methods employing the Lagrange and theKarhunen-Loève proper orthogonal decomposition (POD)basis functions are implemented on the model. Finally, a statefeedback method was developed and investigated computationally for both the full order and reduced ordermodels.
Fault Detection and Model Identification in Linear Dynamical Systems
(2001-04-05) Horton, Kirk Gerritt; S.L. Campbell, Chair; R. Smith, Member; K. Ito, Member; H.T. Tran, Member; E. Chukwu, Member
Linear dynamical systems, Ex'+Fx=f(t), in which E is singular, are useful in a wide variety of applications. Because of this wide spread applicability, much research has been done recently to develop theory for the design of linear dynamical systems. A key aspect of system design is fault detection and isolation (FDI). One avenue of FDI is via the multi-model approach, in which the parameters of the nominal, unfailed model of the system are known, as well as the parameters of one or more fault models. The design goal is to obtain an indicator for when a fault has occurred, and, when more than one type is possible, which type of fault it is. A choice that must be made in the system design is how to model noise. One way is as a bounded energy signal. This approach places very few restrictions on the types of noisy systems which can be addressed, requiring no complex modeling requirement. This thesis applies the multi-model approach to FDI in linear dynamical systems, modeling noise as bounded energy signals. A complete algorithm is developed, requiring very little on-line computation, with which nearly perfect fault detection and isolation over a finite horizon is attained. The algorithm applies techniques to convert complex system relationships into necessary and sufficient conditions for the solutions to optimal control problems. The first such problem provides the fault indicator via the minimum energy detection signal, while the second problem provides for fault isolation via the separating hyperplane. The algorithm is implemented and tested on a suite of examples in commercial optimization software. The algorithm is shownto have promise in nonlinear problems, time varying problems, and certain types of linear problems for which existing theory is not suitable.
Physiologically Based Pharmacokinetic Models for the Systemic Transport of Trichloroethylene
(2001-05-17) Potter, Laura Kay; H.T Banks, Chair; J. Bishir, Member; M.V. Evans, Member; K. Ito, Member; H.T. Tran, Member
Three physiologically based pharmacokinetic (PBPK) models for thesystemic transport of inhaled trichloroethylene (TCE) are presented.The major focus ofthese modeling efforts is the disposition of TCE in the adiposetissue, where TCE is known to accumulate. Adipose tissue is highly heterogeneous, with wide variations in fat cell size, lipid composition, blood flow rates and cellpermeability. Since TCE is highly lipophilic, the uneven distributionof lipids in the adipose tissue may lead to an uneven distribution of TCEwithin the fat. These physiological characteristics suggest that thedynamics of TCE in the adipose tissue may depend on spatial variations within the tissue itself. The first PBPK model for inhaled TCE presented here is a system ofordinary differential equations which includes the standardperfusion-limited compartmental model for each of the adipose, brain,kidney, liver, muscle and remaining tissue compartments.Model simulations predict relatively rapiddecreases in TCE fat concentrations following exposure, which may notreflect the accumulation and relative persistence of TCE inside the fattissue. The second PBPK model is identical to the first except forthe adipose tissue compartment, which is modeled as a diffusion-limited compartment.Although this model yields various concentration profiles for TCE inthe adipose tissue depending on the value of the permeabilitycoefficient, this model may not be physically appropriate for TCE,which is highly lipophilic and has a low molecular weight. Moreover,neither of these two PBPK models is able to capture spatialvariation of TCE concentrations in adipose tissue as suggested bythe physiology. The third model we present is a hybrid PBPK model with adispersion-type model for the transport of TCE in the adipose tissue. Thedispersion model is designed to account for the heterogeneities within fattissue, as well as the corresponding spatial variation of TCE concentrationsthat may occur. This partial differential equation model is based onthe dispersion model of Roberts and Rowland for hepatic uptake andelimination, adapted here for the specific physiology of adipose tissue. Theoretical results are given for the well-posedness of a generalclass of abstract nonlinear parabolic systems which includes the TCEPBPK-hybrid model as a special case. Moreover, theoretical issues related to associated general least squares estimation problems are addressed,including the standard type of deterministic problem and aprobability-based identification problem that incorporates variability inparameters across a population. We also establish thetheoretical convergence of the Galerkin approximations used in our numericalschemes. The qualitative behavior of the TCE PBPK-hybrid model is studied usingmodel simulations and parameter estimation techniques. In general, theTCE PBPK-hybrid model can generate various predictions of the dynamicsof TCE in adipose tissue by varying the adipose model parameters.These predictions include simulations that are similar to the expectedbehavior of TCE in the adipose tissue, which involves a rapid increaseof TCE adipocyte concentrations during the exposure period, followed by aslow decay of TCE levels over several hours. Results are presented for several types of parameter estimationproblems associatedwith the TCE PBPK-hybrid model. We test theseestimation strategies using two types of simulated data: observationsrepresenting TCE concentrations from a single individual, andobservations that simulateinter-individual variability. The latter type of data, which iscommonly found in experiments related to toxicokinetics, assumesvariability in the parameters across a population, and may includeobservations from multiple individuals. Using both deterministic andprobability-based estimation techniques, we demonstrate thatthe probability-based estimation strategiesthat incorporate variability in the parameters may be best suited forestimating adipose model parameters that vary across the population.