Computational Fluid Dynamics 2020

This book presents a collection of works published in a recent Special Issue (SI) entitled “Computational Fluid Dynamics”. These works address the development and validation of existent numerical solvers for fluid flow problems and their related applications. They present complex nonlinear, non-Newtonian fluid flow problems that are (in some cases) coupled with heat transfer, phase change, nanofluidic, and magnetohydrodynamics (MHD) phenomena. The applications are wide and range from aerodynamic drag and pressure waves to geometrical blade modification on aerodynamics characteristics of high-pressure gas turbines, hydromagnetic flow arising in porous regions, optimal design of isothermal sloshing vessels to evaluation of (hybrid) nanofluid properties, their control using MHD, and their effect on different modes of heat transfer. Recent advances in numerical, theoretical, and experimental methodologies, as well as new physics, new methodological developments, and their limitations are presented within the current book. Among others, in the presented works, special attention is paid to validating and improving the accuracy of the presented methodologies. This book brings together a collection of inter/multidisciplinary works on many engineering applications in a coherent manner

Iterative Algorithms for Distributed Optimization with Applications to Multi-Agent Estimation and Control

Optimization is a prevalent tool in control and estimation. This work explores the theoretical and practical challenges in the design and analysis of distributed algorithms to solve optimization problems related to multi-agent systems.We begin by considering a problem related to parameter estimation in sensor networks. We show that the maximum likelihood estimation formulation of several localization problems based on inter-sensor measurements reduces to the form of a common constrained optimization. We then design a distributed algorithm that utilizes only the most recent measurements and the estimates from the neighboring sensors, to iteratively compute the optimal solution. Our analysis shows that the solutions obtained from this algorithm converge locally to the maximum likelihood estimates, nevertheless simulations show that this convergence may occur globally. Furthermore, in experimental results using custom ultra-wideband radio frequency devices, this algorithm outperformed other distributed methods tested for a given localization problem.Next, we consider a multi-agent coordination problem formulated as a finite horizon optimization of the type used in model predictive control. We present two distributed and iterative algorithms, in which each agent is assigned a cost function, which it optimizes to compute its own control action. These cost functions depend on the states and the estimates of the control variables of the agents' neighbors, which are obtained through inter-agent communication. For the first algorithm, the agents are able to receive estimates from 2-hop neighbors, whereas the second algorithm utilizes only 1-hop neighbor information. For the first algorithm, our results show that the local solutions converge to the solution of the original model predictive control problem, regardless of how the algorithm is initialized. Because this convergence is asymptotic, we derive practical conditions for terminating the algorithm in a finite number of iterations, such that the closed-loop system achieves the desired coordination. For the second algorithm, due to more restrictive constraints, the convergence occurs to suboptimal solutions of the model predictive control problem. Nevertheless, simulations demonstrate that the optimality gap is small, and in some cases zero.A key takeaway from these results is that in many problems of multi-agent systems, the communication between the agents can be leveraged to design distributed algorithms that match the quality of solutions that one would obtain from centralized approaches

Design Issues for Generalized Linear Models: A Review

Generalized linear models (GLMs) have been used quite effectively in the modeling of a mean response under nonstandard conditions, where discrete as well as continuous data distributions can be accommodated. The choice of design for a GLM is a very important task in the development and building of an adequate model. However, one major problem that handicaps the construction of a GLM design is its dependence on the unknown parameters of the fitted model. Several approaches have been proposed in the past 25 years to solve this problem. These approaches, however, have provided only partial solutions that apply in only some special cases, and the problem, in general, remains largely unresolved. The purpose of this article is to focus attention on the aforementioned dependence problem. We provide a survey of various existing techniques dealing with the dependence problem. This survey includes discussions concerning locally optimal designs, sequential designs, Bayesian designs and the quantile dispersion graph approach for comparing designs for GLMs.Comment: Published at http://dx.doi.org/10.1214/088342306000000105 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

OptBPPlanner: Automatic Generation of Optimized Business Process Enactment Plans

Unlike imperative models, the specifi cation of business process (BP) properties in a declarative way allows the user to specify what has to be done instead of having to specify how it has to be done, thereby facilitating the human work involved, avoiding failures, and obtaining a better optimization. Frequently, there are several enactment plans related to a specifi c declarative model, each one presenting specifi c values for different objective functions, e.g., overall completion time. As a major contribution of this work, we propose a method for the automatic generation of optimized BP enactment plans from declarative specifi cations. The proposed method is based on a constraint-based approach for planning and scheduling the BP activities. These optimized plans can then be used for different purposes like simulation, time prediction, recommendations, and generation of optimized BP models. Moreover, a tool-supported method, called OptBPPlanner, has been implemented to demonstrate the feasibility of our approach. Furthermore, the proposed method is validated through a range of test models of varying complexity.Ministerio de Ciencia e Innovación TIN2009-1371

Systems approaches and algorithms for discovery of combinatorial therapies

Effective therapy of complex diseases requires control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper we describe the main current and proposed approaches to the design of combinatorial therapies, including the empirical methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.Comment: 25 page

Trajectory Synthesis for Fisher Information Maximization

Estimation of model parameters in a dynamic system can be significantly improved with the choice of experimental trajectory. For general, nonlinear dynamic systems, finding globally "best" trajectories is typically not feasible; however, given an initial estimate of the model parameters and an initial trajectory, we present a continuous-time optimization method that produces a locally optimal trajectory for parameter estimation in the presence of measurement noise. The optimization algorithm is formulated to find system trajectories that improve a norm on the Fisher information matrix. A double-pendulum cart apparatus is used to numerically and experimentally validate this technique. In simulation, the optimized trajectory increases the minimum eigenvalue of the Fisher information matrix by three orders of magnitude compared to the initial trajectory. Experimental results show that this optimized trajectory translates to an order of magnitude improvement in the parameter estimate error in practice.Comment: 12 page

Space Structures: Issues in Dynamics and Control

A selective technical overview is presented on the vibration and control of large space structures, the analysis, design, and construction of which will require major technical contributions from the civil/structural, mechanical, and extended engineering communities. The immediacy of the U.S. space station makes the particular emphasis placed on large space structures and their control appropriate. The space station is but one part of the space program, and includes the lunar base, which the space station is to service. This paper attempts to summarize some of the key technical issues and hence provide a starting point for further involvement. The first half of this paper provides an introduction and overview of large space structures and their dynamics; the latter half discusses structural control, including control‐system design and nonlinearities. A crucial aspect of the large space structures problem is that dynamics and control must be considered simultaneously; the problems cannot be addressed individually and coupled as an afterthought

Estimator Selection: End-Performance Metric Aspects

Recently, a framework for application-oriented optimal experiment design has been introduced. In this context, the distance of the estimated system from the true one is measured in terms of a particular end-performance metric. This treatment leads to superior unknown system estimates to classical experiment designs based on usual pointwise functional distances of the estimated system from the true one. The separation of the system estimator from the experiment design is done within this new framework by choosing and fixing the estimation method to either a maximum likelihood (ML) approach or a Bayesian estimator such as the minimum mean square error (MMSE). Since the MMSE estimator delivers a system estimate with lower mean square error (MSE) than the ML estimator for finite-length experiments, it is usually considered the best choice in practice in signal processing and control applications. Within the application-oriented framework a related meaningful question is: Are there end-performance metrics for which the ML estimator outperforms the MMSE when the experiment is finite-length? In this paper, we affirmatively answer this question based on a simple linear Gaussian regression example.Comment: arXiv admin note: substantial text overlap with arXiv:1303.428