Modeling,simulation and optimization of general solar updraft towers

A model to describe a solar chimney power plant with a generally sloped collector field and for the general situation of humid air is presented. This is a significant development of existing simple models for solar updraft towers with planar collector fields for the situation of purely dry air. The model describing the gas dynamics in the collector and in the chimney includes a turbine model, friction and heat transfer losses, evaporation and condensation models etc. However, the relevant physics can be modeled in one space dimension. It is the result of a fully compressible gas dynamic model in the small Mach number limit. A numerical algorithm is defined which admits very fast simulations. Therefore optimization procedures can easily be applied. Numerical results on optimization with respect to geometric and physical parameters which may be considered both in the planning and the operational phase are presented. The results are compared qualitatively and – if available – quantitatively to prototype data and to simulations from the literature.     

Concurrent optimization in designing for logistics support

Concurrent Engineering has always been an important field within military industry and is gaining recognition within commercial industry as well. Studies have suggested that long-range research efforts need to be directed to the area of integrated logistics. In order to satisfy the overall logistics objectives, product designs must incorporate consideration of the impact that design decisions have on operations, maintenance, transportation and supply. This paper presents a model that carries out a concurrent optimization of a product design and its associated manufacturing and logistics support systems. We offer this model as a framework for decision support of concurrent engineering activities.     

Modeling and analysis of an auction-based logistics market

We consider a logistics spot market where the transportation orders from a number of firms are matched with two types of carriers through a reverse auction. In the spot market, local carriers compete with in-transit carriers that have lower costs. In order to analyze the effects of implementing a logistics spot market on these three parties: firms, local carriers, and in-transit carriers and also the effects of various system parameters, we develop a two-stage stochastic model. We first model the auction in a static setting and determine the expected auction price based on the number of carriers engaging in the auction and their cost distributions. We then develop a continuous-time Markov chain model to evaluate the performance of the system in a dynamic setting with random arrivals and possible abandonment of orders and carriers. By combining these two models, we evaluate the performance measures such as the expected auction price, price paid to the carriers, distribution of orders between local and in-transit carriers, and expected number of carriers and orders waiting at the logistics center in the long run. We present analytical and computational results related to the performance of the system and discuss operation of such a logistics spot market in Turkey.     

Robust bi-level optimization of relief logistics operations

In this paper, a robust bi-level optimization model is developed for a supply–distribution relief network under uncertainty in demand and supply parameters. It optimizes the relief operating costs as well as considering a penalty term for unsatisfied victims’ demands. Moreover, the proposed framework optimizes the relief commodity flow in a relief chain along with the supply risk minimization by identifying the suppliers with a lower risk. This paper proposes an integrated optimization method in which the supply risk value for each supplier is obtained via the TOPSIS method. Next, these values are utilized in a robust bi-level model to select appropriate suppliers and allocate orders. Finally, the robustness and effectiveness of the proposed model are demonstrated by a case of flood disaster.     

Modeling,simulation and control a hydraulic servo system

Hydraulic actuators are widely used in industrial applications due to several advantages like large force and torque, high power to weight ratio, rapid and accurate response. In this paper a nonlinear model of a hydraulic servo system is developed by means of the associated differential equations and then simulated using Matlab techniques. The model describes the behavior of a servo system FESTO TP511 with MOOG-DDV633 servovalve and includes the nonlinearities of friction forces, valve dynamics, oil compressibility and load influence. The nonlinear model is used to design an optimal controller based on estimated state parameters through simulation. A digital control platform based on Atmel ATmega8535 microcontroller is used to compare the behavior of hydraulic system under PD and optimal control. The control platform was designed like an interface between PC and process: the control algorithm runs on PC and the digital platform assures amplifying, filtering and data communication functions. Both simulation and experimental results are provided to show the effectiveness of the proposed model and control method. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Modeling composites on graphs,with parameter optimization

Composites have been modeled by means of graph theory and the concept of a dynamic interfacial layer on both sides of the filler-matrix boundary. Certain assumptions were made in order to eliminate the indeterminacy created by the use of models. The magnitude of the dynamic interfacial layer has been estimated for the fillers and the epoxy matrix. The calculations have been compared with experimental data.Moscow State Open University, Center for Composite Materials, Russia. Translated from Mekhanika Kompozitnykh Materialov, Vol. 32, No. 4, pp. 467–479, July–August, 1996.     

Kidney exchange simulation and optimization

One of the challenges in a kidney exchange program (KEP) is to choose policies that ensure an effective and fair management of all participating patients. In order to understand the implications of different policies of patient allocation and pool management, decision makers should be supported by a simulation tool capable of tackling realistic exchange pools and modeling their dynamic behavior. In this paper, we propose a KEP simulator that takes into consideration the wide typology of actors found in practice (incompatible pairs, altruistic donors, and compatible pairs) and handles different matching policies. Additionally, it includes the possibility of evaluating the impact of positive crossmatch of a selected transplant, and of dropouts, in a dynamic environment. Results are compared to those obtained with a complete information model, with knowledge of future events, which provides an upper bound to the objective values. Final results show that shorter time intervals between matches lead to higher number of effective transplants and to shorter waiting times for patients. Furthermore, the inclusion of compatible pairs is essential to match pairs of specific patient–donor blood type. In particular, O-blood type patients benefit greatly from this inclusion.     

Modeling,analysis and simulation of case hardening of steel

A mathematical model for the gas carburizing in steel is presented. Carbon is dissolved in the surface layer of a low-carbon steel part at a temperature sufficient to render the steel austenitic, followed by quenching to form a martensitic microstructure. We have a nonlinear evolution equation for the temperature, coupled with a nonlinear evolution equation for the carbon concentration, both coupled with two ordinary differential equations to describe the phase fractions. We present mathematical results concerning the well-posedness of the model and finally present a simulation of the process using a finite element approximation. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Modeling,simulation and control of a doubly-fed induction machine

The Hamiltonian model and control of a doubly-fed induction machine is discussed. The geometric properties of the Hamiltonian formalism allow us to develop control algorithms which assure stability and good performance. Experimental results in good accordance with the control goals are presented. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Adaptive sequencing of primal,dual, and design steps in simulation based optimization

Many researchers have used Oneshot optimization methods based on user-specified primal state iterations, the corresponding adjoint iterations, and appropriately preconditioned design steps. Our goal here is to develop heuristics for sequencing these three subtasks, in order to optimize the convergence rate of the resulting coupled iteration cycle. A key ingredient is the preconditioning in the design step by a BFGS approximation of the projected Hessian. We provide a hard bound on the spectral radius of the coupled iteration cycle at local minima satisfying second order sufficiency conditions. Finally, we show how certain problem specific parameters can be estimated by local samples and be used to steer the whole process adaptively. We present limited numerical results that confirm the theoretical analysis.     

Combining modeling,simulation, optimization and analysis under a unique framework

The virtual engineering design process is based on several phases and every phase is supported by special concepts, representations and tools. This process is complex and the transition and communication between phases is difficult because of conversions and transformations. Embodiment is the phase, which more time requires, in order to produce a definitive layout from the concept, hence, we are designing and developing a unique framework where the embodiment phase can be performed, integrated and combined without transformations and conversions. This approach will support the engineers with advanced techniques on modeling meshes, conceptual simulations and real time optimization. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Robust optimization for relief logistics planning under uncertainties in demand and transportation time

Emergency logistics is an essential component of post-disaster relief campaigns. However, there are always various uncertainties when making decisions related to planning and implementing post-disaster relief logistics. Considering the particular environmental conditions during post-disaster relief after a catastrophic earthquake in a mountainous area, this paper proposes a stochastic model for post-disaster relief logistics to guide the tactical design for mobilizing relief supply levels, planning initial helicopter deployments, and creating transportation plans within the disaster region, given the uncertainties in demand and transportation time. We then introduce a robust optimization approach to cope with these uncertainties and deduce the robust counterpart of the proposed stochastic model. A numerical example based on disaster logistics during the Great Sichuan Earthquake demonstrates that the model can help post-disaster managers to determine the initial deployments of emergency resources. Sensitivity analyses explore the trade-off between optimization and robustness by varying the robust optimization parameter values.     

Integrated optimization of customer and supplier logistics at Robert Bosch LLC

Large automotive supply chains typically involve manufacturers pulling materials from their suppliers along the chain, usually by using round-trip truckload routes. The return trips on these routes are used to return empty containers back to the suppliers. The mismatch between the amount of materials and empty containers results in underutilization of the return trips. A supplier can utilize this unused capacity by identifying a subset of promising customer routes that can be combined with its existing supplier routes to save overall costs of the system. Such an integration also leads to other supply chain coordination benefits such as the potential of using crossdocks, more frequent milkruns and ensuing reductions in inventories.     

Modeling and simulation with operator scaling

Self-similar processes are useful models for natural systems that exhibit scaling. Operator scaling allows a different scale factor in each coordinate. This paper develops practical methods for modeling and simulation. A simulation method is developed for operator scaling Lévy processes, based on a series representation, along with a Gaussian approximation of the small jumps. Several examples are given to illustrate the range of practical applications. A complete characterization of symmetries in two dimensions is given, for any exponent and spectral measure, to inform the choice of these model parameters. The paper concludes with some extensions to general operator self-similar processes.     

Modeling and simulation of pollutants transport in rivers

This paper is devoted to mathematical modeling and computer simulation of diffusion and transport of chemicals in rivers. We present one-, two-, and three-dimensional models in terms of time-dependent convection–diffusion–reaction differential equations, further we give the finite difference approximation and appropriate numerical algorithms for these models, and finally we discuss briefly the computer implementation of this methodology in a user friendly software package. To verify the model and the computer code we have used it to study the diffusion and transport of chemicals, in this case NO₃ and PO₄, in two rivers in Western Georgia flowing into the Black Sea. Namely, we considered the river Khobistskali subject to pollution sources Ochkhomuri and Chanistskali river Choga polluted with NO₃.     

Modeling,simulation and validation of material flow on conveyor belts

In this paper a model comparison approach based on material flow systems is investigated that is divided into a microscopic and a macroscopic model scale. On the microscopic model scale particles are simulated using a model based on Newton dynamics borrowed from the engineering literature. Phenomenological observations lead to a hyperbolic partial differential equation on the macroscopic model scale. Suitable numerical algorithms are presented and both models are compared numerically and validated against real-data test settings.     

Modeling and optimization of CSMA/CA in VANET

In this paper, we propose a simple theoretical model to compute the maximum spatial reuse feasible in a VANET. We focus on the ad hoc mode of the IEEE 802.11p standard. Our model offers simple and closed-form formulas on the maximum number of simultaneous transmitters, and on the distribution of the distance between them. It leads to an accurate upper bound on the maximum capacity. In order to validate our approach, results from the analytical models are compared to simulations performed with the network simulator NS-3. We take into account different traffic distributions (traffic of vehicles), and study the impact of this traffic on capacity. An application of this work is the parameterization of the CSMA/CA mechanism. Such an optimization is developed at the end of this paper.