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Melt Flow control Using Magnetic Fields and Magnetic Field Gradients

Melt Flow Control Using Magnetic Fields and Magnetic Field G...

The Sixth World Congress on Computational Mechanics in Conjunction with the Second Asian-Pacific Congress on Computational MechanicsⅠ

作者： N. Zabaras B. Ganapathysubramanian Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering 188 Frank H. T.Rhodes Hall Cornell University Ithaca NY 14853-3801 USA Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering 188 Frank H. T.Rhodes Hall Cornell University Ithaca NY 14853-3801 USA

Solidification of materials to near net shape is one of the most commonly used and economical methods of manufacturing. Different industries impose different restrictions and design objectives on the solidification process. For example, single crystal growth requires a planar growth front whereas casting requires homogenous material distribution. There are various techniques to control the flow in the melt to achieve the required objectives. Magnetic field based techniques have become the most popular commercially used methods for solidification control. Unfortunately, these control methods （uniform magnetic field, rotating magnetic field） are restricted to conducting materials. The extension of magnetic control to non-conducting media is explored in this work. The application of a non-uniform magnetic field produces an extra distributed force term in the governing melt flow equations. This body force, along with the Lorenz force （for conducting media） can be used to effectively control solidification. The physics behind the application of a time varying magnetic gradient on the flow is discussed and a coupled set of equations governing the fluid flow, thermal, concentration and magnetic field evolution is determined. A computational method for the design of solidification is developed such that a prescribed characteristic during solidification is achieved. Various design objectives are considered. As an example, the cost functional is taken as the L norm of an expression representing the deviation of the velocity field in the melt region from conditions corresponding to convection-less growth. The adjoint method for the inverse design of continuum processes is adopted in this framework. A continuum adjoint system is derived to calculate the adjoint temperature, concentration and velocity such that the gradient of the cost functional can be expressed analytically. The cost functional is minimized using the conjugate gradient method with a finite element realization

关键词： Melt Flow control Using Magnetic Fields and Magnetic Field Gradients

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A Variational Multiscale Stabilized Finite Element Method for Stochastic Advection-Diffusion and Stochastic Incompressible Flow

A Variational Multiscale Stabilized Finite Element Method fo...

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The Sixth World Congress on Computational Mechanics in Conjunction with the Second Asian-Pacific Congress on Computational MechanicsⅠ

作者： N. Zabaras V. A. Badri Narayanan Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering Cornell University 188 Frank H. T. Rhodes Hall Ithaca NY 14853-3801 USA Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering Cornell University 188 Frank H. T. Rhodes Hall Ithaca NY 14853-3801 USA

An extension of the deterministic variational multiscale (VMS) approach with algebraic subgrid scale (SGS) modeling is considered for developing stabilized finite element formulations for the linear stochastic scalar advection-diffusion equation and the incompressible stochastic NavierStokes equations. Different models for the intrinsic subgrid time scale will be discussed based on the level of subgrid characterization desired, phase lag and transient behavior restrictions. All these models possess similar behavior in the limits of pure advection and pure diffusion. The model chosen for τin our numerical implementation ensure s that the subgrid scale solution has finite statistical moments. The statistical behavior of τdepends on the kind of probability models chosen for the advection velocity and diffusion coefficient. Most probability models with finite support space are compatible with the proposed intrinsic time scale model. The stabilized formulations are numerically implemented using the spectral stochastic formulation of the finite element method (SSFEM). Generalized polynomial chaos and Karhunen-Loève expansion techniques are used for representation of uncertain quantities. A parallel finite element implementation using cluster specific implementation of BLAS Ⅰand BLAS Ⅱand a preconditioned parallel GMRES solver with restart capability were developed for parallel solution of the stochastic partial differential equations. Though the computational effort involved with the proposed method is an order of magnitude higher than that of a deterministic problem, it was still found to be two orders of magnitude faster than the Monte Carlo simulation for advection-diffusion problems. The proposed stabilized method is applied to various standard advection-diffusion and fluid flow examples with uncertainties in essential boundary conditions. Comparisons are drawn between the numeri cal solutions and Monte Carlo/analytical solutions wherever possible.

关键词： A Variational Multiscale Stabilized Finite Element Method for Stochastic Advection-Diffusion and Stochastic Incompressible Flow

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A Computational Statistics Approach to Stochastic Inverse Problems and Uncertainty Quantification in Heat Transfer

A Computational Statistics Approach to Stochastic Inverse Pr...

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The Sixth World Congress on Computational Mechanics in Conjunction with the Second Asian-Pacific Congress on Computational Mechanics

作者： N.Zabaras Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering Cornell University 188 Frank H.T.Rhodes Hall Ithaca NY 14853 -3801 USA

As most engineering systems and processes operate in an uncertain environment, it becomes increasingly important to address their analysis and inverse design in a stochastic manner using statistical data-driven prior and concurrent information on the system response. Recent advances in computational Bayesian and spatial statistics enable complete and efficient solution procedures to such problems. Herein, a novel framework based on Bayesian inference is presented for the solution of multi-dimension and multi-mode inverse heat transfer problems. The posterior probability density function (PPDF) of unknowns (modeled as random variables or stochastic proces ses), such as the boundary heat flux or heat sources, is computed given finite set of thermocouple temperature measurements. Markov Random Field (MRF) was adopted for prior distribution modeling. Markov Chain Monte Carlo (MCMC) algorithms are exploited to obtain estimates of statistics of random unknowns. One and two-dimensional linear boundary heat flux reconstruction problems are first discussed to demonstrate stochastic regularization through prior modeling and MCMC posterior state space exploration. A one-dimensional inverse heat conduction problem (IHCP) with consideration of uncertainty in thermo-physical properties and sensor location is also presented for complete stochastic description of the IHCP. Finally, a problem of reconstructing heat sources i n three-dimensional radiation heat transfer is presented. This example will demonstrate the need for model reduction in the likelihood calculation when applying MCMC algorithms to complex continuum problems. Simulation results demonstrate the great potenti al of applying a Bayesian approach to stochastic estimation and design problems. Although discussed in the context of thermal systems, the methodology presented is general and applicable to design and estimation problems in diverse areas in engineering.

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Multi‐length scale design of deformation processes for control of orientation (texture) dependent properties

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AIP Conference Proceedings 2004年第1期712卷

作者： Shankar Ganapathysubramanian Nicholas Zabaras Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering 188 Frank H. T. Rhodes Hall Cornell University Ithaca NY 14853‐3801

design of deformation (thermo‐mechanical) processes to achieve desired structural response will be addressed. Plastic deformation is assumed to be accommodated through crystallographic slip and reorientation of crystals. Conventional methodologies towards polycrystal plasticity use an aggregate of single crystals and this choice of the aggregate affects the response of the polycrystal. In order to address this issue, a continuum approach will be presented for the representation of polycrystals through an orientation distribution function over the orientation space. In addition, conventional polycrystal plasticity models do not consider strain rate and temperature effects on the mechanical response of a crystal. In this effort, a constitutive framework for thermo‐elastic‐viscoplastic response will be described along with a coupled macro‐micro, fully implicit algorithm. Numerical examples that highlight the benefits of the proposed approach will be detailed. design/control problems, relevant to industry, and involving microstructure sensitive‐design of processes will be addressed. A novel, coupled macro‐micro continuum sensitivity analysis has been developed for the thermo‐mechanical deformation process and is used in a gradient based optimization framework. The design problems are posed as optimization problems with design variables chosen to characterize macro‐scale deformation. Through these complex examples, we aim to show that accurate modeling and control of microstructure‐sensitive material response is indeed feasible.

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Development of soft sensor coupled to a generic advanced control algorithm based on neural networks

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IFAC Proceedings Volumes 2001年第25期34卷 89-94页

作者： L. Ender R. Madel Filho Chemical Engineering Depanment - Regional University of Blumenau Brazi Laboratory of Optimization Design and Advanced Process Control (LOPCA) Faculty of Chemical Engineering Slate University of Campinas (UNICAMP) Brazil

This work presents a procedure for the development of soft sensors for the concentration control of a fixed bed catalytic reactor, through the definition of the best setpoints for the temperature profile in the reactor, obtained through a optimization procedure where the process is represented by a neural network. This procedure is coupled to a multivariable control strategy based on neural networks and it acts on the temperature of the system. The objective is to control the output concentration of the reactor.

关键词： Soft Sensor Neural Networks Adaptive control

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A Robust Procedure for On‐Line Learning of Neural Networks in control Strategy Development

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Chemie Ingenieur Technik 2001年第6期73卷

作者： L. Ender R. Maciel Filho Chemical Engineering Department — Regional University of Blumenau Brazil Laboratory of Optimization Design and Advanced Process Control (LOPCA) Faculty of Chemical Engineering State University of Campinas (UNICAMP) Brazil

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processing modelling development through artificial neural networks and hybrid models

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Computers & Chemical Engineering 2000年第2期24卷 1355-1360页

作者： L.F.M. Zorzetto R.Maciel Filho M.R. Wolf-Maciel Dupont-Brazil Campinas SP Brazil Laboratory of Optimization Design and Advanced Process Control (LOPCA) School of Chemical Engineering Campinas-SP CP: 6066 CEP 13081-970 Campinas Brazil

Developing fully mechanistic models for bioprocess is expensive and time-consuming. On the other hand, using pure ‘black-box’ approaches can lead to a misuse of available information, because there are aspects of the process that can be accurately described by simple equations as, for example, mass balances. This work analyses the use of different types of ‘black-box’ and hybrid models to outline the dynamics of a batch beer production. The hybrid models, combine mechanistic equations with ‘black-box’ techniques (reserved only for the unclear parts of the system), in order to achieve an efficient use of the available information. The hybrid models can also be called ‘grey-box’ approaches. To generate the hybrid models, different level of information is introduced into the ‘black-box’ models, allowing for an interesting model performance comparison in the end. Results demonstrate that the ‘black-box’ models present a good performance in the range of process conditions used to develop them. However, the inclusion of mechanistic knowledge into the hybrid models increase the model extrapolative capability. In this work, artificial neural networks (ANN) are used as the main technique for both the ‘black-box’ models and the ‘black-box’ parts in the hybrid models.

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Recipe-driven batch processes: event handling in hybrid system simulation

Recipe-driven batch processes: event handling in hybrid syst...

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IEEE International Symposium on Computer Aided control System design (CACSD)

作者： M. Fritz A. Liefeldt S. Engell Software Design and Management GmbH and Company KG Ratingen Germany Germany Process Control Laboratory Department of Chemical Engineering University of Dortmund Dortmund Germany

This paper describes the integration of different simulation methods for batch processes under one single user interface and based upon one core model. We discuss two different simulation concepts with respect to event detection and handling of hybrid systems, especially of recipe-driven batch processes. The notation of event handling is central to both approaches. Examples are given to demonstrate that discrete-event simulation of hybrid systems can produce results which are equivalent to those from continuous simulation and under which conditions it fails.

关键词： Discrete event simulation process control Computational modeling Chemical processes Laboratories Event detection User interfaces Batch production systems Software packages Software design

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FERMENTATION AND GENETIC-ENGINEERING - PROBLEMS AND PROSPECTS

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BIO-TECHNOLOGY 1983年第10期1卷 847-&页

作者： BULL, DN Daniel N. Bull Ph.D. is a consultant in fermentation technology and president of Satori Corporation P.O. Box 1730 Montclair N.J. 07042. (201) 783-9787.REFERENCES Graff G.M. Short H. and Keene J.1983. Gene-splicing methods move from lab to plant. Chem. Eng.90: 22-27.|ISI|Broda P.1979. p. 1-3. Plasmids. W. H. Freeman Oxford and San Francisco.Donoghue D.J. and Sharp P.A.1978. Construction of a hybrid bacteriophage-plasmid recombinant DNA vector. J. Bact.136: 1192-1196.|PubMed|ISI|ChemPort|Bok S.H. Hoppe D. Mueller D.C. and Lee S.E.1983. Improving the production of recombinant DNA proteins through fermentation development. Abstract from 186th ACS Natl. Mtg. Washington D.C. Sept. 1.Maniatis T. Fritsch E.F. and Sam-brook J.1982. p. 88. Molecular Cloning. Cold Spring Harbor Laboratory. Guidelines for research involving recombinant DNA molecules June 1983 Fed. Reg.48: 24556-24581. Modifications of physical containment recommendations for large-scale uses of organisms containing recombinant DNA molecules. 1983. Recomb. DNA Tech. Bull.6: 69-70.Bull D.N. Thoma R.W. and Stinnett T.E.1983. Bioreactors for submerged culture. In:Adv. in Biotechnological Proc. A. Mizrahi and A. L. van Wezel (eds.) 1: 1-30.Schmidli B.L. and Swartz R.W.1982. Design considerations for aseptic fermentation. Presentation at 184th ACS Natl. Mtg. Kanas City MO.Sittig W.1982. The present state of fermentation reactors. J. Chem. Tech. Biotechnol.32: 47-58.|ISI|Strek F.1963. Intl. Chem. Eng.3: 533.Uhl V.W. and Gray J.B.1996. Mixing Theory and Practice Vol. I. Academic Press New York.Peters M.S. and Timmerhaus K.D.1968. p. 542. Plant Design and Economics for Chemical Engineers. McGraw-Hill New York.Dickey D.S. and Hicks R.W. Fundamentals of agitation. Chem. Eng.83: 93-100.Oldshue J.Y.1983. Fluid mixing technology and practice. Chem. Eng.90: 82-108.Kipke K.D.1981. Heat transfer in aerated non-Newtonian fluids. Abstract from 2nd Eur. Cong. Biotech. Eastbourne UK April 5-10.Blakebrough N. McM

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TRANSIM‐A GENERAL PURPOSE PROBLEM SOLVING TOOL

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Naval Engineers Journal 1973年第5期85卷 79-92页

作者： McMICHAEL, DAVID L. ORLEANS, BEATRICE S. Mr. David L. McMichael:is Senior Development Engineer on the Project TRANSIM Staff at the University of California at Los Angeles. He is widely experienced in the application of quantitative analytical methods in particular computer simulation to the solution of real-world problems. Prior to joining the UCLA Staff he was a commissioned officer in the U.S. Navy and was assigned to the Naval Ship Systems Command as a system analyst engaged in the introduction of quantitative analysis methods in the Navy management decision-making process. He has also been Senior Systems Analyst ASSET Inc. Industrial Engineer with the Surface Combustion Corp. and Speiser Engineering Inc. and a lecturer in Mathematics at North Virginia Community College. He received his MS degree in Operations Research at the U.S. Naval Postgraduate School having specialized in gaming and simulation and his BSME degree at Tri-State College Angola Indiana. Miss Beatrice S. Orleans:holds a BA degree in Mathematics and Statistics from Hunter College and a MS degree in Statistics from Columbia University-Graduate School of Business. She has also completed graduate courses at Columbia George Washington and American Universities. Prior to her present position she was employed by the International Statistical Bureau as an economic statistician the Educational Testing Service as a research assistant the Air Force Human Resources Research Laboratory as an aviation psychologist and Head of the Statistics Section and CNO Aviation Plans and Programs as a mathematical statistician. For the past 20 years she has been with the Bureau of Ships and now the Naval Ship System Command in what is known as the Statistical Engineering Branch of which she has been the Branch Head for the last 10 years. She also has worked in the areas of Statistical Quality Control and Design of Experiments and Reliability and in addition has taught a number of sessions of a course in Introduction to Statistical Inference for the Navy the Laboratories and the Army.

This paper describes TRANSIM simulation a a method for solving problems. There is a brief discussion of modeling and its importance. TRANSIM is compared with other general purpose simulators, special purpose simulation, and network analysis techniques such as PERT/CPM. Data requirements for TRANSIM applications are discussed, and a few examples of specific Navy applications are described in a general fashion. In conclusion, the paper details the latest TRANSIM development work in project analysis and output formats for project risk, resource allocation and scheduling. © 1973 American Society of Naval Engineers

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