Adel Mhamdi
RWTH Aachen University
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Publication
Featured researches published by Adel Mhamdi.
Numerical Heat Transfer Part B-fundamentals | 2005
Torsten Lüttich; Adel Mhamdi; Wolfgang Marquardt
ABSTRACT The solution of linear inverse heat conduction problems (IHCP) for the reconstruction of time-dependent, spatially unknown heat fluxes on the boundaries of two- and three-dimensional geometries from several temperature measurements is presented. The solution method is based on the interpretation of the IHCP in the frequency domain. We emphasize the extension of the method to large-scale problems. In particular, we consider the parameterization of the unknown heat fluxes with respect to the number of sensors and their positions and the reduction and stable inversion of the large linear state-space models obtained from semidiscretization of the heat conduction equation. The influence of each of the steps and the choice of their tuning parameters on the quality of the estimated heat flux are discussed. Two- and three-dimensional examples show the good performance of the method with regard to time and space resolution in the presence of noisy measurements.
Inverse Problems | 2009
Herbert Egger; Yi Heng; Wolfgang Marquardt; Adel Mhamdi
In this paper, we consider a three-dimensional transient inverse heat conduction problem arising in pool boiling experiments, i.e., the reconstruction of the surface heat flux from pointwise temperature observations inside a heater. We show that the inverse problem is ill-posed and utilize Tikhonov regularization and conjugate gradient methods together with a discrepancy stopping rule for a stable solution. We investigate the proper choice of regularization terms, which not only affects stability of the reconstructions but also greatly influences the quality of reconstructions in the case of limited observations. For the numerical solution of the governing partial differential equation, a space-time finite element method is used. This allows us to compute exact gradients for the discretized Tikhonov functional, and enables the use of conjugate gradient methods for the solution of the regularized inverse problem. We discuss further aspects of an efficient implementation, including a multilevel optimization strategy, together with an implementable stopping criterion. Finally, the proposed algorithms are applied to the reconstruction of local boiling heat fluxes from experimental data.
Inverse Problems in Science and Engineering | 2010
Yi Heng; Adel Mhamdi; E. Wagner; Peter Stephan; Wolfgang Marquardt
Boiling heat transfer is still hard to model and to predict. Among all kinds of studies on boiling processes, the estimation of the local heat fluxes at the boiling surface is fundamental. In this article, we solve a three-dimensional transient inverse heat conduction problem (IHCP) arising from a single-bubble nucleate boiling experiment to reconstruct the local boiling heat flux from a high-resolution temperature field measured at the back side of a thin heating foil. From the mathematical point of view, this problem belongs to the class of ill-posed inverse problems and cannot be solved straightforwardly by classical numerical methods. Thus, an iterative regularization strategy based on the conjugate gradient method is applied to the solution of the considered IHCP. Ring-shaped local boiling heat fluxes which undergo a significant change during a single-bubble cycle are observed.
IFAC Proceedings Volumes | 2004
Marc Brendel; Adel Mhamdi; Dominique Bonvin; Wolfgang Marquardt
Abstract This paper proposes an incremental approach for the identification of complex reaction kinetics in chemical reactors. The reaction fluxes for the various species are first estimated on the basis of concentration measurements and balance equations. This task represents an ill-posed inverse problem requiring appropriate regularization. In a further step, the reaction rates are estimated without postulating a kinetic structure. Finally, the dependency of the reaction rates on concentrations, i.e. the kinetic laws, are constructed by means of feedforward neural networks. This incremental approach is shown to be both efficient and flexible for utilizing the available process knowledge. The methodology is illustrated on the industrially-relevant acetoacetylation of pyrrole with diketene.
Computer-aided chemical engineering | 2012
Badr Bin Ashoor; Hassan Fath; Wolfgang Marquardt; Adel Mhamdi
Abstract Membrane distillation (MD) for desalination is an emerging thermally driven process exhibiting various advantages in comparison with traditional processes. Most of the MD configuration processes have been modeled as steady-state one-dimensional systems using empirical heat and mass transfer equations. Stationary two-dimensional MD models have been considered only in very few studies. In this work, a dynamic model of a direct contact membrane distillation (DCMD) process in plate-and-frame configuration is developed. It aims at giving insight into the underlying coupled physico-chemical phenomena at a level of detail. The model is implemented in the modeling package gPROMS. Numerical simulations are conducted for different operational parameters at the module inlets such as the feed and permeate temperature or feed and permeate flow rate. The results are compared with experimental data published in the literature.
Inverse Problems | 2010
Yi Heng; Shuai Lu; Adel Mhamdi; Sergei V. Pereverzev
The L-curve method is known as one of the most popular heuristic regularization parameter choice rules in solving discrete ill-posed problems: A modification of the L-curve method proposed by Reginska (1996 SIAM J. Sci. Comput. 17 740–9) consists in finding the minimizer of the functional where −1/μ is the slope at the corner of the L-curve. In this paper we propose a model function approach in the modified L-curve method for the choice of the regularization parameter. The idea is to replace the residual norm and the regularized solution norm with appropriate model functions. With such an approach, the computational cost of the minimization procedure can be essentially reduced. This approach is applied to pool boiling data to reconstruct unknown heat fluxes at the boiling surface.
IFAC Proceedings Volumes | 2004
Adel Mhamdi; Wolfgang Marquardt
Abstract The estimation of reaction rates is an important problem in mechanistic modeling, monitoring and control of chemical reactors. In contrast to standard estimation techniques where a model must be chosen for the reaction rates, we consider them in this work as unknown time-varying functions, which also may be interpreted as inputs. The resulting estimation task is an ill-posed inverse problem. The paper addresses this estimation problem based on systematic methods for nonlinear system inversion and filtering resulting in efficient estimators. A theoretical analysis reveals the conditions for reaction rate reconstruction are those for system invertibility. Our estimation scheme is a regularization method which eliminates the difficulties arising with ill-posed problems. Guidelines for the design of the estimator structure and the selection of the regularization parameters are presented.
SIAM Journal on Scientific Computing | 2011
Maka Karalashvili; Sven Groß; Wolfgang Marquardt; Adel Mhamdi; Arnold Reusken
A rigorous method is presented for the systematic identification of the structure and the parameters of transport coefficient models in three-dimensional, transient convection-diffusion systems using high-resolution measurement data. The transport is represented by a convection term with known convective velocity and a diffusion term with an unknown, generally state-dependent, transport coefficient. The identification of a transport coefficient model constitutes an ill-posed, highly nonlinear inverse problem. In our previous work [Karalashvili et al., SIAM J. Sci. Comput., 30 (2008), pp. 3249-3269], we presented a novel incremental identification method, which decomposes this inverse problem into easier-to-handle inverse subproblems. This way, the incremental identification method not only allows for the identification of the structure and the parameters of the model, but also supports the rigorous decision making on the best-suited transport model structure. Due to the decomposition approach, the identified transport model structure and parameters are subject to errors. To cope with the error propagation inherent in the incremental method, the present work suggests a model correction procedure as a supplement to the incremental identification method of our previous work, which results in a transport model of higher precision. The correction refers to both the model structure and parameters. No a priori knowledge on the unknown transport model structure is necessary. The identification approach is numerically illustrated for a three-dimensional, transient convection-diffusion equation which has its origin in the modeling and simulation of energy transport in a laminar wavy film flow.
SIAM Journal on Scientific Computing | 2008
Maka Karalashvili; Sven Groß; Adel Mhamdi; Arnold Reusken; Wolfgang Marquardt
In this paper, an incremental approach for the identification of a model for transport coefficients in convection-diffusion systems on the basis of high-resolution measurement data is presented. The transport is represented by a convection term with known convective velocity and by a diffusion term with an unknown, generally state-dependent transport coefficient. The identification of the transport model for this transport coefficient constitutes an ill-posed nonlinear inverse problem. We present a novel decomposition approach in which this inverse problem is split into a sequence of inverse subproblems. In the first identification step of this incremental approach a source is estimated by solving an affine-linear inverse problem by means of the conjugate gradient method. In the second identification step a nonlinear inverse problem has to be solved to reconstruct a transport coefficient. A Newton-type method using the conjugate gradient method in its inner iteration is used to solve this nonlinear inverse problem of coefficient estimation. Finally, in the third identification step a transport model structure is proposed and identified on the basis of the model-free transport coefficient reconstructed in the two previous steps. The ill-posedness of each inverse problem is examined by using artificially perturbed transient simulation data and appropriate regularization techniques. The identification methodology is illustrated for a three-dimensional convection-diffusion equation which has its origin in the modeling and simulation of energy transport in a laminar wavy film flow.
IFAC Proceedings Volumes | 1996
Adel Mhamdi; Achim Helbig; Olaf Abel; Wolfgang Marquardt
Abstract In industry limited process knowledge is often an obstacle to the application of model based control and state estimation. In this case study, temperature control of an industrial semibatch reactor assuming unknown reaction kinetics is treated. It is shown that model based techniques can be applied successfully even in this situation if all available knowledge is used in an optimal way. The benefits of easily being able to incorporate inequality constraints into receding horizon optimization based strategies are illustrated. The arising dynamic optimization problems are solved in the unified framework of a Newton-type algorithm.