M. Battistetti, P. Di Barba, F. Dughiero, M. Farina, S. Lupi and A. Savini
Transverse flux induction heating (TFH) is a process advantageously applied for the heat treatment of thin non‐ferrous metal strips. In comparison with the better known…
Abstract
Transverse flux induction heating (TFH) is a process advantageously applied for the heat treatment of thin non‐ferrous metal strips. In comparison with the better known longitudinal flux heating the design of TFH inductors is more complex. In fact both the prediction of power density distribution in the strip and the calculation of the thermal transient during the heating process require a solution of 3D electromagnetic and thermal problems. Moreover the requirements for a good inductor design are in conflict with each other. In the paper a code for the solution of 3D electromagnetic and thermal problems suitable for the design of TFH systems is presented. The analytical‐numerical approach (analytical for the electromagnetic problem, numerical for the thermal one) is suitable for coupling with optimisation algorithms. Both evolutionary strategy and simplex methods and their combination have been used in order to obtain an optimal design for a particular application of TFH.
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Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed…
Abstract
Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.
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P. Di Barba, M. Farina and A. Savini
When a multiobjective optimization problem is tackled using Pareto optima theory, particular care has to be taken to obtain a full sampling of the Pareto Optimal Front. This leads…
Abstract
When a multiobjective optimization problem is tackled using Pareto optima theory, particular care has to be taken to obtain a full sampling of the Pareto Optimal Front. This leads to variability of individuals in both design space and objective space. We compare two different fitness assignment strategies based on two individual sharing procedures in the space domain and in the objective domain on some test cases.
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When tackling the multicriteria optimization of an electromagnetic device, the accurate sampling of the Pareto optimal front implies the use of complex and time‐consuming…
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When tackling the multicriteria optimization of an electromagnetic device, the accurate sampling of the Pareto optimal front implies the use of complex and time‐consuming algorithms. In several cases, however, the identification of a few non‐inferior solutions is often sufficient for the design purposes; as a consequence, the computational cost may be reduced. A simple evolutionary methodology is proposed to obtain a fast approximation of non‐inferior solutions; the optimal shape design of a shielded reactor is presented as a case study.
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Introduces the fourth and final chapter of the ISEF 1999 Proceedings by stating electric and magnetic fields are influenced, in a reciprocal way, by thermal and mechanical fields…
Abstract
Introduces the fourth and final chapter of the ISEF 1999 Proceedings by stating electric and magnetic fields are influenced, in a reciprocal way, by thermal and mechanical fields. Looks at the coupling of fields in a device or a system as a prescribed effect. Points out that there are 12 contributions included ‐ covering magnetic levitation or induction heating, superconducting devices and possible effects to the human body due to electric impressed fields.
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Victor U. Karthik, Sivamayam Sivasuthan, Arunasalam Rahunanthan, Ravi S. Thyagarajan, Paramsothy Jayakumar, Lalita Udpa and S. Ratnajeevan H. Hoole
Inverting electroheat problems involves synthesizing the electromagnetic arrangement of coils and geometries to realize a desired heat distribution. To this end two finite element…
Abstract
Purpose
Inverting electroheat problems involves synthesizing the electromagnetic arrangement of coils and geometries to realize a desired heat distribution. To this end two finite element problems need to be solved, first for the magnetic fields and the joule heat that the associated eddy currents generate and then, based on these heat sources, the second problem for heat distribution. This two-part problem needs to be iterated on to obtain the desired thermal distribution by optimization. Being a time consuming process, the purpose of this paper is to parallelize the process using the graphics processing unit (GPU) and the real-coded genetic algorithm, each for both speed and accuracy.
Design/methodology/approach
This coupled problem represents a heavy computational load with long wait-times for results. The GPU has recently been demonstrated to enhance the efficiency and accuracy of the finite element computations and cut down solution times. It has also been used to speedup the naturally parallel genetic algorithm. The authors use the GPU to perform coupled electroheat finite element optimization by the genetic algorithm to achieve computational efficiencies far better than those reported for a single finite element problem. In the genetic algorithm, coding objective functions in real numbers rather than binary arithmetic gives added speed and accuracy.
Findings
The feasibility of the method proposed to reduce computational time and increase accuracy is established through the simple problem of shaping a current carrying conductor so as to yield a constant temperature along a line. The authors obtained a speedup (CPU time to GPU time ratio) saturating to about 28 at a population size of 500 because of increasing communications between threads. But this far better than what is possible on a workstation.
Research limitations/implications
By using the intrinsically parallel genetic algorithm on a GPU, large complex coupled problems may be solved very quickly. The method demonstrated here without accounting for radiation and convection, may be trivially extended to more completely modeled electroheat systems. Since the primary purpose here is to establish methodology and feasibility, the thermal problem is simplified by neglecting convection and radiation. While that introduces some error, the computational procedure is still validated.
Practical implications
The methodology established has direct applications in electrical machine design, metallurgical mixing processes, and hyperthermia treatment in oncology. In these three practical application areas, the authors need to compute the exciting coil (or antenna) arrangement (current magnitude and phase) and device geometry that would accomplish a desired heat distribution to achieve mixing, reduce machine heat or burn cancerous tissue. This process presented does it more accurately and speedily.
Social implications
Particularly the above-mentioned application in oncology will alleviate human suffering through use in hyperthermia treatment planning in cancer treatment. The method presented provides scope for new commercial software development and employment.
Originality/value
Previous finite element shape optimization of coupled electroheat problems by this group used gradient methods whose difficulties are explained. Others have used analytical and circuit models in place of finite elements. This paper applies the massive parallelization possible with GPUs to the inherently parallel genetic algorithm, and extends it from single field system problems to coupled problems, and thereby realizes practicable solution times for such a computationally complex problem. Further, by using GPU computations rather than CPU, accuracy is enhanced. And then by using real number rather than binary coding for object functions, further accuracy and speed gains are realized.
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P. Di Barba, B. Forghani and D.A. Lowther
Seeks to determine the optimal shape design of an induction heating device.
Abstract
Purpose
Seeks to determine the optimal shape design of an induction heating device.
Design/methodology/approach
Presents, through a case study, the optimal shape design of a multiple coil inductor for surface heating. Resorts to a procedure of automated optimal design, based on evolutionary optimisation and processing both continuous‐valued and discrete‐valued variables.
Findings
Demonstrates that it is possible to solve the design problem routinely using an optimisation tool (OptiNet) that is built to work seamlessly with a coupled electromagnetic‐thermal simulation.
Originality/value
Every design criterion that is likely to be of interest to a designer of industrial applications can be described to the system and optimised in a reasonable time frame.
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P. Di Barba, A. Savini, F. Dughiero and S. Lupi
The paper reports recent experiences of the authors in the automated optimal design of devices and systems for induction heating. The results presented have been obtained in the…
Abstract
The paper reports recent experiences of the authors in the automated optimal design of devices and systems for induction heating. The results presented have been obtained in the frame of a long‐lasting cooperation between Laboratory of Electroheat, University of Padova and Electromagnetic Devices CAD Laboratory, University of Pavia. In particular, two case studies are discussed; in both cases, the shape design of the inductor is carried out in a systematic way, by minimizing user‐defined objective functions depending on design variables and subject to bounds and constraints. When the design problem is characterized by many objectives which are in mutual conflict, the non‐dominated set of solutions is identified.
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F. Dughiero, S. Lupi, A. Mühlbauer and A. Nikanorov
In the years 1999 and 2000 the Universities of Hannover and Padua and four industrial partners from Italy and Germany have developed a common research project on TFH financed by…
Abstract
In the years 1999 and 2000 the Universities of Hannover and Padua and four industrial partners from Italy and Germany have developed a common research project on TFH financed by the EU (Project JOE3‐CT98‐7023). In this paper, the main results obtained are shortly described.