Details
| Original language | English |
|---|---|
| Pages (from-to) | 133-143 |
| Number of pages | 11 |
| Journal | COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering |
| Volume | 39 |
| Issue number | 1 |
| Publication status | Published - 29 Nov 2019 |
Abstract
Purpose: Reliable modeling of induction hardening requires a multi-physical approach, which makes it time-consuming. In designing an induction hardening system, combining such model with an optimization technique allows managing a high number of design variables. However, this could lead to a tremendous overall computational cost. This paper aims to reduce the computational time of an optimal design problem by making use of multi-fidelity modeling and parallel computing. Design/methodology/approach: In the multi-fidelity framework, the “high-fidelity” model couples the electromagnetic, thermal and metallurgical fields. It predicts the phase transformations during both the heating and cooling stages. The “low-fidelity” model is instead limited to the heating step. Its inaccuracy is counterbalanced by its cheapness, which makes it suitable for exploring the design space in optimization. Then, the use of co-Kriging allows merging information from different fidelity models and predicting good design candidates. Field evaluations of both models occur in parallel. Findings: In the design of an induction heating system, the synergy between the “high-fidelity” and “low-fidelity” model, together with use of surrogates and parallel computing could reduce up to one order of magnitude the overall computational cost. Practical implications: On one hand, multi-physical modeling of induction hardening implies a better understanding of the process, resulting in further potential process improvements. On the other hand, the optimization technique could be applied to many other computationally intensive real-life problems. Originality/value: This paper highlights how parallel multi-fidelity optimization could be used in designing an induction hardening system.
Keywords
- Finite element analysis, Induction heating, Multiphysics, Optimal design, Surrogate optimization
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computational Theory and Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
- Mathematics(all)
- Applied Mathematics
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 39, No. 1, 29.11.2019, p. 133-143.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A parallel multi-fidelity optimization approach in induction hardening
AU - Baldan, Marco
AU - Nikanorov, Alexandre
AU - Nacke, Bernard
PY - 2019/11/29
Y1 - 2019/11/29
N2 - Purpose: Reliable modeling of induction hardening requires a multi-physical approach, which makes it time-consuming. In designing an induction hardening system, combining such model with an optimization technique allows managing a high number of design variables. However, this could lead to a tremendous overall computational cost. This paper aims to reduce the computational time of an optimal design problem by making use of multi-fidelity modeling and parallel computing. Design/methodology/approach: In the multi-fidelity framework, the “high-fidelity” model couples the electromagnetic, thermal and metallurgical fields. It predicts the phase transformations during both the heating and cooling stages. The “low-fidelity” model is instead limited to the heating step. Its inaccuracy is counterbalanced by its cheapness, which makes it suitable for exploring the design space in optimization. Then, the use of co-Kriging allows merging information from different fidelity models and predicting good design candidates. Field evaluations of both models occur in parallel. Findings: In the design of an induction heating system, the synergy between the “high-fidelity” and “low-fidelity” model, together with use of surrogates and parallel computing could reduce up to one order of magnitude the overall computational cost. Practical implications: On one hand, multi-physical modeling of induction hardening implies a better understanding of the process, resulting in further potential process improvements. On the other hand, the optimization technique could be applied to many other computationally intensive real-life problems. Originality/value: This paper highlights how parallel multi-fidelity optimization could be used in designing an induction hardening system.
AB - Purpose: Reliable modeling of induction hardening requires a multi-physical approach, which makes it time-consuming. In designing an induction hardening system, combining such model with an optimization technique allows managing a high number of design variables. However, this could lead to a tremendous overall computational cost. This paper aims to reduce the computational time of an optimal design problem by making use of multi-fidelity modeling and parallel computing. Design/methodology/approach: In the multi-fidelity framework, the “high-fidelity” model couples the electromagnetic, thermal and metallurgical fields. It predicts the phase transformations during both the heating and cooling stages. The “low-fidelity” model is instead limited to the heating step. Its inaccuracy is counterbalanced by its cheapness, which makes it suitable for exploring the design space in optimization. Then, the use of co-Kriging allows merging information from different fidelity models and predicting good design candidates. Field evaluations of both models occur in parallel. Findings: In the design of an induction heating system, the synergy between the “high-fidelity” and “low-fidelity” model, together with use of surrogates and parallel computing could reduce up to one order of magnitude the overall computational cost. Practical implications: On one hand, multi-physical modeling of induction hardening implies a better understanding of the process, resulting in further potential process improvements. On the other hand, the optimization technique could be applied to many other computationally intensive real-life problems. Originality/value: This paper highlights how parallel multi-fidelity optimization could be used in designing an induction hardening system.
KW - Finite element analysis
KW - Induction heating
KW - Multiphysics
KW - Optimal design
KW - Surrogate optimization
UR - http://www.scopus.com/inward/record.url?scp=85076190824&partnerID=8YFLogxK
U2 - 10.1108/COMPEL-05-2019-0221
DO - 10.1108/COMPEL-05-2019-0221
M3 - Article
AN - SCOPUS:85076190824
VL - 39
SP - 133
EP - 143
JO - COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering
JF - COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering
SN - 0332-1649
IS - 1
ER -