Controllable thermal state design method of flexible shapes of piston cooling galleries

• Published in: Applied thermal engineering 2021-06, Vol.191, p.116865, Article 116865
• Main Authors: Deng, Xiwen, Chen, Hao, Lei, Jilin, Jia, Dewen, Bi, Yuhua
• Format: Article
• Language: English
• Subjects: Controllable design method; Cooling; Cooling gallery; Deformation; Design optimization; Diesel engine; Genetic algorithms; Multi-objective optimization; Multiple objective analysis; Piston; Piston rings; Regression models; Stability; Support vector machines; Temperature; Temperature distribution; Thermal analysis
• ISSN: 1359-4311; 1873-5606
• DOI: 10.1016/j.applthermaleng.2021.116865

•A controllable thermal state design method is proposed.•A flexible design model is adopted to find the appropriate shape of piston cooling gallery.•Local thermal state of piston cooling gallery is described by multi-objective optimization results.•The local temperature of piston ring zone is effectively controlled.•Controllable thermal state design method of flexible shapes of piston cooling galleries. The uneven temperature field is a great contributor to abnormal deformations and local damages of the piston. The piston gallery cooling is one of the effective ways to achieve an acceptable temperature field of the piston head. However, the traditional design of piston cooling gallery cannot accurately control the local thermal state of the piston, such as the temperature in the piston ring zone. In this paper, a flexible design model is adopted to control local thermal state of piston cooling gallery, and 10 parameters are used to completely determine the cross-section shape and the position of the cooling gallery. The maximum temperature and the maximum temperature gradient of the piston are employed as optimization targets to describe the thermal state of the whole piston. Most importantly, the temperature of the first ring zone is adopted as controllable target to monitor the local thermal state of the piston ring zone. The optimal structure variables and position variables were acquired by a Multi-Objective Genetic Algorithm technique. The results show that support vector machine for regression (SVR) model has a very excellent generalization ability with the determination coefficient greater than 0.90 and the relative deviation less than 7%. The SVR model can quickly obtain the cooling performance of each sample point to save calculation cost. The results illustrate that the controllable thermal state design method is effective, resulting in decreasing thermal load of the piston and adding reliability of the engine. For special focusing on the maximum temperature of the first ring zone, three distinctive optimal solutions (denoted A, B, C) are much lower than original model and other optimization models. Solution A has the lowest the temperature of the first ring zone in three optimal solutions to meet stricter cooling requirements of ring zone. Meanwhile, the temperature of the piston is also affected by the controllable thermal state design method, which have the large reduction with 5.40%. The temperature and temperature gradient are contradictory