Optimizasyon tabanlı tork konvertör hidrodinamik tasarımı
Optimization based torque converter hydrodynamic design
Yazar: MEHMET CAN DURUKAN
Danışman: DOÇ. DR. LEVENT ALİ KAVURMACIOĞLU
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Makine Mühendisliği Ana Bilim Dalı / Isı-Akışkan Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Makine Mühendisliği = Mechanical Engineering
Dizin: Akış analizi = Flow analysis ; Geometri optimizasyonu = Geometry optimisiation ; Güç aktarımı = Power transfer

Özet:
Modern bir paletli zırhlı araç aktarma sisteminde kullanılmaya uygun performans isterleri ve boyut kısıtlarına sahip bir tork konvertör, sunulan optimizasyon tabanlı hidrodinamik dizayn süreci takip edilerek tasarlandı. Pompa, türbin ve stator bileşenlerinden oluşan tek kademeli ve üç bileşenli tork konvertör tasarımının kanat ve meridyonel geometrileri oluşturuldu. Analitik hesaplar sonucu performans hedeflerine uygun sonuçlar vereceği öngörülen bir girdi seti oluşturuldu. Tasarım süreci oluşturulan bu temel tasarımla başlatıldı. Tork konvertör geometrisi yüksek güvenilirlikte uygun geometri çıkarak biçimde parametrize edildi. Oluşturulan tork konvertör tasarımlarının performanslarının öngörülmesi için HAD analizleri gerçekleştirildi. Benzetilmiş Tavlama (Simulated Annealing) algoritmasından esinlenerek oluşturulan bir optimizasyon algoritması tasarım iyileştirmesi sürecinde kullanıldı. Toplam 4 performans parametresi iyileştirme hedefleri olarak seçildi. Bir hedef fonksiyonu kullanılarak çoklu hedefli optimizasyon problemi tek hedefli optimizasyon problemine indirgendi. Bu fonksiyonla hesaplanan puanın yükseltilmesi hedefiyle iyileştirme süreci ilerletildi. Yapay Sinir Ağları (YSA, Artificial Neural Network) yöntemi kullanılarak oluşturulan benzetim modelleri ile tork konvertör performansının öngörülmesinde kullanıldı, böylece iyileştirme süreci için gerekli HAD analiz sayısı azaltıldı. Toplam 6 tasarım parametresi, her üç bileşen için kanat giriş ve çıkış açıları, optimizasyon sürecinde değiştirilmeye açık girdiler olarak kullanıldı. Tasarım parametrelerinin alabileceği değer aralıkları sınırları belirlendi. YSA modellerinin ilklendirilmesi için bir veri tabanı oluşturuldu. Veri tabanının oluşturulmasında en az sayıda tasarımla tasarım uzayının homojen bir şekilde taranmasını sağlamak için Latin Hiperküp Örnekleme (LHÖ, Latinized Hypercube Sampling) metodu kullanıldı. Optimizasyon iç iterasyonları YSA modelleri ve optimizasyon algoritması arasında gerçekleştirildi. Optimizasyon algoritması ile oluşturulan alternatif tasarımlar öncelikle YSA modelleri tarafından değerlendirildi, yalnızca performans artışı beklenen tasarımlar için HAD analizleri gerçekleştirildi. Analiz sonuçları tasarım girdisi parametrelerle birlikte veri tabanına eklenerek sıradaki iterasyonlar için YSA modellerinin tahmin yetenekleri iyileştirildi. Bu döngü elde edilen maksimum skor yakınsayana dek sürdürüldü. Çalışma sonucu, uygulanan bu yöntemin modern bir tork konvertör tasarımını başarıyla oluşturabileceğini ortaya konuldu. Elde edilen tork konvertör performans hedeflerinden en fazla %3.4 sapma gösterdi. Yüksek öncelikli tork oranı hedefleri %3 sapma içerisinde sağlandı.

Abstract:
An optimization based hydrodynamic design process is carried out in order to develop a torque converter design with performance targets and packaging constraints fit for an automatic transmission of a tracked armored vehicle. Design process is initiated with analytical turbomachinery calculations and it is finalized with an optimization study. Blade and meridional geometry design of a three-stage torque converter, consisted from an impeller, a turbine and a stator, is created with this study. Total of 4 performance parameters, torque ratios and capacity factors at speed ratios of 0.0 and 0.8, are set as performance targets.
Main dimensions for the initial design is calculated with analytical equations found in the torque converter literature. Blade angles and meridional geometry dimensions are calculated according to performance targets and packaging constraints. An initial geometrical parameter set is created With these calculations, which describes the base geometry for the optimization process.
2D cascade design for the base design is created according to calculated input parameter set. The created planar design is then transformed in to a 3D point cloud with the presented method. Blade and meridional geometries are fully parameterized in a way which will stably produce valid geometries. With the help of a CAD software, 3D points clouds for the initial and later designs are used to create wet surface data corresponding to the input data set. CAD geometry creation process is fully automatized with the use of various scripts.
CFD analyses are performed for evaluating hydrodynamic performances of created torque converter designs. Analyses are conducted with full or periodic geometries, depending on the required accuracy and computational cost saving. Hydrodynamic performance predictions for the initial and the final torque converter designs are obtained with full geometry models. Analyses for the numerous database creation and optimization iteration designs, on the other hand, are performed with periodic models in order to reduce the computational cost.
CFD analysis results for the base design show that the hydrodynamic performance parameters of the base design does not fit the performance targets satisfactorily. This can be attributed to the torque converter literature being not as wast as literature of some other more widely researched turbomachinery types. It is for this reason that this study employs an optimization based design process in order to finalize the torque converter design. 
In order to improve the performance of the initial design, a simulated annealing based optimization algorithm is used. Since CFD analyses are time costly, Artificial Neural Network (ANN) models are used as surrogate models. These models are used for predicting torque converter performance in order to reduce the number of CFD analyses required. Total time required for the optimization process to finish is reduced significantly since these models, after they are constructed, take only several seconds to form a performance prediction.  
As mentioned, total of 4 hydrodynamic performance parameters are optimization targets of this study. With the use of an objective function, the multi-objective optimization problem is reduced to a single-objective optimization problem. Optimization is carried out in order to maximize the score calculated with this function. Total of 6 input parameters, input and output angles of three stages, selected for manipulation during optimization. The design space is defined as ±20° around the base design blade angles.
Before the start of the optimization process, a database of torque converter designs is created. Main benefits of this study are discovering the design space, giving insight to the objective function defining process and creating the required database to initialize ANN models. For database creation, Latinized Hypercube Sampling (LHS) method is used in order to scan the design space thoroughly with least number of samples. LHS algorithm is used to create 160 homogeneously spaced input parameter sets. Number is kept relatively high considering the input parameter sets that will not produce valid geometries. Additional measures are taken during the optimization in order to ensure that newly created input parameter sets produce valid geometries, yet during database creation these measures are not applied in order to discover the design space more thoroughly. Out of initial 160 samples, 25 input sets did not produce valid geometries. CFD analysis for one of the remaining designs did not produce valid results, and thus the database creation process is finalized with the total of 134 samples.
CFD analyses are performed in order to form performance predictions for the database. These analyses are conducted with periodic models. Full geometry and periodic CFD models have some significant unavoidable geometrical and modelling differences, as a result of this their results for the same design differ considerably. On the other hand the computational cost reduction provided by the periodic model is too great to be abandoned. As a solution, performance predictions are formed with both periodic and full geometry models for 10 randomly selected designs. Difference of results between two models for 10 designs are averaged and performance targets are calibrated according to deviation between them.
Results of CFD analyses for the database give insight about which performance targets will prove harder to achieve. Results obtained for this study clearly showed that 3 of the 4 performance targets, capacity factors at both 0.0 and 0.8 speed ratios and the torque ratio at 0.0 speed ratio, is rather easy to achieve, since designs that are randomly created for the database produced values closer to performance targets. On the other hand the database did not include any design which produced torque ratio at 0.8 speed ratio equal to the performance target. Since these results indicated that the torque ratio at 0.8 speed ratio is the hardest to achieve target for this study, weight for this parameter in the objective function is defined higher than the coefficients of other performance parameters.
After the creation of the database, ANN models are constructed and the optimization process is started. Optimization process is conducted with inner and outer iterations. Optimization inner iterations are carried out between the optimization algorithm and ANN models, in order to search for the optimum input parameter set. Input parameter sets are created randomly by the simulated annealing based optimization algorithm, then performance predictions for these designs are created by ANN models. When inner iterations converge to a parameter set, both the surface data and the CFD analysis model for this torque converter design are created automatically with scripts and performance results are obtained. Like database designs, CFD analyses for optimization iterations are carried out with periodic geometries in order to reduce the computational cost. Obtained results are added to the database, thus ending one outer iteration. ANN models for the next iteration is updated with the database which now includes the CFD analysis results of the last outer iteration. This process is continued until the maximum score is converged.
Optimization iterations are carried out with periodic CFD models, yet full geometry models are more reliable in terms of accuracy. Because of this, top performing 10 designs are reevaluated with full geometry CFD analyses after the optimization is finished.
Performance values of the final design deviates from target values for 3.4% at most. Torque ratio values, which are high importance targets, are within 3% range to target values. Results of the study the show that the proposed methodology is suitable for creating state-of-art torque converter designs.