The Role of Kriging-Based Metamodeling in Engineering Design: Data Sources, Methodological Advances, and Applications

Abstract

This study is an interdisciplinary review that comprehensively examines the current application areas, data sources, and methodological developments of Kriging-based metamodeling techniques in engineering design. In modern design processes—where the direct use of FEA, CFD, and multiphysics analyses is limited due to increasing computational costs—the interpolative structure, uncertainty quantification capability, and high predictive accuracy of Kriging models are evaluated in detail. The performance of Kriging in applications such as aerodynamic optimization, structural reliability analysis, fatigue life prediction, crashworthiness assessment, and manufacturing process parameterization within aerospace, mechanical, and automotive engineering is presented through comparative literature examples. The findings indicate that Kriging provides high accuracy for low-sample problems, significantly accelerates optimization when integrated with uncertainty-driven adaptive sampling strategies, and achieves an optimal balance between cost and accuracy when combined with multi-fidelity modeling frameworks. Furthermore, emerging trends such as non-stationary covariance models and hybrid Kriging architectures integrated with deep learning offer notable advantages, particularly for high-dimensional and strongly nonlinear design problems. By synthesizing fragmented knowledge across literature into a unified framework, this study identifies methodological gaps, current limitations, and future research directions for Kriging-based design optimization.

Keywords

• Kriging Metamodeling
• Engineering Design
• Multi-Fidelity Optimization

Kriging Tabanlı Metamodellemenin Mühendislik Tasarımındaki Rolü: Veri Kaynakları, Yöntemsel Gelişmeler ve Uygulamalar

Abstract

Bu çalışma, Kriging tabanlı metamodelleme tekniklerinin mühendislik tasarımındaki güncel kullanım alanlarını, veri kaynaklarını ve yöntemsel gelişmelerini kapsamlı biçimde inceleyen bir derlemedir. Artan hesaplama maliyetleri nedeniyle FEA, CFD ve çoklu fizik tabanlı analizlerin doğrudan kullanımının sınırlı kaldığı modern tasarım süreçlerinde, Kriging modellerinin sağladığı interpolatif yapı, belirsizlik tahmini ve yüksek doğruluk kapasitesi ayrıntılı olarak değerlendirilmiştir. Havacılık, makine ve otomotiv mühendisliği gibi alanlarda aerodinamik optimizasyon, yapısal güvenilirlik analizi, yorulma ömrü tahmini, çarpışma dayanımı değerlendirmesi ve imalat süreç parametrizasyonu gibi uygulamalarda Kriging’in sunduğu performans literatür örnekleri üzerinden karşılaştırmalı olarak ortaya konmuştur. Elde edilen bulgular, Kriging yönteminin düşük örneklemli problemler için yüksek doğruluk sağladığını, belirsizlik temelli adaptif örnekleme stratejileriyle entegrasyonunun optimizasyon süreçlerini önemli ölçüde hızlandırdığını ve çoklu-fidelite modelleme çerçeveleriyle birleştirildiğinde maliyet–doğruluk dengesini optimize ettiğini göstermektedir. Ayrıca, durağan olmayan kovaryans modelleri ve derin öğrenme ile hibritleştirilmiş Kriging mimarileri gibi güncel eğilimlerin özellikle yüksek boyutlu ve doğrusal olmayan tasarım problemlerinde belirgin avantajlar sunduğu belirlenmiştir. Çalışma, literatürdeki parçalı bilgi birikimini bütüncül bir çerçeve altında birleştirerek, Kriging tabanlı tasarım optimizasyonuna yönelik metodolojik boşlukları, mevcut sınırlılıkları ve gelecekteki araştırma yönelimlerini açıkça ortaya koymaktadır.

Keywords

• Kriging Metamodelleme
• Mühendislik Tasarımı
• Çoklu-Fidelite Optimizasyon

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