Multi-dimensional surrogate based aft form optimization of ships using high fidelity solvers

Abstract

Surrogate (metamodel) based optimization has numerous potential applications in the field of naval architecture.It is aimed here to establish a methodology for the aft form optimization for minimum viscous resistance, thus the present study is focused on the aft form where the viscous effects become dominant.It is necessary to solve this problem within acceptable time span from practical naval architectural point of view which requires metamodeling techniques currently under investigation.Accordingly, the present paper investigates the metamodeling ability of the Kriging interpolation and attempts to explore its capabilities and limitations in the aft form optimization from viscous resistance point of view.As metamodeling techniques become more widely used, their constraints are more apparent.Especially in highly nonlinear design spaces, the effect of dimensionality should be taken into consideration.Taking all those factors into account, the present paper is to examine the capabilities of Kriging and to establish the learning performance in terms of RMS error, correlation coefficient and required number of training points according to selected optimization algorithm for multidimensional ship design problem.The results show that, at least 5 % reduction in viscous pressure drag can be attained by the present optimization methodology.Surrogate (metamodel) based optimization has numerous potential applications in the field of naval architecture.It is aimed here to establish a methodology for the aft form optimization for minimum viscous resistance, thus the present study is focused on the aft form where the viscous effects become dominant.It is necessary to solve this problem within acceptable time span from practical naval architectural point of view which requires metamodeling techniques currently under investigation.Accordingly, the present paper investigates the metamodeling ability of the Kriging interpolation and attempts to explore its capabilities and limitations in the aft form optimization from viscous resistance point of view.As metamodeling techniques become more widely used, their constraints are more apparent.Especially in highly nonlinear design spaces, the effect of dimensionality should be taken into consideration.Taking all those factors into account, the present paper is to examine the capabilities of Kriging and to establish the learning performance in terms of RMS error, correlation coefficient and required number of training points according to selected optimization algorithm for multidimensional ship design problem.The results show that, at least 5% reduction in viscous pressure drag can be attained by the present optimization methodology.Surrogate (metamodel) based optimization has numerous potential applications in the field of naval architecture.It is aimed here to establish a methodology for the aft form optimization for minimum viscous resistance, thus the present study is focused on the aft form where the viscous effects become dominant.It is necessary to solve this problem within acceptable time span from practical naval architectural point of view which requires metamodeling techniques currently under investigation.Accordingly, the present paper investigates the metamodeling ability of the Kriging interpolation and attempts to explore its capabilities and limitations in the aft form optimization from viscous resistance point of view.As metamodeling techniques become more widely used, their constraints are more apparent.Especially in highly nonlinear design spaces, the effect of dimensionality should be taken into consideration.Taking all those factors into account, the present paper is to examine the capabilities of Kriging and to establish the learning performance in terms of RMS error, correlation coefficient and required number of training points according to selected optimization algorithm for multidimensional ship design problem.The results show that, at least 5% reduction in viscous pressure drag can be attained by the present optimization methodology.Surrogate (metamodel) based optimization has numerous potential applications in the field of naval architecture.It is aimed here to establish a methodology for the aft form optimization for minimum viscous resistance, thus the present study is focused on the aft form where the viscous effects become dominant.It is necessary to solve this problem within acceptable time span from practical naval architectural point of view which requires metamodeling techniques currently ander investigation.Accordingly, the present paper investigates the metamodeling ability of the Kriging interpolation and attempts to explore its capabilities and limitations in the aft form optimization from viscous resistance point of view.As metamodeling techniques become more widely used, their constraints are more apparent.Especially in highly nonlinear design spaces, the effect of dimensionality should be taken into consideration.Taking all those factors into account, the present paper is to examine the capabilities of Kriging and to establish the learning performance in terms of RMS error, correlation coefficient and required number of training points according to selected optimization algorithm for multidimensional ship design.The results show that, at least 5% reduction in viscous pressure drag can be attained by the present optimization methodology.يحتوي التحسين البديل (metamodel) على العديد من التطبيقات المحتملة في مجال الهندسة المعمارية البحرية. ويهدف هنا إلى وضع منهجية لتحسين الشكل الخلفي للحد الأدنى من المقاومة اللزجة، وبالتالي تركز هذه الدراسة على الشكل الخلفي حيث تصبح التأثيرات اللزجة مهيمنة. من الضروري حل هذه المشكلة في غضون فترة زمنية مقبولة من وجهة نظر معمارية بحرية عملية تتطلب تقنيات metamodeling قيد التحقيق حاليًا. وفقًا لذلك، تبحث هذه الورقة في قدرة النمذجة التحولية لاستيفاء كريجينج وتحاول استكشاف قدراتها وحدودها في تحسين النموذج الخلفي من وجهة نظر المقاومة اللزجة. مع استخدام تقنيات النمذجة التحولية على نطاق أوسع، تكون قيودها أكثر وضوحًا. خاصة في مساحات التصميم غير الخطية للغاية، يجب مراعاة تأثير الأبعاد. مع مراعاة جميع هذه العوامل، تهدف هذه الورقة إلى فحص قدرات كريجينج وتحديد أداء التعلم من حيث خطأ نظام إدارة ورشة الحفر ومعامل الارتباط والعدد المطلوب من نقاط التدريب وفقًا لخوارزمية التحسين المختارة لمشكلة تصميم السفن متعددة الأبعاد. تظهر النتائج أنه يمكن تحقيق انخفاض بنسبة 5 ٪ على الأقل في سحب الضغط اللزج من خلال منهجية التحسين الحالية.