کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6915816 | 1447409 | 2017 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A direct simulation algorithm for a class of beta random fields in modelling material properties
ترجمه فارسی عنوان
یک الگوریتم شبیه سازی مستقیم برای یک کلاس از زمینه های تصادفی بتا در خواص مواد مدل سازی
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کلمات کلیدی
اموال مواد، تنوع فضایی، فیلد تصادفی توزیع بتا، عملکرد خودکار سازی، شبیه سازی مونت کارلو،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Numerous translation approaches have been developed to generate non-Gaussian random fields; however, non-translation or direct simulation methods are much rare. The translation approaches invoke the memoryless transformation from an underlying Gaussian random field to a target non-Gaussian random field. The correlation structure is changed by the memoryless transformation from a Gaussian to a non-Gaussian field and as a result the non-Gaussian correlation structure is obtained after amending the Gaussian correlation structure. In addition, the underlying Gaussian random field is often generated by series expansion approaches (e.g. spectral representation method), which require a sum of infinite terms. Consequently, the computational efforts involved in generating the Gaussian random fields can be significant. After considering the difficulties in simulating a translation random field, a direct approach without utilizing the memoryless transformation is proposed to generate a beta random field. Though the marginal distribution is restricted to the beta distribution, the proposed approach is simple and efficient. This would make it attractive in simulating large-scale random fields for material properties. This is exemplified with an engineering application.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods in Applied Mechanics and Engineering - Volume 326, 1 November 2017, Pages 642-655
Journal: Computer Methods in Applied Mechanics and Engineering - Volume 326, 1 November 2017, Pages 642-655
نویسندگان
Yong Liu, Jun Hu, Hong Wei, Ay-Lee Saw,