Danial Faghihi

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Acknowledgments Words cannot express my gratitude for the endless help and unfailing encouragement of those without whom I would not have accomplished this feat. I would like to thank my undergraduate advisor, Michael Holst, who believed in me before I did and offered me continuous advice and encouragement throughout my graduate career. I must also thank my(More)
In the present study a general Dynamic Data-Driven Application System (DDDAS) is developed for real-time monitoring of damage in composite materials using methods and models that account for uncertainty in experimental data, model parameters, and in the selection of the model itself. The methodology involves (1) data of the uniaxial tensile experiments(More)
This chapter summarizes the results of a feasibility study exploring the development of a stochastic Dynamic Data-Driven Application System (DDDAS) for prediction and monitoring of material damage in composite materials common to many types of contemporary high-performance military aircraft. The methodology involves (1) acquiring data from mechanical(More)
*Correspondence: oden@ices.utexas.edu Institute for Computational Engineering and Sciences The University of Texas at Austin, 201 East 24th St, Stop C0200 POB 4.102, 78712 Austin, TX, USA Abstract Background: The use of coarse-grained approximations of atomic systems is the most common methods of constructing reduced-order models in computational science.(More)
In the present study, a general probabilistic design framework is developed for cyclic fatigue life prediction of metallic hardware using methods that address uncertainty in experimental data and computational model. The methodology involves (i) utilization of fatigue tests data conducted on coupons of Ti6Al4V material (ii) continuum damage mechanics based(More)
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