Inclusion of explicit thermal requirements in optimum structural design

A finite-element based procedure is described for obtaining minimum mass designs of structures subjected to combined thermal and mechanical loading and both strength and thermal constraints. The procedure is based on a mathematical programming method using the Sequence of Unconstrained Minimizations Technique (SUMT) in which design requirements are incorporated by an exterior penalty function. The procedure is limited to steady-state temperatures which are controlled by structural sizing only. The optimization procedure is demonstrated by the design of a structural wing box with both mechanical loading and external heating, subject to design constraints on stress, minimum gage, and temperature. The final design for these conditions is compared with a corresponding design in which temperature constraints are omitted

Fundamental of cryogenics (for superconducting RF technology)

This review briefly illustrates a few fundamental concepts of cryogenic engineering, the technological practice that allows reaching and maintaining the low-temperature operating conditions of the superconducting devices needed in particle accelerators. To limit the scope of the task, and not to duplicate coverage of cryogenic engineering concepts particularly relevant to superconducting magnets that can be found in previous CAS editions, the overview presented in this course focuses on superconducting radio-frequency cavities.Comment: 20 pages, contribution to the CAS - CERN Accelerator School: Course on High Power Hadron Machines; 24 May - 2 Jun 2011, Bilbao, Spai

Weld sequence optimization: the use of surrogate models for solving sequential combinatorial problems

The solution of combinatorial optimization problems usually involves the consideration of many possible design configurations. This often makes such approaches computationally expensive, especially when dealing with complex finite element models. Here a surrogate model is proposed that can be used to reduce substantially the computational expense of sequential combinatorial finite element problems. The model is illustrated by application to a weld path planning problem

Variable-cell method for stress-controlled jamming of athermal, frictionless grains

A new method is introduced to simulate jamming of polyhedral grains under controlled stress that incorporates global degrees of freedom through the metric tensor of a periodic cell containing grains. Jamming under hydrostatic/isotropic stress and athermal conditions leads to a precise definition of the ideal jamming point at zero shear stress. The structures of tetrahedra jammed hydrostatically exhibit less translational order and lower jamming-point density than previously described `maximally random jammed' hard tetrahedra. Under the same conditions, cubes jam with negligible nematic order. Grains with octahedral symmetry jam in the large-system limit with an abundance of face-face contacts in the absence of nematic order. For sufficiently large face-face contact number, percolating clusters form that span the entire simulation box. The response of hydrostatically jammed tetrahedra and cubes to shear-stress perturbation is also demonstrated with the variable-cell method.Comment: 10 pages, 8 figure

Quantile-based optimization under uncertainties using adaptive Kriging surrogate models

Uncertainties are inherent to real-world systems. Taking them into account is crucial in industrial design problems and this might be achieved through reliability-based design optimization (RBDO) techniques. In this paper, we propose a quantile-based approach to solve RBDO problems. We first transform the safety constraints usually formulated as admissible probabilities of failure into constraints on quantiles of the performance criteria. In this formulation, the quantile level controls the degree of conservatism of the design. Starting with the premise that industrial applications often involve high-fidelity and time-consuming computational models, the proposed approach makes use of Kriging surrogate models (a.k.a. Gaussian process modeling). Thanks to the Kriging variance (a measure of the local accuracy of the surrogate), we derive a procedure with two stages of enrichment of the design of computer experiments (DoE) used to construct the surrogate model. The first stage globally reduces the Kriging epistemic uncertainty and adds points in the vicinity of the limit-state surfaces describing the system performance to be attained. The second stage locally checks, and if necessary, improves the accuracy of the quantiles estimated along the optimization iterations. Applications to three analytical examples and to the optimal design of a car body subsystem (minimal mass under mechanical safety constraints) show the accuracy and the remarkable efficiency brought by the proposed procedure

A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction

Physics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on remaining useful life estimation results. A similarity-based prognostic model is modified to employ the most similar training samples for RUL estimations on each time instance. The presented model is tested on; Virkler’s fatigue crack growth dataset, a drilling process degradation dataset, and a sliding chair degradation of a turnout system dataset. Prediction performances are compared utilizing an evaluation metric. Efficiency analysis of optimization results show that the modified similarity-based model performs better than the original definition