SEISMIC RANDOM VIBRATION ANALYSIS OF STOCHASTIC STRUCTURES USING RANDOM FACTOR METHOD

Seismic random vibration analysis of stochastic truss structures is presented. A new method called random factor method is used for dynamic analysis of structures with uncertain parameters, due to variability in their material properties and geometry. Using the random factor method, the natural frequencies and modeshapes of a stochastic structure can be respectively described by the product of two parts, corresponding to the random factors of the structural parameters with uncertainty, and deterministic values of the natural frequencies and modeshapes obtained by conventional finite element analysis. The stochastic truss structure is subjected to stationary or non-stationary random earthquake excitation. Computational expressions for the mean and standard deviation of the mean square displacement and mean square stress are developed by means of the random variable＇s functional moment method and the algebra synthesis method. An antenna and a truss bridge are used as practical engineering examples to illustrate the application of the random factor method in the seismic response analysis of random structures under stationary or non-stationary random earthquake excitation.

Uncertainty analysis and robust design optimization for the heat-assisted bending of high-strength titanium tube

Heat-assisted rotary draw bending(HRDB)is a promising technique for manufacturing difficult-to-form tubular components comprising high-strength titanium tubes(HSTTs)with small bending radii.However,as a multidie constrained and thermomechanical coupled process with many uncertainty factors,a high risk of several defects,such as cross-section distortion,over wall thinning,or even cracking,is present.Achieving the robust design optimization(RDO)of complex forming processes remains a nontrivial and challenging scientific issue.Herein,considering a high-strength Ti-3Al-2.5V titanium alloy tube as a case material,the five significant uncertainty factors in HRDB,i.e.,temperature distribution,tube geometrical characteristics,tube material parameters,tube/tool friction,and boost velocity had been analyzed.Subsequently,considering the preheating and HRDB of HSTT,a whole-process thermomechanical three-dimensional finite element model was established and validated for virtual experiments.Further,considering the maximum section distortion Q and maximum wall-thickness thinning t as the optimization objectives and the mean and variance of material and forming parameters,an RDO model was established.Finally,the Pareto optimal solutions were obtained using the nondominated sorting genetic algorithm II,and a minimum distance selection method was employed to obtain the satisfactory solution.Results show that the optimized solutions considering the uncertainty factors reduce the maximum section distortion rate of HSTT after bending by 38.1%and the maximum wallthickness thinning rate by 27.8%.

Evaluation method for helicopter maritime search and rescue response plan with uncertainty

Helicopter plays an increasingly significant role in Maritime Search and Rescue(MSAR),and it will perform MSAR mission based on response plans when an accident occurs.Thus the rationality of response plan determines the success of MSAR mission to a large extent.However,with the impact of many uncertainty factors,it is difficult to evaluate response plans comprehensively before performing them.Aiming at these problems,an evaluation framework of helicopter MSAR response plan named UMAD is proposed in this paper,which reveals the influence mechanism of uncertainty factors based on Multi-Agent method and analyzes the mission flow based on Discrete Event System(DEVS)method.Furthermore,the evaluation criterion and indicators of response plan are extracted from the aspects of safety and effectiveness.Meanwhile,the Monte Carlo method is adapted to calculate the probability distribution and robustness of response plan comprehensive result.Finally,in order to illustrate the validity of this method,it is discussed and verified by an application example of evaluating multiple response plans to the same MSAR scenario.The results show that this method can analyze the influence of uncertainty more systematically and optimize response plans more comprehensively.

An Initiative Learning Algorithm Based on System Uncertainty

Initiative-learning algorithms are characterized by and hence advantageousfor their independence of prior domain knowledge. Usually, their induced results could moreobjectively express the potential characteristics and patterns of information systems.Initiative-learning processes can be effectively conducted by system uncertainty, becauseuncertainty is an intrinsic common feature of and also an essential link between information systemsand their induced results. Obviously, the effectiveness of such initiative-learning framework isheavily dependent on the accuracy of system uncertainty measurements. Herein, a more reasonablemethod for measuring system uncertainty is developed based on rough set theory and the conception ofinformation entropy; then a new algorithm is developed on the bases of the new system uncertaintymeasurement and the Skowron's algorithm for mining prepositional default decision rules. Theproposed algorithm is typically initiative-learning. It is well adaptable to system uncertainty. Asshown by simulation experiments, its comprehensive performances are much better than those ofcongeneric algorithms.