scholarly journals Approximation of the Monte Carlo Sampling Method for Reliability Analysis of Structures

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Mahdi Shadab Far ◽  
Yuan Wang
Keyword(s):  
Monte Carlo ◽  
Structural Reliability ◽  
Sampling Method ◽  
Computational Cost ◽  
Simple Algorithm ◽  
Monte Carlo Sampling ◽  
Analysis And Design ◽  
Structural Capacity ◽  
The One ◽  
Load Types

Structural load types, on the one hand, and structural capacity to withstand these loads, on the other hand, are of a probabilistic nature as they cannot be calculated and presented in a fully deterministic way. As such, the past few decades have witnessed the development of numerous probabilistic approaches towards the analysis and design of structures. Among the conventional methods used to assess structural reliability, the Monte Carlo sampling method has proved to be very convenient and efficient. However, it does suffer from certain disadvantages, the biggest one being the requirement of a very large number of samples to handle small probabilities, leading to a high computational cost. In this paper, a simple algorithm was proposed to estimate low failure probabilities using a small number of samples in conjunction with the Monte Carlo method. This revised approach was then presented in a step-by-step flowchart, for the purpose of easy programming and implementation.

scholarly journals An integrated inverse adaptive neural fuzzy system with Monte-Carlo sampling method for operational risk management

2018 ◽  
Vol 98 ◽  
pp. 11-26 ◽  
Author(s):  
Alejandro Peña ◽  
Isis Bonet ◽  
Christian Lochmuller ◽  
Francisco Chiclana ◽  
Mario Góngora
Keyword(s):  
Risk Management ◽  
Monte Carlo ◽  
Fuzzy System ◽  
Sampling Method ◽  
Operational Risk ◽  
Monte Carlo Sampling ◽  
Operational Risk Management ◽  
Neural Fuzzy

Receptor–Ligand Binding Free Energies from a Consecutive Histograms Monte Carlo Sampling Method

2020 ◽  
Vol 16 (10) ◽  
pp. 6645-6655
Author(s):  
Hao Liu ◽  
Jianpeng Deng ◽  
Zhou Luo ◽  
Yawei Lin ◽  
Kenneth M. Merz ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Sampling Method ◽  
Monte Carlo Sampling ◽  
Free Energies ◽  
Receptor Ligand ◽  
Binding Free Energies

Modeling and implementation of spin diode based on two dimensional materials using Monte Carlo sampling method

Circuit World ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Swapnali Makdey ◽  
Rajendra Patrikar ◽  
Mohammad Farukh Hashmi
Keyword(s):  
Magnetic Field ◽  
Monte Carlo ◽  
Density Functional ◽  
Sampling Method ◽  
Spin Current ◽  
Electrical Current ◽  
Monte Carlo Sampling ◽  
Equilibrium Density ◽  
Content Type ◽  
Spin Diode

Purpose A “spin-diode” is the spintronics equivalent of an electrical diode: applying an external magnetic field greater than the limit of spin-diode BT flips the spin-diode between an isolating state and a conducting state [1]. While conventional electrical diodes are two-terminal devices with electrical current between the two terminals modulated by an electrical field, these two-terminal magneto resistive devices can generally be referred to as “spin-diodes” in which a magnetic field modulates the electrical current between the two terminals. Design/methodology/approach Current modulation and rectification are an important subject of electronics as well as spintronics spin diode is two-terminal magnetoresistive devices in which change in resistance in response to an applied magnetic field; this magnetoresistance occurs due to a variety of phenomena and with varying magnitudes and directions. Findings In this paper, an efficient rectifying spin diode is introduced. The resulting spin diode is formed from graphene gallium and indium quantum dots and antimony-doped molybdenum disulfide. Converting an alternating bias voltage to direct current is the main achievement of this model device with an additional profit of rectified spin-current. The non-equilibrium density functional theory with a Monte Carlo sampling method is used to evaluate the flow of electrons and rectification ratio of the system. Originality/value The results indicate that spin diode displaying both spin-current and charge-current rectification should be possible and may find practical application in nanoscale devices that combine logic and memory functions.

Capacity Expansion Problem by Monte Carlo Sampling Method

2009 ◽  
Vol 13 (6) ◽  
pp. 697-703
Author(s):  
Takayuki Shiina ◽  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Sampling Method ◽  
Sampling Technique ◽  
Capacity Expansion ◽  
Monte Carlo Sampling ◽  
Previous Approach ◽  
Importance Sampling Technique ◽  
Stochastic Vector ◽  
Additive Form

We consider the stochastic programming problem with recourse in which the expectation of the recourse function requires a large number of function evaluations, and its application to the capacity expansion problem. We propose an algorithm which combines an L-shaped method and a Monte Carlo method. The importance sampling technique is applied to obtain variance reduction. In the previous approach, the recourse function is approximated as an additive form in which the function is separable in the components of the stochastic vector. In our approach, the approximate additive form of the recourse function is perturbed to define the new density function. Numerical results for the capacity expansion problem are presented.

Development of a Pressure-Based Coupled CFD Solver for Turbulent and Compressible Flows in Turbomachinery Applications

2014 ◽  
Author(s):  
Luca Mangani ◽  
Marwan Darwish ◽  
Fadl Moukalled
Keyword(s):  
Compressible Flows ◽  
Stokes Equations ◽  
Computational Cost ◽  
Simple Algorithm ◽  
Numerical Data ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Analysis And Design ◽  
Flow Problems ◽  
Matrix Coefficients

In this paper we present a fully coupled algorithm for the resolution of compressible flows at all speed. The pressure-velocity coupling at the heart of the Navier Stokes equations is accomplished by deriving a pressure equation in similar fashion to what is done in the segregated SIMPLE algorithm except that the influence of the velocity fields is treated implicitly. In a similar way, the assembly of the momentum equations is modified to treat the pressure gradient implicitly. The resulting extended system of equations, now formed of matrix coefficients that couples the momentum and pressure equations, is solved using an algebraic multigrid solver. The performance of the coupled approach and the improved efficiency of the novel developed code was validated comparing results with experimental and numerical data available from reference literature test cases as well as with segregated solver as exemplified by the SIMPLE algorithm. Moreover the reference geometries considered in the validation process cover the typical aerodynamics applications in gas turbine analysis and design, considering Euler to turbulent flow problems and clearly indicating the substantial improvements in terms of computational cost and robustness.

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