scholarly journals Adaptation of Selected Formulas for Local Scour Maximum Depth at Bridge Piers Region in Laboratory Conditions

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2663
Author(s):  
Marta Kiraga ◽  
Janusz Urbański ◽  
Sławomir Bajkowski
Keyword(s):  
Monte Carlo ◽  
Relative Error ◽  
Sampling Method ◽  
Laboratory Data ◽  
Monte Carlo Sampling ◽  
Local Scour ◽  
Maximum Depth ◽  
Engineering Structures ◽  
Mean Relative Error ◽  
Technical Requirements

The study aimed to adapt selected formulas for the estimation of the maximum depth of local scour in the area of the bridge pillar model: Begam formula, Laursen and Toch equation and the equation included in the Regulation of the Polish Minister of Transport and Marine Economy of 30 May 2000 on technical requirements for road engineering structures and location of these structures. The results of own laboratory tests were used for the adaptation. A total of 19 series of measurements with different durations, water flow rates and water depths were performed. The tests were carried out on a model of a washable flume model with a sandy bed, with a single cylindrical bridge pier. The formulas were optimized using the Monte Carlo sampling method. The best match among the original formulas was obtained for Laursen and Toch’s formula (mean relative error 15.3%). For Begam’s formula, an average relative error of 21.6% was received, and for calculations using the Regulation equation, a relative error of 30.1% was obtained. Optimization of formulas using the Monte Carlo sampling method resulted in a formula that describes laboratory data with a mean relative error of 8.8% based on the Begam equation, a mean relative error of 13.8% based on the Laursen–Toch equation, and 28.5% for the formula based on equation included in the Regulation.

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.

scholarly journals Monte Carlo Sampling Method for a Class of Box-Constrained Stochastic Variational Inequality Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Pei-Yu Li
Keyword(s):  
Monte Carlo ◽  
Variational Inequality ◽  
Optimization Problem ◽  
Sampling Method ◽  
Monte Carlo Sampling ◽  
Merit Function ◽  
Variational Inequality Problems ◽  
Sufficient Condition ◽  
Existence Of A Solution ◽  
Stochastic Variational Inequality

This paper uses a merit function derived from the Fishcher–Burmeister function and formulates box-constrained stochastic variational inequality problems as an optimization problem that minimizes this merit function. A sufficient condition for the existence of a solution to the optimization problem is suggested. Finally, this paper proposes a Monte Carlo sampling method for solving the problem. Under some moderate conditions, comprehensive convergence analysis is included as well.

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