Ship Damage Stability Approval Document Generation by a Amonte Carlo Method

2021 ◽  
Author(s):  
Stefan Krüger ◽  
Katja Aschenberg
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Information Needs ◽  
Computational Time ◽  
Strong Impact ◽  
Design Phase ◽  
Efficient Design ◽  
Stability Problems ◽  
Initial Design ◽  
Damage Stability

Abstract The revised SOLAS 2020 damage stability regulations have a strong impact on possible future ship designs. To cope with these requirements, damage stability investigations must become a central part of the initial design phase, and many internal subdivision concepts need to be investigated. Unfortunately, if damage stability calculations are performed in the classical way, they are very time consuming with respect to modelling and computational time. This fact has impeded the consequent subdivision optimization in the past. Therefore, a simulation procedure for damage stability problems was developed which treats damage stability as a stochastic process which was modeled by a Monte Carlo simulation. If statistical damage distributions are once known, the Monte Carlo simulation delivers a population of damages which can be automatically related to certain damage cases. These damage cases can then be investigated with respect to their survivability. Applying this principle to damage stability problems reduces the computational effort drastically where at the same time no more manual modelling is required. This development does especially support the initial design phase of the compartmentation and leads to a safer and more efficient design. If this very efficient simulation principle shall now also be used after the initial design phase for the generation of approval documents, additional information needs to be generated by the simulation method which is not directly obtained during the simulation: This includes detailed individual probabilities in all three directions and the integration of all damage cases into predefined damage zones. This results in fact in a kind of reverse engineering of the manual damage stability process to automatically obtain this required information. It can be demonstrated that the time to obtain the final documents for the damage stability approval can be drastically reduced by implementing this principle.

A Monte Carlo Based Simulation Method for Damage Stability Problems

2019 ◽  
Author(s):  
Stefan Krüger ◽  
Hendrik Dankowski
Keyword(s):  
Monte Carlo ◽  
Simulation Method ◽  
Computational Effort ◽  
Computational Time ◽  
Design Phase ◽  
Efficient Design ◽  
Stability Calculation ◽  
Initial Design ◽  
Damage Stability ◽  
Implementation Problems

Abstract To cope with future developments of the SOLAS 2009 B1, damage stability investigations must become a central part of the initial design phase. If damage stability calculations are performed in the classical way, they are very time consuming with respect to both modelling and computational time. To overcome this problem, damage stability can be treated as a stochastic process, where the probability of a damage case and the survivability of that particular damage case need to be determined. This task can be solved by direct numerical simulations based on the Monte Carlo principle. If statistical damage distributions are once known, the Monte Carlo simulation delivers a population of damages which can be automatically related to certain damage cases. These damage cases can then be investigated with respect to their survivability. Applying this principle to SOLAS 2009 damage stability calculations leads to a number of implementation problems which must be solved to guarantee that the MC simulation delivers exactly the same results as the manual, zone based damaged stability calculation. If these problems are solved, the MC based damage stability calculations can be used during the initial design phase until the damage stability approval. The proposed method reduces the computational effort drastically which supports the initial design phase of the ship’s compartmentation. The method further leads to higher attained indices and consequently to a safer and more efficient design.

Parallel Monte Carlo Simulation Using Desktop Computers

1999 ◽  
Vol 5 (S2) ◽  
pp. 80-81
Author(s):  
John Henry J. Scott ◽  
Robert L. Myklebust ◽  
Dale E. Newbury
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Electron Scattering ◽  
Information Needs ◽  
Image Interpretation ◽  
Scattered Electron ◽  
Electron Trajectories ◽  
X Ray ◽  
Desktop Computers ◽  
Computationally Expensive

Monte Carlo simulation of electron scattering in solids has proven valuable to electron microscopists for many years. The electron trajectories, x-ray generation volumes, and scattered electron signals produced by these simulations are used in quantitative x-ray microanalysis, image interpretation, experimental design, and hypothesis testing. Unfortunately, these simulations are often computationally expensive, especially when used to simulate an image or survey a multidimensional region of parameter space.Here we present techniques for performing Monte Carlo simulations in parallel on a cluster of existing desktop computers. The simulation of multiple, independent electron trajectories in a sample and the collateral calculation of detected x-ray and electron signals falls into a class of computational problems termed “embarrassingly parallel”, since no information needs to be exchanged between parallel threads of execution during the calculation. Such problems are ideally suited to parallel multicomputers, where a manager process distributes the computational burden over a large number of nodes.

Supply Chain Inventory Coordination under Uncertain Demand via Combining Monte Carlo Simulation and Fitness Inheritance PSO

2015 ◽  
Vol 6 (1) ◽  
pp. 1-22
Author(s):  
Heting Cao ◽  
Xingquan Zuo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Function ◽  
Supply Chain ◽  
Simulation Model ◽  
Uncertain Demand ◽  
Computational Time ◽  
Arbitrary Distribution ◽  
Demand Distribution ◽  
Inventory Coordination

Supply chain coordination consists of multiple aspects, among which inventory coordination is the most widely used in practice. Inventory coordination is challenging due to the uncertainty of customers' demand. Existing researches typically assume that the demand is either a deterministic constant or a stochastic variable following a known distribution function. However, the former cannot reflect the practical costumers' demand, and the later make the model inaccurate when the demand distribution is ambiguous or highly variable. In this paper, the authors propose a Monte Carlo simulation model of such problem, which can mimic the inventory changing procedure of a supply chain with uncertain demand following an arbitrary distribution function. Then, a PSO is combined with the simulation model to achieve a coordination decision scheme to minimize the total inventory cost. Experiments show that their approach is able to produce a high quality solution within a short computational time and outperforms comparative approaches.

scholarly journals Parallel Monte Carlo Simulation Using Desktop Computers

2000 ◽  
Vol 8 (2) ◽  
pp. 34-35
Author(s):  
John Henry J. Scott ◽  
Robert L. Myklebust ◽  
Dale E. Newbury
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Electron Scattering ◽  
Information Needs ◽  
Image Interpretation ◽  
Scattered Electron ◽  
Electron Trajectories ◽  
X Ray ◽  
Desktop Computers ◽  
Computationally Expensive

Monte Carlo simulation of electron scattering in solids has proven valuable to electron microscopists for many years. The electron trajectories, x-ray generation volumes, and scattered electron signals produced by these simulations are used in quantitative x-ray microanalysis, image interpretation, experimental design, and hypothesis testing. Unfortunately, these simulations are often computationally expensive, especially when used to simulate an image or survey a multidimensional region of parameter space.Here we present techniques for performing Monte Carlo simulations in parallel on a cluster of existing desktop computers. The simulation of multiple, independent electron trajectories in a sample and the collateral calculation of detected xray and electron signals fall into a class of computational problems termed “embarrassingly parallel”, since no information needs to be exchanged between parallel threads of execution during the calculation.

Brittle Intergranular Failure in Grain Aggregates - A Computer Simulation by Means of the Graphics Processing Unit

2009 ◽  
Vol 409 ◽  
pp. 386-389
Author(s):  
Miriam Kupková ◽  
Samuel Kupka
Keyword(s):  
Computer Simulation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Graphics Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
Binary Variable ◽  
Cubic Lattice ◽  
Increasing Temperature ◽  
Graphics Processing

Within a model considered, each of bonds between contacting grains is treated as a two-state system and represented by a binary variable. Its two values refer to the two possible states of bond – intact or broken. A Monte Carlo simulation of fracture is carried out on a set of binary variables arranged to a cubic lattice. The transition from one configuration of broken bonds to another is governed by a Griffith-like energy associated with each of configurations. The results demonstrate i) the capability of the model to provide a useful information (e.g. the increase in roughness of fracture surface with increasing temperature, that is the transition from “brittle” to “plastic” failure), and ii) the advantage of simulation by using the graphics processing unit (saving of a computational time).

Variation Simulations for Digital Panel Assembly

Manufacturing ◽  
2003 ◽  
Author(s):  
Wayne Cai ◽  
Yufeng Long ◽  
Ching Hsieh
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Case Studies ◽  
Series Expansion ◽  
Taylor Series ◽  
Taylor Series Expansion ◽  
Computational Time ◽  
Proprietary Software ◽  
Pros And Cons ◽  
Variation Simulation

This paper presents methodologies and results of variation simulations for digital panel assembly, using GM proprietary software called EAVS. EAVS provides two alternative algorithms for variation simulation of digital panel assembly, i.e., Taylor Series Expansion (TSE) based and Monte Carlo Simulation (MCS) based. In this paper, algorithms of the two methods are reviewed, and pros and cons are studied. Several case studies are presented to illustrate the capabilities of the two methods. Based on the case studies, the EAVS variation simulation guidelines will be established ensuring analysis accuracy at reasonable computational time.

Probabilistic Analysis of Hull Girder Stresses Using an Analytical Approach Verified by the Monte Carlo Simulation

2007 ◽  
Author(s):  
Lyuben D. Ivanov ◽  
Ge Wang ◽  
Joe Foster
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Analytical Approach ◽  
Method Comparison ◽  
Simulation Method ◽  
Computational Time ◽  
Hull Girder ◽  
Structure Reliability ◽  
Hull Structure ◽  
Probabilistic Study

The paper presents a probabilistic study of the hull girder bending stresses using an analytical approach verified by the Monte Carlo simulation method. Comparison of these two approaches was performed for the calculations of the still water, wave-induced and total bending moments and the deck stresses. The analytical methods are simple for application, especially when the application of Monte Carlo simulation requires an excessively long computational time, especially in the asymptotic tails of the probabilistic distributions of loads and stresses that are most important for assessing the hull structure reliability.

Prediction of Failure Rate of Rotary Machine Using Computer Simulations

2005 ◽  
Vol 127 (4) ◽  
pp. 768-772 ◽  
Author(s):  
Jie Chen ◽  
Gang Bai ◽  
Zhongmin Shen ◽  
Xiaofeng Li ◽  
David Fulton ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Failure Rate ◽  
Computer Simulations ◽  
Design Phase ◽  
Rotary Machine ◽  
Rotary Machines

Failures can occur in brushless alternators due to interferences between the moving and stationary parts, called “rub.” Improper design of component target dimensions and tolerances can result in interferences. A method was developed to evaluate the component tolerances that can create interference in a brushless alternator. Mathematic models were created to relate target dimensions to the interferences. Monte Carlo simulation was utilized to statistically evaluate the effects of tolerances on failure rate. This method allows a designer to avoid potential rub failures while still in the design phase. It can also be used to analyze rotary machines of similar designs.

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