Update on Probabilistic Assessment of Multi-year Sea Ice Loads on Vertical-faced Structures

Thijssen, Jan; Fuglem, Mark; Muggeridge, K.; Morrison, Tom; Spencer, Paul

doi:10.4043/25520-ms

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Also, they applied Monte-Carlo simulations on design of many models, based on the impact forces between sea ice and offshore structures. They did not have any implementations for sea ice load by using Monte-Carlo simulation on GPUs [19].…”

Section: Related Workmentioning

confidence: 99%

GPU-Based Monte-Carlo Simulation for a Sea Ice Load Application

Ayubian

Alawneh

Thijssen

2016

2016 Summer Computer Simulation Conference (SCSC 2016)

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High Performance Computing (HPC) has recently been considerably improved, for instance General Purpose computation on Graphics Processing Units (GPGPU) has been developed to accelerate parallel computing by using hundreds of cores simultaneously. GPU computing with Compute Unified Device Architecture (CUDA) is a new approach to solve complex problems and transform the GPU into a massively parallel processor. The present study applies this new technology to a Monte-Carlo simulation for a sea ice load application. The goal of this study is to measure the performance of the GPU and Multi-GPU against the serial Central Processing Unit (CPU), parallel CPU (OpenMP), MATLAB and MATLAB (Parallel for) implementations. Results show a speedup of up to 89,000 times, and reduction in elapsed time from about 3 hours to approximately 0.1 second.

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Section: Related Workmentioning

confidence: 99%

GPU-Based Monte-Carlo Simulation for a Sea Ice Load Application

Ayubian

Alawneh

Thijssen

2016

2016 Summer Computer Simulation Conference (SCSC 2016)

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Probabilistic Assessment of Multi-Year Sea Ice Loads on Upward Sloping Arctic Structures

Thijssen

Fuglem

Croasdale³

2016

All Days

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Multi-year (MY) ridge and level ice interactions with sloping and conical structures involve complex ice feature shapes and ice failure mechanisms. The limited available field data makes calibration of associated load models difficult. To account for associated randomness and uncertainty, models may tend to be on the conservative side. New deterministic algorithms were recently developed to calculate loads more accurately for interactions of MY level ice and MY ridges with an upward sloping structure. This paper presents the application of these recently developed formulations in a probabilistic framework using SILS. SILS is a Monte-Carlo type simulator developed by C-CORE following the general procedures outlined in ISO 19906. Ice and metocean input parameters are defined by the user either as a fixed value (e.g. friction coefficients) or a random distribution (e.g. ice drift speed, floe size). The yearly encounter frequency is first estimated for these ice features for the site of interest. The ice loads are then determined for each of simulated interaction event occurring over the model timespan, using the deterministic load formulations. By simulating a large number of years of ice interactions, design ice loads can be determined that correspond to various low annual probability of exceedances. This paper demonstrates how complex loading scenarios, modelled in terms of idealized deterministic models, can be incorporated within a Monte-Carlo framework to provide design level loads. During the model implementation and analysis of results, significant improvements were identified and implemented in the deterministic model, resulting in a more robust model and better design estimates. The results provide valuable insights regarding model inputs and behaviour corresponding to the extreme design ice loads. An example of a full probabilistic analysis is presented in the paper to illustrate the models. Here the probabilistic framework of SILS is used to assess a Base Case scenario and a number of sensitivity cases using different environmental inputs and model parameters.

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Accelerating Numerical Ice Engineering Tools Using GPGPU

2016

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C-CORE is engaged in understanding the iceberg and sea ice design loads needs of the energy sector. As the energy industry ventures into oceans with greater ice cover and more icebergs, there is a significant need for efficient engineering tools to plan and manage operations in exploration, production, and safety. Industry requires a range of scenarios for their risk assessments, where existing simulations can be computationally and time intensive. C-CORE has recently started using the benefit of the General Purpose Computing on Graphical Processing Units (GPGPU) approach. This approach has shown significant speed up of several numerical ice engineering applications related to icebergs and sea ice. The investigated model types are Monte-Carlo type approaches for probabilistic design method, and quadratic discriminant. GPU computing with Compute Unified Device Architecture (CUDA) is a new approach to solve complex problems and transform the GPU into a massively parallel processor. The present study applies the GPGPU technology to a Monte-Carlo simulation, used for a sea ice load application. The objective of this study is to measure the performance of the GPU using CUDA, and compare against the serial Central Processing Unit (CPU) using C++ and MATLAB implementations. Results show a speedup of up to 2,600 times of the GPGPU implementation compared to the MATLAB implementation, reducing the elapsed time from about 1.5 hour to about 2 seconds. This strongly indicates that the GPGPU approach can help the industry to significantly reduce the time required for the simulations.

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Update on Probabilistic Assessment of Multi-year Sea Ice Loads on Vertical-faced Structures

Cited by 4 publications

References 4 publications

GPU-Based Monte-Carlo Simulation for a Sea Ice Load Application

GPU-Based Monte-Carlo Simulation for a Sea Ice Load Application

Probabilistic Assessment of Multi-Year Sea Ice Loads on Upward Sloping Arctic Structures

Accelerating Numerical Ice Engineering Tools Using GPGPU

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