Researchers at the University of Agder in Norway are using MapleSim simulation software, from Maplesoft, Waterloo, Ont., Canada, to predict the performance of complex offshore material-handling equipment. In the short term, the work helps designers pick components. In the long run, it aims to automate more of the design process.
Offshore oil and gas-drilling rigs cost millions of dollars a day, so crews need to get the job done as quickly as possible. Crews must assemble thousands of meters of flexible pipe while working safely on a remote platform with limited space in all kinds of weather.
Modern drilling platforms use highly specialized materialhandling equipment to move components quickly and keep the rig on schedule. Equipment is typically hydraulically operated. Most modern platforms also have sophisticated electronic controls that simplify operation and help support automation.
The design of the control systems can be challenging because the behavior of cranes in motion depends on how the control valves and hydraulic actuators operate, the crane’s inertia and its load, and the complex interactions among components. Researchers hope to simplify designs by giving engineers a way to build and run detailed simulations of equipment before they assemble a single part.
“The model-based-design approach lets users model the entire structure and control system in sufficient detail to get a realistic idea of a drilling rig’s performance,” says project head Morten Kollerup Bak. “MapleSim can divide the whole system into mechanical, hydraulic actuation, and electrical-control models.” The software includes a large library of standard elements and also lets engineers easily incorporate custom parts.
“We had hoped to build hydraulic models from standard catalog data,” says Bak. “But component manufacturers don’t always provide the data needed to model the behavior of components in dynamic conditions.” To get this data, Bak built custom models of key components such as control valves and validated their accuracy by conducting tests on single components.
Once Bak is confident of the custom elements’ performance, he incorporates them into MapleSim models of the actuation system and evaluates the complete crane’s likely performance. “We already built a model of an existing crane and demonstrated that it accurately predicts the real crane’s behavior. The model lets us study the likely impact of design changes to individual components.”
And to use the model for design automation, researchers input performance requirements and the software searches the component library for the best option. The software studies thousands of parameters, a task designers would find dull, difficult, and time consuming.
Searches like these need efficient search algorithms. Bak plans to use the Complex Method found in Matlab and Simulink, from MathWorks, Natick, Mass., to which MapleSim directly links. “This populates the simulation with a number of randomly generated designs and evaluates the performance of each,” he says. “The algorithm also picks the poorest performing design and factors in that data.” The process repeats until the solutions converge on the optimal result.
Currently, Bak uses stability and accuracy as performance criteria. Therefore, the design yielding the lowest level of hydraulic oscillations that can precisely follow the position reference is best. In the future, Bak plans to add other criteria such as price and long-term reliability.