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Content archived on 2024-06-16

Development of new and novel low cost robot inspection methods for in-service inspection of nuclear installation

Objective

The safe operation of the 450 nuclear power plants around the world and 210 in Europe depends on the regular in-service inspection of the reactor pressure vessels, which contain the nuclear fuel. Reactor Pressure Vessels (RPVs) which house the fuel of a nuclear power plant are made of thick steel sections welded together. This steel becomes brittle with age and is therefore more susceptible to the rapid growth of cracks.

These RPVs contain water under high pressure and are particularly susceptible to a process known as 'stress-corrosion cracking'. Current methods of inspection have major drawbacks because:
- They require large and heavy robots costing millions of Euros. In most cases these robots require the use of a central mast manipulator and insertion of this mast is a difficult and time-consuming operation, requiring the use of the Plant's polar crane and causing disruption to other activities during the plant outage thus increasing time of inspection.
- These require several large inspection sensors placed in complex "probe pans" to inspect large areas. The probe pans have to be changed several times thus increasing time of inspection.
- They can sometimes miss defects or wrongfully size them. All the above current drawbacks mean that large amounts of time consuming manual intervention is required during inspection thus requiring operators to work in radiation hazardous areas. This also increases inspection time, which carries a huge economic cost.

To overcome the above drawbacks this project satisfies an urgent need to develop new and novel inspection methods, which can speed up inspection times, improve defect detectability and reduce operator exposure to dangerous radiation whilst working inside the reactor containment.

Call for proposal

FP6-2002-SME-1
See other projects for this call

Coordinator

TWI LIMITED
EU contribution
No data
Total cost
No data

Participants (16)