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reliability engineering PhD Projects, Programs & Scholarships

We have 55 reliability engineering PhD Projects, Programs & Scholarships

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  Enhancing the performance, condition, safety and reliability of offshore wind energy infrastructure systems using optimal design, inspections, structural health monitoring and structural control
  Dr P Omenzetter, Dr P Dunning, Dr S S Aphale
Applications accepted all year round

Funding Type

PhD Type

Wind turbines are exposed to frequent structural damage increasing the cost of wind energy generation. To drive these costs down, this project will develop new automated methods for detection of structural damage to wind turbines by examining vibration responses measured by sensors attached to the structure.
  Marine risers: experimental damage detection, monitoring and numerical modelling using digital twins
  Dr P Omenzetter, Dr D Van der A, Prof E Pavlovskaia
Applications accepted all year round

Funding Type

PhD Type

Marine risers are critical components of any offshore oil and gas production operations linking subsea field developments with production and drilling equipment atop fixed or floating facilities.
  Advanced design of fibre reinforced composites - using multiscale reliability analysis and optimisation
  Dr P Dunning, Dr S Sriramula
Applications accepted all year round

Funding Type

PhD Type

This project aims to advance the design of components made from fibre reinforced composite materials, by combining techniques in multiscale analysis, structural reliability and design optimisation.
  Evolutionary algorithms for engineering applications: developing computational methods and optimisation frameworks to solve engineering optimisation problems
  Research Group: Mechanical and Process Engineering
  Dr JP Li
Applications accepted all year round

Funding Type

PhD Type

This highly multidisciplinary PhD project will develop computational methods and optimisation frameworks to solve engineering optimisation problems.
  Robust power converter design and analysis
  Dr C Crabtree, Dr P Matthews
Applications accepted all year round

Funding Type

PhD Type

Fully-Funded Doctoral Studentship in Offshore Wind Energy Power Converters and Reliability. Department of Engineering, Durham University.
  Predicting failure in crystalline materials using machine learning techniques
  Dr A Roy, Dr M Pacella
Applications accepted all year round

Funding Type

PhD Type

Single crystal materials are extensively used in components ranging from the smallest (e.g. micro-lens, MEMS devices) to the largest sizes (e.g.
  Reliability of pipelines under spatially varying corrosion defects
  Dr S Sriramula, Dr H Tan
Applications accepted all year round

Funding Type

PhD Type

The integrity of pipelines is greatly affected by the initiation and progression of corrosion defects. It has been widely recognised that random variations in these characteristics can be rationally quantified with probabilistic approaches.
  Stochastic response characterisation and reliability driven optimisation of floating wind turbines
  Dr S Sriramula, Dr A Maheri
Applications accepted all year round

Funding Type

PhD Type

As the wind turbine installations move to deep-water environments, there is an increasing focus on the design considerations for floating wind turbines (FWTs).
  Mechanics of Advanced Materials: the response of advanced engineering and bio-materials to various types of external loading and environmental conditions - Ref: VS2UF2018
  Prof V Silberschmidt
Application Deadline: 3 April 2020

Funding Type

PhD Type

In the last two decades, Mechanics of Materials as a discipline has experienced a type of revival. The main reason for this has been a continuing introduction of new materials (or even their classes) with extraordinary microstructures, properties and performance.
  Computational materials engineering of interfaces in alloys
  Dr C Brandl
Applications accepted all year round

Funding Type

PhD Type

Research field. We are working in Computational Materials Science which is an interdisciplinary area. It’s a challenging mix of physics, materials science, engineering, mathematics, and computational science.
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