Piping Systems Design and Analysis: A Review of Codes, Computational Methods and Emerging Digital Practice
Afeez A. Salawudeen
Department of Mechanical Engineering, Teesside University, Middlesbrough, United Kingdom.
Olorunshogo B. Ogundipe
University of Hertfordshire, Hatfield, Hertfordshire, AL10 9AB, United Kingdom.
Riliwan A. Adebayo
Enlight10 Limited, Factory 3, Driffield Business Park, Kelleythorpe, Driffield, YO25 9HD, United Kingdom.
Joshua B. Ajewole *
Department of Mechanical and Mechatronics Engineering, Landmark University, Omu-Aran, Nigeria.
Olutosin A. Ogunleye
Nigerian Defence Academy, Kaduna, Nigeria.
Abosede J. Awolope
Department of Mechanical Engineering, Redeemers’ University, Ede, Osun State, Nigeria.
Samuel B. Adeleye
Department of Mechanical Engineering, Federal University, Oye-Ekiti, Ekiti State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Piping systems are used to transport fluids in power generation, the oil and gas sector, chemical processing and manufacturing industries, and water-delivery facilities. The design of these systems has a substantial influence on the safety and efficiency of industrial operations. This review brings together established piping design and analysis principles with research from the last five years on computational tools, monitoring technologies, and materials. Design standards, their application and accessibility, and flexibility and stress requirements are examined. The review highlights four major areas that are changing the field: the use of algorithms for improved route planning, digital twins integrated with real-time sensor data, machine learning for predicting corrosion and failures, and material challenges associated with composites and the use of steels in hydrogen pipelines. Recent studies show that conventional stress-analysis software can determine whether piping systems satisfy safety requirements, while experiments on components such as bellows expansion joints can improve understanding of their stress and strain capacity during design. The review also examines how machine learning is used to improve piping-condition prediction from available data. Research further indicates that hydrogen embrittlement requires careful monitoring and assessment before existing pipelines are repurposed. The novelty of this review lies in integrating established piping design and analysis codes and principles with emerging digital engineering practices, including computational tools, real-time monitoring, machine learning, digital twins, and advanced materials for hydrogen service. It addresses the fragmented nature of the literature and highlights practical challenges related to cost, technical skills, and data access, particularly in smaller engineering organisations.
Keywords: Piping design, stress analysis, computational fluid dynamics, digital practices, machine learning, pipeline integrity, composite pipes