Doctoral theses at NTNU, 2021:81 Lars Ivar Hatledal Lars Ivar Hatledal Protocols and Standards for Simulation and Co-simulation Doctoral thesis Doctoral For Demanding Maritime Operations ISBN 978-82-326-5777-3 (printed ver.) ISBN 978-82-326-5231-0 (electronic ver.) ISSN 1503-8181 (printed ver.) ISSN 2703-8084 (online ver.) Doctoral theses at NTNU, 2021:81 theses at NTNU, Doctoral NTNU Engineering Philosophiae Doctor Faculty of Engineering Thesis for the Degree of Thesis for the Degree Department of Ocean Operations and Civil Department of Ocean Operations Norwegian University of Science and Technology Lars Ivar Hatledal Protocols and Standards for Simulation and Co-simulation For Demanding Maritime Operations Thesis for the Degree of Philosophiae Doctor Trondheim, March 2021 Norwegian University of Science and Technology Faculty of Engineering Department of Ocean Operations and Civil Engineering NTNU Norwegian University of Science and Technology Thesis for the Degree of Philosophiae Doctor Faculty of Engineering Department of Ocean Operations and Civil Engineering © Lars Ivar Hatledal ISBN 978-82-326-5777-3 (printed ver.) ISBN 978-82-326-5231-0 (electronic ver.) ISSN 1503-8181 (printed ver.) ISSN 2703-8084 (online ver.) Doctoral theses at NTNU, 2021:81 Printed by NTNU Grafisk senter Abstract There is a strong demand for innovation and efficiency within operations, life cycle services, and design of maritime systems. Modern vessels operate increasingly autonomously through strongly interacting sub-systems. These systems are dedicated to a specific, primary objective of the vessel or may be part of the general essential ship operations. The sub-systems exchange data and make coordinated operational decisions, ideally without any user interaction. The task of designing, operating, and integrating life cycle services for such vessels is a complex engineering task that requires an efficient development approach, which must consider the mu- tual interaction between the inherent multi-disciplinary on-board sub-systems. Digitalization thus has become a key aspect of making the maritime industry more innovative, efficient, and fit for future operations. However, no one simulation tool is suitable for all purposes and the plethora of modeling tools within different disciplines exists for very good reasons. Issues related to integration of heterogeneous systems and hardware, memory, and CPU utilization makes implementing complex-cyber-physical systems, like vessels, in a monolithic or centralized manner undesirable. Co-simulation alleviates this issue, allowing different sub-systems to be modeled independently, but simulated together. Co-simulation refers to an enabling technique, where different sub- systems making up a global simulation are being modeled and run in a distributed fashion. Each sub-system is a simulator and is broadly defined as a black box capable of exhibiting behavior, consuming inputs, and producing outputs. A crucial point is that it allows users to simulate models exported from different tools in a unified manner. Compared to more traditional monolithic simulations, co-simulation encourages re-usability, model sharing, and fusion of simulation domains. Co-simulation can be expanded into the realm of digital twins by feeding sensor data measured from the real world into the models, which in turn closes the loop by providing actionable feedback. A digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. As the digital twin mimics its physical counterpart, it can be used to estimate a vessels performance before running any tests in the real world. This not only offers flexibility, but also cuts down costs to a great extent. These proxies of the physical world will help companies in the maritime industry in developing enhancements to existing products, operations, and services, and can even help drive business innovation. This dissertation aims to drive adoption of co-simulation standards and development of use- cases by providing software that makes co-simulation simpler and more intuitive. This includes enabling technology for building standard-conforming models and systems, and subsequent tools for simulating them. The case studies presented show the effectiveness of the proposed approach. i Acknowledgment I conducted the research for this dissertation at the Norwegian University of Science and Tech- nology in Ålesund within the Department of Ocean Operations and Civil Engineering (IHB). My Ph.D. position relied primarily on the project “SFI Offshore Mechatronics” for funding and the “SFI MOVE” and “Digital Twins For Vessel Life Cycle Service” projects provided ancillary support. These projects correspond to the Research Council of Norway grant nos. 280703, 237896, and 237929, respectively. Beyond these funding sources, I’m grateful for the opportunity to pursue a Ph.D. degree under the supervision of Prof. Houxiang Zhang, Prof. Geir Hovland, and Arne Styve. The guidance and support I received during the last three and a half years are highly appreciated. Especially, I would like to thank my main supervisor, Prof. Houxiang Zhang, for shaping me into an independent researcher. Also, I would like to thank Prof. Hans Petter Hildre and Siri Schulerud for their administrative support. Thanks to my colleagues at the Intelligent Systems Lab at NTNU in Ålesund. It has been a privilege working with you. Especially, I would like to thank Dr. Guoyuan Li and Robert Skulstad with their help on Paper A5. For valuable input with regards on this paper, I’d also like to thank Martin Rindarøy and Stian Skjong from SINTEF Ocean. I’d also like to thank my friend, and former colleague, Dr. Yingguang Chu at SINTEF Ålesund for his help in realizing Paper A6, and also Frédéric Collonval for his help in making the PythonFMU package presented in Paper A4 more pythonic. I’ve also learned a lot while working on the OSP project together with the OSP participants. In particular I’d like to mention Levi Jamt, Kristoffer Eide and Halvor Platou from DNV-GL, and Lars Kyllingstad from SINTEF Ocean. A big thanks goes to the Offshore Simulator Centre (OSC) for providing a visualization friendly version of the Gunnerus 3D model used to enhance some of the simulations performed. Finally, I give my most warmest thanks to my beloved partner, Caroline Remø Dahl, and our mesmerising twin sons Aron and Iver. You have truly shown me the meaning of life. Also, I give a special thanks to my supporting family and friends. Mom and dad, I love you. Making a run for a PhD title during a twin birth, COVID-19 and house building has been quite the experience. iii Contents Abstract i Acknowledgment iii List of Abbreviations vii List of Figures ix List of Tables xi 1 Introduction 1 1.1 Background and motivation ............................. 1 1.2 Objectives . .................................... 4 1.3 List of publications .................................. 6 1.4 Structure of the Dissertation ............................. 7 2 Co-simulation to support demanding maritime operations 9 2.1 Co-simulation fundamentals ............................. 9 2.2 Literature review . .................................. 12 2.2.1 Benefits and challenges of co-simulation .................. 12 2.3 Scope of work ..................................... 13 2.4 Model accumulation, limitations, and assumptions ................ 15 2.4.1 Maritime reference models .......................... 15 3 Fundamental technologies for co-simulation 17 3.1 Open standards for co-simulation . ......................... 17 3.1.1 The Functional Mock-up Interface ..................... 17 3.1.2 High Level Architecture ........................... 18 3.1.3 System Structure and Parameterization .................. 18 3.1.4 Open Simulation Platform - Interface Specification ............ 19 3.1.5 Open Simulation Platform - System Structure ............... 19 3.1.6 Distributed Co-simulation Protocol . ................... 19 3.1.7 Standards considered in this work . ..................... 19 3.2 FMI-based co-simulation libraries and tools .................... 20 3.2.1 Overview of existing libraries ........................ 20 v 3.2.2 FMI4j ..................................... 20 3.2.3 FMI4cpp ................................... 21 3.2.4 PythonFMU ................................. 21 3.2.5 SSPgen .................................... 22 3.2.6 FMU-proxy .................................. 23 4 Open-source co-simulation platforms 27 4.1 Overview of open-source FMI based co-simulation platforms . ........ 27 4.2 The open simulation platform ............................ 27 4.2.1 libcosim .................................... 28 4.2.2 cosim4j .................................... 29 4.3 Vico . ........................................ 29 4.3.1 FMI & SSP support ............................. 30 4.3.2 3D visuals ................................... 31 4.3.3 Scenarios . ................................ 31 4.4 Differences between the OSP and Vico ....................... 32 5 Case studies 33 5.1 Accuracy and performance benchmark ....................... 33 5.2 Co-simulation of the RV Gunnerus ......................... 36 6 Conclusion 47 6.1 Summary of contributions . ............................. 47 6.2 Directions for future work .............................. 49 References 51 Appendix 57 A Paper A1 59 B Paper A2 67 C Paper A3 77 D Paper A4 91 E Paper A5 97 F Paper A6 113 vi List of Abbreviations API Application