research paper about marine engineering

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Advertisement

Machine learning for naval architecture, ocean and marine engineering

• Review article
• Published: 28 November 2022
• Volume 28 , pages 1–26, ( 2023 )

Cite this article

• J P PANDA   ORCID: orcid.org/0000-0003-2839-6185 1 , 2  

1763 Accesses

15 Citations

Explore all metrics

Machine learning (ML)-based techniques have found significant impact in many fields of engineering and sciences, where data-sets are available from experiments and high-fidelity numerical simulations. Those data-sets are generally utilised in a machine learning model to extract information about the underlying physics and derive functional relationships mapping input variables to target quantities of interest. Commonplace machine learning algorithms utilised in scientific machine learning (SciML) include neural networks, support vector machines, regression trees, random forests, etc. The focus of this article is to review the applications of ML in naval architecture, ocean and marine engineering problems; and identify priority directions of research. We discuss the applications of machine learning algorithms for different problems such as wave height prediction, calculation of wind loads on ships, damage detection of offshore platforms, calculation of ship-added resistance and various other applications in coastal and marine environments. The details of the data-sets including the source of data-sets utilised in the ML model development are included. The features used as the inputs to the ML models are presented in detail and finally, the methods employed in optimisation of the ML models were also discussed. Based on this comprehensive analysis, we point out future directions of research that may be fruitful for the application of ML to ocean and marine engineering problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Application of machine learning in ocean data

A comparative analysis of machine learning algorithms for predicting wave runup

Predicting Sea Level Rise Using Artificial Intelligence: A Review

Explore related subjects.

• Artificial Intelligence
• Automotive Engineering

Cherkassky Vladimir, Mulier Filip M (2007) Learning from data: concepts, theory, and methods . John Wiley & Sons

Brunton Steven L, Noack Bernd R, Petros Koumoutsakos (2020) Machine learning for fluid mechanics. Ann Rev Fluid Mechan 52:477–508

MathSciNet   MATH   Google Scholar  

Hartigan John A, Wong Manchek A (1979) Algorithm as 136: A k-means clustering algorithm. J Royal Statist Soc Series C Appl Statisti 28(1):100–108

Google Scholar  

Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Bharath Anil A (2018) Generative adversarial networks: An overview. IEEE Signal Proc Magaz 35(1):53–65

Schmidhuber Jürgen (2015) Deep learning in neural networks: An overview. Neural Networks 61:85–117

Beck Andrea, Flad David, Munz Claus-Dieter (2019) Deep neural networks for data-driven les closure models. J Comput Phys 398:108910

MathSciNet   Google Scholar  

Goodfellow Ian, Bengio Yoshua, Courville Aaron (2016) Deep learning . MIT press

Choi Heejeong, Park Minsik, Son Gyubin, Jeong Jaeyun, Park Jaesun, Mo Kyounghyun, Kang Pilsung (2020) Real-time significant wave height estimation from raw ocean images based on 2d and 3d deep neural networks. Ocean Eng 201:107129

Leo Breiman (2001) Random forests. Mach Learn 45(1):5–32

MATH   Google Scholar  

Shan Suthaharan (2016) Support vector machine. In Machine learning models and algorithms for big data classification, pp 207–235, Springer

Smola Alex J, Bernhard Schölkopf (2004) A tutorial on support vector regression. Statist Comput 14(3):199–222

Fukami Kai, Fukagata Koji, Taira Kunihiko (2020) Assessment of supervised machine learning methods for uid ows. Mathemat Soc Sci

Sandhya KG, Balakrishnan Nair TM, Bhaskaran Prasad K, Sabique L, Arun N, Jeykumar K (2014) Wave forecasting system for operational use and its validation at coastal puducherry, east coast of india. Ocean Eng 80:64–72

Mafi Somayeh, Amirinia Gholamreza (2017) Forecasting hurricane wave height in gulf of mexico using soft computing methods. Ocean Eng 146:352–362

Etemad-Shahidi A, Javad Mahjoobi (2009) Comparison between m5 model tree and neural networks for prediction of significant wave height in lake superior. Ocean Eng 36(15–16):1175–1181

Chandra Deka Paresh, Prahlada R (2012) Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time. Ocean Eng 43:32–42

Wang Wenxu, Tang Ruichun, Li Cheng, Liu Peishun, Luo Liang (2018) A bp neural network model optimized by mind evolutionary algorithm for predicting the ocean wave heights. Ocean Eng 162:98–107

Law YZ, Santo H, Lim KY, Chan ES (2020) Deterministic wave prediction for unidirectional sea-states in real-time using artificial neural network. Ocean Eng 195:106722

Oliveira PBA, Lotufo ADP, Lopes MLM, Maciel GF (2020) Impact wave predictions by a fuzzy artmap neural network. Ocean Eng 202:107165

Deshmukh Aditya N, Deo MC, Bhaskaran Prasad K, Balakrishnan Nair TM, Sandhya KG (2016) Neural-network-based data assimilation to improve numerical ocean wave forecast. IEEE J Ocean Eng 41(4):944–953

Deo Mc C, Abhay Jha, Chaphekar AS, Ravikant K (2001) Neural networks for wave forecasting. Ocean Eng 28(7):889–898

Deo MC, Sridhar Naidu C (1998) Real time wave forecasting using neural networks. Ocean Eng 26(3):191–203

Bishnupriya Sahoo, Bhaskaran Prasad K (2019) Prediction of storm surge and coastal inundation using artificial neural network-a case study for 1999 odisha super cyclone. Weather Climate Extremes 23:100196

Mandal S, Prabaharan N (2006) Ocean wave forecasting using recurrent neural networks. Ocean Eng 33(10):1401–1410

Tsai Jen-Chih, Tsai Cheng-Han (2009) Wave measurements by pressure transducers using artificial neural networks. Ocean Eng 36(15–16):1149–1157

Subba Rao, Mandal S (2005) Hindcasting of storm waves using neural networks. Ocean Eng 32(5–6):667–684

Jihee Oh, Suh Kyung-Duck (2018) Real-time forecasting of wave heights using eof-wavelet-neural network hybrid model. Ocean Eng 150:48–59

Sundaramoorthy Rajasekaran, Gayathri S, Lee T-L (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35(16):1578–1587

Iman Malekmohamadi, Reza Bazargan-Lari Mohammad, Reza Kerachian, Reza Nikoo Mohammad, Mahsa Fallahnia (2011) Evaluating the efficacy of svms, bns, anns and anfis in wave height prediction. Ocean Eng 38(2–3):487–497

Doong Dong-Jiing, Peng Jen-Ping, Chen Ying-Chih (2018) Development of a warning model for coastal freak wave occurrences using an artificial neural network. Ocean Eng 169:270–280

Tyagi Amit, Sen Debabrata (2006) Calculation of transverse hydrodynamic coefficients using computational fluid dynamic approach. Ocean Eng 33(5–6):798–809

Avilash Sahoo, Dwivedy Santosha K, Robi PS (2019) Advancements in the field of autonomous underwater vehicle. Ocean Eng 181:145–160

Zhang Jialei, Xiang Xianbo, Zhang Qin, Li Weijia (2020) Neural network-based adaptive trajectory tracking control of underactuated auvs with unknown asymmetrical actuator saturation and unknown dynamics. Ocean Eng 218:108193

Zhang Jianjun, Liu Weidong, Li Le, Li Zeyu et al (2018) The master adaptive impedance control and slave adaptive neural network control in underwater manipulator uncertainty teleoperation. Ocean Eng 165:465–479

Gao Jian, An Xuman, Proctor Alison, Bradley Colin (2017) Sliding mode adaptive neural network control for hybrid visual servoing of underwater vehicles. Ocean Eng 142:666–675

Zhenzhong Chu, Daqi Zhu, Jan Gene Eu (2016) Observer-based adaptive neural network control for a class of remotely operated vehicles. Ocean Eng 127:82–89

Tapabrata Ray, Gokarn RP, Sha OP (1996) Neural network applications in naval architecture and marine engineering. Artific Intellig Eng 10(3):213–226

Margari Vasiliki, Kanellopoulou Aphrodite, Zaraphonitis George (2018) On the use of artificial neural networks for the calm water resistance prediction of marad systematic series’ hullforms. Ocean Eng 165:528–537

Cepowski Tomasz (2020) The prediction of ship added resistance at the preliminary design stage by the use of an artificial neural network. Ocean Eng 195:106657

Zhang Xin-Guang, Zou Zao-Jian (2013) Estimation of the hydrodynamic coefficients from captive model test results by using support vector machines. Ocean Eng 73:25–31

Haitong Xu, Guedes Soares C (2019) Hydrodynamic coefficient estimation for ship manoeuvring in shallow water using an optimal truncated ls-svm. Ocean Eng 191:106488

Shuai Yonghui, Guoyuan Li Xu, Cheng Robert Skulstad, Jinshan Xu, Liu Honghai, Zhang Houxiang (2019) An efficient neural-network based approach to automatic ship docking. Ocean Eng 191:106514

Weilin Luo, Lúcia Moreira, Guedes Soares C (2014) Manoeuvring simulation of catamaran by using implicit models based on support vector machines. Ocean Eng 82:150–159

Tang Huang, Yin Yong, Shen Helong (2019) A model for vessel trajectory prediction based on long short-term memory neural network. J Marine Eng Technol , pages 1–10

Volkova Tamara A, Balykina Yulia E, Alexander Bespalov (2021) Predicting ship trajectory based on neural networks using ais data. J Marine Sci Eng 9(3):254

Valčić Marko, Prpić-Oršić Jasna (2016) Hybrid method for estimating wind loads on ships based on elliptic fourier analysis and radial basis neural networks. Ocean Eng 122:227–240

Parkes AI, Sobey AJ, Hudson DA (2018) Physics-based shaft power prediction for large merchant ships using neural networks. Ocean Eng 166:92–104

Gkerekos Christos, Lazakis Iraklis, Theotokatos Gerasimos (2019) Machine learning models for predicting ship main engine fuel oil consumption: A comparative study. Ocean Eng 188:106282

Farag Yasser BA, Ölçer Aykut I (2020) The development of a ship performance model in varying operating conditions based on ann and regression techniques. Ocean Eng 198:106972

Farag Yasser Bayoumy Abdelwahab (2017) A decision support system for ship’s energy efficient operation: based on artificial neural network method. Thesis

Gkerekos Christos, Lazakis Iraklis (2020) A novel, data-driven heuristic framework for vessel weather routing. Ocean Eng 197:106887

Karagiannidis Pavlos, Themelis Nikos (2021) Data-driven modelling of ship propulsion and the effect of data pre-processing on the prediction of ship fuel consumption and speed loss. Ocean Eng 222:108616

Yuan Zhi, Liu Jingxian, Zhang Qian, Liu Yi, Yuan Yuan, Li Zongzhi (2021) Prediction and optimisation of fuel consumption for inland ships considering real-time status and environmental factors. Ocean Eng 221:108530

Gao Miao, Shi Guo-You (2020) Ship collision avoidance anthropomorphic decision-making for structured learning based on ais with seq-cgan. Ocean Eng 217:107922

Yang Dong, Lingxiao Wu, Wang Shuaian (2021) Can we trust the ais destination port information for bulk ships?-implications for shipping policy and practice. Transport Res Part E Logist Transport Rev 149:102308

Panduranga Kottala, Koley Santanu, Sahoo Trilochan (2021) Surface gravity wave scattering by multiple slatted screens placed near a caisson porous breakwater in the presence of seabed undulations. Appl Ocean Res 111:102675

Kaligatla RB, Manisha Sharma, Trilochan Sahoo (2021) Wave interaction with a pair of submerged floating tunnels in the presence of an array of submerged porous breakwaters. J Offshore Mechan Arctic Eng 143(2):021402

Vijay KG, Neelamani S, Sahoo T, Al-Salem K, Nishad CS (2020) Scattering of gravity waves by a pontoon type breakwater with a series of pervious and impervious skirt walls. Ships and Offshore Structures , pages 1–13

Dong Hyawn Kim and Woo Sun Park (2005) Neural network for design and reliability analysis of rubble mound breakwaters. Ocean Eng 32(11–12):1332–1349

Van Der Meer Jentsje W (1990) Rock slopes and gravel beaches under wave attack. Report

Hyawn Kim Dong, Jin Kim Young, Soo Hur Dong (2014) Artificial neural network based breakwater damage estimation considering tidal level variation. Ocean Eng 87:185–190

Chung Minwoong, Kim Seungjun, Lee Kanghyeok et al (2020) Detection of damaged mooring line based on deep neural networks. Ocean Eng 209:107522

Rezaniaiee Aqdam Hamed, Mohammad Ettefagh Mir, Reza Hassannejad (2018) Health monitoring of mooring lines in floating structures using artificial neural networks. Ocean Eng 164:284–297

Prabhu JJ, Nagarajan V, Sunny MR, Sha OP (2017) On the fluid structure interaction of a marine cycloidal propeller. Applied Ocean Research 64:105–127

Kumar S, Nagarajan V, Sha OP (2017) Measurement of flow characteristics in propeller slipstream of a twin propeller twin rudder model ship. Int Shipbuild Prog 63(1–2):1–40

Subhashis Nandy, Vishwanath Nagarajan, Prakash Sha Om (2018) On the heuristic based electronic control of marine cycloidal propeller. Appl Ocean Res 78:134–155

Kumars Mahmoodi, Hassan Ghassemi, Hashem Nowruzi, Mahdi Shora Mohammad (2019) Prediction of the hydrodynamic performance and cavitation volume of the marine propeller using gene expression programming. Ships Offshore Str 14(7):723–736

Mahdi Shora Mohammad, Hassan Ghassemi, Hashem Nowruzi (2018) Using computational fluid dynamic and artificial neural networks to predict the performance and cavitation volume of a propeller under different geometrical and physical characteristics. J Marine Eng Technol 17(2):59–84

Kim Ji-Hye, Lee Hyoungseok, Hur Jea-Wook (2021) A study on the risk of propeller cavitation erosion using convolutional neural network. J Soc Naval Archit Korea 58(3):129–136

Donal Ryan, Hamill Gerard A, Johnston Harold T (2013) Determining propeller induced erosion alongside quay walls in harbours using artificial neural networks. Ocean Eng 59:142–151

Bao Xingxian, Fan Tongxuan, Shi Chen, Yang Guanlan (2021) One-dimensional convolutional neural network for damage detection of jacket-type offshore platforms. Ocean Eng 219:108293

Osama Abdeljaber, Onur Avci, Serkan Kiranyaz, Moncef Gabbouj, Inman Daniel J (2017) Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J Sound Vibrat 388:154–170

Isabel López, Luis Aragonés, Yolanda Villacampa, Patricia Compañ, Satorre R (2015) Morphological classification of microtidal sand and gravel beaches. Ocean Eng 109:309–319

Tan Yanghui, Tian Hui, Jiang Ruizheng, Lin Yejin, Zhang Jundong (2020) A comparative investigation of data-driven approaches based on one-class classifiers for condition monitoring of marine machinery system. Ocean Eng 201:107174

The case of tanker vessels (2017) Aris Pagoropoulos, Anders H Møller, and Tim C McAloone. Applying multi-class support vector machines for performance assessment of shipping operations. Ocean Eng 140:1–6

Taudal Poulsen René, Hannes Johnson (2016) The logic of business vs. the logic of energy management practice: understanding the choices and effects of energy consumption monitoring systems in shipping companies. J Clean Product 112:3785–3797

Le Anh Vu, Phone Thiha Kyaw, Veerajagadheswar Prabakaran, Viraj Muthugala MA, J, Elara Mohan Rajesh, Kumar Madhu, Huu Khanh Nhan Nguyen, (2021) Reinforcement learning-based optimal complete water-blasting for autonomous ship hull corrosion cleaning system. Ocean Eng 220:108477

Sutton Richard S, Barto Andrew G (2018) Reinforcement learning: An introduction . MIT press

Bento PMR, Pombo JAN, Mendes RPG, Calado MRA, Mariano SJPS (2021) Ocean wave energy forecasting using optimised deep learning neural networks. Ocean Eng 219:10837

Mousavi Seyed Milad, Ghasemi Majid, Manshadi Mahsa Dehghan, Mosavi Amir (2021) Providing a prediction model to the output power of a wave en-ergy converter by artificial neural network. Preprint

He Jun (2020) Coherence and cross-spectral density matrix analysis of random wind and wave in deep water. Ocean Eng 197:106930

Vieira Filipe, Taveira-Pinto Francisco, Rosa-Santos Paulo (2021) Novel time-efficient approach to calibrate varans-vof models for simulation of wave interaction with porous structures using artificial neural networks. Ocean Eng 235:109375

Yuwang Xu, Fenerci Aksel, Øiseth Ole, Moan Torgeir (2020) Efficient prediction of wind and wave induced long-term extreme load effects of floating suspension bridges using artificial neural networks and support vector machines. Ocean Eng 217:107888

Dripta Sarkar, Osborne Michael A, Adcock Thomas AA (2018) Prediction of tidal currents using bayesian machine learning. Ocean Eng 158:221–231

Immas Alexandre, Do Ninh, Alam Mohammad-Reza (2021) Real-time in situ prediction of ocean currents. Ocean Eng 228:108922

Sumangala Dhanya, Warrior Hari (2020) Coastal modelling incorporating artificial neural networks for improved velocity prediction. ISH J Hydraul Eng , pages 1–11

Sumangala Dhanya, Joshi Apurva, Warrior Hari (2022) Modelling freshwater plume in the bay of bengal with artificial neural networks. Curr Sci (00113891) , 123(1)

Prakash Panda Jyoti, Warrior Hari V (2021) Numerical studies on drag reduction of an axisymmetric body of revolution with antiturbulence surface. J Offshore Mech Arct Eng 143(6):064501

Mitra A, Panda JP, Warrior HV (2019) The effects of free stream turbulence on the hydrodynamic characteristics of an auv hull form. Ocean Eng 174:148–158

Arindam Mitra, Prakash Panda Jyoti, Vijayan Warrior Hari (2020) Experimental and numerical investigation of the hydrodynamic characteristics of autonomous underwater vehicles over sea-beds with complex topography. Ocean Eng 198:106978

Piyush Mohapatra, Vijay KG, Anirban Bhattacharyya, Trilochan Sahoo (2021) Performance of a shore fixed oscillating water column device for different bottom slopes and front wall drafts: A study based on computational fluid dynamics and biem. J Offshore Mechan Arctic Eng 143(3):032002

Mohapatra Piyush, Sahoo Trilochan (2020) Hydrodynamic performance analysis of a shore fixed oscillating water column wave energy converter in the presence of bottom variations. Proc Inst Mech Eng Part M J Eng Maritime Environ 234(1):37–47

ANSYS Fluent. Fluent 14.0 user’s guide. ANSYS FLUENT Inc , 2011

S CD-adapco. Star ccm+ user guide version 12.04. CD-Adapco: New York, NY, USA , 2017

Larsson L, Broberg L, Zhang DH (1989) Shipflow–a cfd system for ship design. In International Symposium on Practical Design of Ships and Mobile Units (PRADS), 4th , pages 53–65

Vinothkumar Sekar, Qinghua Jiang, Chang Shu, Cheong Khoo Boo (2019) Fast flow field prediction over airfoils using deep learning approach. Phys Fluids 31(5):057103

Hui Xinyu, Bai Junqiang, Wang Hui, Zhang Yang (2020) Fast pressure distribution prediction of airfoils using deep learning. Aerospace Sci Technol 105:105949

Ashwin Renganathan S, Romit Maulik, Vishwas Rao (2020) Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil. Phys Fluids 32(4):047110

Kong Chen, Chang Juntao, Wang Ziao, Li Yunfei, Bao Wen (2021) Data-driven super-resolution reconstruction of supersonic flow field by convolutional neural networks. AIP Adv 11(6):065321

Kong Chen, Chang Juntao, Li Yunfei, Wang Ziao (2021) A deep learning approach for the velocity field prediction in a scramjet isolator. Phys Fluids 33(2):026103

Lee Sangseung, You Donghyun (2021) Analysis of a convolutional neural network for predicting unsteady volume wake flow fields. Phys Fluids 33(3):035152

Hasegawa Kazuto, Fukami Kai, Murata Takaaki, Fukagata Koji (2020) Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes. Theoret Comput Fluid Dynam 34(4):367–383

Nakamura Taichi, Fukami Kai, Hasegawa Kazuto, Nabae Yusuke, Fukagata Koji (2021) Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow. Phys Fluids 33(2):025116

Leer Michael, Kempf Andreas (2021) Fast flow field estimation for various applications with a universally applicable machine learning concept. Flow Turbul Combust 107(1):175–200

Sun Luning, Gao Han, Pan Shaowu, Wang Jian-Xun (2020) Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data. Comput Methods Appl Mech Eng 361:112732

Ali Kashefi, Davis Rempe, Guibas Leonidas J (2021) A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries. Phys Fluids 33(2):027104

Moin Parviz, Kim John (1997) Tackling turbulence with supercomputers. Scient Am 276(1):62–68

Pope SB (2000) Turbulent Flows. Cambridge University Press, New York

Speziale Charles G (1991) Analytical methods for the development of reynolds-stress closures in turbulence. Ann Rev Fluid Mechan 23(1):107–157

Speziale Charles G, Gatski Thomas B, Mac Giolla Mhuiris Nessan (1990) A critical comparison of turbulence models for homogeneous shear flows in a rotating frame. Physf Fluids A Fluid Dynam 2(9):1678–1684

Ananda Mishra Aashwin, Karthik Duraisamy, Gianluca Iaccarino (2019) Estimating uncertainty in homogeneous turbulence evolution due to coarse-graining. Phys Fluids 31(2):025106

Ananda Mishra Aashwin, Sharath Girimaji (2019) Linear analysis of non-local physics in homogeneous turbulent flows. Phys Fluids 31(3):035102

Launder BE, Reece G Jr, Rodi W (1975) Progress in the development of a reynolds-stress turbulence closure. J Fluid Mechan 68(3):537–566

Speziale Charles G, Sutanu Sarkar, Gatski Thomas B (1991) Modelling the pressure-strain correlation of turbulence:an invariant dynamical systems approach. J Fluid Mechan 227:245–272

Sutanu Sarkar, Speziale Charles G (1990) A simple nonlinear model for the return to isotropy in turbulence. Phys Fluids A Fluid Dynam 2(1):84–93

Mishra Aashwin A, Girimaji Sharath S (2013) Intercomponent energy transfer in incompressible homogeneous turbulence:multi-point physics and amenability to one-point closures. J Fluid Mechan 731:639–681

Mishra Aashwin A, Girimaji Sharath S (2017) Toward approximating non-local dynamics in single-point pressure-strain correlation closures. J Fluid Mechan 811:168–188

Mishra Aashwin A, Girimaji Sharath S (2010) Pressure-strain correlation modeling: towards achieving consistency with rapid distortion theory. Flow Turbulence Combust 85(3–4):593–619

Mishra Aashwin A, Girimaji Sharath S (2014) On the realizability of pressure-strain closures. J Fluid Mechan 755:535–560

Jiménez Javier (2018) Machine-aided turbulence theory. J Fluid Mechan , 854

Duraisamy Karthikeyan, Zhang Ze J, Singh Anand Pratap (2015) New approaches in turbulence and transition modeling using data-driven techniques. In 53rd AIAA Aerospace Sciences Meeting , page 1284

Pratap Singh Anand, Shivaji Medida, Karthik Duraisamy (2017) Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils. AIAA J 55(7):2215–2227

Ling Julia, Kurzawski Andrew, Templeton Jeremy (2016) Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. J Fluid Mechan 807:155–166

Snoek Jasper, Larochelle Hugo, Adams Ryan P (2012) Practical bayesian optimization of machine learning algorithms. Adv Neural Inform Process Syst , 25

Wang Jian-Xun, Jin-Long Wu, Xiao Heng (2017) Physics-informed machine learning approach for reconstructing reynolds stress modeling discrepancies based on dns data. Phys Rev Fluids 2(3):034603

Jin-Long Wu, Xiao Heng, Paterson Eric (2018) Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework. Phys Rev Fluids 3(7):074602

Kaandorp Mikael LA, Dwight Richard P (2020) Data-driven modelling of the reynolds stress tensor using random forests with invariance. Comput Fluids , 202:104497

Zhu Linyang, Zhang Weiwei, Sun Xuxiang, Liu Yilang, Yuan Xianxu (2021) Turbulence closure for high reynolds number airfoil flows by deep neural networks. Aerospace Sci Technol 110:106452

Panda JP, Warrior HV (2021) Modelling the pressure strain correlation in turbulent flows using deep neural networks. arXiv preprint arXiv:2103.00907

Panda JP, Warrior HV (2018) A representation theory-based model for the rapid pressure strain correlation of turbulence. J Fluid Eng 140(8):081101

Panda Jyoti Prakash, Mitra Arindam, Warrior Hari V (2020) A review on the hydrodynamic characteristics of autonomous underwater vehicles. Proce Institut Mech Eng Part M J Eng Maritime Environ , page 1475090220936896

Panda JP, Warrior HV (2021) Evaluation of machine learning algorithms for predictive reynolds stress transport modeling. arXiv preprint arXiv:2105.13641

Wang Zhuo, Luo Kun, Li Dong, Tan Junhua, Fan Jianren (2018) Investigations of data-driven closure for subgrid-scale stress in large-eddy simulation. Phys Fluids 30(12):125101

Yuan Zelong, Xie Chenyue, Wang Jianchun (2020) Deconvolutional artificial neural network models for large eddy simulation of turbulence. Phys Fluids 32(11):115106

Xie Chenyue, Yuan Zelong, Wang Jianchun (2020) Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence. Phys Fluids 32(11):115101

Xinyue Hu, Haoji Hu, Verma Saurabh, Zhang Zhi-Li (2020) Physics-guided deep neural networks for power flow analysis. IEEE Transact Power Syst 36(3):2082–2092

Baker Nathan, Alexander Frank, Bremer Timo, Hagberg Aric, Kevrekidis Yannis, Najm Habib, Parashar Manish, Patra Abani, Sethian James, Wild Stefan et al (2019) Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence. Technical report, USDOE Office of Science (SC), Washington, DC (United States)

Shehtab Zaman M (2020) Scientific Deep Learning: Deep Learning for Quantum Physics . PhD thesis, State University of New York at Binghamton

Arun Shantaram Hegde (2020) Quantifying the agreement between computational models and experimental data under uncertainty. University of California, Berkeley

Download references

Author information

Authors and affiliations.

School of Mechanical and Aerospace Engineering, Gyeongsang National University, Jinju, South Korea

Department of Mechanical Engineering, DIT University, Dehradun, Uttarakhand, India

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to J P PANDA .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

PANDA, J.P. Machine learning for naval architecture, ocean and marine engineering. J Mar Sci Technol 28 , 1–26 (2023). https://doi.org/10.1007/s00773-022-00914-5

Download citation

Received : 09 April 2022

Accepted : 06 November 2022

Published : 28 November 2022

Issue Date : March 2023

DOI : https://doi.org/10.1007/s00773-022-00914-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Artificial intelligence
• Machine learning
• Data driven modeling
• Naval architecture
• Ocean engineering
• Marine engineering
• Find a journal
• Publish with us
• Track your research

Maritime Engineering

• Submit your paper
• Author guidelines
• Editorial team
• Indexing & metrics

Manuscript preparation guidelines for journal authors - Engineering

Our engineering journal titles report the latest research and current practice for the benefit of the international civil engineering profession and related disciplines. We also cover historical research and lessons learned from past events. Each paper is independently assessed and peer-reviewed.

All of our engineering titles broadly follow the guidelines below. More specific instruction may be found on the journal homepage of the title that you are submitting to.

Types of content

• Paper (including research, case study or project papers). A Research article is an original presentation of findings from an investigation. A case study looks at the effects of the implementation of, for example, a system and analyses it, in context of the situation.
• State-of-the-art review.  A state-of-the-art review is an up-to-date summary of knowledge on a particular subject or issue and represents an overview of recent developments.
• Briefing articles.  Short, topical updates, which are not sufficiently comprehensive or novel to be submitted as a research article. Typically, briefings are used to provide authoritative updates of relevant technical, regulatory and professional developments. They can introduce new ideas, explain new legislation, reflect on industry trends, provide the background to a new product or service, discuss anniversaries and events, or simply report a short case history.
• Book review.  A book review provides a short description of an academic title and evaluates its quality and contribution to the field in question.
• Discussion.  This article format allows reader to comment on previously published papers. Authors of the paper being discussed are given the right to reply. All correspondence is peer-reviewed by a member of the Editorial Board or Panel.

Length (excluding abstract and reference list)

• Research articles have a maximum length of 5,000 words (excluding article title, abstract and reference list).
• Briefing articles have a word count limit of 1,800, except for the Civil Engineering journal which has a limit of 600 words.
• Across all journals there is a word count limit of 500 for discussions and book reviews.

If your article exceeds these restrictions, you can upload the additional information as supplementary data. Please note, that this is only published online and not in the print version of the journal. You can find out more information by reading our supplementary information policy .

Format and elements of submitted texts

Please prepare your main text document in Microsoft Word, text should be double-line spaced, line numbered and pages should be numbered. We have a template available should you need it.

We also accept Latex files; you may use the following template:  Proceedings of ICE journal . Latex file manuscripts must be submitted using our template  along with a PDF copy of the manuscript.

Please note that the style that you submit your paper in (e.g. any additional italics or bold fonts, bullet points, etc.) may be changed on publication to accommodate our house style.

• The text should be written in UK English, in the third person and all spelling follow the latest edition of The Concise Oxford English Dictionary, with a preference for ‘s’ rather than ‘z’ spellings, e.g. specialise.
• The manuscript should be able to be readily understood by a civil engineer and avoid any colloquialisms.
• The terms, including nomenclature and abbreviations, and style should be consistent throughout the text. Please bear this in mind when collaborating with other authors on the text.
• Referring directly to the names of individuals, organisations, products or services is forbidden unless essential to the comprehension of the manuscript. Gratuitous flattery or derogatory remarks about any person/organisation should not be included.
• Principal participants in a project should be listed separately in a table or acknowledgement at the end of the text. If a person/client is involved, you should seek their permission to detail the project.
• We do not accept footnotes.
• Symbols and Units: SI and derived units should be used, including for historical structures.
• Abbreviations: the use of internationally recognised abbreviations is allowed in the text provided they are defined on first use. Abbreviations should not be used in the title unless a commonly used, non-specialist term. Any abbreviations which can be pronounced as a word (i.e. acronyms) should generally have an upper-case initial only (e.g. Defra). Symbols for chemical elements and compounds should not be used as abbreviations unless in the context of a chemical equation. In particular, ‘carbon dioxide’ should not be abbreviated to ‘CO2’ or ‘carbon’.
• Use bullet points rather than numbered lists.
• Text should be 1.5 spacing or double spaced.

The following is a detailed manuscript preparation guide for research articles to ICE Publishing’s engineering titles; however, they can, in the most part, be used as a basis for other article types amending to concur with the word limit and premise of the formats, as appropriate.

On the first page of your main text document please provide:

• The date that the text was written or revised
• Title of paper (please see below for guidance on titles)
• Full names and post-nominal letters of author(s)
• Positions, affiliations and ORCID number of author(s)
• Contact address and email addresses of all authors 
• Number of words in the main text (excluding abstract and references) and the number of figures and tables.
• Please DO NOT include your personal telephone number on the title page.

Titles are limited to 90 characters, including spaces. Please avoid the use of any abbreviations, acronyms or formulae. Titles should clearly reflect the content of the manuscript and any search terms that readers may use should be considered and incorporated.

Please provide a 150–200 word summary of the submission (briefings, research articles and letters only). This should be a concise reflection of the aims, findings, conclusions and any interesting or important results. Take care to incorporate any terms that may be used by potential interested readers to improve the article’s discoverability online (search engine optimisation). This should contain no references; abbreviations that are not commonly used should be defined (for the benefit of the non-specialist reader) at first use.

List of notations

Please provide a list of symbols and definitions used in the text that would be helpful for the reader.

These are used for indexing your article on ICE Virtual Library (this website). Please select a minimum of three keywords from this MS Excel file (if it displays as symbols on a webpage, try opening them in a browser other than Internet Explorer). When you submit your article, you may also type in keywords not on this list.

Introduction

A concise, accurate, but not exhaustive, summary of current knowledge, with reference to relevant previous and recent works in the field should be presented. This should be accompanied with the aims of and justification for the work contained in the submitted manuscript.

The methods and processes applied to investigate and achieve the aims should be communicated in sufficient detail that readers could repeat the work successfully. The results should be reported clearly and logically, must be interpreted accurately and discussed fairly. Figures/tables can be used to support these findings, but data must not be reproduced in more than one form.

It is a requirement that all research articles include a section at the end of the main text that highlights the contribution of the findings to the field and any potential applications.

All research articles, case studies and project papers should discuss how the work relates to mitigation of or adaptation to climate change. Where relevant, a section on health and safety should be included.

In general, we recommend one figure per 500 words of text. Examples of figures and guidance on filetypes can be seen on our Figure Guidance page . For specific advice and step by step guidance on accepted file formats and our figure requirements please open, download and save our  figure guidelines  PDF.

All figures are published in colour online. The following four journals also have a black and white printed version: Bridge Engineering, Géotechnique, Ground Improvement and Magazine of Concrete Research . This should be considered when trying to convey information through colour, use greyscale where necessary. If you wish, you can pay a charge of 750 GBP for colour printing. To do so, send this form  to the journal office.

If reproducing or adapting figures from other published work, this must be referenced in the caption and appropriate permissions sought. Please see our  copyright page  for more information.

Conclusions

A concise summary of the findings or, in the instance of case studies or project papers, the lessons learned. No new information should be introduced here. If necessary, you should explain here the applicability / relevance of your article to readers in other countries.

Research papers must explain the practical relevance and potential applications of the work described. This is important to readers working in civil engineering and related practice.

Similarly, case studies and project papers must highlight the relevance of the work described and summarise the lessons learned. As with research papers, they must also include relevant references to demonstrate how previous research and practice has been used. These references could be standards, codes or relevant past ICE Publishing journal papers.

Additional information, such as tables or mathematical calculations/derivations can be included and should be clearly referred to, from the main text, as belonging to the appendix. These will be included in the print and online versions of the article.

Acknowledgements

Please provide details from those (individuals and institutions) other than co-authors that contributed to the paper. Additional details required by funding bodies can be placed here too, as well as information about the source of the work (i.e., based on a presentation etc.)

Please add a list of literature cited in the manuscript at the end of the text. Harvard style (author, date) referencing is used in engineering papers. Further details about Harvard referencing .

Unpublished material should not be included in the Reference list.

•    If an article has been submitted but not yet accepted, it should only be cited within the text and not the reference list. For example, at the first citation ‘(see ‘Title of publication’ by Author, submitted to Journal’). Subsequent citations can be presented as ‘Author (submitted)’ or ‘(Author, submitted)’.

•    If an article has been submitted and accepted but is not yet published, it should be included in the reference list with 'in press' at the end. A DOI number should be included where possible.  

Mathematical equations

Only relevant equations should be included in the main text and should be numbered – anything else can be added as an appendix or as supplementary information. Simple, single line equations can be written using word; an equation editor program is required for more complex formulae.

Figures and tables caption list: Please supply a figure caption list at the end of your main text document. Figures and tables must be mentioned in the text in consecutive order, but as different sets (i.e., Figure 1, Table 1 etc.) All figures must have a brief title accompanied with a short description that can be able to be understood without reference to the main text.

Author Photos

Authors are encouraged to provide a passport style photograph of themselves. These will be published only if a file for every named author is provided.

Corresponding Authors

We only permit one corresponding author per submission. Co-authors can be added, and their email addresses and institutions must be provided. 

Supplementary information

Additional information, data and other material that may enhance the manuscript but is not necessary to the conclusions can be uploaded as supplementary material. Any reference to supplementary information in the main text should be referred to as, e.g., Figure S1. Further details, please read our supplementary information policy .

Once you have completed your manuscript preparation, please go through this submission checklist . When you are ready, please upload your MS Word document text, and separate high-resolution image files, to the journal submission website. All of our titles use ReView, a manuscript management system - all articles must be uploaded through this.

We have more instructions on how to submit your article . This will save you emailing large files through to us. Please do not submit all of your files as one PDF. You will receive a confirmation email once you have successfully submitted your paper online.

Copyright Information

Information on copyright, including text extracts and the reuse of permission published elsewhere, can be found via our Copyright and Permissions page

If you have any queries, please contact the editorial office.

• Tom Bruce University of Edinburgh - United Kingdom
• Tiago João Fazeres Marques Ferradosa University of Porto - Portugal
• Facheng Wang Tsinghua University - P.R. China

Editorial Board

• Caroline Barford Coastal Partners - United Kingdom
• Maria Di Leo Mott MacDonald - UK
• David Finch APBmer - United Kingdom
• Shixiao Fu Shanghai Jiao Tong University - P.R. China
• Fuping Gao Chinese Academy of Sciences - P.R. China
• Nadia Genovese COWI - United Kingdom
• Dawei Guan Hohai University - P. R. China
• James Hernon MetOceanWorks Ltd. - United Kingdom
• Kazuhiro Iijima Osaka University - Japan
• Abbas Khayyer Kyoto University - Japan
• Dongfang Liang University of Cambridge - United Kingdom
• Pedro Lomonaco Oregon State University - USA
• Gillian Millar GHD - USA
• Dezhi Ning Dalian University of Technology - P.R. China
• Muk Chen Ong University of Stavanger - Norway
• James Palmer Beckett Rankine - United Kingdom
• Vijay Panchang Texas A&M University - USA
• Dimitrios Pavlou Norwegian Academy of Technological Sciences and University of Stavanger - Norway
• Bryson Robertson Oregon State University - USA
• Sudath Siriwardane University of Stavanger - Norway
• Nicholas Tavouktsoglou HR Wallingford - United Kingdom
• Ming Zhao Western Sydney University - Australia
• Luis Augusto Ferreira Rodrigues de Macedo Instituto Superior de Engenharia do Porto - Portugal

Commissioning Editor

• Becky Rivers Emerald Publishing - UK [email protected]

Journal Editorial Office (For queries related to pre-acceptance)

• Dhanashree  Bhingarde Emerald Publishing - India [email protected]

Supplier Project Manager (For queries related to post-acceptance)

• Ritu Maity Emerald Publishing - India [email protected]

Citation metrics

CiteScore 2023

Further information

CiteScore is a simple way of measuring the citation impact of sources, such as journals.

Calculating the CiteScore is based on the number of citations to documents (articles, reviews, conference papers, book chapters, and data papers) by a journal over four years, divided by the number of the same document types indexed in Scopus and published in those same four years.

For more information and methodology visit the Scopus definition

CiteScore Tracker 2024

(updated monthly)

CiteScore Tracker is calculated in the same way as CiteScore, but for the current year rather than previous, complete years.

The CiteScore Tracker calculation is updated every month, as a current indication of a title's performance.

2023 Impact Factor

The Journal Impact Factor is published each year by Clarivate Analytics. It is a measure of the number of times an average paper in a particular journal is cited during the preceding two years.

For more information and methodology see Clarivate Analytics

5-year Impact Factor (2023)

A base of five years may be more appropriate for journals in certain fields because the body of citations may not be large enough to make reasonable comparisons, or it may take longer than two years to publish and distribute leading to a longer period before others cite the work.

Actual value is intentionally only displayed for the most recent year. Earlier values are available in the Journal Citation Reports from Clarivate Analytics .

Publication timeline

Time to first decision

Time to first decision , expressed in days, the "first decision" occurs when the journal’s editorial team reviews the peer reviewers’ comments and recommendations. Based on this feedback, they decide whether to accept, reject, or request revisions for the manuscript.

Data is taken from submissions between 1st January 2023 and 31st December 2023

Acceptance to publication

Acceptance to publication , expressed in days, is the average time between when the journal’s editorial team decide whether to accept, reject, or request revisions for the manuscript and the date of publication in the journal.

Acceptance rate

The acceptance rate is a measurement of how many manuscripts a journal accepts for publication compared to the total number of manuscripts submitted expressed as a percentage %

Data is taken from submissions between 1st January 2023 and 31st December 2023 .

Indexing and metrics content displayed here

Maritime Engineering publishes peer-reviewed papers on civil engineering in port, estuarine, coastal and offshore environments.

Aims and scope

Maritime Engineering publishes peer-reviewed papers relevant to civil engineering in port, estuarine, coastal and offshore environments.

Relevant to consulting, client and contracting engineers as well as researchers and academics, the journal focuses on safe and sustainable engineering in the salt-water environment and comprises papers regarding management, planning, design, analysis, construction, operation, maintenance and applied research. The journal publishes papers and articles from industry and academia that conveys advanced research that those developing, designing or constructing schemes can begin to apply, as well as papers on good practices that others can learn from and utilise.

Awards: Each year, the paper rated best by the Editorial Panel is given the ICE's Halcrow Prize .

Open access:  This is a Plan S compliant journal through its zero-month embargo period. This is a hybrid journal allowing for green or gold open access. Find out more about  publishing open access with us , our article processing charges (APCs) and generous waivers. 

This title is aligned with our sustainable structures and infrastructures goal

We recognise the transformative power of sustainable engineering, design and building practices in creating a world where our planet and its inhabitants can thrive.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Journal Menu

• Aims & Scope

Editorial Board

• Reviewer Board

Topical Advisory Panel

• Instructions for Authors

Special Issues

• Article Processing Charge
• Indexing & Archiving
• Editor’s Choice Articles
• Most Cited & Viewed
• Journal Statistics
• Journal History
• Journal Awards
• Society Collaborations
• Conferences
• Editorial Office

Journal Browser

• arrow_forward_ios Forthcoming issue arrow_forward_ios Current issue
• Vol. 12 (2024)
• Vol. 11 (2023)
• Vol. 10 (2022)
• Vol. 9 (2021)
• Vol. 8 (2020)
• Vol. 7 (2019)
• Vol. 6 (2018)
• Vol. 5 (2017)
• Vol. 4 (2016)
• Vol. 3 (2015)
• Vol. 2 (2014)
• Vol. 1 (2013)

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

Ocean Engineering

A section of Journal of Marine Science and Engineering (ISSN 2077-1312).

Following special issues within this section are currently open for submissions:

• Advanced Technologies for New (Clean) Energy Ships (Deadline: 15 September 2024 )
• Recent Advances in Applied Ship Hydrodynamics (Deadline: 15 September 2024 )
• Machine Learning Methodologies and Ocean Science (Deadline: 20 September 2024 )
• Ship Hydrodynamics and Wave Resistance in Ship Design (Deadline: 25 September 2024 )
• Advanced Condition Monitoring and Intelligent Operation & Maintenance Technologies in Ships and Offshore Facilities (Deadline: 25 September 2024 )
• Ship Performance in Actual Seas (Deadline: 25 September 2024 )
• Fluid-Structure Interaction (FSI) Issues in Floating Offshore Wind Turbines (Deadline: 25 September 2024 )
• Advances in Ships and Marine Structures (Deadline: 25 September 2024 )
• Design and Analysis of New and Retrofitted Eco-Friendly Ships and Offshore Structures (Deadline: 30 September 2024 )
• Research Progress on Ocean Observations Technology and Information Systems (Deadline: 30 September 2024 )
• AI-Empowered Marine Energy (Deadline: 30 September 2024 )
• Advances in Marine Computational Fluid Dynamics (Deadline: 30 September 2024 )
• Application of Advanced Technologies in Maritime Safety—Second Edition (Deadline: 30 September 2024 )
• Applications of Remote Sensing in Coastal and Marine Conservation (Deadline: 30 September 2024 )
• Application of CFD Simulations to Marine Hydrodynamic Problems (2nd Edition) (Deadline: 1 October 2024 )
• Ocean Observations (Deadline: 1 October 2024 )
• Marine Technology: Latest Advancements and Prospects (Deadline: 1 October 2024 )
• Performance and Emission Characteristics of Marine Engines (Deadline: 1 October 2024 )
• Advanced Research on the Sustainable Maritime Transportation (2nd Edition) (Deadline: 5 October 2024 )
• Navigation and Detection Fusion for Autonomous Underwater Vehicles (Deadline: 5 October 2024 )
• Intelligent Approaches to Marine Engineering Research (Deadline: 10 October 2024 )
• Applications of Artificial Intelligence in Marine Machinery (Deadline: 10 October 2024 )
• Recent Advances in Autonomous Underwater Vehicles (Deadline: 10 October 2024 )
• Exploring Offshore Wind Innovation: Breakthroughs in Renewable Energy Research (Deadline: 10 October 2024 )
• Applications of Bubble Dynamics in Ocean Engineering: Theory, Experiment and Simulation (Deadline: 15 October 2024 )
• Nonlinear Wave–Structure Interactions and the Development of Advanced Numerical Models (Deadline: 15 October 2024 )
• Safety and Reliability of Ship and Ocean Engineering Structures (Deadline: 15 October 2024 )
• Advanced Research in Flexible Riser and Pipelines (Deadline: 20 October 2024 )
• Advances in Marine Mechanical and Structural Engineering—2nd Edition (Deadline: 20 October 2024 )
• Advances in Wireless Communication Technology in Oceanic Turbulence (Deadline: 20 October 2024 )
• Innovative Technologies in Safety and Reliability of Marine Engineering (Deadline: 25 October 2024 )
• Research Progress in Wave–Structure Interactions in Nearshore Areas (Deadline: 30 October 2024 )
• Advances in Ship and Marine Hydrodynamics (Deadline: 31 October 2024 )
• Modelling Techniques for Floating Offshore Wind Turbines (Deadline: 31 October 2024 )
• Green Shipping Corridors and GHG Emissions (Deadline: 1 November 2024 )
• Progress in Sensor Technology for Ocean Sciences (Deadline: 1 November 2024 )
• Resilience and Capacity of Waterway Transportation (Deadline: 5 November 2024 )
• Models and Simulations of Ship Manoeuvring (Deadline: 5 November 2024 )
• Advances in the Safety and Security of Intelligent Ships and Offshore Structures (Deadline: 5 November 2024 )
• Powering the Seas: Revolutionizing Shipboard Power Systems with Advanced Control, Alternative Fuels, and Renewable Energy (Deadline: 5 November 2024 )
• Hydrodynamic Response to the Effect of Current Loads on Floating Offshore Platform (Deadline: 10 November 2024 )
• Wave Loads on Offshore Structure (Deadline: 15 November 2024 )
• Development of Theories and Systems in Underwater Communications and Networks (Deadline: 20 November 2024 )
• Advances in Marine Geological and Geotechnical Hazards (Deadline: 20 November 2024 )
• Hydroelastic Behaviour of Floating Offshore Structures (Deadline: 20 November 2024 )
• Dynamic Stability and Safety of Ships in Waves (Deadline: 25 November 2024 )
• Global Navigation Satellite System for Maritime Applications (Deadline: 30 November 2024 )
• Hydraulic Modeling, Optimization and Intelligent Maintenance of Marine Fluid Machinery and Systems (Deadline: 1 December 2024 )
• Underwater Observation Technology in Marine Environment (Deadline: 1 December 2024 )
• Novel Maritime Techniques and Technologies, and Their Safety (Deadline: 1 December 2024 )
• Artificial Intelligence and Its Applications in Intelligent Ship Navigation (Deadline: 5 December 2024 )
• Impact of Ocean Wave Loads on Marine Structures (Deadline: 5 December 2024 )
• Anti-explosion, Anti-impact and Vibration Isolation Advanced Protection Design in Naval Architecture and Ocean Engineering (Deadline: 10 December 2024 )
• Unmanned Marine Vehicles: Perception, Planning, Control and Swarm (Deadline: 10 December 2024 )
• Design and Analysis of Mooring System for Floating Offshore Structures (Deadline: 10 December 2024 )
• Advances in Navigability and Mooring (2nd Edition) (Deadline: 10 December 2024 )
• Numerical Simulation of Fluid-Structure Interactions by CFD (2nd Edition) (Deadline: 10 December 2024 )
• Advances in the Performance of Ships and Offshore Structures (Deadline: 15 December 2024 )
• Advanced Research in Shipping Informatics and Communications—2nd Edition (Deadline: 15 December 2024 )
• Maritime Logistics and Green Shipping (Deadline: 15 December 2024 )
• Advancements in Power Management Systems for Hybrid Electric Vessels (Deadline: 15 December 2024 )
• Ship Wireless Sensor (Deadline: 20 December 2024 )
• Theories and Techniques in Intelligent Digital Twins in Marine Science and Engineering (Deadline: 20 December 2024 )
• Unmanned Marine Vehicles: Navigation, Control and Sensing (Deadline: 20 December 2024 )
• Recent Advances on Intelligent Maintenance and Health Management in Ocean Engineering (Deadline: 25 December 2024 )
• Offshore Renewable Energy, Second Edition (Deadline: 25 December 2024 )
• Advanced Studies in Marine Mechanical and Naval Engineering (Deadline: 25 December 2024 )
• Data-Driven Methods for Marine Structures (Deadline: 25 December 2024 )
• Future Maritime Transport: Trends and Solutions (Deadline: 25 December 2024 )
• Monitoring and Evaluation of Marine Engineering Equipment and Structures (Deadline: 25 December 2024 )
• Cavitation on Marine Propellers: Control, Modelling and Applications (Deadline: 30 December 2024 )
• The State of the Art of Marine Risers and Pipelines (Deadline: 30 December 2024 )
• Computational Marine Hydrodynamics (CMH) (Deadline: 30 December 2024 )
• Advances in Marine Gas Hydrate Exploration and Discovery (Deadline: 31 December 2024 )
• Optimal Maneuvering and Control of Ships—2nd Edition (Deadline: 31 December 2024 )
• Novel Technologies and Achievements of Transport and Logistics in Marine Science (Deadline: 1 January 2025 )
• Numerical Analysis and Modeling of Floating Structures (Deadline: 1 January 2025 )
• Deep-Sea Mining Technologies: Recent Developments and Challenges (Deadline: 1 January 2025 )
• Fatigue, Failure and Integrity of Marine Vessels and Marine Structures (Deadline: 1 January 2025 )
• New Challenges in Offshore Geotechnical Engineering Developments, Second Edition (Deadline: 5 January 2025 )
• Track Planning with Automatic Obstacle Recognition and Avoidance for Maritime Vessels (Deadline: 5 January 2025 )
• Analysis and Design of Marine Structures (Deadline: 5 January 2025 )
• Management and Control of Ship Traffic Behaviours (Deadline: 10 January 2025 )
• Risk Assessment in Maritime Transportation (Deadline: 10 January 2025 )
• Computational Fluid Dynamics and Acoustic Design Methods for Ship (Deadline: 10 January 2025 )
• The Interaction of Ocean Waves and Offshore Structures (Deadline: 10 January 2025 )
• Advances in Maritime Shipping (Deadline: 15 January 2025 )
• Maritime Transport and Port Management (Deadline: 15 January 2025 )
• Marine Geophysical Exploration and Underwater Digital Twin Technology Application (Deadline: 18 January 2025 )
• Intelligent Measurement and Control System of Marine Robots (Deadline: 20 January 2025 )
• Mobile Offshore Drilling Unit (Deadline: 25 January 2025 )
• Advanced Technologies of Ship Power Plants and Infrastructure of Seaports (Deadline: 25 January 2025 )
• Numerical Modeling of Fluid-Structure Interactions in Ocean Engineering (Deadline: 30 January 2025 )
• Advance in Marine Geotechnical Engineering—2nd Edition (Deadline: 30 January 2025 )
• Wave/Current–Structure–Seabed Interactions around Offshore Foundations (Deadline: 1 February 2025 )
• Application of Autonomous Underwater Robotics in Ocean Observation (Deadline: 1 February 2025 )
• Maritime Artificial Intelligence Convergence Research (Deadline: 1 February 2025 )
• Offshore Geotechnics: Offshore Foundations and Soil–Structure Interactions (Deadline: 5 February 2025 )
• Design and Application of Underwater Robots for Navigation and Manipulation (Deadline: 10 February 2025 )
• Collision Avoidance and Path Planning for Marine Vehicles (Deadline: 15 February 2025 )
• Innovations in Underwater Robotic Software Systems (Deadline: 20 February 2025 )
• Advances in Marine Engineering Hydrodynamics (Deadline: 20 February 2025 )
• Intelligent Systems for Marine Transportation (Deadline: 25 February 2025 )
• Advances in Marine Vehicles, Automation and Robotics—2nd Edition (Deadline: 28 February 2025 )
• Maritime Security and Risk Assessments—2nd Edition (Deadline: 28 February 2025 )
• Fatigue Performance and Ultimate Strength of Ships and Marine Structures (Deadline: 28 February 2025 )
• Motion Control and Path Planning of Marine Vehicles—3rd Edition (Deadline: 1 March 2025 )
• Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling (Deadline: 30 June 2025 )
• New Era in Offshore Wind Energy (Deadline: 31 August 2025 )

Papers Published

Further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Naval Architecture and Marine Engineering: Essentials

• Books & eBooks
• Trending Article Feed

Click the Books & EBooks Tab Above to see even more books. 

Professional Associations

Start your research here.....

• Request a TDNet/OpenAthens Account Note: Available to Research Commons users at Atlantic, Carderock, Corona, Crane, DTRA, Indian Head, Keyport, Newport, Panama City, US Naval Observatory, and Office of Naval Research.

Find Even More Books!

• Request a Book

Search Google Scholar

Questions? Contact Us

• Next: Books & eBooks >>
• Last Updated: Apr 19, 2024 11:50 AM
• URL: https://navsea-navy-mil.libguides.com/c.php?g=895436

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Introduction to Marine Engineering

RELATED PAPERS

Gasper Valdi

Farich Firmansyah

2006 Annual Conference & Exposition Proceedings

Rodrigo Castro

CALIFA7SEAS MARITIME ACADEMIA HANDBOOK - PART 2: PRODUCTION AND CONVERSION

Engr. Khairulmuzammil YUZRI

Edwina Santoso

Bell Bella Rahman

Debby Debby

emmanuel bassey

Jorge C Palma , Jorge Palma

F. M. Hocker and C.A Ward,eds.,The Philosophy of Shipbuilding: Conceptual Approaches to the Study of Wooden Ships

George F Bass

Naval Engineers Journal

jack wilson

Alfred Ekpenyong

Shafiishuna Silvanus

Proceedings of the Institute of Marine Engineering, Science, and Technology. Part A, Journal of marine engineering and technology

Vaughan Pomeroy

Sulaiman Bbaale

CALIFA7SEAS MARITIME ACADEMIA HANDBOOK - PART 5: BUSINESS COMMERCIAL AND ECONOMY

Max van der Graaf

Sumit Narayan

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024