Machine Learning Based Fluid-Transportation Monitoring and Controlling
Article Sidebar
Main Article Content
Abstract
The discipline of fluid mechanics is developing quickly, propelled by previously unheard-of data volumes from experiments, field measurements, and expansive simulations at various spatiotemporal scales. The field of machine learning (ML) provides a plethora of methods for gleaning insights from data that can be used to inform our understanding of the fluid dynamics at play. As an added bonus, ML algorithms can be used to automate duties associated with flow control and optimization, while also enhancing domain expertise. This article provides a review of the background, current state, and potential future applications of ML in fluid mechanics. We provide an introduction to the most fundamental ML approaches and describe their applications to the study, modelling, optimization, and management of fluid flows. From the standpoint of scientific inquiry, which treats data as an integral aspect of modelling, experiments, and simulations, the benefits and drawbacks of these approaches are discussed. Since ML provides a robust information-processing framework, it can supplement and potentially revolutionize conventional approaches to fluid mechanics study and industrial applications.
Article Details
References
Agrawal, A.; Choudhary, A. Perspective: Materials Informatics and Big Data: Realization of the “Fourth Paradigm” of Science in Materials Science. APL Mater. 2016, 4, 053208.
Agrawal, A.; Deshpande, P.D.; Cecen, A.; Basavarsu, G.P.; Choudhary, A.N.; Kalidindi, S.R. Exploration of Data Science Techniques to Predict Fatigue Strength of Steel from Composition and Processing Parameters. Integr. Mater. Manuf. Innov. 2014, 3, 90–108.
Wei, J.; Chu, X.; Sun, X.; Xu, K.; Deng, H.; Chen, J.; Wei, Z.; Lei, M. Machine Learning in Materials Science. InfoMat 2019, 1, 338–358.
Paulson, N.H.; Zomorodpoosh, S.; Roslyakova, I.; Stan, M. Comparison of Statistically-Based Methods for Automated Weighting of Experimental Data in CALPHAD- Type Assessment. Calphad 2020, 68, 101728.
Frank, M.; Drikakis, D.; Charissis, V. Machine-Learning Methods for Computational Science and Engineering. Computation 2020, 8, 15.
Wang, T.; Zhang, C.; Snoussi, H.; Zhang, G. Machine Learning Approaches for Thermoelectric Materials Research. Adv. Funct. Mater. 2020, 30, 1906041.
Alexiadis, A. Deep Multiphysics: Coupling Discrete Multiphysics with Machine Learning to Attain Self-Learning in-Silico Models Replicating Human Physiology. Artif. Intell. Med. 2019, 98, 27–34.
Brenner, M.P.; Eldredge, J.D.; Freund, J.B. Perspective on Machine Learning for Advancing Fluid Mechanics. Phys. Rev. Fluids 2019, 4, 100501.
Schmid, M.; Altmann, D.; Steinbichler, G. A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding. Polymers 2021, 13, 2652.
Goh, G.D.; Sing, S.L.; Yeong, W.Y. A Review on Machine Learning in 3D Printing: Applications, Potential, and Challenges. Artif. Intell. Rev. 2021, 54, 63–94.
Kailkhura, B.; Gallagher, B.; Kim, S.; Hiszpanski, A.; Han, T.Y.-J. Reliable and Explainable Machine-Learning Methods for Accelerated Material Discovery. npj Comput. Mater. 2019, 5, 108.
Sofos, F. A Water/Ion Separation Device: Theoretical and Numerical Investigation. Appl. Sci. 2021, 11, 8548.
Roscher, R.; Bohn, B.; Duarte, M.F.; Garcke, J. Explainable Machine Learning for Scientific Insights and Discoveries. IEEE Access 2020, 8, 42200–42216.
Vasudevan, R.K.; Choudhary, K.; Mehta, A.; Smith, R.; Kusne, G.; Tavazza, F.; Vlcek, L.; Ziatdinov, M.; Kalinin, S.V.; HattrickSimpers, J. Materials Science in the Artificial Intelligence Age: High-Throughput Library Generation, Machine Learning, and a Pathway from Correlations to the Underpinning Physics. MRS Commun. 2019, 9, 821–838
Craven, G.T.; Lubbers, N.; Barros, K.; Tretiak, S. Machine Learning Approaches for Structural and Thermodynamic Properties of a Lennard-Jones Fluid. J. Chem. Phys. 2020, 153, 104502.
Sun, W.; Zheng, Y.; Yang, K.; Zhang, Q.; Shah, A.A.; Wu, Z.; Sun, Y.; Feng, L.; Chen, D.; Xiao, Z.; et al. Machine Learning-Assisted Molecular Design and Efficiency Prediction for High-Performance Organic Photovoltaic Materials. Sci. Adv. 2019, 5, eaay4275.
Zhang, W.-W.; Noack, B.R. Artificial Intelligence in Fluid Mechanics. Acta Mech. Sin. 2022, 1–3. [CrossRef]
Raissi, M.; Perdikaris, P.; Karniadakis, G.E. Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. J. Comput. Phys. 2019, 378, 686–707.
Papastamatiou, K.; Sofos, F.; Karakasidis, T.E. Machine Learning Symbolic Equations for Diffusion with Physics-Based Descriptions. AIP Adv. 2022, 12, 025004.
Brunton, S.L.; Noack, B.R.; Koumoutsakos, P. Machine Learning for Fluid Mechanics. Annu. Rev. Fluid Mech. 2020, 52, 477–508.
Brunton, S.L. Applying Machine Learning to Study Fluid Mechanics. Acta Mech. Sin. 2022, 1–9.
Jirasek, F.; Hasse, H. Perspective: Machine Learning of Thermophysical Properties. Fluid Phase Equilib. 2021, 549, 113206. [CrossRef]
Arief, H.A.; Wiktorski, T.; Thomas, P.J. A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation. Sensors 2021, 21, 2801.
Pandey, S.; Schumacher, J.; Sreenivasan, K.R. A Perspective on Machine Learning in Turbulent Flows. J. Turbul. 2020, 21, 567–584.
Mr. Dharmesh Dhabliya, Prof. Ojaswini Ghodkande. (2016). Prevention of Emulation Attack in Cognitive Radio Networks Using Integrated Authentication . International Journal of New Practices in Management and Engineering, 5(04), 06 - 11. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/48
Botu, V.; Ramprasad, R. Learning Scheme to Predict Atomic Forces and Accelerate Materials Simulations. Phys. Rev. B Condens. Matter Mater. Phys. 2015, 92, 094306.
Behler, J.; Parrinello, M. Generalized Neural-Network Representation of High- Dimensional Potential-Energy Surfaces. Phys. Rev. Lett. 2007, 98, 146401.
Noé, F.; Olsson, S.; Köhler, J.; Wu, H. Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning. Science 2019, 365, eaaw1147.
Sofos, F.; Karakasidis, T.E.; Liakopoulos, A. Fluid Flow at the Nanoscale: How Fluid Properties Deviate from the Bulk. Nanosci. Nanotechnol. Lett. 2013, 5, 457–460.
Sofos, F.; Karakasidis, T.E.; Giannakopoulos, A.E.; Liakopoulos, A. Molecular Dynamics Simulation on Flows in Nano-Ribbed and Nano-Grooved Channels. Heat Mass Transf./Waerme Stoffuebertragung 2016, 52, 153–162.
Mueller, T.; Hernandez, A.; Wang, C. Machine Learning for Interatomic Potential Models. J. Chem. Phys. 2020, 152, 050902.
Krems, R.V. Bayesian Machine Learning for Quantum Molecular Dynamics. Phys. Chem. Chem. Phys. 2019, 21, 13392–13410.
Mishin, Y. Machine-Learning Interatomic Potentials for Materials Science. Acta Mater. 2021, 214, 116980.
Veit, M.; Jain, S.K.; Bonakala, S.; Rudra, I.; Hohl, D.; Csányi, G. Equation of State of Fluid Methane from First Principles with Machine Learning Potentials. J. Chem. Theory Comput. 2019, 15, 2574–2586.
Peng, G.C.Y.; Alber, M.; Buganza Tepole, A.; Cannon, W.R.; De, S.; Dura-Bernal, S.; Garikipati, K.; Karniadakis, G.; Lytton, W.W.; Perdikaris, P.; et al. Multiscale Modeling Meets Machine Learning: What Can We Learn? Arch. Comput. Methods Eng. 2021, 28, 1017–1037.
Tchipev, N.; Seckler, S.; Heinen, M.; Vrabec, J.; Gratl, F.; Horsch, M.; Bernreuther, M.; Glass, C.W.; Niethammer, C.; Hammer, N.; et al. TweTriS: Twenty Trillion-Atom Simulation. Int. J. High Perform. Comput. Appl. 2019, 33, 838–854.
Naaz, S. ., ShivaKumar, K. B., & B. D., P. . (2023). Aggregation Signature of Multi Scale Features from Super Resolution Images for Bharatanatyam Mudra Classification for Augmented Reality Based Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 224–234. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2565
Mortazavi, B.; Podryabinkin, E.V.; Roche, S.; Rabczuk, T.; Zhuang, X.; Shapeev, A.V. Machine-Learning Interatomic Potentials Enable First-Principles Multiscale Modeling of Lattice Thermal Conductivity in Graphene/Borophene Heterostructures. Mater. Horiz. 2020, 7, 2359– 2367.
Holland, D.M.; Lockerby, D.A.; Borg, M.K.; Nicholls, W.D.; Reese, J.M. Molecular Dynamics Pre-Simulations for Nanoscale Computational Fluid Dynamics. Microfluid. Nanofluid. 2015, 18, 461–474.
Lin, C.; Li, Z.; Lu, L.; Cai, S.; Maxey, M.; Karniadakis, G.E. Operator Learning for Predicting Multiscale Bubble Growth Dynamics. J. Chem. Phys. 2021, 154, 104118.
Thomas, C., Wright, S., Hernandez, M., Flores, A., & García, M. Enhancing Student Engagement in Engineering Education with Machine Learning. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/123
Wang, Y.; Ouyang, J.; Wang, X. Machine Learning of Lubrication Correction Based on GPR for the Coupled DPD–DEM Simulation of Colloidal Suspensions. Soft Matter 2021, 17, 5682–5699.
Sofos, F.; Chatzoglou, E.; Liakopoulos, A. An Assessment of SPH Simulations of Sudden Expansion/Contraction 3-D Channel Flows. Comput. Part. Mech. 2022, 9, 101–115.
Albano, A.; Alexiadis, A. A Smoothed Particle Hydrodynamics Study of the Collapse for a Cylindrical Cavity. PLoS ONE 2020, 15, e0239830.
Aisha Ahmed, Machine Learning in Agriculture: Crop Yield Prediction and Disease Detection , Machine Learning Applications Conference Proceedings, Vol 2 2022.
Bai, J.; Zhou, Y.; Rathnayaka, C.M.; Zhan, H.; Sauret, E.; Gu, Y. A Data-Driven Smoothed Particle Hydrodynamics Method for Fluids. Eng. Anal. Bound. Elem. 2021, 132, 12–32.
Wang, J.; Olsson, S.; Wehmeyer, C.; Pérez, A.; Charron, N.E.; De Fabritiis, G.; Noé, F.; Clementi, C. Machine Learning of Coarse- Grained Molecular Dynamics Force Fields. ACS Cent. Sci. 2019, 5, 755–767.