Publications

Journals

  1. Sørbø, S., Blakseth, S.S., Rasheed, A., Kvamsdal, T., San, O., Enhancing Elasticity Models with Deep Learning: A Novel Corrective Source Term Approach for Accurate Predictions, Applied Soft Computing, 111312, 2024
  2. Føre., M., Alver, M.O., Alfredsen, J.A., Rasheed, A., Hukkelås, T., Bjelland, H.V., Su, B., Ohrem, S.J., Kelasidi, E., Norton, T., Papandroulakis, N., Digital twins in intensive aquaculture – Challenges, opportunities and future prospects, Computers and Electronics in Agriculture, 218, 108676, 2024
  3. Alshantti, A. Varagnolo, D., Rasheed, A., Rahmati, A., Westad, F., CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis, IEEE Access, 12, 13213–13232, 2024
  4. Haugstvedt, E.J., Calero, A.M., Lundby, E.T.B., Rasheed, Gravdahl, J. T., A Comparative Study of Sparsity Promoting Techniques in Neural Network for Modeling Non-Linear Dynamics, IEEE Access, 11, 131435–131452, 2023
  5. San, O., Pawar, S., Rasheed, A. Decentralized digital twins of complex dynamical systems. Scientific Reports 13, 20087, 2023
  6. Stadtmann, F., Rasheed A., Kvamsdal, T., Johannessen, K.A., San, O., Tande, J.G.O., et al, Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions, IEEE Access, 11, 110762-110795, 2023
  7. Menges, D., Rasheed, A., An environmental disturbance observer framework for autonomous ships, Ocean Engineering 2023, 285, 115412
  8. Gupta, P., Kim, Y.R., Steen, S., Rasheed, A.,  Streamlined Semi-automatic Data Processing Framework for Ship Performance Analysis, International Journal of Naval Architecture and Ocean Engineering, 2023, 100550, 2023
  9. Midjiyawa, Z., Venås, J.V., Kvamsdal, T., Kvarving, A.M., Midtbø, K.H., Rasheed, A., Nested computational fluid dynamic modeling of mean turbulent quantities estimation in complex topography using AROME-SIMRA, Journal of Wind Engineering and Industrial Aerodynamics, 2023, 240 (105497)
  10. Robinson, H., Lundby, E.T.B., Rasheed, A., Gravdahl, J.T., Deep learning assisted physics-based modeling of aluminum extraction process. Engineering Applications of Artificial Intelligence 2023, 125 (106623)
  11. Keilegavlen, E., Fonn, E., Johannessen, K.A., Eikehaug, K., Both, J.W., Fernø, M., Kvamsdal, T. Rasheed, A., Nordbotten, J.M., PoroTwin: A digital twin for a FluidFlower rig. Transport in Porous Media 2023
  12. Elfarri, E.M., Rasheed, A., San, O., Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality. IEEE Access 2023 ;Volum 11. s. 35035-35058
  13. Belay, M.A., Blakseth, S.S., Rasheed, A., Salvo Rossi, P., Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions. Sensors 2023 , 23 (5)
  14. Lundby, E.T.B., Rasheed, A., Gravdahl, J.T., Halvorsen, I.J., Sparse deep neural networks for modeling aluminum electrolysis dynamics, Applied Soft Computing, 134, 109989, 2023
  15. Ahmed, S.E., San, O., Rasheed, A., Iliescu, T., Veneziani, A., Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling, SIAM Journal of Scientific Computing, 45(3), 283-313, 2023
  16. Pawar, S., San, O., Rasheed, A., Frame-invariant neural network closures for Kraichnan turbulence, Physica A: Statistical Mechanics and its Applications, 609, 128327, 2022
  17. San, O., Pawar, S. and Rasheed, A. Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems, Scientific Reports, 12, 17947, 2022
  18. Blakseth, S.S., Rasheed, A., Kvamsdal, T. and San, O., Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach, Applied Soft Computing, 128, 109533, 2022
  19. Robinson H., Pawar, S., Rasheed, A., San, O., Physics guided neural networks for modelling of non-linear dynamics, Neural Networks, 154, 333-345, 2022
  20. San, O., Pawar, S., Rasheed, A., Prospects of federated machine learning in fluid dynamics. AIP Advances, 2022
  21. Heiberg, A., Larsen, T.N., Meyer, E., Rasheed, A., San, O., Varagnolo, D., Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning, Neural Networks, 152, 17-33, 2022
  22. Gupta, P., Rasheed, A., Steen, S., Ship Performance Monitoring using machine-learning, Ocean Engineering, 254, 111094, 2022
  23. Pawar, S., San, O., Vedula, P., Rasheed, A., Kvamsdal, T., Multi-fidelity information fusion with concatenated neural networks, Scientific Report, 12, 5900, 2022
  24. Blakseth, S.S., Rasheed, A., Kvamsdal, T., San, O. Deep neural network enabled corrective source term approach to hybrid analysis and modeling, Neural Networks, 146, 181-199, 2021
  25. Alshantti, A.A.S., Rasheed, A. Self-organising map based framework for investigating accounts sus- pected of money laundering, Frontiers in Artificial Intelligence, 2021
  26. Ahmed, S. E., San, O., Rasheed, A. and Iliescu, T. Nonlinear proper orthogonal decomposition for convection-dominated flows, Physics of Fluids, 33, 121702, 2021.
  27. Larsen, T.N., Teigen, H.Ø., Laache, T., Varagnolo, D., and Rasheed, A., Comparing Deep Reinforcement Learning Algorithms’ Ability to Safely Navigate Challenging Waters, Frontiers in Robotics and Artificial Intelligence, 8, 287, 2021
  28. Lundby, E.T.B., Rasheed, A., Gravdahl, J.T., Halvorsen, I.J., A novel hybrid analysis and modeling approach applied to aluminum electrolysis process, Journal of Process Control, 105, 62–77, 2021.
  29. Gupta, P., Taskar, B., Steen, S., Rasheed, A., Statistical modeling of Ship’s hydrodynamic performance indicator, Applied Ocean Research, 111, 102623, 2021.
  30. Ahmed, S., Pawar, S., San, O., Rasheed, A., Iliescu, T., and Noack, B., On closures for reduced order models – a spectrum of first-principle to machine-learned avenues, Physics of Fluids, 33, 091301, 2021.
  31. Pawar, S., San, O., Rasheed, A., Navon, I.M., A nonintrusive hybrid neural-physics modeling of incomplete dynamical systems: Lorenz equations, International Journal of Geomathematics, 12, 17, 2021
  32. Pawar, S., San, O., Aditya, N., Rasheed, A., Kvamsdal, T., Model fusion with physics-guided machine learning: projection based reduced order modeling, Physics of Fluids, 33, 067123, 2021. (Editor’s Pick)
  33. San, O., Rasheed, A., Kvamsdal, T. Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution, GAMM Mitteilungen, 44, e202100007, 2021.
  34. Sundby, T., Graham, J. M., Rasheed, A., Tabib, M., San, O., Geometric change detection in digital twins, Digital, 1 (2), 111-129, 2021.
  35. Ahmed, S. E., Pawar, S., San, O., Rasheed, A., Tabib, M., A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction, Computers and Fluids, 221, 104895, 2021.
  36. Stavelin, H., Rasheed, A., San, O., Hestnes, A. J., Applying object detection to marine data and exploring explainability of a fully convolutional neural network using principal component analysis, Ecological Informatics, 62, 101269, 2021.
  37. Ahmed, S. E., San, O., Kara, K., Younis, R., Rasheed, A., Multifidelity computing for coupling full and reduced order models, PLOS ONE, 16(2), e0246092, 2021.
  38. Havenstrøm, S. T., Rasheed, A., San, O. Deep reinforcement learning controller for 3D path following and collision avoidance by autonomous underwater vehicles, Frontiers in Robotics and AI, 7, 566037, 2021.
  39. Pawar, S., San, O., Aksoylu, B., Rasheed, A., Kvamsdal, T. Physics guided machine learning using simplified theories, Physics of Fluids, 33, 011701, 2021.
  40. Ahmed, S. E., San, O., Kara, K., Younis, R., Rasheed, A., Interface learning of multiphysics and multiscale systems, Physical Review E, 102, 053304, 2020.
  41. Ahmed, S. E., Bhar, K., San, O., Rasheed, A. Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction, Physical Review E, 102, 043302, 2020.
  42. Meyer, E., Heiberg, A., Rasheed, A., San, O., COLREG-Compliant Collision Avoidance for Un- manned Surface Vehicle using Deep Reinforcement Learning, IEEE Access, 8, 165344-165364, 2020.
  43. Pawar, S., Ahmed, S. E., San, O., Rasheed, A., Navon I. M., Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows, Physics of Fluids, 32, 076606, 2020.
  44. Pawar, S., Ahmed, S. E., San, O., Rasheed, A. An evolve-then-correct reduced order model for hidden fluid dynamics, Mathematics, 8(4), 570, 2020.
  45. Ahmed, S. E., San, O., Rasheed, A., Iliescu, T., A long short-term memory embedding for hybrid uplifted reduced order models, Physica D: Nonlinear Phenomena, 409, 132471, 2020.
  46. Pawar, S., Ahmed, S. E., San, O., Rasheed, A., Data-driven recovery of hidden physics in reduced order modeling of fluid flows, Physics of Fluids, 32, 036602, 2020.
  47. Meyer, E., Robinson, H., Rasheed, A., San, O., Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning, IEEE Access, 8, 41466-41481, 2020.
  48. Rasheed, A., San, O., Kvamsdal, T., Digital twin: values, challenges and enablers from a modeling perspective, IEEE Access, 8, 21980-22012, 2020.
  49. Pawar, S., San, O., Rasheed, A., Vedula, P., A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence, Theoretical and Computational Fluid Dynamics, 34, 429-455, 2020.
  50. Vaddireddy, H., Rasheed, A., Staples, A. E., San, O., Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data, Physics of Fluids, 32, 015113, 2020. (Editor’s Pick)
  51. Siddiqui, M.S., Rasheed, A., Kvamsdal, T., Numerical assessment of RANS turbulence models for the development of data driven reduced order models, Ocean Engineering, 196, 106799, 2020
  52. Ahmed, S. E., Rahman, S. M., San, O., Rasheed, A., Navon, I. M., Memory embedded non-intrusive reduced order modeling of non-ergodic flows, Physics of Fluids, 31, 126602, 2019.
  53. Rahman, S. M., Pawar, S., San, O., Rasheed, A., Iliescu, T., Nonintrusive reduced order modeling framework for quasigeostrophic turbulence, Physical Review E, 100, 053306, 2019.
  54. Pawar, S., Rahman, S. M., Vaddireddy, H., San, O., Rasheed, A., Vedula, P., A deep learning enabler for non-intrusive reduced order modeling of fluid flows, Physics of Fluids, 31, 085101, 2019. (Featured Article)
  55. Maulik, R., San, O., Rasheed, A. and Vedula, P. Sub-grid modelling for two-dimensional turbulence using neural networks, Journal of Fluid Mechanics, 858, 122-144, 2019.
  56. Siddiqui, M.S., Rasheed, A., Kvamsdal, T. Validation of the numerical simulations of flow around a scaled-down turbine using experimental data from wind tunnel, Wind and Structures, 29, 405–416, 2019
  57. Siddiqui, M.S., Fonn, E., Kvamsdal, T., Rasheed, A., Finite Volume high-fidelity simulation com- bined with finite-element-based reduced order modeling of incompressible flow problems, Energies, 12, 1271, 2019
  58. Fonn, E., Brummelen, H.van, Kvamsdal, T., Rasheed, A., Fast divergence-conforming reduced basis methods for steady Navier–Stokes flow, Computer Methods in Applied Mechanics and Engineering, 346, 486–512, 2019
  59. Siddiqui, M.S., Rasheed, A., Tabib, M.V., Kvamsdal, T., Numerical investigation of modeling frame- works and geometric approximations on NREL 5MW wind turbine, Renewable Energy, 132, 1058– 1075, 2019
  60. Maulik, R., San, O., Rasheed, A., Vedula, P., Data-driven deconvolution for large eddy simulations of Kraichnan turbulence, Physics of Fluids, 30, 125109, 2018.
  61. Rahman, S. M., San, O., Rasheed, A., A hybrid approach for model order reduction of barotropic quasi-geostrophic turbulence, Fluids, 3(4), 86, 2018.
  62. Rahman, S. M., Rasheed, A., San, O., A hybrid analytics paradigm combining physics-based mod- eling and data-driven modeling to accelerate incompressible flow solvers, Fluids, 3(3), 50, 2018.
  63. Nordanger, K., Rasheed, A., Okstad, K.M., Kvarving, A.M., Holdahl, R., Kvamsdal, T., Numer- ical benchmarking of fluid–structure interaction: An isogeometric finite element approach. Ocean Engineering, 124, 324–339, 2016
  64. Nordanger, K., Holdahl, R., Kvarving, A.M., Kvamsdal, T. Rasheed, A., Simulation of airflow past a 2D NACA0015 airfoil using an isogeometric incompressible Navier–Stokes solver with the Spalart– Allmaras turbulence model, Computer Methods in Applied Mechanics and Engineering, 290, 183–208, 2015
  65. Nordanger, K., Holdahl, R., Kvarving, A.M., Rasheed, A., Kvamsdal, T., Implementation and com- parison of three isogeometric Navier–Stokes solvers applied to simulation of flow past a fixed 2D NACA0012 airfoil at high Reynolds number, Computer Methods in Applied Mechanics and Engi- neering, 284, 664–688, 2014
  66. Rasheed, A., Mushtaq, A. Numerical analysis of the flying condition at the Alta airport, Norway,Aviation, 18, 109–119, 2014.
  67. Rasheed, A., Karstein S., CFD Analysis of terrain induced turbulence at Kristiansand airport, kjevik, Aviation, 17, 104–112, 2013.
  68. Rasheed, A., Robinson, D., Characterization of dispersive fluxes in mesoscale modelsusing les of flow over an array of cubes, International Journal of Atmospheric Sciences, 17, 898095, 2013.
  69. Rasheed, A., Robinson, D., Clappier, A., Narayanan, C., Lakehal, D., Representing complexities in urban geometry in mesoscale modeling, International Journal of Climatology, 31, 289–301, 2011.

Conference proceedings

  1. Lundby, E.T.B., Robinson, H., Rasheed, A., Halvorsen, I.J. and Gravdahl, J.T., Sparse Neural Networks with Skip-Connections for Identification of Aluminum Electrolysis Cell, 2023 62nd IEEE Conference on Decision and Control (CDC), Singapore, Singapore, 2023, pp. 5506-5513
  2. Stadtmann, Florian; Mahalingam, Hary Pirajan; Rasheed, Adil. (2023) Data Integration Framework for Virtual Reality Enabled Digital Twins. IEEE 9th World Forum on Internet of Things , Aveiro, Portugal 2023-10-12 – 2023-10-27
  3. Tabib, M., Skare, K., Bruaset, E., Rasheed, A., Data-Driven Spatio-Temporal Modelling and Opti- mal Sensor Placement for a Digital Twin Set-Up. Eng. Proc. 2023, 39, 98.
  4. Nivlet, P., Bjorkevoll, K.S., Tabib, M., Skogestad, J.O., Lund, B., Nybo, R., and Rasheed, A., Towards Real-Time Bad Hole Cleaning Problem Detection Through Adaptive Deep Learning Models, Middle East Oil, Gas and Geosciences Show, Manama, Bahrain
  5. Zhang, W, Vatn, J., Rasheed, A., Statistical analysis of offshore wind turbine failures, 32nd European Safety and Reliability Conference
  6. Stadtman, F., Rasheed, A., Rasmussen, T., Standalone, Descriptive, and Predictive Digital Twin of an Onshore Wind Farm in Complex Terrain, Journal of Physics Conference Series, 2626, 012030
  7. Bjørkøy, H., Engmark, H.A., Rasaheed, A., Varagnolo, D., Regularization when modelling with biased simulation data as a prior, IFAC WC 2023 IFAC-PapersOnLine, 56(2), 4000-4005, 2023
  8. Larsen, T.N., Hansen, H., Rasheed, A., Risk-based Convolutional Perception Models for Colli- sion Avoidance in Autonomous Marine Surface Vessels using Deep Reinforcement Learning, IFAC- PapersOnLine, 56(2), 10033-10038, 2023
  9. Vaaler, A, Robinson, H, Tengesdal, T, and Rasheed, A. Modular Collision Avoidance Using Pre- dictive Safety Filters. Proceedings of the ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. Volume 1: Offshore Technology. Melbourne, Australia. June 11–16, 2023. V001T01A016. ASME.
  10. Menges, D, Sætre, SM, and Rasheed, A. Digital Twin for Autonomous Surface Vessels to Generate Situational Awareness. Proceedings of the ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. Volume 5: Ocean Engineering. Melbourne, Australia. June 11–16, 2023. V005T06A025. ASME.
  11. Stadtmann, F, Wassertheurer, HAG, and Rasheed, A. Demonstration of a Standalone, Descrip- tive, and Predictive Digital Twin of a Floating Offshore Wind Turbine. Proceedings of the ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. Volume 8: Ocean Renewable Energy. Melbourne, Australia. June 11–16, 2023. V008T09A039. ASME.
  12. Tabib, MV, Nivlet, P, Skogestad, JO, Nybø, R, and Rasheed, A. A Hybrid Approach to Detect Bad Hole Cleaning. Proceedings of the ASME 2023 42nd International Conference on Ocean, Off- shore and Arctic Engineering. Volume 9: Offshore Geotechnics; Petroleum Technology. Melbourne, Australia. June 11–16, 2023. V009T11A009. ASME.
  13. Tabib, M.V., Nivlet, P., Bjørkboll, K.S., Skogestad, J.O., Nybø, R. and Rasheed, A, An Intrusive Hybrid-Analytics and Modelling Approach Involving Deep-learning for Efficient and Accurate Pre- dictions of Hole-cleaning Process During the Wellbore Drilling Simulations, SPE EuropEC – Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition
  14. Tabib, M.V., Stene, J.K., Rasheed, A., Langeland, O., Gundersen, F., Machine Learning for Capacity Utilization Along the Routes of an Urban Freight Service. In: Sanfilippo, F., Granmo, OC., Yayilgan, S.Y., Bajwa, I.S. (eds) Intelligent Technologies and Applications. INTAP 2021. Communications in Computer and Information Science, vol 1616. Springer, Cham. 2022
  15. Bjørkøy, H.B., Engmark, H., Rasheed, A., Varagnolo, D., Wienerization Based Control of Nonlinear Systems, 2022 European Control Conference (ECC), 2022, pp. 641-648
  16. Wanwan Zhang, Vatn, J., Rasheed, A., A review of failure prognostics for predictive maintenance of offshore wind turbines, Journal of Physics Conference Series, 2022
  17. Tabib, M.V., Tsiolakis, V., Pawar S., Ahmed, S.E., Rasheed, A., Kvamsdal, T., San, O., Hybrid deep- learning POD-based parametric reduced order model for flow around wind-turbine blade, Journal of Physics: Conference Series, 2022
  18. Jain, R.P., Brekke, E.F., Rasheed, A., Unsupervised Clustering of Marine Vessel Trajectories in Historical AIS Database, 2022 25th International Conference on Information Fusion (FUSION), 2022, pp. 1-6,
  19. Engmark, H., Bjørkøy, H., Rasheed, A., Varagnolo, D., Wienerization of systems in nonlinear control canonical normal form, IEEE 61st Conference on Decision and Control (CDC), Cancun, Mexico, 4485-4492, 2022
  20. Busetto, R., Thomas, T.N., Rasheed, A., Varagnolo, D., Formentin, S., A receding-horizon estima- tion and control framework for the content sequencing problem, 2022 European Control Conference (ECC), 2022, pp. 1908-1915
  21. Thomas, T.N., Busetto, R., Varagnolo, D., Formentin, S., Rasheed, A., A receding horizon approach for curriculum management in higher education, 13th IFAC Symposium on Advances in Control Education 2022
  23. Pawar, S., Ahmed, S.E., San, O., Rasheed, A. Hybrid analysis and modeling for next generation of digital twins. Journal of Physics: Conference Series, 2018(1):012031, 2021
  24. Tabib, M.V., Pawar, S., Ahmed, S.E., Rasheed, A., San., O., A non-intrusive parametric reduced order model for urban wind flow using deep learning and Grassmann manifold. Journal of Physics: Conference Series, 2018(1):012038, 2021
  25. Tabib, M.V., Midtbø, K.H., Rasheed, A., Kvamsdal, T., Skaslien, T.,A nested multi-scale model for assessing urban wind conditions : Comparison of Large Eddy Simulation versus RANS turbulence models when operating at the finest scale of the nesting. Journal of Physics: Conference Series, 2018(1):012039, 2021
  26. Tsiolakis, V., Kvamsdal, T., Rasheed, A., Fonn, E., van Brummelen, H., Reduced order models for finite-volume simulations of turbulent flow around wind-turbine blades. Journal of Physics: Conference Series, 2018(1):012042, sep 2021
  27. Tabib, M.V., Rasheed, A., Uteng, T.P., Machine learning with subsequent physics-based analytics for guiding transport planning, International Conference on Applied Artificial Intelligence (ICAPAI), 2021, pp. 1-6,
  28. Ahmed, S.E., Pawar, S., San, O., Rasheed, A., Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning. AIAA AVIATION 2020. June 15–19, 2020.
  29. Fonn, E., van Brummelen, H., Kvamsdal, T., Rasheed, A., Fast divergence-conforming reduced basis methods for stationary and transient flow problems. Journal of Physics Conference Series, 1669:012031, 2020
  30. Tran, D. T., Robinson, H., Rasheed, A., San, O., Tabib, M., Kvamsdal, T., GANs enabled super- resolution reconstruction of wind field. Journal of Physics Conference Series, 1669:012029, 2020 (Best poster award)
  31. Siddiqui, M.S., Kvamsdal, T. and Rasheed, A., High fidelity computational fluid dynamics assess- ment of wind tunnel turbine test. Journal of Physics: Conference Series, 1356:012044, 2019
  32. Tabib, M., Rasheed, A. and Kvamsdal, T. High resolution CFD modelling nad prediction of terrain induced wind shear and turbulence for aviaiton safety. MekIT’19 – 10th National Conference on Computational Mechanics. International Conference on Offshore Mechanics and Arctic Engineering (CIMNE), ISBN 978-84-949194-9-7:323–341, 2019
  33. Rasheed, A., San, O. and Kvamsdal, T., Hybrid Analysis and Modeling as an enabler for Big Data Cybernetics. Proceeding of the 32nd Nordic Seminar on Computational Mechanics, ISBN 978-952- 62-2420-6:21–24, 2019
  34. Tabib, M.V., Rasheed, A., and Fonn, E., A computational framework involving CFD and data mining tools for analyzing disease in carotid artery bifurcation. Progress in Applied CFD – CFD2017 Selected papers from 12th International Conference on Computational Fluid Dynamics in the Oil & Gas, Metallurgical and Process Industries, ISBN 978-82-536-1544-8:125 – 132, 2017
  35. Siddiqui, M.S., Rasheed, A., Tabib, M.V., and Kvamsdal, T., Numerical Modeling Framework for Wind Turbine Analysis & Atmospheric Boundary Layer Interaction. AIAA 2017-1162. 35th Wind Energy Symposium. January 2017.
  36. Siddiqui, M.S., Rasheed, A., Kvamsdal, T., Tabib, M.V., Influence of tip speed ratio on wake flow characteristics utilizing fully resolved CFD methodology. Journal of Physics: Conference Series, 854:012043, May 2017
  37. Tabib, M.V., Siddiqui, M.S., Fonn, E., Rasheed, A., and Kvamsdal, T., Near wake region of an industrial scale wind turbine: comparing LES-ALM with LES-SMI simulations using data mining (POD). Journal of Physics: Conference Series, 854:012044, May 2017
  38. Gupta, P., Steen, S. and Rasheed, A., Big Data Analytics As a Tool to Monitor Hydrodynamic Performance of a Ship. Proceedings of the ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. Volume 7A: Ocean Engineering. Glasgow, Scotland, UK. June 9–14, 2019. V07AT06A059. ASME.
  39. Fonn, E., Rasheed, A., Tabib, M., and Kvamsdal, T., A Step Towards a Reduced Order Modelling of Flow Characterized by Wakes Using Proper Orthogonal Decomposition, Proceedings of the ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. Volume 1: Offshore Technology. Trondheim, Norway. June 25–30, 2017. V001T01A011. ASME.
  40. Fonn, E., Tabib, M., Siddiqui, M.S., Rasheed, A. and Kvamsdal, T., A step towards reduced order modelling of flow characterized by wakes using proper orthogonal decomposition. Energy Procedia, 137:452 – 459, 2017. 14th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’2017
  41. Siddiqui, M.S., Rasheed, A., Tabib, M.V., Fonn, E. and Trond Kvamsdal. On Interactions Between Wind Turbines and the Marine Boundary Layer. Proceedings of the ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. Volume 10: Ocean Renewable Energy. Trondheim, Norway. June 25–30, 2017. V010T09A052. ASME.
  42. Tabib, M., Rasheed, A., and Fuchs, F.G., Analysis of Unsteady Hydrodynamics Related to Vortex Induced Vibrations on Bluff-Bodied Offshore Structure. Proceedings of the ASME 2017 36th In- ternational Conference on Ocean, Offshore and Arctic Engineering. Volume 2: Prof. Carl Martin Larsen and Dr. Owen Oakley Honoring Symposia on CFD and VIV. Trondheim, Norway. June 25–30, 2017. V002T08A027. ASME.
  43. Rasheed, A., Süld, J.K., and Tabib, M. Effect of Uni- and Bi-Directional Coupling of Ocean-Met Interaction on Significant Wave Height and Local Wind. Proceedings of the ASME 2017 36th In- ternational Conference on Ocean, Offshore and Arctic Engineering. Volume 7B: Ocean Engineering. Trondheim, Norway. June 25–30, 2017. V07BT06A052. ASME.
  44. Rasheed, A., Tabib, M.V., and Kristiansen, J. Wind Farm Modeling in a Realistic Environment Using a Multiscale Approach. Proceedings of the ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. Volume 10: Ocean Renewable Energy. Trondheim, Norway. June 25–30, 2017. V010T09A051. ASME.
  45. Rasheed, A., Süld, J.K., Tabib, M.V., Kvamsdal, T., and Kristiansen J., Demonstrating the impact of bidirectional coupling on the performance of an ocean-met model. Energy Procedia, 137:443 – 451, 2017. 14th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’2017
  46. Tabib, M.V., Siddiqui, M.S., Rasheed, A., and Kvamdal T., Industrial scale turbine and associated wake development comparison of RANS based actuator line vs sliding mesh interface vs multiple reference frame method. Energy Procedia, 137:487-–496, 2017. 14th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’2017
  47. Tabib, M.V., Rasheed, A., Siddiqui, M.S., and Kvamsdal, T., A full-scale 3D Vs 2.5D Vs 2D analysis of flow pattern and forces for an industrial-scale 5MW NREL reference wind-turbine. Energy Pro- cedia, 137:477 – 486, 2017. 14th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’2017
  48. Siddiqui, M.S., Rasheed, A., Kvamsdal, T., and Tabib, M.V., Quasi-static & dynamic numerical modeling of full scale NREL 5MW wind turbine. Energy Procedia, 137:460 – 467, 2017. 14th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’2017
  49. Tabib, M.V., Rasheed, A., and Uteng, T.P., Methodology for assessing cycling comfort during a smart city development, Energy Procedia, 122:361 – 366, 2017. CISBAT 2017 International Conference Future Buildings & Districts – Energy Efficiency from Nano to Urban Scale
  50. Rasheed, A. and Tabib, M.V., Flow characterization in complex terrain. International Journal of Aerospace and Mechanical Engineering, 3(1), 2016
  51. Fuchs, F.G., Rasheed, A., Tabib, M. and Fonn, E., Wake modeling in complex terrain using a hybrid Eulerian-Lagrangian Split Solver. Journal of Physics: Conference Series, 753:082031, sep 2016
  52. Siddiqui, M.S., Rasheed, A., Tabib, M.V., and Kvamsdal, T., Numerical Analysis of NREL 5MW Wind Turbine: A Study Towards a Better Understanding of Wake Characteristic and Torque Gen- eration Mechanism. Journal of Physics: Conference Series, 753:032059, Sep 2016
  53. Tabib, M.V., Rasheed, A. and Fuchs, F., Analyzing complex wake-terrain interactions and its im- plications on wind-farm performance. Journal of Physics: Conference Series, 753:032063, sep 2016
  54. Tabib, M.V., Rasheed, A. and Kvamsdal, T., LES and RANS simulation of onshore bessaker wind farm: analysing terrain and wake effects on wind farm performance. Journal of Physics: Conference Series, 625:012032, June 2015
  55. van Opstal, T., Fonn, E., Holdahl, R. Kvamsdal, T., Kvarving, A.M., Mathisen, K.M., Nordanger, K., Okstad, K.M., Rasheed, A., and Tabib, M.V., Isogeometric methods for CFD and FSI-simulation of flow around turbine blades. Energy Procedia, 80:442-–449, 2015. 12th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’2015
  56. Süld, J.K., Rasheed, A., Kristiansen, J., Sætra, Ø., Carrasco, A., and Kvamsdal, T., Mesoscale Numerical Modelling of Met-ocean Interactions. Energy Procedia, 80:433 – 441, 2015. 12th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’2015
  57. Siddiqui, M.S., Rasheed, A., Kvamsdal, T., and Tabib, M.V., Effect of turbulence intensity on the performance of an offshore vertical axis wind turbine. Energy Procedia, 80:312 – 320, 2015. 12th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’2015
  58. Tabib, M.V., Rasheed, A., and Kvamsdal, T., Investigation of the impact of wakes and stratification on the performance of an onshore wind farm. Energy Procedia, 80:302 – 311, 2015. 12th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’2015
  59. Fonn, E., Rasheed, A., Kvarving, A.M., and Trond Kvamsdal. Spline based mesh generator for wind turbine blades. 27th Nordic Seminar on Computational Mechanics, Stockholm, Sweden, 2014
  60. Rasheed, A., Süld, J., and Kvamsdal, T., A hybrid numerical and statistical model for wind power forecasting. Grand Renewable Energy 2014, International Conference and Exhibition, 2014
  61. Rasheed, Adil, Jakob Kristoffer Süld, and Trond Kvamsdal. A multiscale wind and power forecast system for wind farms. Energy Procedia, 53:290 – 299, 2014. EERA DeepWind’ 2014, 11th Deep Sea Offshore Wind R&D Conference
  62. Rasheed, Adil, Runar Holdahl, Trond Kvamsdal, and Espen Åkervik. A comprehensive simulation methodology for fluid-structure interaction of offshore wind turbines. Energy Procedia, 53:135 – 145, 2014. EERA DeepWind’ 2014, 11th Deep Sea Offshore Wind R&D Conference
  63. Rasheed, A., Sørli, K., Süld, J., and Midtbø, K.H., Downscaling as a way to predict hazardous conditions for aviation activities. SESAR Innovation Day, 2013
  64. Rasheed, A., Sørli, K., Holdahl, R., and Kvamsdal, T., A multiscale approach to micrositing of wind turbines. Energy Procedia, 14:1458 – 1463, 2012. 2011 2nd International Conference on Advances in Energy Engineering (ICAEE)
  65. Rasheed, A., Robinson, D., and Lakehal, D., On the effects of complex urban geome- tries on mesoscale modeling. Proceeding of the International Symposium on Computational Wind Engi- neering, Chapel Hill, North Carolina, USA, 2010
  66. Robinson, D., Haldi, F., Kaempf, J., Leroux, P., Perez, D., Rasheed, A., and Wilke, U., From the neighborhood to the city: Resource flow modeling for urban sustainability. Proceeding of the CISBAT 2009, Lausanne, Switzerland, 2009
  67. Rasheed, A., Robinson, D., Narayanan, C., and Lakehal, D., On the effects of complex urban geometries on mesoscale modeling. Proceeding of the seventh International Conference on Urban Climate, Yokohama, Japan, 2009
  68. Rasheed, A., Robinson, D., and Clappier, A., A new urban canopy model. Proceeding of the seventh International Conference on Urban Climate, Yokohama, Japan, 2009
  69. Rasheed, A. and Robinson, D., Multiscale modeling of urban climate. Proceeding of the Eleventh International IBPSA Conference: Building Simulation, Glasgow, UK, 2009
  70. Robinson, D., Haldi, F., Kaempf, J., Leroux, P., Rasheed, A., and Wilke, U., Citysim: Comprehen- sive micro simulation of resource flows for sustainable urban planning. Proceeding of the Eleventh International IBPSA Conference: Building Simulation, Glasgow, UK, 2009
  71. Rasheed, A., Robinson, D., and Clappier, A., On the sensitivity of building performance to the urban heat island effect. Proceeding of the CISBAT, Lausanne, Switzerland, 2007