Zobrazeno 1 - 10
of 281
pro vyhledávání: '"Dimitris Drikakis"'
Publikováno v:
Fluids, Vol 9, Iss 10, p 241 (2024)
Figures: In Section 5, we aligned Figures 14–18 by consistently adding all the modelling parameters inside the labels [...]
Externí odkaz:
https://doaj.org/article/ef717fd00c63465b9aa5987f92cd39ec
Publikováno v:
Fluids, Vol 9, Iss 4, p 94 (2024)
Understanding of dusty fluids for different Brinkman numbers in porous media is limited. This study examines the Darcy–Brinkman model for two-dimensional magneto-hydrodynamic fluid flow across permeable stretching/shrinking surfaces with heat trans
Externí odkaz:
https://doaj.org/article/64ce607498db410b9986030c7063d3d2
Publikováno v:
Energies, Vol 17, Iss 6, p 1385 (2024)
This paper introduces an innovative and eco-friendly computational methodology to assess the wind potential of a location with the aid of high-resolution simulations with a mesoscale numerical weather prediction model (WRF), coupled with the statisti
Externí odkaz:
https://doaj.org/article/ac489da1cf0b4f818c22eebf2ffbab8f
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract The design of rigid vortex generators (RVG) influences the thermal performance of various technologies. We employed Discrete Adjoint-Based Optimization to show the optimal development of vortex generators. Under turbulent flow conditions, di
Externí odkaz:
https://doaj.org/article/8d9992f2e34049e29a81328a7fd700e6
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract According to WHO, by 2050, at least one person out of two will suffer from an allergy disorder resulting from the accelerating air pollution associated with toxic gas emissions and climate change. Airborne pollen, and associated allergies, a
Externí odkaz:
https://doaj.org/article/28c7422a36684caf92e672efe439e194
Autor:
Konstantinos Poulinakis, Dimitris Drikakis, Ioannis W. Kokkinakis, S. Michael Spottswood, Talib Dbouk
Publikováno v:
Computation, Vol 12, Iss 1, p 4 (2024)
This paper concerns the application of a long short-term memory model (LSTM) for high-resolution reconstruction of turbulent pressure fluctuation signals from sparse (reduced) data. The model’s training was performed using data from high-resolution
Externí odkaz:
https://doaj.org/article/f2d763249bb94cfc9a59e4109f51b309
Autor:
Nicholas Christakis, Dimitris Drikakis
Publikováno v:
Mathematics, Vol 11, Iss 17, p 3637 (2023)
This paper discusses using unsupervised learning in classifying particle-like dispersion. The problem is relevant to various applications, including virus transmission and atmospheric pollution. The Reduce Uncertainty and Increase Confidence (RUN-ICO
Externí odkaz:
https://doaj.org/article/7ed63d4b23384825adce9bc64db71f11
Autor:
Nicholas Christakis, Dimitris Drikakis
Publikováno v:
Mathematics, Vol 11, Iss 14, p 3063 (2023)
This paper presents the development of a novel algorithm for unsupervised learning called RUN-ICON (Reduce UNcertainty and Increase CONfidence). The primary objective of the algorithm is to enhance the reliability and confidence of unsupervised clust
Externí odkaz:
https://doaj.org/article/c369c84c826e429f858b6188b1c7668a
Autor:
Dimitris Drikakis, Filippos Sofos
Publikováno v:
Fluids, Vol 8, Iss 7, p 212 (2023)
The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dyna
Externí odkaz:
https://doaj.org/article/2a0dac5935a74989b3de69880768656f
Autor:
Konstantinos Poulinakis, Dimitris Drikakis, Ioannis W. Kokkinakis, Stephen Michael Spottswood
Publikováno v:
Mathematics, Vol 11, Iss 1, p 236 (2023)
Experimental and computational data and field data obtained from measurements are often sparse and noisy. Consequently, interpolating unknown functions under these restrictions to provide accurate predictions is very challenging. This study compares
Externí odkaz:
https://doaj.org/article/509d9d5dc64a429bb4c9525c6fe36f9e