| 1. |
- Amor, Christian, et al.
(författare)
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Higher-order dynamic mode decomposition on-the-fly : A low-order algorithm for complex fluid flows
- 2023
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Ingår i: Journal of Computational Physics. - : Elsevier BV. - 0021-9991 .- 1090-2716. ; 475
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Tidskriftsartikel (refereegranskat)abstract
- This article presents a new method to identify the main patterns describing the flow motion in complex flows. The algorithm is an extension of the higher-order dynamic mode decomposition (HODMD), which compresses the snapshots from the analysed database and progressively updates new compressed snapshots on-the-fly, so it is denoted as HODMD on -the-fly (HODMD-of). This algorithm can be applied in parallel to the numerical simulations (or experiments), and it exhibits two main advantages over offline algorithms: (i) it automatically selects on-the-fly the number of necessary snapshots from the database to identify the relevant dynamics; and (ii) it can be used from the beginning of a numerical simulation (or experiment), since it uses a sliding-window to automatically select, also on-the-fly, the suitable interval to perform the data analysis, i.e. it automatically identifies and discards the transient dynamics. The HODMD-of algorithm is suitable to build reduced order models, which have a much lower computational cost than the original simulation. The performance of the method has been tested in three different cases: the axi-symmetric synthetic jet, the three-dimensional wake of a circular cylinder and the turbulent wake behind a wall-mounted square cylinder. The obtained speed-up factors are around 7 with respect to HODMD; this value depends on the simulation and the configuration of the hyperparameters. HODMD-of also provides a significant reduction of the memory requirements, between 40 - 80% amongst the two-and three-dimensional cases studied in this paper.
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| 2. |
- Amor, Christian, et al.
(författare)
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Modeling the Turbulent Wake Behind a Wall-Mounted Square Cylinder
- 2020
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Ingår i: Logic journal of the IGPL (Print). - : Oxford University Press (OUP). - 1367-0751 .- 1368-9894. ; 30:2, s. 263-276
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Tidskriftsartikel (refereegranskat)abstract
- This article introduces some soft computing methods generally used for data analysis and flow pattern detection in fluid dynamics. These techniques decompose the original flow field as an expansion of modes, which can be either orthogonal in time (variants of dynamic mode decomposition), or in space (variants of proper orthogonal decomposition) or in time and space (spectral proper orthogonal decomposition), or they can simply be selected using some sophisticated statistical techniques (empirical mode decomposition). The performance of these methods is tested in the turbulent wake of a wall-mounted square cylinder. This highly complex flow is suitable to show the ability of the aforementioned methods to reduce the degrees of freedom of the original data by only retaining the large scales in the flow. The main result is a reduced-order model of the original flow case, based on a low number of modes. A deep discussion is carried out about how to choose the most computationally efficient method to obtain suitable reduced-order models of the flow. The techniques introduced in this article are data-driven methods that could be applied to model any type of non-linear dynamical system, including numerical and experimental databases.
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| 3. |
- Amor, Christian, et al.
(författare)
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Soft Computing Techniques to Analyze the Turbulent Wake of a Wall-Mounted Square Cylinder
- 2020
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Ingår i: 14th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2019. - Cham : Springer. ; , s. 577-586
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Konferensbidrag (refereegranskat)abstract
- This paper introduces several methods, generally used in fluid dynamics, to provide low-rank approximations. The algorithm describing these methods are mainly based on singular value decomposition (SVD) and dynamic mode decomposition (DMD) techniques, and are suitable to analyze turbulent flows. The application of these methods will be illustrated in the analysis of the turbulent wake of a wall-mounted cylinder, a geometry modeling a skyscraper. A brief discussion about the large and small size structures of the flow will provide the key ideas to represent the general dynamics of the flow using low-rank approximations. If the flow physics is understood, then it is possible to adapt these techniques, or some other strategies, to solve general complex problems with reduced computational cost. The main goal is to introduce these methods as machine learning strategies that could be potentially used in the field of fluid dynamics, and that can be extended to any other research field.
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| 4. |
- Atzori, Marco, et al.
(författare)
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High-resolution simulations of a turbulent boundary layer impacting two obstacles in tandem
- 2023
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Ingår i: Physical Review Fluids. - : American Physical Society (APS). - 2469-990X. ; 8:6
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Tidskriftsartikel (refereegranskat)abstract
- High-fidelity large-eddy simulations of the flow around two rectangular obstacles are carried out at a Reynolds number of 10 000 based on the freestream velocity and the obstacle height. The incoming flow is a developed turbulent boundary layer. Mean-velocity components, turbulence fluctuations, and the terms of the turbulent-kinetic-energy budget are analyzed for three flow regimes: skimming flow, wake interference, and isolated roughness. Three regions are identified where the flow undergoes the most significant changes: the first obstacle's wake, the region in front of the second obstacle, and the region around the second obstacle. In the skimming-flow case, turbulence activity in the cavity between the obstacles is limited and mainly occurs in a small region in front of the second obstacle. In the wake-interference case, there is a strong interaction between the freestream flow that penetrates the cavity and the wake of the first obstacle. This interaction results in more intense turbulent fluctuations between the obstacles. In the isolated-roughness case, the wake of the first obstacle is in good agreement with that of an isolated obstacle. Separation bubbles with strong turbulent fluctuations appear around the second obstacle.
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| 5. |
- Corrochano, Adrian, et al.
(författare)
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Flow Structures on a Planar Food and Drug Administration (FDA) Nozzle at Low and Intermediate Reynolds Number
- 2021
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Ingår i: Fluids. - : MDPI. - 2311-5521. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present a general description of the flow structures inside a two-dimensional Food and Drug Administration (FDA) nozzle. To this aim, we have performed numerical simulations using the numerical code Nek5000. The topology patters of the solution obtained, identify four different flow regimes when the flow is steady, where the symmetry of the flow breaks down. An additional case has been studied at higher Reynolds number, when the flow is unsteady, finding a vortex street distributed along the expansion pipe of the geometry. Linear stability analysis identifies the evolution of two steady and two unsteady modes. The results obtained have been connected with the changes in the topology of the flow. Finally, higher-order dynamic mode decomposition has been applied to identify the main flow structures in the unsteady flow inside the FDA nozzle. The highest-amplitude dynamic mode decomposition (DMD) modes identified by the method model the vortex street in the expansion of the geometry.
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| 6. |
- Eivazi, Hamidreza, et al.
(författare)
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Non-Linear Orthogonal Modal Decompositions in Turbulent Flows via Autoencoders
- 2022
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Ingår i: 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022. - : International Symposium on Turbulence and Shear Flow Phenomena, TSFP.
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Konferensbidrag (refereegranskat)abstract
- We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of nonlinear modes from high-fidelity turbulent-flow data. Our approach is based on β-variational autoencoders (β-VAEs) and convolutional neural networks (CNNs), which enable extracting non-linear modes from multi-scale turbulent flows while encouraging the learning of independent latent variables and penalizing the size of the latent vector. Moreover, we introduce an algorithm for ordering VAE-based modes with respect to their contribution to the reconstruction. We apply this method for non-linear mode decomposition of the turbulent flow through a simplified urban environment. We demonstrate that by constraining the shape of the latent space, it is possible to motivate the orthogonality and extract a set of parsimonious modes sufficient for high-quality reconstruction. Our results show the excellent performance of the method in the reconstruction against linear-theory-based decompositions. We show the ability of our approach in the extraction of near-orthogonal modes with the determinant of the correlation matrix equal to 0.99, which may lead to interpretability.
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| 7. |
- Eivazi, Hamidreza, et al.
(författare)
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Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
- 2022
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Ingår i: Expert systems with applications. - : Elsevier BV. - 0957-4174 .- 1873-6793. ; 202, s. 117038-
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Tidskriftsartikel (refereegranskat)abstract
- Modal-decomposition techniques are computational frameworks based on data aimed at identifying a low-dimensional space for capturing dominant flow features: the so-called modes. We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of non-linear modes from high-fidelity turbulent-flow data useful for flow analysis, reduced-order modeling and flow control. Our approach is based on beta-variational autoencoders (beta-VAEs) and convolutional neural networks (CNNs), which enable extracting non-linear modes from multi-scale turbulent flows while encouraging the learning of independent latent variables and penalizing the size of the latent vector. Moreover, we introduce an algorithm for ordering VAE-based modes with respect to their contribution to the reconstruction. We apply this method for non-linear mode decomposition of the turbulent flow through a simplified urban environment, where the flow-field data is obtained based on well-resolved large-eddy simulations (LESs). We demonstrate that by constraining the shape of the latent space, it is possible to motivate the orthogonality and extract a set of parsimonious modes sufficient for high-quality reconstruction. Our results show the excellent performance of the method in the reconstruction against linear-theory-based decompositions, where the energy percentage captured by the proposed method from five modes is equal to 87.36% against 32.41% of the POD. Moreover, we compare our method with available AE-based models. We show the ability of our approach in the extraction of near-orthogonal modes with the determinant of the correlation matrix equal to 0.99, which may lead to interpretability.
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| 8. |
- Lazpita, Eneko, et al.
(författare)
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On the generation and destruction mechanisms of arch vortices in urban fluid flows
- 2022
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Ingår i: Physics of fluids. - : AIP Publishing. - 1070-6631 .- 1089-7666. ; 34:5, s. 051702-
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Tidskriftsartikel (refereegranskat)abstract
- This study uses higher-order dynamic mode decomposition to analyze a high-fidelity database of the turbulent flow in an urban environment consisting of two buildings separated by a certain distance. We recognize the characteristics of the well-known arch vortex forming on the leeward side of the first building and document this vortex's generation and destruction mechanisms based on the resulting temporal modes. We show that the arch vortex plays a prominent role in the dispersion of pollutants in urban environments, where its generation leads to an increase in their concentration; therefore, the reported mechanisms are of extreme importance for urban sustainability.& nbsp;Published under an exclusive license by AIP Publishing
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| 9. |
- Le Clainche, Soledad, et al.
(författare)
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Improving aircraft performance using machine learning : A review
- 2023
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Ingår i: Aerospace Science and Technology. - : Elsevier BV. - 1270-9638 .- 1626-3219. ; 138
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Forskningsöversikt (refereegranskat)abstract
- This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring. We review the state of the art, gathering the advantages and challenges of ML methods across different aerospace disciplines and provide our view on future opportunities. The basic concepts and the most relevant strategies for ML are presented together with the most relevant applications in aerospace engineering, revealing that ML is improving aircraft performance and that these techniques will have a large impact in the near future.
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| 10. |
- Mamchur, Dmytro, et al.
(författare)
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Analysis of the state of the art on non-intrusive object-screening techniques
- 2022
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Ingår i: Przeglad Elektrotechniczny. - : Wydawnictwo SIGMA-NOT, sp. z.o.o.. - 0033-2097 .- 2449-9544. ; 98:2, s. 168-173
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Tidskriftsartikel (refereegranskat)abstract
- The paper is devoted to an analysis of the modern methods and techniques used for non-intrusive object screening. First, currently used technology and the principle of equipment operation are described. Next, the ways for improving the reliability and efficiency of the screening process and ways for its automation are indicated. Finally, a schematic of an automated screening system that uses additional sensors and implements AI-based analysis for automatic detection and distinguishing between legal, illegal and illicit items inside the object under inspection is proposed.
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| 11. |
- Mamchur, Dmytro, et al.
(författare)
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Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection
- 2022
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 22:6
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Tidskriftsartikel (refereegranskat)abstract
- Increase in trading and travelling flows has resulted in the need for non-intrusive object inspection and identification methods. Traditional techniques proved to be effective for decades; however, with the latest advances in technology, the intruder can implement more sophisticated methods to bypass inspection points control techniques. The present study provides an overview of the existing and developing techniques for non-intrusive inspection control, current research trends, and future challenges in the field. Both traditional and developing methods, techniques, and technologies were analyzed with the use of traditional and novel sensor types. Finally, it was concluded that the improvement of non-intrusive inspection experience could be gained with the additional use of novel types of sensors (such as biosensors) combined with traditional techniques (X-ray inspection).
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| 12. |
- Martinez Sanchez, Alvaro, et al.
(författare)
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Causality analysis of large-scale structures in the flow around a wall-mounted square cylinder
- 2023
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Ingår i: Journal of Fluid Mechanics. - : Cambridge University Press (CUP). - 0022-1120 .- 1469-7645. ; 967
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this work is to analyse the formation mechanisms of large-scale coherent structures in the flow around a wall-mounted square cylinder, due to their impact on pollutant transport within cities. To this end, we assess causal relations between the modes of a reduced-order model obtained by applying proper orthogonal decomposition to high-fidelity simulation data of the flow case under study. The causal relations are identified using conditional transfer entropy, which is an information-theoretical quantity that estimates the amount of information contained in the past of one variable about another. This allows for an understanding of the origins and evolution of different phenomena in the flow, with the aim of identifying the modes responsible for the formation of the main vortical structures. Our approach unveils that vortex-breaker modes are the most causal modes, in particular, over higher-order modes, and no significant causal relationships were found for vortex-generator modes. We validate this technique by determining the causal relations present in the nine-equation model of near-wall turbulence developed by Moehlis et al. (New J. Phys., vol. 6, 2004, p. 56), which are in good agreement with literature results for turbulent channel flows.
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| 13. |
- Martínez Sánchez, Álvaro, et al.
(författare)
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Data-driven assessment of arch vortices in simplified urban flows
- 2023
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Ingår i: International Journal of Heat and Fluid Flow. - : Elsevier BV. - 0142-727X .- 1879-2278. ; 100
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Tidskriftsartikel (refereegranskat)abstract
- Understanding flow structures in urban areas is widely recognized as a challenging concern due to its effect on urban development, air quality, and pollutant dispersion. In this study, state-of-the-art data-driven methods for modal analysis of simplified urban flows are used to study the dominant flow processes in these environments. Higher order dynamic mode decomposition (HODMD), a highly-efficient method to analyze turbulent flows, is used together with traditional techniques such as proper-orthogonal decomposition (POD) to analyze high-fidelity simulation data of a simplified urban environment. Furthermore, the spatio-temporal Koopman decomposition (STKD) will be applied to the temporal modes obtained with HODMD to perform spatial analysis. The flow interaction within the canopy influences the flow structures, particularly the arch vortex. The latter is a vortical structure generally found downstream of wall-mounted obstacles, which is generated as a consequence of flow separation. Therefore, the main objective of the present study is to characterize the mechanisms that promote these phenomena in urban areas with different geometries. Remarkably, among all the vortical structures identified by the HODMD algorithm, low- and high-frequency modes are classified according to their relation with the arch vortex. They are referred to as vortex-generator and vortex-breaker modes, respectively. This classification implies that one of the processes driving the formation and destruction of major vortical structures in between the buildings is the interaction between low- and high-frequency structures. The high energy revealed by the POD for the vortex-breaker modes points to this destruction process as the mechanism driving the flow dynamics. Furthermore, the results obtained with the STKD method show how the generating- and breaking-mechanisms originated along with the streamwise and spanwise directions.
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| 14. |
- Torres, Pablo, et al.
(författare)
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On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows
- 2021
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Ingår i: Energies. - : MDPI AG. - 1996-1073. ; 14:5
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.
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| 15. |
- Vinuesa, Ricardo, et al.
(författare)
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Machine-Learning Methods for Complex Flows
- 2022
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Ingår i: Energies. - : MDPI AG. - 1996-1073. ; 15:4
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- We are delighted to introduce this Special Issue focused on novel machine-learning (ML) methods aimed at predicting, modeling, and controlling a variety of complex fluid flow scenarios [...]
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