Cartographing dynamic stall with machine learning

Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge. Eventually the shear layer rolls up, and then a coherent vortex forms and then sheds downstream with its low-pressure core causing a lift overshoot and moment drop. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analyze cycle-to-cycle variations. Modern data science and machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures the fact that secondary and tertiary vorticity vary strongly, and in static stall with surging flow the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.TU Berlin, Open-Access-Mittel – 202

Artificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control

We present the first application of an Artificial Neural Network trained through a Deep Reinforcement Learning agent to perform active flow control. It is shown that, in a 2D simulation of the Karman vortex street at moderate Reynolds number (Re = 100), our Artificial Neural Network is able to learn an active control strategy from experimenting with the mass flow rates of two jets on the sides of a cylinder. By interacting with the unsteady wake, the Artificial Neural Network successfully stabilizes the vortex alley and reduces drag by about 8%. This is performed while using small mass flow rates for the actuation, on the order of 0.5% of the mass flow rate intersecting the cylinder cross section once a new pseudo-periodic shedding regime is found. This opens the way to a new class of methods for performing active flow control

A Data-Driven Approach for Generating Vortex Shedding Regime Maps for an Oscillating Cylinder

Recent developments in wind energy extraction methods from vortex-induced vibration (VIV) have fueled the research into vortex shedding behaviour. The vortex shedding map is vital for the consistent use of normalized amplitude and wavelength to validate the predicting power of forced vibration experiments. However, there is a lack of demonstrated methods of generating this map at Reynolds numbers feasible for energy generation due to the high computational cost and complex dynamics. Leveraging data-driven methods addresses the limitations of the traditional experimental vortex shedding map generation, which requires large amounts of data and intensive supervision that is unsuitable for many applications and Reynolds numbers. This thesis presents a data-driven approach for generating vortex shedding maps of a cylinder undergoing forced vibration that requires less data and supervision while accurately extracting the underlying vortex structure patterns. The quantitative analysis in this dissertation requires the univariate time series signatures of local fluid flow measurements in the wake of an oscillating cylinder experiencing forced vibration. The datasets were extracted from a 2-dimensional computational fluid dynamic (CFD) simulation of a cylinder oscillating at various normalized amplitude and wavelength parameters conducted at two discrete Reynolds numbers of 4000 and 10,000. First, the validity of clustering local flow measurements was demonstrated by proposing a vortex shedding mode classification strategy using supervised machine learning models of random forest and -nearest neighbour models, which achieved 99.3% and 99.8% classification accuracy using the velocity sensors orientated transverse to the pre-dominant flow (), respectively. Next, the dataset of local flow measurement of the -component of velocity was used to develop the procedure of generating vortex shedding maps using unsupervised clustering techniques. The clustering task was conducted on subsequences of repeated patterns from the whole time series extracted using the novel matrix profile method. The vortex shedding map was validated by reproducing a benchmark map produced at a low Reynolds number. The method was extended to a higher Reynolds number case of vortex shedding and demonstrated the insight gained into the underlying dynamical regimes of the physical system. The proposed multi-step clustering methods denoted Hybrid Method B, combining Density-Based Clustering Based on Connected Regions with High Density (DBSCAN) and Agglomerative algorithms, and Hybrid Method C, combining -Means and Agglomerative algorithms demonstrated the ability to extract meaningful clusters from more complex vortex structures that become increasingly indistinguishable. The data-driven methods yield exceptional performance and versatility, which significantly improves the map generation method while reducing the data input and supervision required

Data driven techniques for modal decomposition and reduced-order modelling of fluids

In this thesis, a number of data-driven techniques are proposed for the analysis and extraction of reduced-order models of fluid flows. Throughout the thesis, there has been an emphasis on the practicality and interpretability of data-driven feature-extraction techniques to aid practitioners in flow-control and estimation. The first contribution uses a graph theoretic approach to analyse the similarity of modes extracted using data-driven modal decomposition algorithms to give a more intuitive understanding of the degrees of freedom in the underlying system. The method extracts clusters of spatially and spectrally similar modes by post-processing the modes extracted using DMD and its variants. The second contribution proposes a method for extracting coherent structures, using snapshots of high dimensional measurements, that can be mapped to a low dimensional output of the system. The importance of finding such coherent structures is that in the context of active flow control and estimation, the practitioner often has to rely on a limited number of measurable outputs to estimate the state of the flow. Therefore, ensuring that the extracted flow features can be mapped to the measured outputs of the system can be beneficial for estimating the state of the flow. The third contribution concentrates on using neural networks for exploiting the nonlinear relationships amongst linearly extracted modal time series to find a reduced order state, which can then be used for modelling the dynamics of the flow. The method utilises recurrent neural networks to find an encoding of a high dimensional set of modal time series, and fully connected neural networks to find a mapping between the encoded state and the physically interpretable modal coefficients. As a result of this architecture, the significantly reduced-order representation maintains an automatically extracted relationship to a higher-dimensional, interpretable state.Open Acces

Custom-Designed Biohybrid Micromotor for Potential Disease Treatment

Micromotors are recognized as promising candidates for untethered micromanipulation and targeted cargo transport. Their future application is, however, hindered by the low efficiency of drug encapsulation and their poor adaptability in physiological conditions. To address these challenges, one potential solution is to incorporate micromotors with biological materials as the combination of functional biological entities and smart artificial parts represents a manipulable and biologically friendly approach. This dissertation focuses on the development of custom-designed micromotors combined with sperm and their potential applications on targeted diseases treatment. By means of 2D and 3D lithography methods, microstructures with complex configurations can be fabricated for specific demands. Bovine and human sperm are both for the first time explored as drug carriers thanks to their high encapsulation efficiency of hydrophilic drugs, their powerful self-propulsion and their improved drug-uptake relying on the somatic-cell fusion ability. The hybrid micromotors containing drug loaded sperm and constructed artificial enhancements can be self-propelled by the sperm flagella and remotely guided and released to the target at high precision by employing weak external magnetic fields. As a result, micromotors based on both bovine and human sperm show significant anticancer effect. The application here can be further broadened to other biological environments, in particular to the blood stream, showing the potential on the treatment of blood diseases like blood clotting. Finally, to enhance the treatment efficiency, in particular to control sperm number and drug dose, three strategies are demonstrated to transport swarms of sperm. This research paves the way for the precision medicine based on engineered sperm-based micromotors

Multiple mechanisms of spiral wave breakup in a model of cardiac electrical activity

It has become widely accepted that the most dangerous cardiac arrhythmias are due to re- entrant waves, i.e., electrical wave(s) that re-circulate repeatedly throughout the tissue at a higher frequency than the waves produced by the heart's natural pacemaker (sinoatrial node). However, the complicated structure of cardiac tissue, as well as the complex ionic currents in the cell, has made it extremely difficult to pinpoint the detailed mechanisms of these life-threatening reentrant arrhythmias. A simplified ionic model of the cardiac action potential (AP), which can be fitted to a wide variety of experimentally and numerically obtained mesoscopic characteristics of cardiac tissue such as AP shape and restitution of AP duration and conduction velocity, is used to explain many different mechanisms of spiral wave breakup which in principle can occur in cardiac tissue. Some, but not all, of these mechanisms have been observed before using other models; therefore, the purpose of this paper is to demonstrate them using just one framework model and to explain the different parameter regimes or physiological properties necessary for each mechanism (such as high or low excitability, corresponding to normal or ischemic tissue, spiral tip trajectory types, and tissue structures such as rotational anisotropy and periodic boundary conditions). Each mechanism is compared with data from other ionic models or experiments to illustrate that they are not model-specific phenomena. The fact that many different breakup mechanisms exist has important implications for antiarrhythmic drug design and for comparisons of fibrillation experiments using different species, electromechanical uncoupling drugs, and initiation protocols.Comment: 128 pages, 42 figures (29 color, 13 b&w

Data-Driven and Hybrid Methods for Naval Applications

The goal of this PhD thesis is to study, design and develop data analysis methods for naval applications. Data analysis is improving our ways to understand complex phenomena by profitably taking advantage of the information laying behind a collection of data. In fact, by adopting algorithms coming from the world of statistics and machine learning it is possible to extract valuable information, without requiring specific domain knowledge of the system generating the data. The application of such methods to marine contexts opens new research scenarios, since typical naval problems can now be solved with higher accuracy rates with respect to more classical techniques, based on the physical equations governing the naval system. During this study, some major naval problems have been addressed adopting state-of-the-art and novel data analysis techniques: condition-based maintenance, consisting in assets monitoring, maintenance planning, and real-time anomaly detection; energy and consumption monitoring, in order to reduce vessel consumption and gas emissions; system safety for maneuvering control and collision avoidance; components design, in order to detect possible defects at design stage. A review of the state-of-the-art of data analysis and machine learning techniques together with the preliminary results of the application of such methods to the aforementioned problems show a growing interest in these research topics and that effective data-driven solutions can be applied to the naval context. Moreover, for some applications, data-driven models have been used in conjunction with domain-dependent methods, modelling physical phenomena, in order to exploit both mechanistic knowledge of the system and available measurements. These hybrid methods are proved to provide more accurate and interpretable results with respect to both the pure physical or data-driven approaches taken singularly, thus showing that in the naval context it is possible to offer new valuable methodologies by either providing novel statistical methods or improving the state-of-the-art ones

Abstracts to be Presented at the 2016 Supercomputing Conference

No abstract availabl

Ny metodikk for kobling av mesoskala og stasjonære CFD modeller for vindressurskartlegging

The estimation of the energy production of wind farms is a key factor for the development of wind energy projects. Currently, these estimations utilize only a few onsite measurement points to estimate the wind resource at the location of the wind turbines by means of a wind flow model. One of the most advanced wind flow models utilized in the wind energy industry for this purpose are the steady-state computational fluid dynamic (CFD) models. These models have proven to be successful in modelling the wind flow in complex terrain. Nevertheless, there are some limitations in their applicability at sites with complex weather patterns. In this PhD thesis, these limitations are addressed by coupling a CFD model with a mesoscale meteorological model (MMM). MMMs are widely used for weather forecast and can reproduce the complex weather phenomena that a CFD model lacks. In this study, the framework to couple both models consists in utilizing the mesoscale simulation results to compute the boundary conditions of the CFD model. Two variants of the meso-microscale coupling approach are here studied. The first approach consists in utilizing the average values of the mesoscale fields by wind directional sector. It is shown that this approach improves the wind estimations in complex terrain and in areas that are located at the wake of the terrain features of a site. Nevertheless, the approach presents important limitations in sites where the wind blows from few wind directions. The second approach addresses this limitation by extracting weather patterns from the mesoscale simulations by means of a fully automated clustering methodology. This classification technique is capable of extracting the predominant weather patterns and organizing them in a meaningful way. Overall, by downscaling the extracted patterns the modelling error is reduced compared with the mesoscale model. Such a methodology has a lot of potential for wind turbine wake studies as well as for forecasting solutions that utilize CFD models

GPU-accelerated Modeling of Microscale Atmospheric Flows over Complex Terrain

With installed wind power capacities steadily on the rise, balancing the loads on electrical grids is challenging due to the intermittency of the wind. Short-term wind power forecasting can be a valuable tool for better informing grid operators on the available wind power. Current short-term wind forecasting techniques typically adopt mesoscale weather forecasting models with spatial resolutions on the order of a kilometer. On relatively flat terrain, use of mesoscale models may prove effective, but application to complex terrain induces large forecasting errors. To address this issue, a baseline incompressible flow solver for GPU (graphics processing unit) clusters is extended to simulate neutrally-stable atmospheric flows over complex terrain with the ultimate goal of developing a comprehensive short-term wind fore-casting capability that can resolve winds at turbine hub height. In the extended wind model, the large-eddy simulation (LES) technique with a Lagrangian dynamic subgrid-scale (SGS) model is implemented to better capture the effects of atmospheric turbulence over complex terrain. Additionally, the immersed boundary method (IBM) is adopted to numerically represent the complex terrain on a Cartesian mesh. Validation is performed using common benchmark cases. Performance results obtained from simulating the Bolund Hill Experiment demonstrates that faster than real-time computations are realized with GPU clusters. While the results are encouraging and justifies the foundation for a short-term wind forecasting capability, the work does not account for all factors in wind forecasting and the results can be considered as a first attempt requiring further improvements