Instance Flow Based Online Multiple Object Tracking

We present a method to perform online Multiple Object Tracking (MOT) of known object categories in monocular video data. Current Tracking-by-Detection MOT approaches build on top of 2D bounding box detections. In contrast, we exploit state-of-the-art instance aware semantic segmentation techniques to compute 2D shape representations of target objects in each frame. We predict position and shape of segmented instances in subsequent frames by exploiting optical flow cues. We define an affinity matrix between instances of subsequent frames which reflects locality and visual similarity. The instance association is solved by applying the Hungarian method. We evaluate different configurations of our algorithm using the MOT 2D 2015 train dataset. The evaluation shows that our tracking approach is able to track objects with high relative motions. In addition, we provide results of our approach on the MOT 2D 2015 test set for comparison with previous works. We achieve a MOTA score of 32.1

Sparse optical flow regularisation for real-time visual tracking

Optical flow can greatly improve the robustness of visual tracking algorithms. While dense optical flow algorithms have various applications, they can not be used for real-time solutions without resorting to GPU calculations. Furthermore, most optical flow algorithms fail in challenging lighting environments due to the violation of the brightness constraint. We propose a simple but effective iterative regularisation scheme for real-time, sparse optical flow algorithms, that is shown to be robust to sudden illumination changes and can handle large displacements. The algorithm proves to outperform well known techniques in real life video sequences, while being much faster to calculate. Our solution increases the robustness of a real-time particle filter based tracking application, consuming only a fraction of the available CPU power. Furthermore, a new and realistic optical flow dataset with annotated ground truth is created and made freely available for research purposes

Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and tracking of particles is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH) sensors. To address this issue, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that predicts particle positions over time as a function of the estimated velocity field. The model can account for non-ideal effects like drag on inertial particles. A statistical data loss that compares the tracked and advected particle positions, accounting for arbitrary localization and tracking uncertainties, is derived and approximated. We implement our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss, a Navier-Stokes physics loss, and a wall boundary loss, where appropriate. Results are reported for simulated and experimental DIH-PTV measurements of laminar and turbulent flows. Our statistical approach significantly improves the accuracy of PTV reconstructions compared to a conventional data loss, resulting in an average reduction of error close to 50%. Furthermore, our framework can be readily adapted to work with other data assimilation techniques like state observer, Kalman filter, and adjoint-variational methods

Lagrangian and inertial transport in atmospheric and chaotic flows

This thesis presents a compendium of publications related to transport studies analyzed from the perspective of dynamical systems. The goal is to address the role that particle properties and the flow have on the organization of trajectories and hence the transport. To observe how transport is structured, we focus on the most widely used method: the Finite Time Lyapunov Exponents. These exponents measure the separation rate of the particles starting from nearby initial positions, estimating the hyperbolicity of the trajectories. This method allows us to make a first approach to the problem, obtaining the borders or frontiers between regions with different dynamics given a simplified vision of transport. The transport structures related with this method, are called Lagrangian Coherent Structures. In the first study, the Lagrangian transport in the troposphere was analyzed. The atmospheric flow is characterized by being turbulent in a continuum of spatiotemporal scales. Within these scales, it was observed that there are structures such as the Atmospheric Rivers that maintain a spatial and temporal coherence of the order of days acting as organizers of water vapor transport and therefore dominating the dynamics of the region at the moment they occur. At the same time, the persistence and repetition of these structures, together with all the other tropospheric structures, introduce mixing into the atmosphere. Those areas in middle latitudes where these structures develop have higher mixing variability. This is mainly due to seasonal changes. However, those regions with less variability, such as the equatorial zones, the mixing and its variability on day scales, are mainly associated with inter-annual variability events such as El Ni ˜no or La Ni ˜na or the Intertropical Convergence Zone (ITCZ). In addition, the mixing information of the air masses from a climatic point of view, was used as a predictor of rainfall for the Iberian region. The Atlantic margin is characterized by an intense activity of Atmospheric Rivers, being one of the main causes of precipitation. However, the problem of determining the activity of rainfall months in advance is complex, for this reason the use of new variables as potential predictors is required. It has been obtained that the mixing, in the Atlantic region, is related to the precipitation on the Iberian Peninsula. Addressing on the second study, we focus on the influence of forces on the particles motion so the resolution of motion equation is required to obtain the trajectories they describe. The particles are modeled as small spheres with mass, but the fact that their movement is decoupled from the flow makes their trajectories depend initially on other properties such as the initial velocity. It was observed that this dependence, for certain flows, is even higher than small perturbations in its position, mainly in those regions where there is a high spatial variability of the fluid such as regions with shear. The same happens for bubbles where flotation effects appear. They are very sensitive to the inertial effects and especially to the disturbances of the radius as well as the effects of merging with other bubbles, being especially relevant in the initial instants of the movement. In addition, it has been observed that particles properties and their collective motion play a key role in the synchronization of finite-size chemical oscillators. To experimentally support some of the aforementioned behaviors, experimental data are needed to measure the trajectories of the particles. Particle Tracking Velocimetry (PTV) methods, track the trajectories of individual particles in three-dimensional space. In the last part of this thesis, we present an experimental setup and some preliminary results of trajectories of the particles mentioned above in a high turbulent flow

IMPROVING EFFICIENCY AND SCALABILITY IN VISUAL SURVEILLANCE APPLICATIONS

We present four contributions to visual surveillance: (a) an action recognition method based on the characteristics of human motion in image space; (b) a study of the strengths of five regression techniques for monocular pose estimation that highlights the advantages of kernel PLS; (c) a learning-based method for detecting objects carried by humans requiring minimal annotation; (d) an interactive video segmentation system that reduces supervision by using occlusion and long term spatio-temporal structure information. We propose a representation for human actions that is based solely on motion information and that leverages the characteristics of human movement in the image space. The representation is best suited to visual surveillance settings in which the actions of interest are highly constrained, but also works on more general problems if the actions are ballistic in nature. Our computationally efficient representation achieves good recognition performance on both a commonly used action recognition dataset and on a dataset we collected to simulate a checkout counter. We study discriminative methods for 3D human pose estimation from single images, which build a map from image features to pose. The main difficulty with these methods is the insufficiency of training data due to the high dimensionality of the pose space. However, real datasets can be augmented with data from character animation software, so the scalability of existing approaches becomes important. We argue that Kernel Partial Least Squares approximates Gaussian Process regression robustly, enabling the use of larger datasets, and we show in experiments that kPLS outperforms two state-of-the-art methods based on GP. The high variability in the appearance of carried objects suggests using their relation to the human silhouette to detect them. We adopt a generate-and-test approach that produces candidate regions from protrusion, color contrast and occlusion boundary cues and then filters them with a kernel SVM classifier on context features. Our method exceeds state of the art accuracy and has good generalization capability. We also propose a Multiple Instance Learning framework for the classifier that reduces annotation effort by two orders of magnitude while maintaining comparable accuracy. Finally, we present an interactive video segmentation system that trades off a small amount of segmentation quality for significantly less supervision than necessary in systems in the literature. While applications like video editing could not directly use the output of our system, reasoning about the trajectories of objects in a scene or learning coarse appearance models is still possible. The unsupervised segmentation component at the base of our system effectively employs occlusion boundary cues and achieves competitive results on an unsupervised segmentation dataset. On videos used to evaluate interactive methods, our system requires less interaction time than others, does not rely on appearance information and can extract multiple objects at the same time

Velocimetry-based pressure information for spray analysis – novel experimental, processing and evaluation strategies

In der vorliegenden Arbeit wurde der Spraytransport von komplexen Benzindirekteinspritzungssprays (GDI) mittels auf Geschwindigkeitmessung basierter Druckauswertung untersucht. Für diesen Zweck wurden neue Versuchs-, Verarbeitungs- und Auswertestrategien eingeführt, um eine Druckauswertung der Spray-induzierten Strömung zu befähigen und deren Möglichkeiten auszuweiten. Dies umfasst unter anderem ein statistisches Verfahren auf Basis der Unsteady Reynolds-Averaged Navier-Stokes (URANS) Gleichungen und Ensemble-Mittelung, welche die Druckauswertung transienter, statistisch stationärer Strömungen mittels konventioneller Particle Image Velocimetry (PIV) ermöglicht. Darüber hinaus wurde eine neuartige Technik namens Dual-Plane-Stereo-Astigmatismus (DPSA) entwickelt, die die Auswertung momentaner Druckfelder und damit die Analyse einzelner Einspritzereignisse unter Verwendung eines stereoskopischen Aufbaus und einer einzigen Lichtquelle ermöglicht. Abschließend wurde die Methode der Physics-Informed Neural Networks (PINNs) erfolgreich aus dem Bereich des Deep Learnings in die experimentelle Strömungsmechanik und Spray-Analyse übertragen. Das PINN-Verfahren weitet die Möglichkeiten der bisherigen auf Geschwindigkeitsmessung basierenden Druckauswertung aus und ermöglicht die Auswertung von bislang nicht auswertbaren Strömungsbereichen, sowohl in Raum und Zeit. Unter Verwendung der beschriebenen Methoden wurde die Wechselwirkung zwischen Spray und Umgebungsgasströmung für unterschiedliche Betriebsbedingungen und Sprayauslegungen untersucht. Es zeigte sich, dass der Impulsaustausch mit höherem Einspritzdruck, Gasdichte, Kraftstofftemperatur, größerer Relativgeschwindigkeit, Spray-Gas-Grenzfläche, Sprayexpansion und stärkerer Zerstäubung bzw. Flash-Boiling zunimmt. Als eine wesentliche Erkenntnis wurde festgestellt, dass die Ablenkung von Sprays bzw. das Phänomen der Strahl-zu-Strahl-Wechselwirkung und Spraykontraktion auf einen Nettoimpuls zurückzuführen ist, der auf einzelne Spraykeulen infolge von induzierten Druckkräften wirkt. In diesem Zusammenhang wurde das Vorhandensein eines Niederdruckgebiets im Zentrum von Mehrlochsprays experimentell bestätigt. Es wurde aufgezeigt, dass das Ausmaß der Strahl-zu-Strahl-Wechselwirkung und der Spraykontraktion durch eine enge Spritzlochanordnung und -ausrichtung, eine starke Zerstäubung und ein erhöhtes Tropfen-Folgeverhalten begünstigt wird

Flowing matter

This open access book, published in the Soft and Biological Matter series, presents an introduction to selected research topics in the broad field of flowing matter, including the dynamics of fluids with a complex internal structure -from nematic fluids to soft glasses- as well as active matter and turbulent phenomena.Flowing matter is a subject at the crossroads between physics, mathematics, chemistry, engineering, biology and earth sciences, and relies on a multidisciplinary approach to describe the emergence of the macroscopic behaviours in a system from the coordinated dynamics of its microscopic constituents.Depending on the microscopic interactions, an assembly of molecules or of mesoscopic particles can flow like a simple Newtonian fluid, deform elastically like a solid or behave in a complex manner. When the internal constituents are active, as for biological entities, one generally observes complex large-scale collective motions. Phenomenology is further complicated by the invariable tendency of fluids to display chaos at the large scales or when stirred strongly enough. This volume presents several research topics that address these phenomena encompassing the traditional micro-, meso-, and macro-scales descriptions, and contributes to our understanding of the fundamentals of flowing matter.This book is the legacy of the COST Action MP1305 “Flowing Matter”

Sparse variational regularization for visual motion estimation

The computation of visual motion is a key component in numerous computer vision tasks such as object detection, visual object tracking and activity recognition. Despite exten- sive research effort, efficient handling of motion discontinuities, occlusions and illumina- tion changes still remains elusive in visual motion estimation. The work presented in this thesis utilizes variational methods to handle the aforementioned problems because these methods allow the integration of various mathematical concepts into a single en- ergy minimization framework. This thesis applies the concepts from signal sparsity to the variational regularization for visual motion estimation. The regularization is designed in such a way that it handles motion discontinuities and can detect object occlusions

LES Eulerian diffuse-interface modeling of fuel dense sprays near- and far-field

[EN] Engine fuel spray modeling still remains a challenge, especially in the dense near-nozzle region. This region is difficult to experimentally access and also to model due to the complex and rapid liquid and gas interaction. Modeling approaches based on Lagrangian particle tracking have failed in this area, while Eulerian modeling has proven to be particularly useful. Interface resolved methods are still limited to primary atomization academic configurations due to excessive computational requirements. To overcome those limitations, the single-fluid diffuse interface model known as Sigma-Y, arises as a single-framework for spray simulations. Under the assumption of scale separation at high Reynolds and Weber numbers, liquid dispersion is modeled as turbulent mixing of a variable density flow. The concept of surface area density is used for representing liquid structures, regardless of the complexity of the interface. In this work, a LES based implementation of the Sigma-Y model in the OpenFOAM CFD library is applied to simulate the ECN Spray A configuration. Model assessment is performed for both near- and far-field spray development regions using different experimental diagnostics available from ECN database. The CFD model is able to capture near-nozzle fuel mass distribution and, after Sigma equation constant calibration, interfacial surface area. Accurate predictions of spray far-field evolution in terms of liquid and vapor tip penetration and local velocity can be simultaneously achieved. Model accuracy is lower when compared to mixture fraction axial evolution, despite radial distribution profiles are well captured.This work was partially funded by the Spanish Ministerio de Economia y Competitividad within the frame of the CHEST (TRA2017-89139-C2-1-R) project. The computations were partially performed on the Tirant III cluster of the Servei d'Informatica of the University of Valencia (vlc38-FI-2018-2-0006). Authors acknowledge the computer resources at Picasso and the technical support provided by Universidad de Malaga (UMA) (RES-FI-2018-1-0039).Desantes Fernández, JM.; García-Oliver, JM.; Pastor Enguídanos, JM.; Olmeda-Ramiro, I.; Pandal, A.; Naud, B. (2020). LES Eulerian diffuse-interface modeling of fuel dense sprays near- and far-field. International Journal of Multiphase Flow. 127:1-13. https://doi.org/10.1016/j.ijmultiphaseflow.2020.103272S113127Andreini, A., Bianchini, C., Puggelli, S., & Demoulin, F. X. (2016). Development of a turbulent liquid flux model for Eulerian–Eulerian multiphase flow simulations. International Journal of Multiphase Flow, 81, 88-103. doi:10.1016/j.ijmultiphaseflow.2016.02.003Anez, J., Ahmed, A., Hecht, N., Duret, B., Reveillon, J., & Demoulin, F. X. (2019). Eulerian–Lagrangian spray atomization model coupled with interface capturing method for diesel injectors. International Journal of Multiphase Flow, 113, 325-342. doi:10.1016/j.ijmultiphaseflow.2018.10.009Baldwin, E. T., Grover, R. O., Parrish, S. E., Duke, D. J., Matusik, K. E., Powell, C. F., … Schmidt, D. P. (2016). String flash-boiling in gasoline direct injection simulations with transient needle motion. International Journal of Multiphase Flow, 87, 90-101. doi:10.1016/j.ijmultiphaseflow.2016.09.004Bardi, M., Payri, R., Malbec, L. M., Bruneaux, G., Pickett, L. M., Manin, J., … Genzale, C. (2012). ENGINE COMBUSTION NETWORK: COMPARISON OF SPRAY DEVELOPMENT, VAPORIZATION, AND COMBUSTION IN DIFFERENT COMBUSTION VESSELS. Atomization and Sprays, 22(10), 807-842. doi:10.1615/atomizspr.2013005837Battistoni, M., Som, S., & Powell, C. F. (2019). Highly resolved Eulerian simulations of fuel spray transients in single and multi-hole injectors: Nozzle flow and near-exit dynamics. Fuel, 251, 709-729. doi:10.1016/j.fuel.2019.04.076Beheshti, N., Burluka, A. A., & Fairweather, M. (2007). Assessment of Σ−Y liq model predictions for air-assisted atomisation. Theoretical and Computational Fluid Dynamics, 21(5), 381-397. doi:10.1007/s00162-007-0052-3Chesnel, J., Reveillon, J., Menard, T., & Demoulin, F.-X. (2011). LARGE EDDY SIMULATION OF LIQUID JET ATOMIZATION. Atomization and Sprays, 21(9), 711-736. doi:10.1615/atomizspr.2012003740CMT, 2018. Virtual injection rate generator.Crua, C., Heikal, M. R., & Gold, M. R. (2015). Microscopic imaging of the initial stage of diesel spray formation. Fuel, 157, 140-150. doi:10.1016/j.fuel.2015.04.041Crua, C., Manin, J., & Pickett, L. M. (2017). On the transcritical mixing of fuels at diesel engine conditions. Fuel, 208, 535-548. doi:10.1016/j.fuel.2017.06.091Dahms, R. N., Manin, J., Pickett, L. M., & Oefelein, J. C. (2013). Understanding high-pressure gas-liquid interface phenomena in Diesel engines. Proceedings of the Combustion Institute, 34(1), 1667-1675. doi:10.1016/j.proci.2012.06.169Demoulin, F.-X., Beau, P.-A., Blokkeel, G., Mura, A., & Borghi, R. (2007). A NEW MODEL FOR TURBULENT FLOWS WITH LARGE DENSITY FLUCTUATIONS: APPLICATION TO LIQUID ATOMIZATION. Atomization and Sprays, 17(4), 315-345. doi:10.1615/atomizspr.v17.i4.20Demoulin, F.-X., Reveillon, J., Duret, B., Bouali, Z., Desjonqueres, P., & Menard, T. (2013). TOWARD USING DIRECT NUMERICAL SIMULATION TO IMPROVE PRIMARY BREAK-UP MODELING. Atomization and Sprays, 23(11), 957-980. doi:10.1615/atomizspr.2013007439Desantes, J. M., García-Oliver, J. M., Pastor, J. M., Pandal, A., Baldwin, E., & Schmidt, D. P. (2016). Coupled/decoupled spray simulation comparison of the ECN spray a condition with the -Y Eulerian atomization model. International Journal of Multiphase Flow, 80, 89-99. doi:10.1016/j.ijmultiphaseflow.2015.12.002Dukowicz, J. K. (1980). A particle-fluid numerical model for liquid sprays. Journal of Computational Physics, 35(2), 229-253. doi:10.1016/0021-9991(80)90087-xDuret, B., Reveillon, J., Menard, T., & Demoulin, F. X. (2013). Improving primary atomization modeling through DNS of two-phase flows. International Journal of Multiphase Flow, 55, 130-137. doi:10.1016/j.ijmultiphaseflow.2013.05.004ECN, 2014. LVF data archive.ECN, 2018. Engine combustion network data archive.Garcia-Oliver, J. M., Pastor, J. M., Pandal, A., Trask, N., Baldwin, E., & Schmidt, D. P. (2013). DIESEL SPRAY CFD SIMULATIONS BASED ON THE Σ-Υ EULERIAN ATOMIZATION MODEL. Atomization and Sprays, 23(1), 71-95. doi:10.1615/atomizspr.2013007198Gorokhovski, M., & Herrmann, M. (2008). Modeling Primary Atomization. Annual Review of Fluid Mechanics, 40(1), 343-366. doi:10.1146/annurev.fluid.40.111406.102200Hussein, H. J., Capp, S. P., & George, W. K. (1994). Velocity measurements in a high-Reynolds-number, momentum-conserving, axisymmetric, turbulent jet. Journal of Fluid Mechanics, 258, 31-75. doi:10.1017/s002211209400323xIlavsky, J., & Jemian, P. R. (2009). Irena: tool suite for modeling and analysis of small-angle scattering. Journal of Applied Crystallography, 42(2), 347-353. doi:10.1107/s0021889809002222Jasak, H., Weller, H. G., & Gosman, A. D. (1999). High resolution NVD differencing scheme for arbitrarily unstructured meshes. International Journal for Numerical Methods in Fluids, 31(2), 431-449. doi:10.1002/(sici)1097-0363(19990930)31:23.0.co;2-tKastengren, A., Ilavsky, J., Viera, J. P., Payri, R., Duke, D. J., Swantek, A., … Powell, C. F. (2017). Measurements of droplet size in shear-driven atomization using ultra-small angle x-ray scattering. International Journal of Multiphase Flow, 92, 131-139. doi:10.1016/j.ijmultiphaseflow.2017.03.005Kastengren, A. L., Tilocco, F. Z., Powell, C. F., Manin, J., Pickett, L. M., Payri, R., & Bazyn, T. (2012). ENGINE COMBUSTION NETWORK (ECN): MEASUREMENTS OF NOZZLE GEOMETRY AND HYDRAULIC BEHAVIOR. Atomization and Sprays, 22(12), 1011-1052. doi:10.1615/atomizspr.2013006309Kastengren, A. L., Tilocco,F. Z., Duke, D. J., Powell, C. F., Seoksu, M., Xusheng, Z., 2012b. Time-resolved x-ray radiography of diesel injectors from the engine combustion network. ICLASS Paper (1369).Kastengren, A. L., Powell, C. F., Wang, Y., Im, K.-S., & Wang, J. (2009). X-RAY RADIOGRAPHY MEASUREMENTS OF DIESEL SPRAY STRUCTURE AT ENGINE-LIKE AMBIENT DENSITY. Atomization and Sprays, 19(11), 1031-1044. doi:10.1615/atomizspr.v19.i11.30Klein, M., Sadiki, A., & Janicka, J. (2003). A digital filter based generation of inflow data for spatially developing direct numerical or large eddy simulations. Journal of Computational Physics, 186(2), 652-665. doi:10.1016/s0021-9991(03)00090-1Kraichnan, R. H. (1970). Diffusion by a Random Velocity Field. Physics of Fluids, 13(1), 22. doi:10.1063/1.1692799Lacaze, G., Misdariis, A., Ruiz, A., & Oefelein, J. C. (2015). Analysis of high-pressure Diesel fuel injection processes using LES with real-fluid thermodynamics and transport. Proceedings of the Combustion Institute, 35(2), 1603-1611. doi:10.1016/j.proci.2014.06.072Lebas, R., Menard, T., Beau, P. A., Berlemont, A., & Demoulin, F. X. (2009). Numerical simulation of primary break-up and atomization: DNS and modelling study. International Journal of Multiphase Flow, 35(3), 247-260. doi:10.1016/j.ijmultiphaseflow.2008.11.005Ma, P. C., Wu, H., Jaravel, T., Bravo, L., & Ihme, M. (2019). Large-eddy simulations of transcritical injection and auto-ignition using diffuse-interface method and finite-rate chemistry. Proceedings of the Combustion Institute, 37(3), 3303-3310. doi:10.1016/j.proci.2018.05.063Macian, V., Bermudez, V., Payri, R., & Gimeno, J. (2003). NEW TECHNIQUE FOR DETERMINATION OF INTERNAL GEOMETRY OF A DIESEL NOZZLE WITH THE USE OF SILICONE METHODOLOGY. Experimental Techniques, 27(2), 39-43. doi:10.1111/j.1747-1567.2003.tb00107.xManin, J., Bardi, M., Pickett, L. M., & Manin, J. (2012). SP2-4 Evaluation of the liquid length via diffused back-illumination imaging in vaporizing diesel sprays(SP: Spray and Spray Combustion,General Session Papers). The Proceedings of the International symposium on diagnostics and modeling of combustion in internal combustion engines, 2012.8(0), 665-673. doi:10.1299/jmsesdm.2012.8.665Matheis, J., & Hickel, S. (2018). Multi-component vapor-liquid equilibrium model for LES of high-pressure fuel injection and application to ECN Spray A. International Journal of Multiphase Flow, 99, 294-311. doi:10.1016/j.ijmultiphaseflow.2017.11.001Naber, J., Siebers, D., 1996. Effects of gas density and vaporization on penetration and dispersion of diesel sprays. SAE Technical Paper (960034).Nicoud, F., Toda, H. B., Cabrit, O., Bose, S., & Lee, J. (2011). Using singular values to build a subgrid-scale model for large eddy simulations. Physics of Fluids, 23(8), 085106. doi:10.1063/1.3623274Oefelein, J., Dahms, R., & Lacaze, G. (2012). Detailed Modeling and Simulation of High-Pressure Fuel Injection Processes in Diesel Engines. SAE International Journal of Engines, 5(3), 1410-1419. doi:10.4271/2012-01-1258Pandal, A., Pastor, J. M., Payri, R., Kastengren, A., Duke, D., Matusik, K., … Schmidt, D. (2017). Computational and Experimental Investigation of Interfacial Area in Near-Field Diesel Spray Simulation. SAE International Journal of Fuels and Lubricants, 10(2), 423-431. doi:10.4271/2017-01-0859Pandal, A., Payri, R., García-Oliver, J. M., & Pastor, J. M. (2017). Optimization of spray break-up CFD simulations by combining Σ-Y Eulerian atomization model with a response surface methodology under diesel engine-like conditions (ECN Spray A). Computers & Fluids, 156, 9-20. doi:10.1016/j.compfluid.2017.06.022Pastor, J. V., Garcia-Oliver, J. M., Pastor, J. M., & Vera-Tudela, W. (2015). ONE-DIMENSIONAL DIESEL SPRAY MODELING OF MULTICOMPONENT FUELS. Atomization and Sprays, 25(6), 485-517. doi:10.1615/atomizspr.2014010370Pickett, L. M., Manin, J., Genzale, C. L., Siebers, D. L., Musculus, M. P. B., & Idicheria, C. A. (2011). Relationship Between Diesel Fuel Spray Vapor Penetration/Dispersion and Local Fuel Mixture Fraction. SAE International Journal of Engines, 4(1), 764-799. doi:10.4271/2011-01-0686Pickett, L. M., Manin, J., Kastengren, A., & Powell, C. (2014). Comparison of Near-Field Structure and Growth of a Diesel Spray Using Light-Based Optical Microscopy and X-Ray Radiography. SAE International Journal of Engines, 7(2), 1044-1053. doi:10.4271/2014-01-1412Poinsot, T. ., & Lelef, S. . (1992). Boundary conditions for direct simulations of compressible viscous flows. Journal of Computational Physics, 101(1), 104-129. doi:10.1016/0021-9991(92)90046-2Pope, S. B. (2004). Ten questions concerning the large-eddy simulation of turbulent flows. New Journal of Physics, 6, 35-35. doi:10.1088/1367-2630/6/1/035Poursadegh, F., Lacey, J. S., Brear, M. J., & Gordon, R. L. (2017). On the Fuel Spray Transition to Dense Fluid Mixing at Reciprocating Engine Conditions. Energy & Fuels, 31(6), 6445-6454. doi:10.1021/acs.energyfuels.7b00050Ricou, F. P., & Spalding, D. B. (1961). Measurements of entrainment by axisymmetrical turbulent jets. Journal of Fluid Mechanics, 11(1), 21-32. doi:10.1017/s0022112061000834Robert, A., Martinez, L., Tillou, J., & Richard, S. (2013). Eulerian – Eulerian Large Eddy Simulations Applied to Non-Reactive Transient Diesel Sprays. Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles, 69(1), 141-154. doi:10.2516/ogst/2013140Schmidt, D. P., Gopalakrishnan, S., & Jasak, H. (2010). Multi-dimensional simulation of thermal non-equilibrium channel flow. International Journal of Multiphase Flow, 36(4), 284-292. doi:10.1016/j.ijmultiphaseflow.2009.11.012Shin, D., Sandberg, R. D., & Richardson, E. S. (2017). Self-similarity of fluid residence time statistics in a turbulent round jet. Journal of Fluid Mechanics, 823, 1-25. doi:10.1017/jfm.2017.304Shinjo, J., & Umemura, A. (2010). Simulation of liquid jet primary breakup: Dynamics of ligament and droplet formation. International Journal of Multiphase Flow, 36(7), 513-532. doi:10.1016/j.ijmultiphaseflow.2010.03.008Taub, G. N., Lee, H., Balachandar, S., & Sherif, S. A. (2013). A direct numerical simulation study of higher order statistics in a turbulent round jet. Physics of Fluids, 25(11), 115102. doi:10.1063/1.4829045Trask, N., Schmidt, D. P., Lightfoot, M., & Danczyk, S. (2012). Compressible Modeling of the Internal Two-Phase Flow in a Gas-Centered Swirl Coaxial Fuel Injector. Journal of Propulsion and Power, 28(4), 685-693. doi:10.2514/1.b34102Wehrfritz, A., Kaario, O., Vuorinen, V., & Somers, B. (2016). Large Eddy Simulation of n-dodecane spray flames using Flamelet Generated Manifolds. Combustion and Flame, 167, 113-131. doi:10.1016/j.combustflame.2016.02.019Weller, H. G., Tabor, G., Jasak, H., & Fureby, C. (1998). A tensorial approach to computational continuum mechanics using object-oriented techniques. Computers in Physics, 12(6), 620. doi:10.1063/1.168744Xue, Q., Battistoni, M., Powell, C. F., Longman, D. E., Quan, S. P., Pomraning, E., … Som, S. (2015). An Eulerian CFD model and X-ray radiography for coupled nozzle flow and spray in internal combustion engines. International Journal of Multiphase Flow, 70, 77-88. doi:10.1016/j.ijmultiphaseflow.2014.11.012Xue, Q., Som, S., Senecal, P. K., & Pomraning, E. (2013). LARGE EDDY SIMULATION OF FUEL-SPRAY UNDER NON-REACTING IC ENGINE CONDITIONS. Atomization and Sprays, 23(10), 925-955. doi:10.1615/atomizspr.201300832

Machine Learning Methods for Autonomous Flame Detection in Videos

Fire detection has attracted increasing attention from the public because of the huge loss caused by fires every year. Compared with the traditional fire detection techniques based on smoke or heat sensors, the frameworks using machine learning methods in videos for fire detection have the advantages of higher efficiency and accuracy of detection, robustness to various environments, and lower cost of the systems. The uniqueness of these frameworks stems from the developed machine learning approaches for autonomous information extraction and fire detection in sequential video frames. A framework for flame detection is proposed based on the synergy of the Horn-Schunck optical flow estimation method, a probabilistic saliency analysis approach and a temporal wavelet analysis scheme. The estimated optical flows, together with the saliency analysis method, work effectively in selecting moving regions by well describing the dynamic property of flames, which contributes to accurate detection of flames. Additionally, the temporal wavelet transform based analysis increases the robustness of the framework and provides reliable results by discarding non-flame pixels according to their temporally changing patterns. Apart from the dynamic characteristic of flames, the property of colours is also of crucial importance in describing flames. However, the colours of flames usually vary significantly with different illumination or burning material, which results in a wide diversity. To well model the various colours, a novel flame colour model is proposed based on the Dirichlet process Gaussian mixture model. The distribution of flame colours is represented by a Gaussian mixture model, of which the number of mixture components is learned from the training data autonomously by setting a Dirichlet process as the prior. Compared with those methods which set the number of mixture components empirically, the developed model can access a more accurate estimation of the distribution of flame colours. The inference is successfully implemented by two methods, i.e., the Gibbs sampling and variational inference algorithms, to manage different quantities of training data. The colour model can be incorporated into the framework of flame detection and the results show that the colour model achieves a highly accurate estimation of the distribution of flame colours, which contributes to the good performance of the whole framework. All the proposed approaches are tested on real videos of various environments and proved to be capable of accurate flame detection