A system and method for optimizing passive control strategies of oscillatory instabilities in turbulent systems using finite-time Lyapunov exponents are disclosed. The method includes receiving data from one or more measuring devices connected to the turbulent flow system incorporating a control strategy in the flow field. One or more flow characteristics are determined from the data obtained from the measuring devices. The method involves computing critical dynamics from backward time finite-time Lyapunov exponent (FTLE) field based on the one or more flow characteristics. Next, one or more regions of critical dynamics associated with impending oscillatory instabilities in the turbulent flow system are identified. The identified region of critical dynamics is disrupted the control the onset of oscillatory instabilities in the turbulent flow system.

Imaging of complex, non-stationary three dimensional (3D) flow velocities is achieved by encoding depth into color. A flow volume 22 is illuminated with a continuum 40 of light planes 42 whereby each depth corresponds to a respective light plane 14 having a specific wavelength of light. A diffractive component 46 in the camera 24 optics, which records the trajectories of illuminated particles 20 within the flow volume 22, ensures that all light planes 42 are in focus simultaneously. The setup permits a user to track 3D) trajectories of particles 20 within the flow volume 22 by combining two dimensional (2D) spatial and one dimensional (1D) color information. For reconstruction, an image formation model for recovering stationary ID particle positions is provided. 3D velocity estimation is achieved with a variant of a 3D optical flow approach that accounts for both physical constraints as well as the color (rainbow) image formation model.

Apparatus and methods for measuring velocity perturbation in a fluid include one or more sensor vehicle adapted to be deployed in the fluid and a device that obtains position and acceleration data of each sensor vehicle. A physical model of the one or more sensor vehicle behaviour is used to transform the obtained data into a velocity field of the fluid, and output a map of the velocity field in the fluid. Sensor vehicles may be adapted to move passively through the fluid and/or to be transported by the fluid. The apparatus and methods may be used in liquid and gaseous environments, and the velocity field map may be used to track movement of species and particulate matter of interest in the fluid in real time.

A portable electronic device is operable in a particulate matter concentration mode where the portable electronic device uses a self-mixing interferometry sensor to emit a beam of coherent light from an optical resonant cavity, receive a reflection or backscatter of the beam into the optical resonant cavity, produce a self-mixing signal resulting from a reflection or backscatter of the beam of coherent light, and determine a particle velocity and/or particulate matter concentration using the self-mixing signal. The portable electronic device is also operable in an absolute distance mode where the portable electronic device determines whether or not an absolute distance determined using the self-mixing signal is outside or within a particulate sensing volume associated with the beam of coherent light. If not, the portable electronic device may determine a contamination and/or obstruction is present that may result in inaccurate particle velocity and/or particulate matter concentration determination.

Various sensors, including particulate matter sensors, are described. One particulate matter sensor includes a self-mixing interferometry sensor and a set of one or more optical elements. The set of one or more optical elements is positioned to receive an optical emission of the self-mixing interferometry sensor, split the optical emission into multiple beams, and direct each beam of the multiple beams in a different direction. The self-mixing interferometry sensor is configured to generate particle speed information for particles passing through respective measurement regions of the multiple beams.

A wind flow sensing system determines a first approximation of the velocity field at each of the altitudes by simulating computational fluid dynamics (CFD) of the wind flow with operating parameters reducing a cost function of a weighted combination of errors, determines a horizontal derivative of vertical velocity at each of the altitudes from the first approximation of the velocity fields, and determines a second approximation of the velocity fields using geometric relationships between a velocity field for each of the altitudes, projections of the measurements of radial velocities on the three-dimensional axes, and the horizontal derivative of vertical velocity for the corresponding velocity field. In the cost function of the CFD, each error corresponds to one of the altitudes and includes a difference between measured velocities at the line-of-site points at the corresponding altitude and simulated velocities at the line-of-site points simulated by the CFD for the corresponding altitude. At least some errors in the weighted combination have different weights.

Imaging of complex, non-stationary three dimensional (3D) flow velocities is achieved by encoding depth into color. A flow volume 22 is illuminated with a continuum 40 of light planes 42 whereby each depth corresponds to a respective light plane 14 having a specific wavelength of light. A diffractive component 46 in the camera 24 optics, which records the trajectories of illuminated particles 20 within the flow volume 22, ensures that all light planes 42 are in focus simultaneously. The setup permits a user to track 3D trajectories of particles 20 within the flow volume 22 by combining two dimensional (2D) spatial and one dimensional (1D) color information. For reconstruction, an image formation model for recovering stationary 3D particle positions is provided. 3D velocity estimation is achieved with a variant of a 3D optical flow approach that accounts for both physical constraints as well as the color (rainbow) image formation model.

The present invention provides techniques for recovering hydrocarbon fluids in a process flow, including recovering bitumen from a coarse tailings line. The apparatus includes a signal processor that responds to signaling containing information about the presence of a hydrocarbon fluid in a process flow; and determines corresponding signaling to control the diversion of the hydrocarbon fluid from the process flow remaining based on the signaling received. The hydrocarbon fluid may be bitumen, including bitumen flowing in a course tailings line. The signal processor receives the signaling from a velocity profile meter having sensors arranged around a circumference of a process pipe containing information about a fluid flow velocity at various levels or heights within the process pipe, including a wrap-around velocity profile meter having multiple sensing arrays located radially at a top position of 0, a bottom position of 180, and intermediate positions 45, 90 and 135.

A method for predicting arrival of a wind event at an aeromechanical structure includes sensing wind velocity in an atmospheric volume moving towards the aeromechanical structure to obtain a time series of spatially distributed wind velocity measurements, determining presence of the wind event from at least one of the distributed wind velocity measurements, and tracking the wind event based upon the time series of spatially distributed wind velocity measurements to estimate arrival time of the wind event at the aeromechanical structure. A wind-predicting system uses a lidar and a wind-predicting unit to implement this method. An aeromechanical apparatus integrates this wind-predicting system to predict wind events at the aeromechanical apparatus.

Embodiments include apparatus and methods for modeling air flow from flight responses in aerial vehicles. Sensor data is received for aerial vehicles in a geographic area. The pose (e.g., roll, pitch, and yaw) of the aerial vehicles is calculated from the sensor data. One or more wind vectors are calculated based, at least in part, on the pose. An air flow model is generated from the wind vectors.