Search results

Develop a method for the optimal placement of sensors in order to detect the largest number of contaminant release scenarios with the minimum amount of sensors.

The method considers the general sensor placement problem. Assuming a given number of sensors, every release scenario leads to a sensor input. The data recorded from all the possible release scenarios at all possible sensor locations allow the identification of the best or optimal sensor locations. Clearly, if only one sensor is to be placed, it should be at the location that recorded the highest number of releases. This argument can be used recursively by removing from further consideration all releases already recorded by sensors previously placed.

The method developed works well. Examples showing the effect of different wind conditions and release locations demonstrate the effectiveness of the procedure.

The method can be used to design sensor systems for cities, subway stations, stadiums, concert halls, high value residential areas, etc.

The method is general, and can be used with other physics‐based models (puff, mass‐conservation, RANS, etc.). The investigation also shows that first‐principles CFD models have matured sufficiently to be run in a timely manner on PCs, opening the way to optimization based on detailed physics.

The combined use of damage criteria, genetic algorithms and advanced CFD solvers provides an effective strategy to identify locations of releases that produce maximum damage. The implementation is simple and does not require any change to flow solvers. A rather general criterion has been formulated to determine the damage inflicted by the intentional or unintentional release of contaminants. Results of two typical cases show that damage can vary considerably as a function of release location, implying that genetic algorithms are perhaps the only techniques suited for this type of optimization problem.

A parallel linelet preconditioner has been implemented to accelerate finite element (FE) solvers for incompressible flows when highly anisotropic meshes are used. The convergence of the standard preconditioned conjugate gradient (PCG) solver that is commonly used to solve the discrete pressure equations, greatly deteriorates due to the presence of highly distorted elements, which are of mandatory use for high Reynolds‐number flows. The linelet preconditioner notably accelerates the convergence rate of the PCG solver in such situations, saving an important amount of CPU time. Unlike other more sophisticated preconditioners, parallelization of the linelet preconditioner is almost straighforward. Numerical examples and some comparisons with other preconditioners are presented to demonstrate the performance of the proposed preconditioner.