Inversion Algorithm of Fiber Bragg Grating for Nanofluid Flooding Monitoring In the current study, we developed an adaptive algorithm that can predict oil mobilization in a porous medium on the basis of optical data. Associated mechanisms based on tuning the electromagnetic response of magnetic and dielectric nanoparticles are also discussed. This technique is a promising method in rational magnetophoresis toward fluid mobility via fiber Bragg grating (FBG). The obtained wavelength shift due to Fe3O4 injection was 75% higher than that of dielectric materials. This use of FBG magnetooptic sensors could be a remarkable breakthrough for fluidflow tracking in oil reservoirs. Our computational algorithm, based on piecewise linear polynomials, was evaluated with an analytical technique for homogeneous cases and achieved 99.45% accuracy. Theoretical values obtained via coupledmode theory agreed with our FBG experiment data of at a level of 95.23% accuracy.

Evaluation of Silica Nanofluids in Static and Dynamic Conditions by an Optical Fiber Sensor This work presents an optical fiber dynamic light scattering sensor capable of simultaneously assessing concentration and flow speed of nanofluids. Silica nanoparticles (189 nm) in water were tested, yielding a sensitivity of 0.78288 10xc2xb3 sxe2x88x921 for static conditions. Then, the sensor was submitted to situations that simulate spatial concentration changes, offering better results than those obtained by traditional mathematical models. Finally, in flow tests, the light backscattered by the nanoparticles were collected by a fiber probe placed parallel to the streamline, whereas intensity values were processed by artificial neural networks. The sensor provides average errors of 0.09 wt% and 0.26 cms for concentration and speed measurements, respectively, and can be further applied to assess different types of nanofluids and inline processes.