An evolutionary optimization approach to prevent electronics burnout in subsea oil and gas equipment

The electronics burnout in subsea engineering equipment caused by the excessive heating of electronics due to improper cooling mechanism is an area of major concern in subsea oil and gas fields. Very often the electronic canisters are encapsulated by insulation to prevent hydrate formation in the subsea completion equipment. The electronic equipment with a set of sensors is usually deployed subsea for live monitoring of data and to regulate the functioning of the equipment. This study presents a numerical methodology to predict and prevent electronics burnout in a pressure/temperature transmitter (PT/TT) that is truly representative of a wide class of PT/TT deployed subsea. An optimization study of the insulation system around the PT/TT sensors that encompasses the various contradicting constraints that are routinely encountered in subsea engineering has been presented for the benefit of the readers. In the present study, the optimal design of the insulation system around the electronics equipment is generated using a combination of thermal finite element analysis and evolutionary optimization algorithms. The results obtained show that the proposed methodology can yield results which could be a tremendous improvement in the traditional means of designing the insulation systems for such electronics equipment. It is also shown that locating the electronic housing far from the production fluid in the PT/TT sensors can lead to proper cooling and thereby avoid the burnout to a significant extent.

Extracting Thin-film Optical Parameters from Spectrophotometric Data by Evolutionary Optimization

Extracting optical parameters from spectrophotometric measurements is a challenging task. In a photometric setup, an unknown thin-film is subjected to an incident light beam for a range of admissible wavelengths, which outputs reflectance and transmittance spectra. The current work attempts to solve an inverse problem of extracting thin-film thickness and complex refractive index from reflectance and transmittance spectra for an incident angle of light. The film thickness is a scalar quantity, and the complex refractive index is composed of real and imaginary parts as functions of wavelengths. We leverage evolutionary optimization techniques to solve the underlying inverse problem, which determines the desired parameters associated with two optical dispersion models: ensemble of Tauc-Lorentz (TL) and ensemble of Gaussian oscillators, such that the generated spectra accurately fit the input data. The optimal parameters involved in the adopted models are determined using efficient evolutionary algorithms (EAs). Numerical results validate the effectiveness of the proposed approach in estimating the optical parameters of interest.