No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Electrochemical models are essential to solving many problems involving lithium ion batteries. However, they require many parameters, some of which cannot be directly measured and must be inferred by fitting to cell testing data. Battery performance is strongly affected by temperature which is well documented in the literature and can be mathematically represented by the Arrhenius equation. When fitting data to a cell model, the temperature must be constant, known, and uniform, to be mathematically consistent. However, it is impossible to test a cell without causing its surface temperature to vary. This will eventually induce thermal gradients which could affect the cell performance. Despite this, there is a significant amount of literature on fitting test data to isothermal models. It is difficult to quantify the non-uniformity inside the cell, therefore a battery model which accounts for internal and surface region has been adopted. By using this model, the deviation of current between surface and internal region can be up to 4% at the constant surface temperature of 5 degree Celsius. To explore the thermal variation issues, the effect of applying different thermal boundary conditions, using forced air convection and surface cooling plates, was investigated experimentally. This was not to evaluate cooling effectiveness, but rather to show the effect of using data generated with different thermal boundary conditions on parameter estimation when fitting to an isothermal model. When fitting the model to the data, the estimated diffusion coefficient of the positive electrode was four times larger using the data gathered using forced air convection compared to surface cooling at low operating temperature. This was achieved by using a four-point surface cooling rig, which was designed to maintain tabs/surfaces of a pouch cell at the constant temperature, to be as close as experimentally possible to isothermal conditions without interfering with the cell.

The detection and characterisation of corrosion type defects on pipelines is a major challenge for the petrochemical industry, especially in regions with poor accessibility. Guided wave tomography is one feasible approach to inspect areas with restricted access by transmitting guided waves through the area and then processing the measured wave field into a thickness map of the pipeline wall, without having to take measurements at all points on the surface. The key objective of this research project is to develop, implement and test a prototype guided wave tomography system based on the A0 Lamb mode. For the development of a guided wave tomography system a low-frequency, omni-directional A0 Lamb wave ElectroMagnetic Acoustic Transducer (EMAT) is developed, and operates at 0.50MHzmm on a steel plate. For that, a parametric Finite Element (FE) model is implemented in a commercially available FE software and a numerical optimization process employing a genetic algorithm is set up to optimised the EMAT design for an improved A0 mode selectivity. The FE model is validated against measurements on an aluminium plate and on a steel plate. A two-step model-based design approach is proposed whereby only the Lorentz force is used in the first step for the optimisation and then in a second step, a realistic estimate of the mode selectivity can be obtained by additionally considering the magnetisation force. The optimised design fulfils the S0 suppression design requirement and is integrated into the guided wave tomography system consisting of two ring arrays. The developed guided wave tomography system is tested on two steel pipes with smooth and well defined defect. The repeatability of measurements is assessed and the robustness of the guided wave tomography measurements to sensor position errors is investigated. It is demonstrated that there is a small influence on the thickness reconstruction for fairly large systematic and unsystematic position errors. Similar results are obtained for single sensor failures or gaps in the arrays and an increase in sensor spacing is found to increase reconstruction artefacts. With Golay complementary sequences, a signal processing technique is presented that allows for a significant increase in the data capture speed with the same performance as time averaging. Three areas with restricted access, support locations, pipe clamps and STOPAQ(R) coatings, are considered and their influence on the thickness reconstruction is investigated relative to a reference configuration and only a small influence is found in the experiments.

No description available.