Tension-Leg Platform (TLP) is a vertically moored floating structure. The platform is permanently moored by tendons. Surge equation of motion of TLP is highly nonlinear because of large displacement and it should be solved with perturbation parameter in time domain. This paper compare the dynamic motion responses of a TLP in regular sea waves obtained by applying three method in time domain using MATLAB soft ware. In this paper Lindstedt-Poincare method (L-P method) is used to solve nonlinear differential equation of surge motion considering first-order perturbation. Also modified Euler method (MEM) is used for solving nonlinear equation of motion as numerical method and ordinary differential equation is used for linear equation of motion (without nonlinear term). The results were obtained as responses represent good accordance between results of L-P method and MEM.

Tension Leg Platform (TLP) is a comparative platform used for extracting petroleum in deep waters. It is necessary to study Caspian Sea's TLPs in deep water conditions due to its deep oil-rich areas. One of the most important parameters in TLP design is to calculate horizontal, vertical and angular relocations of platform. Relocations have enormous effect on tendon stresses. Since the main characteristic of TLP is to restrain these platforms by tendons, it is important to calculate these relocations and monitor the tension of tendons in all steps of design and production. By considering the conditions of the Caspian Sea’s waves on the platform modeled by ABAQUS software, the wave force have been calculated by Morison’s equation and the platform’s response had been studied for surge, sway and heave motions and roll, pitch and yaw angular motions by dynamic and static analyses. By changing the angle of incident wave and also platform’s loading, different responses of structure have been shown. The ideal of this research is analyses dynamic and static for a kind of this platform in deep water of Caspian Sea and show the final result of the platform’s response in different motions regarding to hydrodynamic calculations of platform.

Monopile platforms are rather slim structures which are sensitive to wave loadings and the natural period of them are within the range of sea waves' periods. Therefore, suitable dynamic analysis and wave modeling for these platforms is necessary. There are two methods for analyzing such flexible structures under wave loading, i.e., deterministic and random methods. In deterministic method, a regular wave representing the sea state is used while random analysis considers irregular wave including spectral and time history analysis. A comprehensive research has been carried out by applying the above mentioned methods for several monopile platforms to evaluate the validity of these methods. Results show that time history random method has the most reliable responses because of accurate modeling of waves and considering nonlinearities in the analysis. However, spectral method accuracy is acceptable if inertia term of wave loading is dominated and natural period of platform is far from wave period. Deterministic method can be used when platform has high rigidity.      

Hydrodynamic forces generated from waves and currents on slender cylinders are of great concern in designing offshore structures. Numerous analytical and experimental methods such as the Morison equation have been suggested to estimate the wave forces on slender structures. However, such methods have shown some inaccuracy in their estimations. In recent years, artificial neural networks have received a great deal of attention and have been used effectively in many of engineering subjects. In this paper, artificial neural networks have been used to predict waves and current forces on slender cylinders. The data generated from laboratory experiments were used to train and evaluate the prediction performance of the artificial neural networks used in this paper. A number of neural networks were designed, trained and evaluated. Results indicated the success of application of neural networks approach which can efficiently predict the waves and current forces on slender cylinders after conducting appropriate training. Furthermore, the results are quite acceptable in comparison with test results and better than the results obtained from other methods such as the Morison equation.