Numerical investigation of interaction between propulsion system behaviour and manoeuvrability of a large containership

In actual operation process of a ship, the engine-propeller-hull is an integrated system with internal coupling effects, and thus there is a close interaction between the diesel engine propulsion system operation conditions and the ship manoeuvring motions. The propulsion system can experience large power fluctuation during manoeuvring, with considerable torque increase with regard to the stabilized value in straight course. However, the diesel engine propulsion system behaviour and ship manoeuvrability are usually studied separately as they are considered to belong to different disciplines. Thus, it is difficult to reflect the actual operating characteristics of the propulsion system and ship manoeuvring motion under coupled conditions in actual operation. To investigate the interaction between the propulsion system behaviour and the manoeuvrability of a large containership, this paper proposed a multi-disciplinary ship mobility model capable of coupling the marine diesel engine model and the ship manoeuvring model. In the engine model, the mean value modelling approach was adopted to simulate the two-stroke marine diesel engine considering the fact that it can capture the performance of the engine sub-systems including scavenging receiver, exhaust gas receiver, turbocharger, etc. In the manoeuvring model, the MMG-based method was used to simulate the ship manoeuvring motion with three degrees-of-freedom. The engine model and manoeuvring model were coupled through the propeller model that transferring propeller speed and torque between the two models. The coupled model was validated against the engine shop test data and the sea trial results. By applying this coupled model, a series of simulations of turning circle manoeuvres under various scenarios were performed. The simulation results presented the dynamic response of engine internal sub-systems during turning circle manoeuvring, explained the effect of the torque limiter on engine performance and ship manoeuvring motion, and analyse the influence of different propulsion system control strategies on the ship turning circle manoeuvrability. Although the presented case study has been validated on a specific ship, most of the discussed models have a general application.

Accurate parametric sensitivity of stochastic biochemical systems

Computational and Systems Biology are experiencing a rapid development in recent years. Mathematical and computational modelling are critical tools for studying cellular dynamics. Molecular interactions in a cell may display significant random fluctuations when some key species have low amounts (RNA, DNA), making the traditional model of the deterministic reaction rate equations insufficient. Consequently, stochastic models are required to accurately represent the biochemical system behaviour. Nonetheless, stochastic models are more challenging to simulate and analyse than the deterministic ones. Parametric sensitivity is a powerful tool for exploring the system behaviour, such as system robustness with respect to perturbations in its parameters. We present an accurate method for estimating parametric sensitivities for stochastic discrete models of biochemical systems using a high order Coupled Finite Difference scheme and illustrate its advantages compared to the existing techniques

Accurate parametric sensitivity of stochastic biochemical systems

Computational and Systems Biology are experiencing a rapid development in recent years. Mathematical and computational modelling are critical tools for studying cellular dynamics. Molecular interactions in a cell may display significant random fluctuations when some key species have low amounts (RNA, DNA), making the traditional model of the deterministic reaction rate equations insufficient. Consequently, stochastic models are required to accurately represent the biochemical system behaviour. Nonetheless, stochastic models are more challenging to simulate and analyse than the deterministic ones. Parametric sensitivity is a powerful tool for exploring the system behaviour, such as system robustness with respect to perturbations in its parameters. We present an accurate method for estimating parametric sensitivities for stochastic discrete models of biochemical systems using a high order Coupled Finite Difference scheme and illustrate its advantages compared to the existing techniques

Sensitivity Study of Greitzer Model Based on Physical System Parameters of Radial Compressing Units

Centrifugal compressors are key elements of energy systems and industrial installations including fluid flow. Their operating range is strictly limited by the surge phenomenon. The Greitzer model is a known way of simulating the compressor’s behaviour at the surge. In this paper, the parametric study of different versions of the Greitzer model is conducted. There are several versions of this model that include 4 to 2 equation models. The system behaviour depends on the features of the compressor itself as well as of the plenum. In this paper, all terms connected with the compressor were grouped into the “Co” parameter, while those associated with the plenum were grouped into the “Pl” parameter. The study shows how each component influences the system stability. The comparison of analytical data with experimental results allowed to draw conclusions regarding the way of choosing the model parameters that provide the best simulation of the real system behaviour. The study shows that the system is well simulated by a model with relatively large values of the Lc parameter. The length of the compressor parameter Lc=3.57 m was performing well for the machine with impeller radius 0.33 m. Possible explanations of this finding are presented and compared to the state of the art. This result may provide possible help in adjusting the model parameters for other machines and designing reliable anti-surge systems based on the Greitzer model suited to energy conversion systems.

Methodical support for investigation of system behaviour by means of analysis techniques—overcoming non-transparency in embodiment design

One challenge in adaptive design of technical systems is insufficient understanding of the mechanical system behavior. The actual system behaviour often differs from the system behaviour expected by the designer. This is due, for example, to influences from manufacturing, wear, or errors in the designer’s understanding. For the analysis of the differences between expected and actual system behavior, the system behaviour can be observed. Special analysis techniques are often necessary for system observation. However, the missing methodical support in the system-specific use of analysis techniques is a challenge. In this contribution, a methodical support for the selection and adaptation of analysis techniques for system observation is developed. For this, known errors that occur during system observation are operationalised and provided as requirements for evaluation of analysis techniques. The support is provided as a Selection Matrix, in which the evaluated analysis techniques can be selected and adapted. This is evaluated considering an accompanying application to the non-transparent system wood screw connection. By using the analysis techniques selected with the method, it was possible to identify the actual system behaviour and gain new insights. Here, the Selection Matrix provided support through a structured evaluation of analysis techniques. The Selection Matrix also supported the adaptation of analysis techniques for improved observation of the system behaviour. No general statements on the quality of the support by the Selection Matrix are yet possible. Also, the operationalisation of the errors should be improved to reduce subjective influences. Therefore, these topics should be investigated in further studies.