Rotor dynamic force coefficients of gas seals strongly depend on the machine operational conditions. These force coefficients influence the overall dynamical response and modal properties of machines, consequently defining the machine vibration levels. Accurate estimations of the rotor dynamic coefficients are required for designing machines with low vibration amplifications and well-defined stability margins throughout the operational range. Experimental methods applied to test benches are used to validate such force coefficients and they normally rely on (i) the quality of the measurements and (ii) the assumption that the mathematical model is able to capture the whole system dynamics. If relevant dynamical contributions in a system are neglected by the mathematical model, the contribution will erroneously be concluded to originate from the seal being tested. The theoretical and experimental investigation in this paper focuses on quantifying and qualifying the effect of neglected system dynamics modelling on the estimation of seals force coefficients and stability margins. The in-situ identification of seal forces shows that the direct stiffness, cross-coupling stiffness, and direct damping coefficient estimations for a gas seal with high preswirl are statistically significantly affected by the baseline model. Nevertheless, the baseline model leads to small deviations of the seal force coefficient estimations. The prediction accuracy of stability margins is found to be more influenced by the baseline model describing the system dynamics than by the deviations between the seal force coefficient estimations.
Due to the harsh weather conditions, severe spatial limitations and extremely high safety requirements, the indoor climate control for offshore oil & gas production platforms is much more challenging than any on-shore situations. For instance, the indoor pressure of man-board quarters should be kept all the way above the ambient pressure according to safety regulations. Meanwhile, the indoor air needs to be regularly changed in order to guarantee the indoor air quality. Both requirements could be possibly achieved by automatically manipulating either the throttle valve located at the terminal of the inlet channel in the considered Heating Ventilation and Air-Condition (HVAC) system, or the pressurization system located inside the inlet channel, or both of them in a coordinated way. A Model-Predictive Control (MPC) solution to control the inlet throttle has been proposed in our previous work. This paper proposes a set of control solutions to regulate the variable speed pressurization fan system such that the energy efficiency of the considered HVAC system can be explicitly considered. A combined feed-forward with a PI-based feedback control solution, and an MPC solution are proposed based on derived simple system models. Some preliminary simulation results show that both control solutions can keep the indoor pressure and the air circulation in a very satisfactory and robust manner, even subject to the presence of severe disturbances.
A coupling between a dynamic mooring solver based on high-order finite element techniques (MooDy) and a radiation-diffraction based hydrodynamic solver (WEC-Sim) is presented. The high-order scheme gives fast convergence resulting in high-resolution simulations at a lower computational cost. The model is compared against a lumped mass mooring code (MoorDyn) that has an existing coupling to WEC-Sim. The two models are compared for a standard test case and the results are similar, giving confidence in the new WEC-Sim-MooDy coupling. Finally, the coupled model is validated using experimental data of a spread moored cylinder with good agreement.
Floating wave energy converters (WECs) operating in the resonance region are strongly affected by non-linearities arising from the interaction between the waves, the WEC motion and the mooring restraints. To compute the restrained WEC motion thus requires a method which readily accounts for these effects. This paper presents a method for coupled mooring analysis using a two-phase Navier-Stokes (VOF-RANS) model and a high-order finite element model of mooring cables. The method is validated against experimental measurements of a cylindrical buoy in regular waves, slack-moored with three catenary mooring cables. There is overall a good agreement between experimental and computational results with respect to buoy motions and mooring forces. Most importantly, the coupled numerical model accurately recreates the strong wave height dependence of the response amplitude operators seen in the experiments.
Cyber-resilience is an increasing concern for autonomous navigation of marine vessels. This paper scrutinizes cyber-resilience properties of marine navigation through a prism with three edges: multiple sensor information fusion, diagnosis of not-normal behaviours, and change detection. It proposes a two-stage estimator for diagnosis and mitigation of sensor signals used for coastal navigation. Developing a Likelihood Field approach, the first stage extracts shoreline features from radar and matches them to the electronic navigation chart. The second stage associates buoy and beacon features from the radar with chart information. Using real data logged at sea tests combined with simulated spoofing, the paper verifies the ability to timely diagnose and isolate an attempt to compromise position measurements. A new approach is suggested for high level processing of received data to evaluate their consistency, which is agnostic to the underlying technology of the individual sensory input. A combined generalized likelihood ratio test using both parametric Gaussian modelling and Kernel Density Estimation is suggested and compared with a detector using only either of two. The paper shows how the detection of deviations from nominal behaviour is possible when the navigation sensor is under attack or defects occur.
The shipping industry is associated with approximately three quarters of all world trade. In recent years, the sustainability of shipping has become a public concern, and various emissions control regulations to reduce pollutants and greenhouse gas (GHG) emissions from ships have been proposed and implemented globally. These regulations aim to drive the shipping industry in a low-carbon and low-pollutant direction by motivating it to switch to more efficient fuel types and reduce energy consumption. At the same time, the cyclical downturn of the world economy and high bunker prices make it necessary and urgent for the shipping industry to operate in a more costeffective way while still satisfying global trade demand. As bunker fuel bunker (e.g., heavy fuel oil (HFO), liquified natural gas (LNG)) consumption is the main source of emissions and bunker fuel costs account for a large proportion of operating costs, shipping companies are making unprecedented efforts to optimize ship energy efficiency. It is widely accepted that the key to improving the energy efficiency of ships is the development of accurate models to predict ship fuel consumption rates under different scenarios. In this study, the ship fuel consumption prediction models presented in the literature (including the academic literature and technical reports, which are a typical type of “grey literature”) are reviewed and compared, and models that optimize ship operations based on fuel consumption prediction results are also presented and discussed. Current research challenges and promising research questions on ship performance monitoring and operational optimization are identified.
Maintaining the condition of a vessel and its equipment guarantees the scheduled completion of voyages and the safety of the crew.
This paper presents condition monitoring techniques for early detection of faults related to piston rings in remote cylinders of two-stroke marine diesel engines. Operational sensor data from the main engine of a container ship are provided by a shipping company.
A graphical approach complimented by correlation heatmaps and feature importance from gradient boosting trees are used for feature selection. Support Vector Machine, Random Forest and Extreme Gradient Boosting Trees are tested for residual generation from the nominal behavior.
The residual time series gives a good indication of the degradation of the system and can be used for alarm raising under strict rules. It is proven that the proposed method could alert the engine crew of a change in the condition of the piston rings much earlier than existing methods.
This study proposes a novel tower damping system to enhance the structural performance of the NREL 5 MW semi-submersible wind turbine under operational and extreme load conditions. Environmental load data from the Norwegian MET center was analyzed to characterize the loading conditions for floating offshore wind turbines (FOWT). The probability density spectrum of sea state data was employed to identify operational load conditions. At the same time, the Inverse First-Order Reliability Method (IFORM) was used to derive the 50-year extreme sea state. Perform a fully coupled Aero-Hydro-Servo-Elastic simulation of the FOWT dynamic model with a damping system using OrcaFlex software. The results reveal that: Under operational sea states, the turbine tower-top displacement was reduced by 60–70%, and acceleration by 30–40%, enhancing tower-top stability. Under extreme loads, tower-top acceleration was reduced by 5–7%, and displacement by 6–8%. Cumulative damage assessments indicate a reduction in fatigue damage of up to 72%, with the effective fatigue life of the tower base extended by 136%. The proposed damping system significantly reduces vibration under fatigue and extreme load conditions.
This study proposes a novel tower damping system to enhance the structural performance of the NREL 5 MW semi-submersible wind turbine under operational and extreme load conditions. Environmental load data from the Norwegian MET center was analyzed to characterize the loading conditions for floating offshore wind turbines (FOWT). The probability density spectrum of sea state data was employed to identify operational load conditions. At the same time, the Inverse First-Order Reliability Method (IFORM) was utilized to derive the 50-year extreme sea state. Perform a fully coupled Aero-Hydro-Servo-Elastic simulation of the FOWT dynamic model with a damping system using OrcaFlex software. The results reveal that: Under operational sea states, the turbine tower-top displacement was reduced by 60–70%, and acceleration by 30–40%, enhancing tower-top stability. Under extreme loads, tower-top acceleration was reduced by 5–7%, and displacement by 6–8%. Cumulative damage assessments indicate a reduction in fatigue damage of up to 72%, with the effective fatigue life of the tower base extended by 136%. The proposed damping system significantly reduces vibration under fatigue and extreme load conditions.
This chapter is about emergent safety hazards in engineering systems. These
hazards are those that emerge from a system without arising from any part of the
system alone, but because of interactions between parts. We distinguish two
approaches to analysing engineering systems: one is to view them as sociotechnical, and the other is to consider them as cyber-physical systems. We
illustrate a great deal of emergent hazardous behaviours and phenomena due to
unknown accident physics, malign actions, chemistry, and biology and due to
deficiencies in managements and organisations. The method that follows the
socio-technical view consists in the representation of a system by sequential
functionally unrelated processes that can in reality influence the performance of each other via sneak paths. The method that follows the cyber-physical systems
view focuses on the analysis of control loops (feedback, feedforward, positive,
and negative) and, especially, interrelated loops. The chapter explores also the
realm of security threats due to malign actions that can trigger safety-threatening events. And finally it gives general guidance for avoiding and eliminating safety hazards when designing engineering systems.