# Digital Twin in the Maritime Domain: A Review and Emerging Trends
## 4 Present Digital Twin Manipulations in Marine Industry
### Vessel Life Cycle Management  
Cost-saving is one main benefit of using the DT, as Condition-Based Predictive Maintenance (CBPM) is powered extensively with networked sensors, actuators, and control systems that are operated at their optimum condition. Prognostic Health Management (PHM) will be used with the projection of the state of the machinery with accurate data fidelity. It will further provide insight into failure modes, threshold settings, health indicators, and risks. With this analytic data, the operator will obtain the total domain awareness, which can be utilized to operate the physical unit at its prime operating condition or to make amends to optimize the performance. It further improves the predictability and man agement of real-time continuous quality control of marine diesel engine (MDE) critical parts with dynamic quality control starting from the machining stage of the components [59]. The historical data derived from the DT will be used to compute the Remaining Useful Life (RUL) of mission-critical components of the ship's systems. In DT-based CBPM solutions, data fusion [60] is highly important to implement formidable evidence-driven big data sets taken using Bayesian Inference, Demster-Shafer reasoning, etc. [61]. [PERSON] et al. proposed intelligent predictive maintenance using Implicit DT (IDT) formed with intelligent sensing, and reliable simulation data mapping with the historical operation data [62]. Critical components such as the main engine can be digitally modelled as a Cycle Mean Value (CMV) model, allowing the monitoring of the performance in both the steady state and transient response [63].  
With the increasing visibility along with the real-time synching of high-fidelity models, monitoring of the physical system can be easily enhanced. Reduction of the time taken for system testing and development has led to the reduction of time to market by eliminating the risks of design failures. DT can provide a complete user experience even before the actual production. The entire operation of a ship within the total lifespan can be manipulated during the design phase with the support of its DT from a multi-user point of view (Passengers, Crew, Owner Company) [64]. Further, real-time connection ensures the optimum operation of the actual system with the ability to analyze the performance of the physical model [65]. It will further evaluate the existing ship's machinery with the support of machine learning with a quantitative assessment of the degradation of the components. This will result in the intelligent diagnosis by an inferential decision model leading to more accurate predictions [66]. These aspects will further support Structural Health Monitoring (SHM) and parameter trending of critical components of any machinery, aircraft, ship, plant, etc. [67]. This augments the Model-Based System Engineering (MBSE) [68; 69] with available tools for simulation, rehearsals, machine learning applications, etc. Vessel Traffic Service (VTS) is also supported by MBSE-based DT solutions. For example, the Diamond model concept proposed by Boeing Defence Company [70] illustrates the DT-based MBSE.