On-board System Research Articles

Techno-Economic Evaluation of Direct Low-Pressure Selective Catalytic Reduction for Boil-Off Gas Treatment Systems of NH3-Fueled Ships

This study proposes a feasible solution for boil-off gas (BOG) treatment to facilitate NH3 fuel use by ocean-going ships, which is currently considered an alternative fuel for ships. Two systems were designed and analyzed for BOG in IMO Type-A NH3 fuel storage tanks for 14,000 TEU container ships. First, BOG lost inside the storage tank minimized economic losses through the onboard re-liquefaction system. The total energy consumed by the system to process NH3 gas generated in the fuel tank at 232.4 kg/h was 51.9 kW, and the specific energy consumption (SEC) was 0.223 kWh/kg. Second, NH3 was supplied to the direct Low-Pressure Selective Catalytic Reduction (LP-SCR) system to treat marine pollutants generated by combustion engines. The feasible design point was determined by calculating the NH3 feed flow rate using three methodologies. The energy consumed by the direct LP-SCR system was 3.89 and 2.39 kW, and the SEC was 0.0144 at 0.0167 kWh/kg at 100% and 25% load, respectively. The feasibility was indicated via economic analysis. Depending on the life cycle cost, the competitiveness of the re-liquefaction system depends on the price of NH3, where a higher price yields a more economical solution. In conclusion, the direct LP-SCR system has a low overall cost because of its low energy consumption when supplying NH3 and its reduced amount of core equipment.

Dispersion and migration characteristics of multisource respirable dust in development panels during tunnelling processes

Underground miners in Australia are facing continued threats from dust-related diseases. To address these issues, improved knowledge of airflow migration patterns and respirable dust dispersion characteristics within a continuous-miner-driven heading under an exhausting ventilation system is required. Based on site-specific conditions of a development heading in New South Wales, a three-dimensional computational fluid dynamics (CFD) model was constructed and validated with onsite dust monitoring data, where a good agreement was achieved. Three scenarios of coal cutting at the middle, floor and roof positions were considered and simulated, with dust generated at four different sources. The simulation results indicated that the operators on the left-hand-side (LHS) with the extraction duct should be equipped with fit-for-purpose personal protective equipment and stay behind the ventilation duct inlet during coal cutting process, while miners standing at the right-hand-side (RHS) of the continuous miner for roof and rib bolting and machine operating should stay immediately behind the roof and rib bolting rig where dust concentration is relatively low. In addition, an increase in airflow rate through the exhausting ventilation duct or a reduction in the distance from the duct inlet to the heading face assisted in reducing dust levels within the heading, particularly at the LHS of the continuous miners. Finally, compared to the scenario of the current ventilation scheme, an on-board exhausting ventilation system could improve dust removal performance, with dust concentration at the breathing level reducing by approximately 43.6%. This modelling study can advance the understanding of multi-source dust diffusion characteristics in the heading face and provide guidance on dust mitigation, thus improving the health and safety of miners and creating a cleaner underground working environment.

Assessment of an auto-routing method for topology generation of aircraft power supply systems

AbstractDuring early phases of aircraft development, overall systems design is performed to estimate relevant system design parameters such as mass, power consumption, and compliance with architectural requirements (redundancy). The GeneSys software framework has been developed for parametric modeling of aircraft on-board systems for overall systems design. To this end, model-based architecture definition, topology generation (components positioning and connections routing), and system sizing are conducted. The topology generation is currently performed using design templates containing knowledge-based heuristics derived from systems topologies of existing aircraft. However, using such design templates limits their generic application to novel aircraft models because exceptions for geometric differences between these models need to be implemented. This especially applies to the topology generation of power supply systems (electric, hydraulic) due to their high number of interfaces to consumer systems. To overcome these limitations, an automated routing method is presented in this paper as a generic approach for topology generation. With this method, the system topology is generated based on a defined routing network, which comprises areas of the aircraft for allowed components positioning and connections routing. Using Dijkstra’s algorithm, the shortest path connections are found within the established routing network. Furthermore, boundary conditions for routing, for instance adding a cost function for power segregation, can be defined. An assessment of the automated routing method applied on the electric power supply system is performed using qualitative parameters such as the capability to adapt the routing network. Quantitative parameters like cable length and system mass are assessed as well.

Аналіз точності самоналаштування динамічної моделі газотурбінного двигуна

One of the most promising directions in the development of aircraft engines is related to the introduction of adaptive automatic control systems (ACS). The defining element of these systems are dynamic mathematical models of engines capable of self-adjustment based on engine operating parameters measured in flight. A number of leading researchers have developed a concept of using such models called STORM (Self-Tuned On-board Real-time Model). However, in the corresponding works, clearly insufficient attention is paid to solving the problem of checking the sufficiency of the information used to ensure the necessary accuracy of the models. This check must be performed a priori (to predict the composition of engine operating modes, and the volume of registered information), as well as posteriori. The subject of this study is the process of forming dynamic mathematical models (MM) of gas turbine engines using real data for the subsequent use of these models to solve problems related to the control and diagnostics of on-board systems. The goal of this study is to determine the dependence of estimation errors of dynamic parameters of mathematical models on influencing factors. Tasks considered in the work: forming the structure of a mathematical model, dividing the identification process into stages according to the structure of the model (estimating the parameters that determine the static and dynamic properties of the object), forming the least-square functional for the assessment task, determining the errors in estimating dynamic coefficients, analyzing influencing factors, and determining dependencies between factors and errors. For this purpose, the methods of the theory of air-jet engines, the theory of linear dynamic systems, and statistical evaluation are used. The following results were obtained: a mathematical model of a turboshaft engine with a reciprocating gas generator was formed, and ratios were obtained that allow determining the errors in estimating the time constant of a reciprocating engine or gas generator. Scientific and practical innovation: for the first time, a ratio was obtained that determines the errors in estimating the time constant based on the specified values of the measurement errors, the intensity of the jump-like change in fuel consumption, and the frequency and duration of observation. These relations are presented in dimensionless coordinates, which makes them universal and able to be applied to any single-stroke turbojet engine or single-stroke gas generator during a priori or a posteriori analysis of results, as well as planning experiments and debugging on-board self-tuning algorithms of models.

Novel virtual sensors development based on machine learning combined with convolutional neural-network image processing-translation for feedback control systems of internal combustion engines

Physical sensors are commonly used to record performance data of internal combustion engines (ICEs) for online feedback control and calibration, but they are prone to diagnostic and increased development costs. Lookup tables are commonly used in conventional calibration and feedback control; however, the table parameters increase with the advancement of ICE technologies under transient operations. Consequently, the calibration and control systems are time-consuming. This work proposes novel virtual sensors to address these issues by predicting the combustion, performance, and emission of ICEs using neural networks and image processing/translation. The novel sensors are targeted for onboard feedback control systems under transient driving. Firstly, a virtual diesel engine (VDE) was developed and calibrated against experimental data taken from a production 2.2 L turbocharged diesel engine. The VDE was calibrated under WLTC, JC08, and NEDC transient operations and was used to generate teaching data. Next, the virtual sensors are developed using five machine learning (ML) regressors. The result shows that the coefficient of determination R2 from all ML regressors exceeded 0.94, and the XG-Boost outperforms other ML techniques with R2 > 0.977. XG-Boost parameter estimations were 8 times faster than that on a desktop simulation. Then, an image classification model using a deep convolutional neural network (D-CNN) is constructed, and the dependency of performance parameters and exhaust emissions with the rate of heat release (R.H.R) and in-cylinder pressure profile is confirmed. The performance parameters and emissions dependency was compared individually with R.H.R. and the in-cylinder pressure profile. As a result, a strong correlation between the performance and R.H.R. was observed. Finally, a generative adversarial network (GAN) model was constructed to translate the in-cylinder pressure profile to R.H.R. profile. A novel method to develop virtual sensors for advanced feedback control of any type of ICEs is proposed for the first time.

A framework for collaborative multi-robot mapping using spectral graph wavelets

The exploration of large-scale unknown environments can benefit from the deployment of multiple robots for collaborative mapping. Each robot explores a section of the environment and communicates onboard pose estimates and maps to a central server to build an optimized global multi-robot map. Naturally, inconsistencies can arise between onboard and server estimates due to onboard odometry drift, failures, or degeneracies. The mapping server can correct and overcome such failure cases using computationally expensive operations such as inter-robot loop closure detection and multi-modal mapping. However, the individual robots do not benefit from the collaborative map if the mapping server provides no feedback. Although server updates from the multi-robot map can greatly alleviate the robotic mission strategically, most existing work lacks them, due to their associated computational and bandwidth-related costs. Motivated by this challenge, this paper proposes a novel collaborative mapping framework that enables global mapping consistency among robots and the mapping server. In particular, we propose graph spectral analysis, at different spatial scales, to detect structural differences between robot and server graphs, and to generate necessary constraints for the individual robot pose graphs. Our approach specifically finds the nodes that correspond to the drift’s origin rather than the nodes where the error becomes too large. We thoroughly analyze and validate our proposed framework using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90% and can recover the onboard estimation from localization failures and even from the degeneracies within its estimation.

Active control method for fluid borne noise in aerospace fluid systems of variable operation statuses

In the fluid system of aerospace vehicles, the fluid-borne noise (FBN) may induce vibration and damage to components. The FBN may limit the performance of the entire onboard system. To achieve a stable active elimination for the FBN is not easy, for many variable operation statuses exist in practical systems, such as varying-frequency and varying-load status. In this work, an improved feed-forward active control method based on narrowband adaptive notch filters, with an online identification module, is proposed. The algorithm includes three adaptive filters: an adaptive notch filter for producing cancelling signal by using filtered-X least mean square (FXLMS) algorithm, one for online identification to model the real-time secondary path, and an error prediction filter for extracting the precise error for online identification. In the aircraft hydraulic system of high pressure, a so-called relief scheme, also known as the bypass structure, is used for the FNB, controlling an active control valve in the branched line to overflow the main fluid line. The system is modelled with software AMESim for the fluid part and Matlab for the control algorithm. Simulation results show that the control method can performance well in the situations of varying frequency and load. The test rig of a hydraulic system is established, which could change pump speed and fluid pressure. The test results show that more than 90% reduction for a certain frequency can be achieved for stable pressure statuses. And more than 50% reduction is achieved for varying frequency with step changes. As for the load-varying status, the reduction of 82.7% and 65.8% are obtained before and after pressure change. The method proposed is validated to be effective with both numerical and experimental tests.

Measurement Techniques, Calculation Methods, and Reduction Measures for Greenhouse Gas Emissions in Inland Navigation—A Preliminary Study

Emissions originating from inland navigation should be reduced to achieve climate targets. This paper aims to identify (1) onboard GHG emission measurement systems, (2) calculation methods for GHG emissions of inland vessels and (3) reduction measures. A systematic literature review, examining 6 databases, yielded 105 initial outcomes, with 17 relevant references. The review reveals a scarcity of studies, with the majority concentrated in Europe and Asia, while North America, Africa, Australia, and South America remain largely unexplored. Four of the seventeen relevant studies focused on real-world GHG emissions measurement. Future research should explore more efficient and calibrated approaches for real-time CO2 insights in inland vessels. In the section on calculating GHG emissions, most papers attempt to adapt the EEDI or EEXI to inland navigation. Reduction measures for GHG emissions concentrate on alternative fuels, like LNG, methanol, hydrogen, or alternative power sources. As the research in this area is limited, prioritizing it in academic discourse is not only essential for advancing our understanding but also imperative for shaping a resilient and environmentally conscious future for inland navigation.

Image quality characterization of an ultra-high-speed kilovoltage cone-beam computed tomography imaging system on an O-ring linear accelerator.

The quality of on-board imaging systems, including cone-beam computed tomography (CBCT), plays a vital role in image-guided radiation therapy (IGRT) and adaptive radiotherapy. Recently, there has been an upgrade of the CBCT systems fused in the O-ring linear accelerators called HyperSight, featuring a high imaging performance. As the characterization of a new imaging system is essential, we evaluated the image quality of the HyperSight system by comparing it with Halcyon 3.0 CBCT and providing benchmark data for routine imaging quality assurance. The HyperSight features ultra-fast scan time, a larger kilovoltage (kV) detector, a more substantial kV tube, and an advanced reconstruction algorithm. Imaging protocols in the two modes of operation, treatment mode with IGRT and the CBCT for planning(CBCTp) mode were evaluated and compared with Halcyon 3.0 CBCT. Image quality metrics, including spatial resolution, contrast resolution, uniformity, noise, computed tomography (CT) number linearity, and calibration error, were assessed using a Catphan and an electron density phantom and analyzed with TotalQA software. HyperSight demonstrated substantial improvements in contrast-to-noise ratio and noise in both IGRT and CBCTp modes compared to Halcyon 3.0 CBCT. CT number calibration error of HyperSight CBCTp mode (1.06%) closely matches that of a full CT scanner (0.72%), making it suitable for adaptive planning. In addition, the advanced hardware of HyperSight, such as ultra-fast scan time (5.9s) or 2.5 times larger heat unit capacity, enhanced the clinical efficiency in our experience. HyperSight represented a significant advancement in CBCT imaging. With its image quality, CT number accuracy, and ultra-fast scans, HyperSight has a potential to transform patient care and treatment outcomes. The enhanced scan speed and image quality of HyperSight are expected to significantly improve the quality and efficiency of treatment, particularly benefiting patients.

WAYS TO REDUCE THE IMPACT OF PULSATIONS IN LOCAL POWER SUPPLY ROBOTIC SYSTEMS

An analysis of the changes in the flow of pulsation transforming the energy-dynamic indicators of on-board power electronics systems has shown that there are several options for solving this problem.
 The article outlines the main options for the current problem of pulsation in the energy systems of autonomous electrical systems, such as UAVs, robotic complexes, and other systems. The effectiveness of changes in pulsation is examined, in addition to directly assessing the effectiveness of stagnation strategies and methods. Also in the statistics, the optimal technical solutions for specific operating minds were identified.
 The prospects and future directives have been predicted based on the proposals for further research in the sphere of changes in pulsation in local life systems.
 In the future, it is predicted and supported by the development of technology and innovation in this country.
 To sum up, we can say that the statistics have analyzed the changes in pulsation in robotic local power supply systems, which is one of the key directions in the field of electrical engineering and electronics. Increased attention to this problem is due to the need for assured stability, reliability and greater energy efficiency of electronic systems. Research in this area is aimed at developing more comprehensive methods and strategies for reducing noise and pulsation in electrical systems.
 Assessing the effectiveness of stagnant methods, promoting the development of new technologies and innovation, as well as developing promising directions in this field opens the way to creating more stable and adaptive living systems. Replacement of advanced electronic filters, voltage stabilizers, selection of new materials and components, integration of intelligent energy management systems and optimization of essential energy resources local power supply systems – the whole purpose is to directly conduct further investigations.
 The growing demand for stable and efficient electronic systems is driven by current research and the introduction of new technologies to change pulsation. Technological developments and scientific research in this area open up broad prospects for reducing the power supply and ensuring the stability of the functioning of modern electronic devices.

Ariane 6 – in-flight safety and space debris mitigation

Ariane 6 is the future large European Launcher launched from the Guiana Space Center (CSG). Besides, Ariane 6 launcher program corresponds to the first full application of the French Space Operations Act from the beginning of an European Launch System development.This paper summarizes how French Space Operations Act requirements have been finally implemented in the Ariane 6 Launcher System. It addresses hardware systems as well as software solutions, but also specific analysis performed to support compliance with the French Space Operations Act requirements. This paper identifies the definition and design of these different on-board systems contributing to flight safety and space debris mitigation along with their validation. Moreover it details the studies that have been performed to assess Ariane 6 compliance to the requirements of French Space Operations Act.This document addresses more specifically safety in flight beginning at lift-off and ending with launcher disposal after its main mission. Afterwards it covers also solutions implemented on Ariane 6 launcher and its mission definition in order to mitigate space debris generation.

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

The automobile industry has a growing need for reliable and safe health monitoring systems equipped with low-cost sensor networks and intelligent algorithms. This paper provides an overview of approaches already exist, used in on-board health monitoring systems for vehicles. It focuses on the methodologies, theories, and applications employed in the data measurement and data analysis systems of vehicle (cars) on-board health monitoring systems. A fault detection and diagnosis system, which is accurate, plays a vital role in ensuring the safety of autonomous vehicles by preventing potentially dangerous situations. This study focuses on emphasizing a fault diagnosis system that utilizes hybrid methods. Among the various options considered in this analysis, internal sensors emerge as the preferred choice due to their numerous benefits, including affordability, durability, widespread availability, ease of access, and low energy consumption. Model-based methods require various techniques that may introduce errors to estimation results, while signal-based methods necessitate a time-consuming process of including all possible conditions in a pre-built database. Based on this review, future development trends in designing new low-cost health monitoring systems for vehicles are also discussed.

Operating modes of grid integrated PV-solar based electric vehicle charging system- a comprehensive review

The impact of air pollution on the world has been highlighted by the World Health Organization (WHO), which has contributed to the growth of emission-free and ecologically friendly transportation options such as electric vehicles (EVs). Between 2019 and 2020, EV sales climbed by 43%, and by 2025, 14 million EVs are expected to be sold worldwide. On the other hand, EVs have drawbacks such as expensive batteries, short lifespans, lack of EV dependability, limited driving range, and long charging periods. Integrating renewable energy sources (RESs) such as biomass, solar, and wind power into EV charging infrastructures is gaining popularity. PV solar-powered EV charging has benefits like cheaper fuel costs, easier installation, less demand on the grid for power, and cost savings. Hybrid and on-board charging systems offer benefits such as reduced weight, faster charging, and improved communication protocols. On-board chargers have higher energy transfer but are more expensive and difficult to integrate with charging stations. Off-board charging systems include public, rapid, induction, and home charging stations. Advanced charging management systems can optimize charging to preserve battery life. It is recommended that future research concentrate on the development of sophisticated control algorithms, integration of wireless charging, extension of fast-charging infrastructure, innovation in materials, compatibility with many vehicles, and optimization of grid interface to enhance efficiency and user experience. The charging of electric vehicles in standalone and grid-connected photovoltaic systems is covered in this paper, along with an explanation of the various modes of operation for these systems. There is also a quick explanation of the various kinds of EV chargers, their power capacities, and their specifications. The research and comparisons in this paper are expected to be useful resources for scholars who are interested in learning more about EV charging facilities.

A context-aware on-board intrusion detection system for smart vehicles

Modern vehicles are becoming more appealing to potential intruders due to two primary reasons. Firstly, they are now equipped with various connectivity features like WiFi, Bluetooth, and cellular connections, e.g., LTE and 5G, which expose them to external networks. Secondly, the growing complexity of on-board software increases the potential attack surface. In this article, we introduce CAHOOTv2, a context-sensitive intrusion detection system (IDS), aiming at enhancing the vehicle’s security and protect against potential intrusions. CAHOOTv2 leverages the vehicle’s sensors data, such as the amount of steering, the acceleration and brake inputs, to analyze driver habits and collect environmental information. To demonstrate the validity of the algorithm, we collected driving data from both an artificial intelligence (AI) and 39 humans. We include the AI driver to demonstrate that CAHOOTv2 is able to detect intrusions when the driver is both a human or an AI. The dataset is obtained using a modified version of the MetaDrive simulator, taking into account the presence of an intruder capable of performing the following types of intrusions: denial of service, replay, spoofing, additive and selective attacks. The sensors present in the vehicle are a numerical representation of the environment. The amount of steering, the acceleration and brake inputs given by the driver are based on the environmental situation. The intruder’s input often contradicts the driver’s wishes. CAHOOTv2 uses vehicle sensors to detect this contradiction. We perform several experiments that show the benefits of hyperparameter optimization. Indeed, we use a hyperparameter tuning paradigm to increase detection accuracy combining randomized and exhaustive search of hyperparameters. As a concluding remark, the results of CAHOOTv2 show great promise in detecting intrusions effectively.

Анализ электромагнитных процессов блока возбуждения трехкаскадного стартер-генератора для авиационной энергосистемы

This article discusses the excitation system of a three-stage synchronous generator for aviation use in two distinctive modes of operation: as an electric power generator for an on-board power supply system and an electric starter for gas turbine engines of the aircraft. The article presents the calculation of the starter and generator operating modes of the excitation system. The basic design relations for the semiconductor converter circuit have been obtained and confirmed by simulation modeling in the PowerSIM environment. The problem of synthesizing an automatic control system for the TSG excitation unit operating as part of an on-board power supply system using a method for calculating the parameters of regulators based on the motion separation method has been solved. A two-circuit control system has been created for the generator mode of operation, including PI and PID regulators with output voltage regulation of the main generating stage. To control the excitation unit in the electric star¬ter mode of operation, a single-circuit control system for the current of the excitation winding with the addition of a resonant component has been designed. The evaluation of the efficiency of the designed control systems was carried out on the basis of simulation modeling using PowerSIM and MATLAB/Simulink application software packages for a generator feeding a three-phase AC network in accordance with the requirements of GOST R 54073-2017.The results of mathematical modeling formed the basis for the design of an experimental sample of the power unit in a hybrid integrated design for the TSG voltage regulation system.

Characterization of hydrogen refueling temperature rise of the on-board hydrogen system under different failure modes

Fuel cell vehicles (FCVs) have the advantages of pollution-free, and excellent fuel economy. However, rapid hydrogen filling will cause the temperature inside the hydrogen cylinder to rise rapidly. The failure of some components of the on-board hydrogen system (OBHS) will increase the accident rate of FCVs during rapid refueling. Valves and pipelines are ignored during the hydrogen filling mode establishment, thereinto the automatic tank valve and hydrogen dispenser are prone to failure due to loose parts, sensor failure, and other problems. In this paper, a two-dimensional (2D) planar computational fluid dynamics (CFD) model is established to simulate and analyze three kinds of failures, such as hydrogen filling blockage, excessive hydrogen filling, and excessive hydrogen flow, to study the temperature rise characteristics of the OBHS. The results show that the failures of excessive hydrogen filling and excessive hydrogen flow are more likely to cause the final temperature of the hydrogen cylinders to exceed 358 K. The maximum temperature of the OBHS caused by excessive hydrogen flow rate is 5.7 % (21 K) and 0.8 % (3 K) higher than hydrogen filling blockage and excessive hydrogen filling, respectively. This indicates that, contrary to what other studies have not found, the hydrogen filling flow has a greater impact on the temperature rise of the hydrogen cylinder than the hydrogen filling pressure and inlet pipe diameter.

Resilience evaluation of train control on-board system based on multi-dimensional continuous-time Bayesian network

The Bayesian network belongs to a commonly used model in the resilience research of Train Control on Board System (TCOBS). In the previous works, component failure is only considered to be affected by time, without showing the trend of resilience changing with time, and less attention paid to whether it can effectively recover to a usable state after failure. In this paper, we propose a multi-dimensional continuous-time Bayesian network to evaluate TCOBS resilience. Firstly, based on multi-dimensional influencing factors, the quantitative function of TCOBS component resilience is constructed. Secondly, the unit step function and impulse function are used to construct a multi-dimensional continuous-time Bayesian network, and the TCOBS resilience is derived and evaluated. A real-world case study is conducted by using the Chinese Train Control System-3 On-Board System as the background, two comparative experiments are conducted. The results show that, in addition to time factor, TCOBS resilience is also influenced by human and environmental factors. Therefore, to enhance the resilience of TCOBS, we should reduce failure rate and improve maintenance rate, and reduce the probability of human failure and environmental interference failure. The new model can find out the continuous trend of TCOBS resilience under the influence of multi-dimensional factors.

Hexa-Propeller Airship for Environmental Surveillance and Monitoring in Amazon Rainforest

This paper proposes a new kind of airship actuator configuration for surveillance and environmental monitoring missions. We present the design and application of a six-propeller electrical airship (Noamini) with independent tilting propellers, allowing improved and flexible maneuverability. The vehicle has different combinations of differential propulsion and can be used in a two-, four- or six-motor configuration. We developed a high-fidelity airship simulator for the Noamini airship, which was used to test and validate a control/guidance approach. Incremental Nonlinear Dynamic Inversion (INDI) is used for the velocity/attitude control to follow a high-level L1 guidance reference in a simulated waypoint-tracking mission with wind and turbulence. The proposed framework will be soon implemented in the onboard control system of the Noamini, an autonomous airship for environmental monitoring and surveillance applications.

Resilience evaluation of train control on-board system considering common cause failure: Based on a beta-factor and continuous-time bayesian network model

Common cause failure is an inevitable factor in studying the reliability or resilience of a system with redundant structures. Train Control On Board System (TCOBS) is a typical redundant structural system. However, there are few studies on the resilience of TCOBS that consider the issue of common cause failure, which means the resilience assessment results might be imprecise. At the same time, previous TCOBS resilience evaluation works also have problems such as unreasonable resilience evaluation functions and low accuracy of resilience evaluation models. To address the above issues, in this paper, we firstly construct the Bayesian network structure of TCOBS and present the resilience function of TCOBS components. Secondly, a Beta-factor model is used to evaluate the common cause failure in TCOBS, and a continuous-time Bayesian network model is used to infer TCOBS resilience and the importance of each component. Finally, CTCS-3OBS is used as an example for case simulation and model comparison analysis. The results show that if the impact of common cause failure on TCOBS resilience is not considered, it will lead to a higher result of resilience assessment and significant differences in component importance assessment, both of which will lead to inaccurate formulation of TCOBS resilience improvement measures.

Design principle, 4E analyses and optimization for onboard CCS system under EEDI framework: A case study of an LNG-fueled bulk carrier

With the accelerated implementation of Energy Efficiency Design Index (EEDI) phase 3 by the International Maritime Organization, shipping carbon reduction is urgently required. This paper explores the possibility to develop the onboard carbon capture and storage (OCCS) system unstraints between EEDI phases 3 and 4. The OCCS is particularly established with the utilization of exhaust gas waste heat and LNG cold energy. The proposed OCCS system is modelled in Aspen HYSYS, laying the foundation for energy, exder the EEDI framework. The OCCS system design principle for an LNG-fueled bulk carrier is determined with the conergy, economic and environment analyses. Furthermore, the multi-objective optimization is based on the energy and exergy efficiency of OCCS system (ηen,OCCS × ηen,OCCS), payback period (PBP) and metric of carbon capture efficiency degree (MCCED) by non-dominated sorting genetic algorithm-Ⅲ. The Pareto frontier indicates that the system is in optimal operation with the exhaust gas mass flow rate of 29,990 kg/h and the liquid-to-gas ratio of 1.04. The carbon capture is 1048.0 kg/h, which is 70.74% over EEDI phase 3 requirements. The optimized ηen,OCCS × ηen,OCCS is 2.92%, the PBP is 13.03 years and the MCCED is 23.00%. This study provides an important basis for the maritime post-combustion carbon reduction compliance.