Efficient System Research Articles

Intelligent Traffic Control Decision-Making Based on Type-2 Fuzzy and Reinforcement Learning

Intelligent traffic control decision-making has long been a crucial issue for improving the efficiency and safety of the intelligent transportation system. The deficiencies of the Type-1 fuzzy traffic control system in dealing with uncertainty have led to a reduced ability to address traffic congestion. Therefore, this paper proposes a Type-2 fuzzy controller for a single intersection. Based on real-time traffic flow information, the green timing of each phase is dynamically determined to achieve the minimum average vehicle delay. Additionally, in traffic light control, various factors (such as vehicle delay and queue length) need to be balanced to define the appropriate reward. Improper reward design may fail to guide the Deep Q-Network algorithm to learn the optimal strategy. To address these issues, this paper proposes a deep reinforcement learning traffic control strategy combined with Type-2 fuzzy control. The output action of the Type-2 fuzzy control system replaces the action of selecting the maximum output Q-value of the target network in the DQN algorithm, reducing the error caused by the use of the max operation of the target network. This approach improves the online learning rate of the agent and increases the reward value of the signal control action. The simulation results using the Simulation of Urban MObility platform show that the traffic signal optimization control proposed in this paper has achieved significant improvement in traffic flow optimization and congestion alleviation, which can effectively improve the traffic efficiency in front of the signal light and improve the overall operation level of traffic flow.

A numerical study on the sustainability and efficiency of Deep Coaxial borehole heat exchanger systems in the Cold Region of Northeast China

A numerical study on the sustainability and efficiency of Deep Coaxial borehole heat exchanger systems in the Cold Region of Northeast China

Electrical resistivity tomography: A reliable tool to monitor the efficiency of different irrigation systems in horticulture field

Water management in agricultural systems is essential for optimal crop yields without incurring excessive water costs and wastage. The choice of irrigation method is crucial for better water management and distribution. The drip system appears to be among the best methods in the field of precision agriculture. In addition to the irrigation system, mulching with ridge plastic film to drain excess water is widely used to increase crop yields in terms of plant water availability. In this study, the time-lapse Electrical Resistivity Tomography (ERT), a not-invasive geophysical technique, is proposed as a simple and reliable method to evaluate the effectiveness of the irrigation systems and to monitor the changes in water content over time and over a volume of soil. ERTs data were compared to moisture ones retrieved from sensors that record continuously over time, but punctually. The ERT investigations were conducted in melon-growing lands in southern Tuscany (Italy). Measurements were carried out on two different fields in two periods: spring and summer. The aim of the work was to evaluate, by means of volumetric measures of the soil conductivity, the effectiveness of three different drip systems and of the mulch ridge. In both the monitored fields the ridge was created in a half portion of the field itself, while the other part of the land was left plat. Geoelectrical investigations associated with humidity sensors have shown that in the summer a too high mulch ridge quickly drains the irrigation water, bringing the root zone into a water deficit. The ERTs also provided good results relating to the irrigation system, demonstrating that a three-lines drip irrigation system, compared to a two-lines one, manages to distribute the irrigation water homogeneously, guaranteeing a constant water content for the plants over time.

Multi-objective optimization of chemical reaction characteristics of selective catalytic reduction in denitrification of diesel engine using ELM-MOPSO methodology

This study systematically investigated the effects of structural parameters in a two-stage selective catalytic reduction (SCR) catalyst on ammonia storage, NO conversion efficiency, and ammonia slip using computational fluid dynamics (CFD) simulations. The rank ordering of the correlations between structural parameters and performance metrics was determined by gray relational analysis (GRA). Both the catalyst diameter and the upstream catalyst porosity had correlations exceeding 0.9, making them key parameters affecting SCR performance. An extreme learning machine (ELM) prediction model was trained using data obtained from CFD simulations. The prediction performance of this ELM model was then evaluated using statistical metrics. Finally, the optimized Pareto frontier diagram was obtained through the multi-objective particle swarm optimization (MOPSO) algorithm, and the optimal results were selected for further CFD modeling analysis. The results showed that the upstream NO conversion efficiency of the SCR system was improved by 7.7 %, the downstream NO conversion efficiency was improved by 18.67 %, the ammonia slip was reduced by 71.97 %, and the upstream ammonia storage capacity was improved by 6.83 % compared with the original model.

Hybrid of imperialist competitive algorithm and particle swarm optimization for parameter extraction of photovoltaic cells

To improve the efficiency of photovoltaic systems, it is essential to obtain the parameters of photovoltaic cells through an identification process. However, due to the nonlinear and multimodal characteristics, accurately and reliably identifying the parameters of photovoltaic cells still remains a challenging task. In this paper, a hybrid of the imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), ICA-PSO, is proposed to effectively identify the parameters of photovoltaic cells. The position updating strategy of PSO is adopted to replace the colony’s position updating strategy in the ICA. The hybrid algorithm ICA-PSO integrates the multi-swarm search characteristic and the powerful exploration ability of PSO together, leading to an enhanced optimization performance. Experimental results of applying ICA-PSO to parameter identification of photovoltaic cells show that ICA-PSO can extract the parameters of photovoltaic cells with higher accuracy and reliability, thus outperforming many other methods presented in the literature.

Enhancing efficiency and economy of hydrogen-based integrated energy system: A green dispatch approach based on exergy analysis

Hydrogen-based integrated energy system holds promise for leveraging the low carbon advantage of hydrogen, but inefficiency of electrolysis hydrogen production leads to significant exergy loss within its operation. Addressing this, this paper focuses on the energy-saving and economic operation of the hydrogen-based integrated energy system, proposes a novel green dispatch approach using loss cost as the objective function and establishes a refined loss cost accounting model based on exergy analysis theory. Firstly, derived from the analogy of exergy loss, the concept and accounting framework of system loss cost are proposed. Secondly, employing exergy analysis and consistent unit exergy cost principle, equipment operation loss cost is calculated through energy, exergy flows analysis and cost allocation. Thirdly, utilizing the total of equipment operation loss cost, wind curtailment penalty cost, and transmission line loss cost as the objective function, a green dispatch model is formulated to minimize overall loss cost while ensuring energy balance and other constraints. Finally, simulations analysis are conducted. The results show that compared to economic dispatch, minimized exergy loss dispatch and multi-objective dispatch methods, proposed approach increases exergy efficiency by 0.54 %, reduces cost by 6.13 % and increases the scale of hydrogen utilization by 3.45 %, respectively.

Performance analysis and evaluation of postquantum secure blockchained federated learning

As the field of quantum computing progresses, traditional cryptographic algorithms such as RSA and ECDSA are becoming increasingly vulnerable to quantum-based attacks, underscoring the need for robust post-quantum security in critical systems like Federated Learning (FL) and Blockchain. In light of this, we propose a novel hybrid approach for blockchain-based FL (BFL) that integrates a stateless signature scheme, such as Dilithium or Falcon, with a stateful hash-based scheme like XMSS. This combination leverages the complementary strengths of both schemes to provide enhanced security. To further optimize performance, we introduce a linear formula-based device role selection method that takes into account key factors such as computational power and stake accumulation. This selection process is reinforced by a verifiable random function (VRF), which strengthens the blockchain consensus mechanism. Our extensive experimental results demonstrate that this hybrid approach significantly enhances both the security and efficiency of BFL systems, establishing a robust framework for the integration of post-quantum cryptography as we transition into the quantum computing era.

E-commerce and Mobile Application Development in the Sports Industry

In order to achieve user recommendations that best match their current contextual needs, the author proposes a mobile service QoS (Quality of Service) hybrid recommendation model based on sports user situational awareness. Cluster the users and service items covered by mobile users based on their location contextual information according to the classification principle of autonomous systems, forming a collaborative filtering and recommendation mechanism for mobile user service items; In response to the cold start problem of new users and new projects in traditional QoS recommendation methods, prediction and recommendation of missing QoS attribute preference values are based on User based and Item based CF; In response to the problem of difficult determination of mixed recommendation weights caused by massive data and uneven distribution of service QoS attribute values in mobile network environments, MapReduce based ant colony neural network weight training is used for CF mixed recommendation. Experimental results have shown that the Hadoop sports industry has improved the operational efficiency of algorithms and reduced the time for global QoS preference prediction; And by comparing the operation results of the 10% and 100% sub datasets, it can also be seen that the acceleration ratio of the algorithm will continuously improve with the increase of data volume, thereby improving the operational efficiency of the recommendation algorithm. It has been proven that the MapReduce based ant colony neural network weight training method significantly reduces the global computation time of the algorithm and improves the operational efficiency of the recommendation system.

An inhibitive system to mitigate the corrosion of AA2024-T3 in 3.5 wt% NaCl solution: Utilizing the synergistic effect of cerium cations and benzimidazole molecules

This study aims to improve the anti-corrosion protection performance of AA2024-T3 using the synergistic effect of organic and inorganic corrosion inhibitors in saline solution. Combining the Ce3+ cations and benzimidazole (BI) molecules was employed as the inhibitive system for the AA2024-T3. Electrochemical properties of the metallic substrate in the presence and absence of the inhibitive system were studied utilizing EIS, PD, and OCP techniques. In addition, GIXRD, ATR-FTIR, XPS, OM, FE-SEM/EDS, Water Contact Angle, and AFM were hired to study the protection mechanism. Based on the results obtained, the inhibitive system has a synergistic effect in improving the anti-corrosion protection performance of AA2024-T3. The synergistic index for the Ce + BI system was calculated to be around 19. Also, the efficiency of the synergistic system in the simultaneous presence of Ce3+ cations and BI molecules as the corrosion inhibitor was 99.3 %, which is higher than that of other inhibitive systems. Finally, the surface analysis outcomes revealed that the inhibitive film forming on the samples' surface during the immersion of the sample in the saline solution in the presence of the inhibitive species is the main factor in boosting the anti-corrosion properties of the aluminum substrate.

Development trends of payment ecosystems as a key link of the e-commerce infrastructure

The article provides an analytical assessment of trends and prospects for the development of payment ecosystems as a key link of e-commerce infrastructure. The factors of the development of electronic payment systems are systematized: the formation of technical and technological prerequisites related to general digitalization (payment infrastructure, network of POS terminals; mobile telephony capabilities; Abstract. The article provides an analytical assessment of trends and prospects for the development of payment ecosystems as a key link of e-commerce infrastructure. The factors of the development of electronic payment systems are systematized: the formation of technical and technological prerequisites related to general digitalization (payment infrastructure, network of POS terminals; mobile telephony capabilities; blockchain technology; creation of robots-consultants; innovative financial services; built-in financing); formation of socio-psychological prerequisites (digital literacy of the population; affordability of mobile phones with appropriate payment services; mental understanding of the importance of the transition to digitization of payments; convenience, security and speed of cashless payments; the impact of advertising). A modern paradigm of the renewed payment ecosystem of Ukraine is proposed, which will lead to its profound positive transformation. The necessity of applying European experience in the organization and operation of payment systems in order to improve the forms of interbank settlements and increase the efficiency of the payment system of Ukraine is substantiated.

Exergy based yearly performance analysis of a poly-generation system for a small-town application

The current study conducts a comprehensive year-round exergy analysis of an integrated system centered around a reversible solid oxide cell (RSOC), aimed at fulfilling the power and fresh water requirements of a small town, equivalent to 2.7 MW and 2000 m³/day, respectively. The integrated system encompasses four subsystems: the solar field, RSOC, organic Rankine cycle coupled with ejector cooling (ORC), and zero liquid discharge multi-effect desalination (MED-ZLD). The proposed system is analyzed using MATLAB. The annual exergy efficiency for each subsystem is examined, yielding average values of 11.9%, 48.11%, and 56.22% for the solar field, ORC cycle, and MED-ZLD, respectively. Notably, the exergy efficiency of the Rankine cycle is higher in colder months compared to warmer ones. This phenomenon is attributed to the cycle's greater heat generation relative to the cooling it produces during the cold season. The RSOC exhibits noteworthy average exergy efficiencies of 93% in electrolysis mode, 69.87% in fuel cell mode, and an overall average exergy efficiency of 79.62% throughout the year. The average exergy efficiency of the overall system is evaluated throughout the day, nighst, and the entire operational period. The results are values of 10.74%, 34.31%, and 23.34%, respectively. This discrepancy is due to the primary energy supply source, which is solar energy during the day and input hydrogen during the night. The highest exergy efficiency is observed in December, while the lowest is in June. Furthermore, the study examines the distribution of exergy destruction and identifies cycles with significant impacts by analyzing the exergy destruction ratio across various months. The solar field is the primary contributor to daytime exergy destruction, peaking at 94.9%. At night, the MED-SET cycle leads in exergy destruction, followed closely by the RSOC cycle operating as a fuel cell. The research also delves into exploring key contributors to exergy destruction in various cycles, shedding light on crucial insights into the intricate dynamics of the integrated system's performance.

Partial replacement of inorganic fertilizer with organic inputs for enhanced nitrogen use efficiency, grain yield, and decreased nitrogen losses under rice-based systems of mid-latitudes

In the rice-based system of mid-latitudes, mineral nitrogen (N) fertilizer serves as the largest source of the N cycle due to an insufficient supply of N from organic sources causing higher N losses due to varying soil and environmental factors. However, aiming to improve soil organic matter (OM) and nutrients availability using the best environmentally, socially, and economically sustainable cultural and agronomic management practices are necessary. This study aimed to enhance nitrogen use efficiency (NUE) and grain yield in rice-based systems of mid-latitudes by partially replacing inorganic N fertilizer with organic inputs. A randomized complete block design (RCBD) was employed to evaluate the effects of sole mineral N fertilizer (urea) and its combinations with organic sources—farmyard manure (FYM) and poultry compost—on different elite green super rice (GSR) genotypes and were named as NUYT-1, NUYT-2, NUYT-3, NUYT-4, NUYT-5, and NUYT-6. The study was conducted during the 2022 and 2023 rice growing seasons at the Rice Research Program, Crop Sciences Institute (CSI), National Agricultural Research Centre (NARC), Islamabad, one of the mid-latitudes of Pakistan. The key objective was to determine the most effective N management strategy for optimizing plant growth, N content in soil and plants, and overall crop productivity. The results revealed that the combined application of poultry compost and mineral urea significantly enhanced soil and leaf N content (1.36 g kg− 1 and 3.06 mg cm− 2, respectively) and plant morphophysiological traits compared to sole urea application. Maximum shoot dry weight (SDW) and root dry weight (RDW) were observed in compost-applied treatment with the values of 77.62 g hill− 1 and 8.36 g hill− 1, respectively. The two-year mean data indicated that applying 150 kg N ha⁻1, with half provided by organic sources (10 tons ha⁻1 FYM or poultry compost) and the remainder by mineral urea, resulted in the highest N uptake, utilization, and plant productivity. Thus, integrated management of organic carbon sources and inorganic fertilizers may sustain the productivity of rice-based systems more eco-efficiently. Further research is recommended to explore root and shoot morphophysiological, molecular, and biochemical responses under varying N regimes, aiming to develop N-efficient rice varieties through advanced breeding programs.

Degradation of carbamazepine in piezo-activation of persulfate systems: Comparison of PDS and PMS

The choice of persulfate (PS) is crucial to the degradation and energy efficiencies for piezo-activation of PS (PE-PS) systems. Herein, a comparison between piezoelectrically activated peroxydisulfate (PDS) and peroxomonosulfate (PMS) using BaTiO3 are comparatively investigated for carbamazepine (CBZ) degradation. By contrast, the PE-PMS system achieves better degradation and mineralization of CBZ, as well as higher reaction stoichiometric efficiencies (%RSE). Nevertheless, the energy consumption of the PE-PDS system is only 9.69% of the PE-PMS system. The quenching test, chemical probe determination, and electron spin resonance (EPR) analysis suggest that the concentration of •OH, SO4∙-, and 1O2 except ∙O2- of the PE-PMS system, is higher than that of the PE-PDS system. The piezoelectric current density and carrier separation efficiency of the PE-PMS system are higher than that of the PE-PDS system, demonstrating that the piezoelectrically activated PMS has higher reactivity than piezoelectrically activated PDS. The ∙O2- and •OH are the primary ROS in the PE-PDS system, while 1O2 and •OH are the major ROS in the PE-PMS system. Additionally, CBZ is effectively degraded by both PE-PMS and PE-PDS systems from actual water sources. This work compares the performance, energy cost, and mechanism of PE-PMS and PE-PDS systems systematically. Hopefully, all of that is designed to tender a helpful reference for the choice of PS in piezocatalytic research and application.

Secure virtual coupling control of connected train platoons under cyber attacks

Virtual coupling of automated trains has emerged as a promising technology to enhance the operational efficiency and line capacity of railway transportation systems. However, the vulnerability of such systems to malicious cyber attacks poses a significant threat to their stability, reliability and applicability. This article addresses the challenge of ensuring secure virtual coupling control of connected train platoons subject to various cyber attacks, including false data injection attacks, denial-of-service attacks, and replay attacks. First, based on the low-dimensional and noisy sensor measurements, novel set-valued observers are designed for each follower train to estimate their inaccessible full longitudinal motion states despite uncertainties and unknown attacks and noises. Then, distributed secure attack-compensation-based virtual coupling control laws are developed to ensure the platoon's stability and resilience against cyber attacks. Additionally, formal stability analysis of the resultant estimation error system and platoon tracking error system is provided. Furthermore, two algorithms are presented to elaborate the offline observer and controller design process as well as their online implementation. Finally, based on the data of a realistic urban rail transit line, simulation experiments are conducted to validate the efficacy of the proposed theoretical findings.

Dog complement system is less effective against Leishmania infantum than human complement

Dogs are important reservoir hosts for Leishmania infantum, the causative agent of visceral leishmaniasis. The complement system, as part of the innate immune defense, is responsible for initiating the fight against pathogens that may invade an organism. A failure of the complement to combat L. infantum may explain, at least in part, why a mammal species is more or less susceptible to visceral leishmaniasis. The objective of this study was to compare the effectiveness of human and dog complement systems against L. infantum parasites. The results showed that dog serum was less effective than human serum at killing promastigote and amastigote-like forms. We also compared the efficiency of human and canine sera in classic and alternative hemolytic assays, as well as the serum efficiency of non-infected and Leishmania-infected dogs. Serum from dogs was less hemolytic than human serum in both pathways tested, but the efficiency of serum from infected dogs was higher than that of non-infected dogs. When testing C3b deposition assays on parasite surfaces, serum from infected dogs was more effective against amastigote-like forms than serum from non-infected dogs. However, both types of serum proved equally effective on promastigotes, while serum from infected dogs was more effective on amastigote-like forms. Considering the efficiency of the complement system, our results indicate that dogs are more susceptible to visceral leishmaniasis than humans are.

Synergetic integration of photothermal and self-cleaning features in a composite phase change material based on layered double hydroxides and polypyrrole

Composite phase change materials (PCMs) with photothermal functionality play a crucial role in efficiently storing and utilizing solar energy. However, the challenge of effectively removing of contaminants on photothermal conversion coatings has long puzzled researchers. In this study, we propose a novel approach to enhancing both photothermal conversion performance and self-cleaning properties of wood-based composite phase change materials, ultimately facilitating the sustainable and effective utilization of solar energy. The method utilized metal-organic frameworks (MOFs) as a precursor to form rough layered double hydroxides (LDH) on the wood surface through hydrolysis-induced exchange. Subsequently, polypyrrole (PPy) was deposited through vapor phase onto the LDH, and paraffin wax (PW) is utilized as the phase change material to prepare a novel composite phase change material. This modified wood structure facilitates leak-proof PW encapsulation and effective heat storage (167.6 J/g). PPy acts as a proficient photothermal converter and improves the composite material's heat transfer efficiency by establishing thermal conduction pathways, resulting in an 87.4 % enhancement in thermal conductivity compared to pure PW. The rough surface of LDH promotes multiple light reflections and scattering within the material, leading to increased light absorption by the PPy coating and achieving a photothermal conversion efficiency of up to 96.4 %. Furthermore, the high roughness of the LDH layer combined with the low surface energy of PW imparts superhydrophobic self-cleaning properties to the material, ensuring sustained effectiveness of the photothermal conversion coating over time. This research presents a viable approach for maintaining the cleanliness and efficiency of solar energy systems.

Artificial Intelligence Plays Crucial Role in Education Sector of Assam

“AI is a mirror, reflection not only our intellect, but our values and fears” Artificial intelligence (AI) is shaping the future of technology and revolutionizing our education system. Due to evolution of artificial intelligence, we have seen the drastic change in our educational sector throughout the globe. It indicates a positive signal for present and future generations of our country and also throughout the globe. Artificial Intelligence (AI) can play a crucial role in the school functioning process, streamlining and optimizing the procedure for both institutions and applicants. Through AI-driven algorithms, schools can efficiently sift through a large pool of applicants, identifying the most suitable candidates based on predefined criteria such as student’s enrollment according to their academic performance, extracurricular activities, and personal attributes. One of the most remarkable advantages of employing AI in the admission process is its ability to eliminate human bias, ensuring fairness and equal opportunities for all applicants. AI systems are programmed to assess candidates solely based on their qualifications and merit, reducing the influence of subjective factor, viz., race, gender, or socioeconomic background. We have seen the drastic changes in case of new admission in our School and College for last 3 to 4 years in the state of Assam and country as well. Moreover, AI facilitates personalized and efficient communication with applicants, providing them with updates on their application status and guiding them through the admission process. Virtual assistants powered by AI can promptly address queries from applicants, offering them the support they need. Furthermore, AI enables predictive analysis, allowing schools to forecast future trends in admission and tailor their strategies accordingly. By analyzing past admission data and trends, AI systems can provide valuable insights into the preferences and behaviors of prospective students, helping schools make informed decisions and develop targeted recruitment strategies. AI is revolutionizing the school admission process by making it more efficient, transparent, and fair. By leveraging AI technologies, schools can streamline their admission procedures, improve the candidate experience, and ultimately ensure that the most qualified and deserving students are admitted. In Assam, the education department of Government of Assam has introduced Shiksha Setu App to enhance communication between students, parents, teachers, and educational institutions. It facilitates access to various educational resources, attendance tracking of both teachers and students, exam schedules, leave for teachers, and performance reports. Its significance lies in improving transparency, efficiency, and accessibility in the whole education system of Assam, and as result contributing to better student outcomes and parental involvement.

Significant statistical model of heat transfer rate in radiative Carreau tri-hybrid nanofluid with entropy analysis using response surface methodology used in solar aircraft

The recent advancement of technologies focuses on the use of solar-based heat radiation and nanotechnology. The primary source of heat is solar energy, which is obtained by absorbing sunlight. In addition, solar thermal planes, photovoltaic cells, and sun-based hybrid nanofluids all help to harness solar energy. Therefore, main concern of the study is to explored the two-dimensional Engine oil-based Carreau tri-hybrid nanofluid (SiO2 (silica dioxide), MOS2 (Molybdenum disulfide), Fe3O4(black iron oxide)) flow via a stretching sheet within solar wings. The flow phenomena of the non-Newtonian fluid enrich for the effects of radiant energy and the external heat source. By adopting similarity transfiguration, the dimensional partial main flow equations are changed into nonlinear dimensionless ordinary differential equations. The most effective and powerful tool of the semi-analytical method ADM (Adomain Decomposition Method) is utilized to solve the nanofluid flow problem. The uniqueness of the proposed study arises due to the analysis of entropy generation obtained by various irreversibility processes. The graphical illustration of outcome of various governing parameters on velocity, temperature, entropy terms and engineering quantities have been presented and theoretically analyzed. The findings reveal that The Carreau ternary hybrid nanofluids enhance the system's thermal conductivity for optimal temperature regulation in solar aircraft and better heat dissipation. The use of Carreau ternary hybrid nanofluids significantly reduces entropy generation, which implies a more reversible process, enhancing the overall efficiency of the energy conversion systems in solar aircraft. The heat flow of the system is accurately reflected by the chosen variables and their interactions, as demonstrated by the constructed model's strong statistical significance. For real-world applications, this improves the model's predictability and dependability.

The formation and post-formation processes of vortex rings discharged from laminar starting jets

Motivated by biological systems, vortex rings can enhance the propulsive efficiency in industrial systems. To study the vortex properties during the formation and post-formation stages, impulsively starting jets are investigated by simulations. The effects of the stroke ratio and nozzle geometry are studied at a fixed jet Reynolds number of 2500. The stroke ratio at the formation number is found to be not enough to produce a vortex ring with maximum circulation. The stroke ratio is suggested to be about twice as large as the formation number. An alternative criterion based on the circulation ratio is proposed to describe the onset of pinch-off. This criterion states that the pinch-off would start when the vortex ring attains about 80% of the total jet circulation. During the formation stage, the scaling laws for vortex trajectories and circulation are proposed for continuous formation. By combining the suggested scaling relations and Saffman's velocity formula, the evolution of non-dimensional energy can be predicted. During the post-formation stage, the scaling laws for vortex properties (e.g., the vortex ring diameter, translational velocity, and circulation) are found to be independent of both the nozzle configuration and the vortex Reynolds number. On the grounds of the invariance of impulse in vortex decay, the scaling laws of vortex motion are derived for the non-dimensional energy, circulation, and diffusivity scale of the vortex core. In consequence, the normalized energy and circulation found in experiments can be successfully derived from the similarity model for both nozzle configurations.

Assessing the reliability of a ship energy performance simulation tool through on-board data

To mitigate the environmental impact of shipping on climate change, it is nowadays urgent to explore energy efficiency and cost-effective measures by means of reliable digital tools. These are becoming essential for assisting the design of new ships and the refurbishment of the existing ones, as well as for assessing and optimizing the energy performance of a ship during its entire life-cycle. This paper focuses on the verification of the reliability of a novel ship energy dynamic simulation tool, developed for the calculation of the energy, economic, and environmental performance of large ships, the design and optimization of energy system layouts and operational logics, as well as the definition of design and management guidelines for ships. The reliability of the developed model was verified through the use of data measured onboard an existing cruise ship. The verification procedure is conducted for assessing the reliability and for exploiting the potential of dynamic analysis for a more precise evaluation of ships loads, temperature levels, and necessary sizes of technologies. The validate tool will enable reaching two different goals: i) to evaluate different energy system layouts to define the optimal one to achieve imposed targets, ii) to obtain a large amount of data that can allow the implementation of the digital twin in the maritime sector. The developed model relies on several well-established software to assess the energy, economic and environmental performance of different types of ships. The model validation was successfully assessed and very low or negligible deviations, lower than 4%, of numerical data from measurements were observed. Thanks to the validated simulation model it was possible to assess the energy performance of the investigated ship, that requires approximately 23.4 GWh of primary energy from polluting fuels over two weeks. The model provided insights into the flow rate, temperature levels, and energy flows of the ship energy system, necessary for identifying the amount of thermal energy to be recovered and potential solutions to enhance the efficiency of the entire system.