This paper presents a new method for improving the assessment of energy and resources of waves in the cases of finite water depths in which the historical and some ongoing sea wave measurements are simply given in forms of scatter diagrams or the forms of (significant) wave heights and the relevant statistical wave periods, whilst the detailed spectrum information has been discarded, thus no longer available for the purpose of analysis. As a result of such simplified wave data, the assessment for embracing the effects of water depths on wave energy and resources becomes either difficult or inaccurate. In many practical cases, the effects of water depths are simply ignored because the formulas for deep-water waves are frequently employed. This simplification may cause large energy under-estimations for the sea waves in finite water depths. To improve the wave energy assessment for such much-simplified wave data, an approximate method is proposed for approximating the effect of water depth in this research, for which the wave energy period or the calculated peak period can be taken as the reference period for implementing the approximation. The examples for both theoretical and measured spectra show that the proposed method can significantly reduce the errors on wave energy assessment due to the approximations and inclusions of the effects of finite water depths.

For cost savings and ease of operation, nearshore regions have been considered as ideal regions for deploying wave energy converters (WECs) and wave farms. As the water depths of these regions may be frequently limited to 50 m or less, they can be considered as being transitional/intermediate to shallow when compared to the wave lengths of interest for wave energy conversion. Since the impact of water depths on propagation of waves is significant, it cannot be ignored in wave energy assessment. According to the basic wave theory, in order to work out accurate wave energy amounts in finite water depth, detailed wave spectral distributions should be given. However, for some practical reasons, there are still some cases where only scatter diagrams and/or the statistical wave parameters are available, whilst the detailed wave spectra are discarded. As a result, the assessments of wave energy and resources are frequently determined by ignoring the effect of water depths or using very simplified approximations. This research paper aims to develop more accurate approximation methods by utilising a number of available parameters such that a better estimate on the wave resource assessment can be achieved even if the detailed wave spectra are not available. As one important goal, the research can provide some important indications on how the measured wave data are effectively presented so that they can be very useful for assessing the wave energy resource, especially in the cases including the effects of finite water depths.

Deep-sea cages are highly susceptible to biofouling due to long-term seawater immersion, which promotes the attachment and growth of marine organisms on nets, significantly reducing fish survival. To address this issue, this study explores the use of low-pressure abrasive water jets (LPAWJs) for cage fouling removal through numerical simulation. Based on a Box-Behnken response surface design, nozzle inlet pressure X₁, nozzle outlet diameter X₂, and target distance X₃ were selected as optimization parameters. The peak jet impact force Z₁, stable jet impact force Z₂, peak abrasive water jet velocity Z₃, and peak abrasive particle velocity Z₄ were chosen as evaluation indicators to characterize the jet’s instantaneous impact ability, sustained action ability, and dynamic particle behavior. Using the entropy method, weights for each indicator were determined, and the jet’s overall removal capability was calculated. A regression model was developed by integrating numerical simulation with the response surface methodology (RSM), and the optimal parameter combination was identified as X₁ = 4.5 MPa, X₂ = 10 mm, and X₃ = 205.396 mm. Compared with the poorest experimental condition (Condition 1), the jet’s overall removal capability obtained under the optimal parameter combination increases by 101.35%. Experimental validation further confirms that the optimized parameters yield the best oyster-removal performance of the low-pressure abrasive jet, with the average removal rate improving by 100.55% relative to Condition 1. The methodology and results of this study provide a theoretical foundation and technical reference for the design and optimization of automated net-cleaning systems or net-cleaning robots equipped with low-pressure abrasive jets. By integrating the proposed model and operating parameters, future robotic systems will be able to predict and dynamically adjust jet conditions according to fouling characteristics, thereby improving the efficiency, cost-effectiveness, and sustainability of maintenance operations in marine aquaculture.

Deep-sea cages are highly susceptible to biofouling due to long-term seawater immersion, which promotes the attachment and growth of marine organisms on nets, significantly reducing fish survival. To address this issue, this study explores the use of low-pressure abrasive-water jets (LPAWJ) for cage fouling removal through numerical simulation. Based on a Box-Behnken response surface design, nozzle inlet pressure X1, nozzle outlet diameter X2, and target distance X3 were selected as optimization parameters. The peak jet impact force Z1, stable jet impact force Z2, peak abrasive-water jet velocity Z3, and peak abrasive particle velocity Z4 were chosen as evaluation indicators to characterize the jet’s instantaneous impact ability, sustained action ability, and dynamic particle behavior. Using the entropy method, weights for each indicator were determined, and the jet’s overall removal capability was calculated. A regression model was developed by integrating numerical simulation with the response surface methodology (RSM), and the optimal parameter combination was identified as X1 = 4.5 MPa, X2 = 10 mm, and X3 = 205.396 mm. Compared with the poorest experimental condition (Condition 1), the jet’s overall removal capability obtained under the optimal parameter combination increases by 101.35%. Experimental validation further confirms that the optimized parameters yield the best oyster-removal performance of the low-pressure abrasive jet, with the average removal rate improving by 100.55% relative to Condition 1. The methodology and results of this study provide a theoretical foundation and technical reference for the design and optimization of automated net-cleaning systems or net-cleaning robots equipped with low-pressure abrasive jets. By integrating the proposed model and operating parameters, future robotic systems will be able to predict and dynamically adjust jet conditions according to fouling characteristics, thereby improving the efficiency, cost-effectiveness, and sustainability of maintenance operations in marine aquaculture.

Droplet-based electricity generators (DEGs) have emerged as a promising technology for harvesting low-frequency ambient mechanical energy from water droplets. However, current research remains predominantly empirical, lacking a unified theoretical framework to predict and rationalize multi-parameter coupled behaviors. To address this gap, we propose a dynamic capacitance-fluid coupling (DCFC) Model that systematically integrates droplet impact dynamics, interfacial charge transfer kinetics and external circuit response. The model identifies three core physical determinants of output: the time-varying contact area A ( t ) , the effective interfacial charge density σ eff ( t ) and the system dynamic capacitance C total ( t ) . The model is validated through two complementary approaches: (1) independent literature data on inclination angle effects using FEP dielectric, and (2) our own experiments on solution pH effects (4.00, 6.86, 9.18) using polyvinyl chloride (PVC) dielectric. The DCFC model successfully reproduces monotonic increase of output with inclination angle and the V-shaped pH dependence centered at the isoelectric point. Leveraging the validated model, we systematically analyze the influence of key operational parameters—inclination angle, flow rate, solution pH, and dielectric material—on DEG performance. This analysis reveals that charge transfer kinetics k ct , rather than intrinsic charge density σ 0 alone, governs device performance: electron-dominated materials (PTFE, FEP) exhibit fast kinetics and superior output, while ion-mediated materials (PI) underperform despite high σ 0 . Based on these insights, we construct performance phase diagrams that map multidimensional parameter spaces to output characteristics, providing clear guidelines for rational DEG design. This work transitions DEG development from empirical optimization to physics-informed, model-guided rational design.