System Model Research Articles

Network Graph Laplacian-Based Sensor Projection in System-Level Modeling of Liquid-Metal Loop

Accurate modeling of nuclear reactor transients is important for improving operation and maintenance planning strategies in advanced reactors. An adequate understanding of the connectivity between various components and sensor signals can provide the means to project plant information even where sensors are not operational. This work presents a method for projecting temperature signals using network graph Laplacian and support vector regression (SVR). A system model of a scaled liquid-metal-cooled reactor experiment facility is used to generate thermal-hydraulic data simulating cold-shock transients into the reactor plenum. Two different flow rates are investigated, 0.03 kg/s and 0.06 kg/s, describing the different degrees of nonlinear disturbances in two cases. The temperatures at 72 locations within the system are used to test the temperature prediction model. The inter-connectivity of these points is described using the network graph Laplacian, which describes a location in the loop as a node and then comprises a weighted matrix of connections between each of these nodes. Additional nodes, called ghost nodes, are used to model the heat transfer of the loop with the surroundings. The graph Laplacian and corresponding nodal sensing data are then used to train a kernel, which can inform the impact of various phenomena at one location in the plant on phenomena at other locations in the plant. The sensing data and graph network of these nodes are used to construct a surrogate model of the liquid-metal loop. This model is then used to predict the behavior at certain nodes where the training data are not provided to the model. These model predictions are compared against the test data for the two different inlet flow rates. When optimized, the average error of the simulated temperature data remained below 5% when 3 of the 72 nodes are predicted using the graph-based SVR model. As the number of unknown sensors is increased, the root-mean-square error increases slightly but still remains below 2% when 24 of the 72 nodes are unknown or tested. The test or unknown sensor location plays a larger role than the number of unknowns, with sensor locations near ghost nodes and near the outlet pipes in the plenum having the largest error, with a maximum recorded error of 7%. The objective of the graph-based SVR model is to not only capture the temperature or field variable accurately but also to capture the relative connectivity between the simulated sensors. The correlation coefficient matrix provides a scaled reference for the correlation between temperatures at two different node locations. The calculated correlation coefficient for the simulated temperature data and actual temperature data is within 5% for most of the system, with a maximum relative error of 15%.

Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0

Abstract. Estimating global river solids, nitrogen (N), and phosphorus (P), in both quantity and composition, is necessary for understanding the development and persistence of many harmful algal blooms, hypoxic events, and other water quality issues in inland and coastal waters. This requires a comprehensive freshwater model that can resolve intertwined algae, solid, and nutrient dynamics, yet previous global watershed models have limited mechanistic resolution of instream biogeochemical processes. Here we develop the global, spatially explicit, and process-based Freshwater Algae, Nutrient, and Solid cycling and Yields (FANSY) model and incorporate it within the Land Model (LM3). The resulting model, LM3-FANSY v1.0, is intended as a baseline for eventual linking of global terrestrial and ocean biogeochemistry in next-generation Earth system models to project global changes that may challenge empirical approaches. LM3-FANSY explicitly resolves interactions between algae, N, P, and solid dynamics in rivers and lakes at 1° spatial and 30 min temporal resolution. Simulated suspended solids (SS), N, and P in multiple forms (particulate or dissolved, organic or inorganic) agree well with measurement-based yield (kg km−2 yr−1), load (kt yr−1), and concentration (mg L−1) estimates across a globally distributed set of large rivers, with an accuracy comparable to other global nutrient and SS models. Furthermore, simulated global river loads of SS, N, and P in different forms to the coastal ocean are consistent with published ranges, though regional biases are apparent. River N loads are estimated to contain approximately equal contributions by dissolved inorganic N (41 %) and dissolved organic N (39 %), with a lesser contribution by particulate organic N (20 %). For river P load estimates, particulate P, which includes both organic and sorbed inorganic forms, is the most abundant form (64 %), followed by dissolved inorganic and organic P (25 % and 11 %). Time series analysis of river solid and nutrient loads in large US rivers for the period ∼ 1963–2000 demonstrates that simulated SS and N loads in different N forms covary with variations of measurement-based loads. LM3-FANSY, however, has less capability to capture interannual variability of P loads, likely due to the lack of terrestrial P dynamics in LM3. Analyses of the model results and sensitivity to components, parameters, and inputs suggest that fluxes from terrestrial litter and soils, wastewater, and weathering are the most critical inputs to the fidelity of simulated river nutrient loads for observation-based estimates. Sensitivity analyses further demonstrate a critical role of algal dynamics in controlling the ratios of inorganic and organic nutrient forms in freshwaters. While the simulations are able to capture significant cross-watershed contrasts at a global scale, disagreement for individual rivers can be substantial. This limitation is shared by other global river models and could be ameliorated through further refinements in nutrient sources, freshwater model dynamics, and observations. Current targets for future LM3-FANSY development include the additions of terrestrial P dynamics, freshwater carbon, alkalinity, enhanced sediment dynamics, and anthropogenic hydraulic controls.

Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0

Abstract. Estimating global river solids, nitrogen (N), and phosphorus (P), in both quantity and composition, is necessary for understanding the development and persistence of many harmful algal blooms, hypoxic events, and other water quality issues in inland and coastal waters. This requires a comprehensive freshwater model that can resolve intertwined algae, solid, and nutrient dynamics, yet previous global watershed models have limited mechanistic resolution of instream biogeochemical processes. Here we develop the global, spatially explicit, and process-based Freshwater Algae, Nutrient, and Solid cycling and Yields (FANSY) model and incorporate it within the Land Model (LM3). The resulting model, LM3-FANSY v1.0, is intended as a baseline for eventual linking of global terrestrial and ocean biogeochemistry in next-generation Earth system models to project global changes that may challenge empirical approaches. LM3-FANSY explicitly resolves interactions between algae, N, P, and solid dynamics in rivers and lakes at 1° spatial and 30 min temporal resolution. Simulated suspended solids (SS), N, and P in multiple forms (particulate or dissolved, organic or inorganic) agree well with measurement-based yield (kg km−2 yr−1), load (kt yr−1), and concentration (mg L−1) estimates across a globally distributed set of large rivers, with an accuracy comparable to other global nutrient and SS models. Furthermore, simulated global river loads of SS, N, and P in different forms to the coastal ocean are consistent with published ranges, though regional biases are apparent. River N loads are estimated to contain approximately equal contributions by dissolved inorganic N (41 %) and dissolved organic N (39 %), with a lesser contribution by particulate organic N (20 %). For river P load estimates, particulate P, which includes both organic and sorbed inorganic forms, is the most abundant form (64 %), followed by dissolved inorganic and organic P (25 % and 11 %). Time series analysis of river solid and nutrient loads in large US rivers for the period ∼ 1963–2000 demonstrates that simulated SS and N loads in different N forms covary with variations of measurement-based loads. LM3-FANSY, however, has less capability to capture interannual variability of P loads, likely due to the lack of terrestrial P dynamics in LM3. Analyses of the model results and sensitivity to components, parameters, and inputs suggest that fluxes from terrestrial litter and soils, wastewater, and weathering are the most critical inputs to the fidelity of simulated river nutrient loads for observation-based estimates. Sensitivity analyses further demonstrate a critical role of algal dynamics in controlling the ratios of inorganic and organic nutrient forms in freshwaters. While the simulations are able to capture significant cross-watershed contrasts at a global scale, disagreement for individual rivers can be substantial. This limitation is shared by other global river models and could be ameliorated through further refinements in nutrient sources, freshwater model dynamics, and observations. Current targets for future LM3-FANSY development include the additions of terrestrial P dynamics, freshwater carbon, alkalinity, enhanced sediment dynamics, and anthropogenic hydraulic controls.

Thermodynamic and life cycle assessment analysis of polymer-containing oily sludge supercritical water gasification system combined with Organic Rankine Cycle

With the development of polymer flooding technology, polymer-containing oily sludge (PCOS) has become a major pollutant on offshore oil platforms. Supercritical water gasification system is an efficient and pollution-free technology compared to conventional PCOS treatment. However, the system model for PCOS has not yet been established and the theoretical model for system optimization is insufficient. In this work, a novel auto-thermal system is established based on supercritical water gasification technology for the treatment of PCOS. Through the thermodynamic analysis, an optimization plan is concluded and used for the establishment of a modified system with Organic Rankine Cycle. Results show that the two-stage reactor reduces the gasification reaction temperature from 652 °C to 502 °C, and the Organic Rankine Cycle system using Isopropanol recover 9.1 % chemical exergy of feedstock with the optimal parameters (superheat, evaporation temperature and circulating pressure). The energy and exergy efficiency of the modified system has increased by 52.4 % and 42.4 % compared to the original system. Besides, a life cycle assessment is adopted for the environment analysis. The results show that the increasing gasification temperature and PCOS slurry concentration can reduce system unit exergy loss. Global Warming Potential reaching the minimum value of 26.1 at 65 wt%.

REMix: A GAMS-based framework for optimizing energy system models

REMix: A GAMS-based framework for optimizing energy system models

Research on Magnetic Levitation Control Method under Elastic Track Conditions Based on Backstepping Method

The vehicle–guideway coupled self-excited vibration of maglev systems is a common control instability problem in maglev traffic while the train is suspended above flexible girders, and it seriously affects the suspension stability of maglev vehicles. In order to solve this problem, a nonlinear dynamic model of a single-point maglev system with elastic track is established in this paper, and a new and more stable adaptive backstepping control method combined with magnetic flux feedback is designed. In order to verify the control effect of the designed control method, a maglev vehicle–guideway coupled experimental platform with elastic track is built, and experimental verifications under rigid and elastic conditions are carried out. The results show that, compared with the state feedback controller based on the feedback linearization controller, the adaptive backstepping control law proposed in this paper can achieve stable suspension under extremely low track stiffness, and that it shows good stability and anti-interference abilities under elastic conditions. This work has an important meaning regarding its potential to benefit the advancement of commercial maglev lines, which may significantly enhance the stability of the maglev system and reduce the cost of guideway construction.

Research on Magnetic Levitation Control Method under Elastic Track Conditions Based on Backstepping Method

The vehicle–guideway coupled self-excited vibration of maglev systems is a common control instability problem in maglev traffic while the train is suspended above flexible girders, and it seriously affects the suspension stability of maglev vehicles. In order to solve this problem, a nonlinear dynamic model of a single-point maglev system with elastic track is established in this paper, and a new and more stable adaptive backstepping control method combined with magnetic flux feedback is designed. In order to verify the control effect of the designed control method, a maglev vehicle–guideway coupled experimental platform with elastic track is built, and experimental verifications under rigid and elastic conditions are carried out. The results show that, compared with the state feedback controller based on the feedback linearization controller, the adaptive backstepping control law proposed in this paper can achieve stable suspension under extremely low track stiffness, and that it shows good stability and anti-interference abilities under elastic conditions. This work has an important meaning regarding its potential to benefit the advancement of commercial maglev lines, which may significantly enhance the stability of the maglev system and reduce the cost of guideway construction.

Seismic mitigation performance of a periodic foundation for nuclear power structures considering soil-structure interactions

Periodic structures may be designed as periodic foundations to reduce the seismic response of engineering structures. The periodic foundation is more suitable for the seismic mitigation of nuclear power structures because of its wide attenuation domain. Based on the dispersion relationship of infinite periodic structures, this research constructs a one-dimensional layered periodic foundation using rubber and concrete materials, determines the attenuation domain frequency band gap that meets the seismic design requirements of nuclear power structures, and optimizes the raft design of nuclear power structures. At the same time, with the scarcity of bedrock sites, the site selection of nuclear power structures is inevitably shifted to soft rock sites. Therefore, this paper also considers the influence of the soil-structure interaction (SSI) on the seismic response of nuclear power structures. A three-dimensional finite element model of the nuclear power structure-periodic foundation-site system was established, and a detailed parameter analysis of the seismic performance of the periodic foundation of nuclear power structures considering the SSI effect was carried out. The results show that the seismic response of nuclear power structures can be significantly reduced by designing the attenuation domain of the periodic foundation.

Dataset of soil hydraulic parameters in the Yellow River Basin based on in situ deep sampling

Soil hydraulic parameters are vital for precisely characterizing soil hydrological processes, which are critical indicators for regulating climate change effects on terrestrial ecosystems and governing feedbacks between water, energy, and carbon–nitrogen cycles. Although many studies have integrated comprehensive soil datasets, data quality and cost challenges result in data completeness deficiencies, especially for deep soil information. These gaps not only impede methodological endeavours but also constrain soil parameter-based ecosystem process studies spanning from local profiles to global earth system models. We established a soil dataset across the entire Yellow River Basin (YRB) (795,000 km2) using high-density in situ field sampling. This observation-based dataset contains records of soil texture (2924), bulk density (2798), saturated hydraulic conductivity (2782), and water retention curve parameters (1035) down to a maximum depth of 5 m. This dataset, which extends the recorded data range for deep soil hydraulic parameters, is valuable as a direct data resource for environmental, agronomical and hydrological studies in the YRB and regions with similar pedological and geological backgrounds around the world.

Data and Management Traffic of IEEE 802.15.4 ZigBee-Based WSN

Wireless sensor networks (WSNs) are an amalgam of wireless technologies. They are extensively utilized in numerous industries, including agriculture, medical, and military fields. In the vast majority of cases, these technologies are deployed in monitoring environmental or physical parameters including sound, pressure, and temperature. WSNs employ various technologies, including radio frequency (RF), Wi-Fi, Bluetooth, ZigBee, and Z-Wave. Zigbee in particular has greater potential for energy-savings in long-distance transmissions, and consequently has emerged as the preferred standard for use in WSNs. In Zigbee-assisted networks, the three primary data-communication devices are ZigBee coordinators, routers, and nodes. The coordinator device gathers, stores, and processes the data before forwarding it to the next appropriate node or the base-station. The system model comprises several zones with each zone containing several sensors. Each sensor node transfers data to the master node, which serves as the ZigBee coordinator. The software used for this simulated investigation is the Riverbed Modeler V17.5. This paper examines the data traffic, management traffic, and load performance of the four modelled systems. The findings demonstrate that whereas the number of coordinators has no effect on data traffic, an increase in the number of routers correspondingly increases both the amount of data sent and received. The MAC follows the same pattern.

Role of the Antarctic Circumpolar Circulation in Current Asymmetric Arctic and Antarctic Warming

AbstractBoth historical observations and recent modeling studies reveal a faster warming in the Arctic compared to the Antarctic. To understand the role of the Antarctic Circumpolar Circulation (ACC) in this warming asymmetry, we simulate the climate mean state and climate response to doubled CO2 under different climate mean ACC states by closing or opening the Drake Passage (DP) with the Community Earth System Model. From closed to open DP, a stronger climate mean ACC leads to a stronger climate mean Atlantic Meridional Overturning Circulation (AMOC), as well as a colder Antarctic but a warmer Arctic in the climate mean state. The less climate mean sea ice coverage in a warmer Arctic implies less extensive sea ice melting under global warming. This causes a reduced asymmetry in warming between the two poles in response to the doubled CO2.

Automatic generation of system model diagrams driven by multi-source heterogeneous data

The demand for functionality, performance, safety, and reliability in complex product domains is continuously increasing, leading to growing product complexity. Traditional document-based systems engineering approaches face numerous challenges in terms of communication efficiency, traceability, and maintenance when dealing with complex product system design. Consequently, Model-Based Systems Engineering (MBSE) has been widely adopted. To improve the efficiency of system modelling, an effective method is needed to automate the process by reusing existing design resources. This study proposes a knowledge graph-based approach for the automatic generation of system model diagrams, consisting of three main steps. Firstly, a system model graph is constructed using an existing system model repository. Then, the required modelling elements are extracted from design documents, and reusable model elements are obtained based on the system model graph. Lastly, a mapping relationship is established between system model elements extracted from multi-source heterogeneous data and SysML metamodel, enabling the automatic generation of system model diagrams based on SysML views. This approach effectively leverages the information from the existing system model repository and achieves automated mapping conversion from multi-source heterogeneous data to the SysML system model. A communication satellite case study is presented to demonstrate the capability of this method.

Design Method of Freeform Surface Optical Systems with Low Coupling Position Error Sensitivity.

Freeform off-axis reflective systems are significantly more difficult to align and assemble owing to their asymmetric surface shapes and system structures. In this study, a freeform surface system design method with low coupling position error sensitivity (FCPESM) was proposed. First, we established a mathematical model of a reflective system when it was perturbed by coupling position errors and used the clustering-microelement method to establish the coupling error sensitivity evaluation function. The evaluation function was then applied to the design process of a freeform surface off-axis three-mirror optical system. The results showed that the FCPESM optical design method can significantly relax the assembly tolerance requirements of optical systems on the basis of ensuring image performance. In this study, the reflective system was perturbed by tilt and decenter simultaneously, and the disturbance mechanism of position errors on optical systems was further improved. Through this research, freeform surface systems with both image performance and error sensitivity can be obtained, which makes freeform off-axis reflective systems with better engineering realizability.

Development of a DNN model using calibrated simulation for the optimal control of HVAC systems

Acquiring actual data for developing data-based prediction models for the optimization of control setpoints (hereinafter referred to as optimal control (OC)) in heating, ventilation, and air conditioning (HVAC) systems is challenging, especially when changing control setpoints while considering all possible boundary conditions. Thus, considering the scalability of OC, it is a promising solution to generate sufficient learning data according to the control settings using a highly accurate simulation model, build a source model, and expand the learning model based on the actual data of the target system. This is the first study to improve the scalability of OC aiming to develop a deep neural network (DNN) model to predict the power consumption of a heat source system. First, this study will construct a calibrated heat source system simulation model and then use the generated learning data to train a DNN model. The actual operation conditions of the target building and heating system located in Vietnam were analyzed using operation data. A physics-based simulation model of the target heat source system was developed and calibrated using actual operation data to improve accuracy. By using the calibrated simulation, sufficient learning data was generated for all control settings allowing for the development of a learning model based on DNN that predicted the power consumption of the heat source system. In addition, a highly accurate prediction model through hyperparameter optimization was secured.

Comparing net zero pathways across the Atlantic A model inter-comparison exercise between the Energy Modeling Forum 37 and the European Climate and Energy Modeling Forum

Europe and North America account for 32 % of current carbon emissions. Due to distinct legacy systems, energy infrastructure, socioeconomic development, and energy resource endowment, both regions have different policy and technological pathways to reach net zero by the mid-century. Against this background, our paper examines the results from the net zero emission scenarios for Europe and North America that emerged from the collaboration of the European and American Energy Modeling Forums. In our analysis, we perform an inter-comparison of various integrated assessments and bottom-up energy system models. A clear qualitative consensus emerges on five main points. First, Europe and the United States reach net zero targets with electrification, demand-side reductions, and carbon capture and sequestration technologies. Second, the use of carbon capture and sequestration is more predominant in the United States due to a steeper decarbonization schedule. Third, the buildings sector is the easiest to electrify in both regions. Fourth, the industrial sector is the hardest to electrify in the United States and transportation in Europe.Fifth, in both regions, the transition in the energy mix is driven by the substitution of coal and natural gas with solar and wind, but to a different extent.

Factorized visual representations in the primate visual system and deep neural networks.

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether ('invariance'), represented in non-interfering subspaces of population activity ('factorization') or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters - lighting, background, camera viewpoint, and object pose - in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.

Operational reliability evaluation integrating prediction models for enhanced situational awareness in wind-integrated system

The extensive implementation of wind power generation (WPG) challenges power systems' flexibility and reliability during operations. Fluctuating wind speeds lead to variability in generation within wind energy systems, directly impacting operational reliability. Therefore, evaluating wind energy systems' situational awareness (SA) is imperative to forecast two-day-ahead operational reliability, facilitating effective power system planning. The foundation of SA for wind energy systems lies in monitoring the wind profile. This involves gaining insights into the system's state through perceiving wind speed and WPG, comprehending the dynamics of its state, and subsequently predicting operational reliability. To achieve this, a Simulink model of the wind system has been developed and integrated with dSPACE hardware through a real-time interface for real-time validation and implementation of a cloud-based IoT platform. This IoT platform aids in acquiring real-time data on wind profiles, enhancing the assessment of SA. The representation of the wind energy system utilizes a multi-state wind speed model, incorporating the inherent randomness of wind energy generation. A time-series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is employed to predict wind speed and WPG for SA-based operational reliability. This model proves instrumental in forecasting the dynamic nature of wind speed and WPG. The proposed approach for operational planning is validated by implementing the wind energy system in real-time on the dSPACE platform and integrating it with IoT. This approach considers the uncertainties associated with wind generation, providing a comprehensive framework for assessing SA and optimizing power system operations.

The control unit of the combined photovoltaic/thermal system

During the last years the design and implementation of energy-saving solutions remains a relevant and integral task for implementation the route of decreasing carbon emissions and reduce the negative impact on the environment. Using combined photovoltaic/thermal systems is limited by their high price connected mainly with the complexity of their control systems, even taking into account availability of government programs for financial support for the use of alternative energy. The novelty of the proposed work consists in the development of a mathematical model and the analytical study of the control system of combined photovoltaic/thermal system for research the operation of control and logic elements and their response to transient and emergency modes during such systems operation. Aim of the work - operational analysis, analytical and optimization studies of the control system of the combined photovoltaic/thermal system to ensure reliable operation in transient and emergency modes and significantly simplify the design of such a system. In this article was implemented mathematical modeling methods and using specialized software. As a result of the work the mathematical model of the control system of combined photovoltaic/thermal system which operates with variable set of control and logic elements was developed. A structural solution of the control system is proposed for further implementation and field testing. The response criteria of the control system to transient and emergency operation modes of the combined photovoltaic/thermal system have been developed. Practical value. It was established the permissible and critical values of a number of parameters characterizing the operation of combined photovoltaic/thermal system in normal mode and deviations from which indicate the presence of an emergency situation are determined. The proposed algorithms of the control system will make it possible to increase the reliability of combined photovoltaic/thermal system and reduce the risk of emergency situations during field tests and further operation.

Recommendation system model based on graph neural network combined with support vector machine and comparative learning for filtering

Recommendation system model based on graph neural network combined with support vector machine and comparative learning for filtering

Research on the design and optimal control of the power take-off (PTO) system for underwater eel-type power generators

Wave energy and ocean current energy are considered stable, reliable, and highly predictable renewable energy sources. The development of ocean current energy conversion technology is crucial in addressing power shortages. However, the low velocity of most ocean currents worldwide, typically less than 1.5 m/s, poses a challenge for traditional ocean current energy capture devices. Current estimations of power generation from underwater devices often significantly differ from actual results. This study presents an eel-type power generation device designed for underwater use, investigates the design and optimization of a hydraulic power take-off (PTO) system suitable for such devices, and examines key components of the hydraulic PTO system like variable hydraulic motors and generators. Analytical research was conducted to understand its operational characteristics, with a focus on components such as the accumulator, speed control valve, and energy storage flywheel. The article also delves into the hydrodynamic and energy conversion characteristics of the device under shallow water wave and current conditions, enhancing the hydraulic PTO system model and establishing an integrated calculation model for real-time data transmission during the calculation process. Through evaluations of pitch angle and power generation under varying sea conditions, the study explores the impact of hydraulic motor displacement and damping coefficient on the hydrodynamic and power generation characteristics of the device, further validated through sea trials. The study findings indicate a high level of parameter adaptation among the components of the hydraulic system. The addition of an accumulator to the system results in smoother output response curves, suggesting that the accumulator absorbs impacts and stabilizes the hydraulic power transmission system. The proposed comprehensive calculation method enables a more precise prediction of the performance of the eel power generation device in real marine environments, capturing its movement behavior and power generation characteristics. Adjusting the motor displacement or damping coefficient under specific conditions can optimize the total damping of the hydraulic PTO system for maximum power output. The optimal power point of the hydraulic power generation system varies for different sea conditions, with a potential power generation efficiency of up to 80.3% under certain circumstances.