Search Results (23)

Underwater vehicles must operate efficiently within the limits of their onboard resources, and travel time is a primary operational objective for extending submerged endurance and range. Ocean currents significantly affect vehicle motion and travel time, either aiding or impeding propulsion depending on their direction and magnitude. This study proposes a cell-based, ocean-current-aware cost formulation for offline three-dimensional (3D) underwater path planning. A 3D grid map integrating ocean current vectors and underwater terrain is constructed, and rather than modifying a specific search algorithm, the proposed approach defines a travel-time-based cost at the cell-transition level by incorporating current effects into the vehicle’s effective velocity. Because the cost operates at the cell-transition level, it can be adopted by standard grid-search planners such as Dijkstra’s, A*, and D* without modification. Simulation experiments over the Tsushima–Jeju corridor using two measured current fields from the Korea Hydrographic and Oceanographic Agency show that identical fields aided westbound transits (−2.4% and −4.4% travel time versus a current-unaware baseline) while opposing eastbound transits (+1.3% and +1.8%), with the planner exploiting favorable flows and detouring to mitigate adverse flows; these effects amplified roughly threefold as the vehicle speed decreased from 15 to 6 knots. A* and Dijkstra’s algorithms returned identical optimal costs under the same formulation, confirming planner independence. The results demonstrate that embedding measured current information at the map level yields realistic, condition-dependent, minimum-travel-time routes for offline mission planning. Full article

The dual-active-bridge (DAB) converter is the core component of the DC micro-grid system; it has the advantages of topological structure symmetry, high efficiency, and high-power density. Model predictive control (MPC) is often employed to improve the dynamic response characteristics of the system, but its strong parameter dependence is a key factor limiting the development of MPC. Therefore, a model-free predictive control (MFPC) method combining an ultra-local model with model predictive control is proposed to solve the problem of strong dependence of traditional MPC on system model parameters. Firstly, establish the ultra-local mathematical model of the DAB converter. The system’s lumped disturbances are identified using the residual prediction method and substituted into the discrete model of the system at the next time step to achieve model-free prediction. Secondly, a minimum back-flow power constraint is added to the cost function to improve the steady-state performance of the converter. Thirdly, in the extended phase shift modulation, the Lagrange multiplier method (LMM) is proposed to reduce the current stress, ultimately achieving the collaborative optimization of the comprehensive performance of the DAB. Finally, a simulation model is built using MATLAB/Simulink, and compared with traditional control methods, the voltage ripple has been reduced by 51.3%, 89.1%, and 85.1%, respectively; the current stress significantly decreases both when the output voltage reference value changes and when the load resistance changes abruptly, and both can basically achieve zero back-flow power operation. The validity and superiority of the proposed strategy have been verified. Full article

Carbon dioxide (CO₂) emissions resulting from natural gas flaring are significant contributors to atmospheric greenhouse gases, posing a substantial risk to the Earth’s climate by exacerbating global warming. As a response, both the oil industry and government authorities are actively exploring cost-effective strategies to address this issue through carbon capture, utilization, and storage (CCUS), as well as reducing natural gas flaring and CO₂ leaks in the oil fields to mitigate the adverse consequences of greenhouse gas emissions. This study presents a numerical investigation of CO₂ utilization for enhanced oil recovery (EOR) and associated CO₂ retention in unconventional reservoirs, using the Bakken Formation as a representative case. A compositional reservoir model is developed to simulate CO₂ Huff-n-Puff (HnP) processes in a fractured horizontal well. The model incorporates dual-porosity and dual-permeability formulations, fluid–rock interactions, and an equation-of-state-based compositional framework to capture multiphase flow behavior. Key operational parameters, including reservoir pressure, injection rate, injection duration, and CO₂ molecular diffusion, are systematically evaluated to assess their impact on oil recovery and CO₂ retention. The results show that lower bottom-hole pressures enhance oil recovery through increased drawdown, while operating pressures near the minimum miscibility pressure (MMP) improve CO₂ solubility and overall retention. Extended injection durations and higher diffusion coefficients increase CO₂ dissolution in the oil phase but exhibit diminishing marginal benefits beyond an optimal injection time. The study quantifies residual and solubility trapping mechanisms during the operational timeframe of CO₂-EOR and provides mechanistic insights into optimizing CO₂-HnP performance in tight formations. The proposed framework establishes a technical basis for integrating CO₂-EOR with emission mitigation strategies in unconventional reservoirs. Full article

The widespread adoption of Electric Vehicles (EVs) is critically dependent on the deployment of efficient charging infrastructure. However, existing facility location models typically treat charging duration as an exogenous parameter, thereby neglecting the traveler’s autonomy to make trade-offs between service time and energy needs based on their Value of Time (VoT). This study addresses this theoretical gap by developing a heterogeneous network design model that endogenizes both charging mode selection and continuous charging duration decisions. A bi-objective optimization framework is formulated to minimize the weighted sum of infrastructure capital expenditure and users’ generalized travel costs. To ensure computational tractability for large-scale networks, an exact linearization technique is applied to reformulate the resulting Mixed-Integer Non-Linear Program (MINLP) into a Mixed-Integer Linear Program (MILP). Application of the model to the Hubei Province highway network reveals a convex Pareto frontier between investment and service quality, providing quantifiable guidance for budget allocation. Empirical results demonstrate that the marginal return on infrastructure investment diminishes rapidly. Specifically, a marginal budget increase from the minimum baseline yields disproportionately large reductions in system-wide dwell time, whereas capital allocation beyond a saturation point yields diminishing returns, offering negligible service gains. Furthermore, sensitivity analysis indicates an asymmetry in technological impact: while extended EV battery ranges significantly reduce user dwell times, they do not proportionally lower the capital required for the foundational infrastructure backbone. These findings suggest that robust infrastructure planning must be decoupled from anticipations of future battery breakthroughs and instead focus on optimizing facility heterogeneity to match evolving traffic flow densities. Full article

During the sudden closure of gates in long-distance gravity flow water supply projects, intense water hammer waves are generated. These waves can cause severe damage to the water supply tunnel structure, posing a significant threat to project safety. To develop an economical and effective hydraulic safety control strategy, this study uses the example of a specific gravity flow water supply project with long-distance and multi-fluctuation pressure tunnels in Zhejiang Province. A novel combined protection strategy was investigated, involving the conversion of construction branch tunnels into branch tunnel surge tanks combined with an overflow surge tank. Numerical simulations of gate closure-induced water hammer pressures were conducted using the method of characteristics. Additionally, the effectiveness of the overflow surge tank on controlling the surge water level in the branch tunnels was analyzed with respect to variations in its height, diameter, and impedance hole diameter. The results indicate that a 300 s linear gate closure without any protective measures induces severe water hammer pressure. Extending the closure time to 1200 s still results in pressures far exceeding the safety threshold. Converting construction branch tunnels into surge tanks effectively controlled the water hammer pressure; however, overflow issues emerged in some branch tunnels. The subsequent addition of an overflow surge tank at the end of the water supply system successfully eliminated the risk of overflow in the branch tunnels. Building upon this, multi-parameter optimization analysis was used to determine the optimal configuration for the overflow surge tank. This solution ensures hydraulic safety while maintaining cost-effectiveness. Both the maximum pressure and the minimum pressure along the water supply tunnel, as well as the surge water levels in all branch tunnels, meet the code requirements. Furthermore, the reduced size of the surge tank significantly lowered construction costs. The findings of this research provide theoretical foundations and technical support for similar long-distance gravity flow water supply projects. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas $ϵ$-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments. Full article

As a cost-effective transitional strategy, the integrated utilization and transportation of hydrogen and natural gas have gained significant attention as a viable pathway toward carbon neutrality. However, hydrogen’s low density, viscosity, and calorific value cause upward migration and accumulation in pipelines, raising embrittlement risks. Its high diffusion and leakage rates also pose significant safety challenges. To address hydrogen–natural gas blending challenges, achieving uniform mixing is crucial. This study systematically examines hydrogen–methane mixing in T-junction pipelines via numerical simulations, analyzing hydrogen mixing ratios (HMR: 10–25%) and methane flow rates (4–10 m/s) to assess flow and mixing dynamics. The coefficient of variation (COV) quantifies mixing uniformity with spatial and temporal analyses, optimizing hydrogen injection for rapid, homogeneous mixing. The key findings are as follows: (1) The uniform mixing length (the minimum axial distance required for the first pipeline cross-section to achieve 95% mixing uniformity) decreases inversely with the HMR, from 100 D to 20.875 D (D represents the pipeline diameter) as the HMR rises from 10% to 25%. (2) Analysis of initial uniform mixing time (defined as the duration required for the first pipeline cross-section to achieve 95% mixing uniformity) shows significant reduction with increasing HMR. While methane flow rate has a less pronounced effect, it nevertheless contributes to reducing the outlet uniform mixing time (defined as the time required to attain 95% mixing uniformity at the pipeline outlet). (3) A fundamental trade-off in engineering applications is established: increasing the HMR reduces mixing length but extends overall mixing time (difference between outlet and initial mixing times), while higher methane flow rates shorten overall mixing time at the cost of increased mixing length. The primary objective of this research is to elucidate the fundamental fluid dynamics of hydrogen–methane mixtures in T-junction pipelines, providing scientific insights for the safe and efficient operation of hydrogen-blended natural gas pipeline systems. Full article

With the rapid development of UAV technology, UAV delivery has gained attention for its potential to reduce labor costs. However, limitations in load capacity and energy restrict UAVs’ distribution capabilities. This paper proposes a cooperative delivery scheme combining traditional trucks and UAVs to extend UAV coverage and improve delivery completion rates. For densely distributed depots in wide-area regions, we develop algorithms for task allocation and path planning in a truck-independent UAV system. Specifically, a minimum-cost, maximum-flow model is constructed to obtain sub-paths covering all delivery tasks, and resource tree-based algorithms are used to construct global paths for UAVs and trucks. Simulation results show that our algorithms reduce total energy consumption by 11.53% and 9.15% under different task points, which suggests that our proposed method can significantly enhance delivery efficiency, offering a promising solution for future logistics operations. Full article

This paper investigates the application of a minimum loss path finding algorithm to determine the maximum flow in generalized networks that are characterized by arc losses or gains. In these generalized network flow problems, each arc has not only a defined capacity but also a loss or gain factor, which must be taken into consideration when calculating the maximum achievable flow. This extension of the traditional maximum flow problem requires a more comprehensive approach, where the maximum amount of flow is determined by accounting for additional factors such as costs, varying arc capacities, and the specific loss or gain associated with each arc. This paper extends the classic Ford–Fulkerson algorithm, adapting it to iteratively identify source-to-sink (s − t) residual directed paths with minimum cumulative loss and generalized augmenting paths (GAPs), thus enabling the efficient computation of maximum flow in such complex networks. Moreover, to enhance the computational performance of the proposed algorithm, we conducted extensive studies on parallelization techniques using graphics processing units (GPUs). Significant improvements in the algorithm’s efficiency and scalability were achieved. The results demonstrate the potential of GPU-accelerated computations in handling real-world applications where generalized network flows with arc losses and gains are prevalent, such as in telecommunications, transportation, or logistics networks. Full article

This paper investigates the optimal remediation process in an aquifer using Modflow 6 software and genetic algorithms. A theoretical confined aquifer has been polluted over a long period of time by unnoticed leakage in a pipeline conveying leachate from an adjacent landfill to a wastewater treatment plant. When the extended leakage and groundwater pollution are discovered, the optimal planning of the remediation strategy is investigated using the pump-and-treat method or/and hydrodynamic control of the pollution. The practical goal is to find the optimal locations and flow rates of two additional pumping wells, which will pump the polluted water or/and control pollution, protecting an existing drinking water pumping well, securing its fully operational mode even during the remediation process with the minimum possible cost, simply represented by the pumped water volume of the additional wells. The remediation process is considered complete when the maximum concentration in the aquifer drops below a certain limit. The Modflow software (handled by the Flopy Python package) simulates the flow field and advective–dispersive mass transport, and a genetic algorithm is used as the optimization tool. The coupled simulation–optimization model, Modflow-GA, complemented by a sophisticated post-processing results analysis, provides optimal and alternate sub-optimal remediation strategies for the decision makers to select from. Full article

Background: With regard to the definition of an optimal transport plan for some material flow in the logistics system in literature and practice, the classical transport task model is developed. The minimization of total transport costs is usually considered for optimality. Some modifications to the classical transport task have also been developed. Methods: The article uses the methods of linear optimization. Based on these methods, two modified transport task models have been constructed, which consider the possibility of planning in advance the quantities available from the suppliers of the transported cargo. These models are applicable in SCM for pharmaceuticals with a national logistics hub. Furthermore, a solver in MS Excel is used to determine the optimal solution of optimization models. Results: Two new (modified, extended) models of the transport task have been constructed, in which a preliminary planning of the available quantities of the transported cargo at the suppliers is made. These quantities shall be planned in such a way as to ensure a minimum total transport cost. Conclusions: By applying the proposed new transport task models, lower total transport costs for carrying out imported pharmaceuticals can be ensured compared to an application of the classical transport task model. Full article

Over the past few years, fuel prices have increased dramatically, and emissions regulations have become stricter in maritime applications. In order to take these factors into consideration, improvements in fuel consumption have become a mandatory factor and a main task of research and development departments in this area. Internal combustion engines (ICEs) can exploit only about 15–40% of chemical energy to produce work effectively, while most of the fuel energy is wasted through exhaust gases and coolant. Although there is a significant amount of wasted energy in thermal processes, the quality of that energy is low owing to its low temperature and provides limited potential for power generation consequently. Waste heat recovery (WHR) systems take advantage of the available waste heat for producing power by utilizing heat energy lost to the surroundings at no additional fuel costs. Among all available waste heat sources in the engine, exhaust gas is the most potent candidate for WHR due to its high level of exergy. Regarding WHR technologies, the well-known Rankine cycles are considered the most promising candidate for improving ICE thermal efficiency. This study is carried out for a six-cylinder marine diesel engine model operating with a WHR organic Rankine cycle (ORC) model that utilizes engine exhaust energy as input. Using expander inlet conditions in the ORC model, preliminary turbine design characteristics are calculated. For this mean-line model, a MATLAB code has been developed. In off-design expander analysis, performance maps are created for different speed and pressure ratios. Results are produced by integrating the polynomial correlations between all of these parameters into the ORC model. ORC efficiency varies in design and off-design conditions which are due to changes in expander input conditions and, consequently, net power output. In this study, ORC efficiency varies from a minimum of 6% to a maximum of 12.7%. ORC efficiency performance is also affected by certain variables such as the coolant flow rate, heat exchanger’s performance etc. It is calculated that with the increase of coolant flow rate, ORC efficiency increases due to the higher turbine work output that is made possible, and the condensing pressure decreases. It is calculated that ORC can improve engine Brake Specific Fuel Consumption (BSFC) from a minimum of 2.9% to a maximum of 5.1%, corresponding to different engine operating points. Thus, decreasing overall fuel consumption shows a positive effect on engine performance. It can also increase engine power output by up to 5.42% if so required for applications where this may be deemed necessary and where an appropriate mechanical connection is made between the engine shaft and the expander shaft. The ORC analysis uses a bespoke expander design methodology and couples it to an ORC design architecture method to provide an important methodology for high-efficiency marine diesel engine systems that can extend well beyond the marine sector and into the broader ORC WHR field and are applicable to many industries (as detailed in the Introduction section of this paper). Full article

Microgrids have become an alternative for integrating distributed generation to supply energy to isolated communities, so their control and optimal management are important. This research designs and simulates the three levels of control of a DC microgrid operating in isolated mode and proposes an Energy Management System (EMS) based on Model Predictive Control (MPC), with real-time measurement feedback for optimal energy dispatch, which ensures power flow distribution and operation at minimum cost while extending the lifespan of the BESS. The EMS can react to disturbances produced in the lower control levels. The microgrid’s performance is analyzed and compared in two scenarios without EMS, and with EMS against changes in irradiation and changes in electricity demand. The fulfillment of the power balance is evaluated by analyzing the power delivered by each generation unit, the operating cost, and the state of charge of the battery (SOC). Full article

Optimal power flow (OPF), a mathematical programming problem extending power flow relationships, is one of the essential tools in the operation and control of power grids. To name but a few, the primary goals of OPF are to meet system demand at minimum production cost, minimum emission, and minimum voltage deviation. Being at the heart of power system problems for half a century, the OPF can be split into two significant categories, namely optimal active power flow (OAPF) and optimal reactive power flow (ORPF). The OPF is spontaneously a complicated non-linear and non-convex problem; however, it becomes more complex by considering different constraints and restrictions having to do with real power grids. Furthermore, power system operators in the modern-day power networks implement new limitations to the problem. Consequently, the OPF problem becomes more and more complex which can exacerbate the situation from mathematical and computational standpoints. Thus, it is crucially important to decipher the most appropriate methods to solve different types of OPF problems. Although a copious number of mathematical-based methods have been employed to handle the problem over the years, there exist some counterpoints, which prevent them from being a universal solver for different versions of the OPF problem. To address such issues, innovative alternatives, namely heuristic algorithms, have been introduced by many researchers. Inasmuch as these state-of-the-art algorithms show a significant degree of convenience in dealing with a variety of optimization problems irrespective of their complexities, they have been under the spotlight for more than a decade. This paper provides an extensive review of the latest applications of heuristic-based optimization algorithms so as to solve different versions of the OPF problem. In addition, a comprehensive review of the available methods from various dimensions is presented. Reviewing about 200 works is the most significant characteristic of this paper that adds significant value to its exhaustiveness. Full article

No study has been previously completed on the range of sites, potential yield, and financial characteristics of poplar short rotation coppice plantations (SRC) in Hungary. This paper conducts a literature survey to reveal the biomass production potential of such plantations and presents a model that is used to analyze their financial performance. The results indicate that the break-even-point of production is between 6 and 8 oven-dry tons per hectare per year once a minimum cost level and wood chip price within a 10% range of the 2020 value are considered. The higher the wood chip price, the lower the break-even-point. Since the model excluded the administrative costs that depend on the type and size of the management organization, the break-even-points can be significantly higher in reality, which suggests that short rotation energy plantations can be a financially reasonable land-use option in above average or even superior poplar-growing sites. The rotation period of industrial poplar plantations that produce high quality veneer logs ranges from 12 to 25 years. Though such sites can provide higher returns on investment, short rotation plantations have the advantage of providing a more evenly distributed cash flow. To facilitate the wider application of poplar SRC, the related policies need to apply specific subsidies and allow the rotation cycle to be extended up to 20–25 years, which is currently limited to 15 years. Full article