Optimal System Research Articles

Exploring aluminum as a solid thermal storage medium for solar cooking application: An experimental investigation coupled with numerical modeling using OpenFOAM

The intermittent nature of solar energy presents a significant challenge to its reliability, particularly in applications that require a consistent energy supply, such as cooking. This issue is especially critical in emerging economies with abundant solar resources, where sustainable energy solutions are needed to reduce reliance on traditional fuels. To address this challenge, this study introduces a novel solar thermal storage (STS), utilizing a metal-based material to accumulate and retain heat for off-sunshine hour cooking. The research focuses on optimizing aluminium as the STS material, evaluating its temperature suitability, efficiency, and heat retention capabilities for household solar cooking applications. Numerical simulations using the OpenFOAM framework were conducted to analyze heat transfer within the cooker, determining the optimal size of the aluminium block based on existing literature and predefined parameters. Practical experiments, including solar-induced heating-cooling cycles and controlled cooking tests, were carried out to validate the findings. Experimental results demonstrate STS's ability to efficiently absorb and retain heat, reaching a maximum of 235 °C during a 5.5-h heating session. The water boiling experiment further confirmed STS's practical utility, effectively transferring stored heat to cooking tasks and sustaining temperatures up to 160 °C even after the test. Additionally, experiments with black lentils and chicken stew highlighted aluminium's suitability for practical cooking applications, showcasing its ability to sustain high temperatures and efficiently transfer stored heat despite longer cooking times. The study's novelty lies in integrating numerical modeling with experimental analysis to optimize STS systems, providing practical guidelines for efficient thermal storage in cooking applications. This research advances beyond previous efforts by providing a validated methodology for the design and optimization of thermal storage systems. It improves the reliability and adaptability of solar energy for cooking applications.

Enhancing safety and performance of hybrid supercapacitors through material system optimization

Enhancing safety and performance of hybrid supercapacitors through material system optimization

Cost-effective and optimal control analysis for mitigation strategy to chocolate spot disease of faba bean

Faba bean is one of the most important grown plants worldwide for human and animal. Despite its various importance, the productivity of faba bean has been constrained by several biotic and abiotic factors. Many faba bean pathogens have been reported so far, of which the most important yield limiting disease is Chocolate Spot Disease (Botrytis fabae). The dynamics of disease transmission and decision-making processes for intervention programs for disease control are now better understood through the use of mathematical modeling. In this paper a deterministic mathematical model for Chocolate Spot disease (CSD) on faba bean plant with an optimal control model was developed and analyzed to examine the best strategy in controlling CSD. The optimal control model is developed with three control interventions, namely prevention (u1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u_{1}$$\\end{document}), quarantine (u2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u_{2}$$\\end{document}) and chemical control (u3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u_{3}$$\\end{document}). The Pontryagin’€™s maximum principle isused to derive the Hamiltonian, the adjoint variables, the characterization of the controls and the optimality system. A cost-effective approach is chosen from a set of possible integrated strategies using the incremental cost-effectiveness ratio (ICER). The forward-backward sweep iterative approach is used to run numerical simulations. We obtained the Hamiltonian, the adjoint variables, the characterization of the controls and the optimality system. The numerical results demonstrate that each integrated strategy can reduce the diseases within the specified period. However due to limited resources, an integrated strategy prevention and uprooting was found to be a best cost-effective strategy to combat CSD. Therefore, attention should be given for the integrated cost-effective and environmentally eco-friendly strategy by stake holders and policy makers to control CSD and disseminate the integrated intervention to the farmers in order to fight the spread of CSD in the Faba bean population and produce the expected yield from the field.

Flexural Behavior of 3D-Printed Carbon Fiber-Reinforced Nylon Lattice Beams.

This study investigates the flexural behavior of 3D-printed multi-topology lattice beams, with a specific emphasis on octet and cube lattice geometries created through fused deposition modeling (FDM). The mechanical properties of these beams were evaluated through quasi-static three-point bending tests. A comparative analysis of load-carrying capacity, energy absorption, and specific energy absorption (SEA) indicates that octet lattice beams exhibit superior performance to cube lattice beams. The octet lattice beam in the triple-layer double-column (TL-DC) arrangement absorbed 14.99 J of energy, representing a 38% increase compared to the 10.86 J absorbed by the cube lattice beam in the same design. The specific energy absorption (SEA) of the octet beam was measured at 0.39 J/g, which exceeds the 0.29 J/g recorded for the cube beam. Two distinct types of deformations were identified for the struts and the beam layers. Octet struts exhibit enhanced performance in stretch-dominated zones, whereas the cube system demonstrates superior efficacy in compressive-dominated regions. The results highlight the enhanced efficacy of octet lattice structures in energy absorption and mechanical stability maintenance. The investigation of sandwich lattice topologies integrating octet and cube structures indicates that while hybrid designs may exhibit efficiency, uniform octet structures yield superior performance. This study provides valuable insights into the structural design and optimization of lattice systems for applications requiring high-energy absorption and mechanical robustness.

An analytical approach for evaluation of conveyor exit strategies in open-pit mines

Transportation is the most expensive task in deep open-pit mining operations. An In-Pit Crushing and Conveying (IPCC) system, as an alternative to the conventional shovel-truck system, requires precise planning to avoid wasting high capital expenditures on trucks. The decision to fit a conveyor system into the pit design is challenging and needs to be addressed in the literature. Further optimisation of the IPCC system requires choosing an appropriate conveyor exit strategy. Conveyor exit schemes include a Dedicated Ramp Slot (DRS), an Existing Ramping System, an inclined tunnel and High-Angle Conveyors (HAC). This paper provides an analytical approach to compare the cost of different conveyor exit strategies in open-pit mines. The results indicate that for a depth up to 100 m, the Present Value of Cost (PVC) for the DRS is the least. For the cases deeper than 300 m, the HAC has the least PVC. Sensitivity analysis showed that for the depths between 100 and 300 m, a more accurate and detailed definition of parameters is needed. Besides, in most cases, the HAC provides the lowest payback period and the highest internal rate of return among the alternatives.

Tetrafunctionalized Azocalix[4]resorcinarene Dye: A Chromogenic Supramolecule Used for the Selective Liquid-Liquid Extraction and Spectrophotometric Determination of Cu(II)

Background: The detection and extraction of trace metal ions, particularly copper(II), are critical for environmental monitoring and industrial processes. Calixresorcinarene, with its unique cavity structure, offers excellent platforms for designing selective chemosensors and extractants. Functionalization of calixresorcinarene with azo groups can enhance their chromogenic properties, enabling both extraction and detection in a single step. Objective: This study aimed to evaluate its (Azocalix[4]resorcinaren) efficacy as a selective chemosensor for the liquid-liquid extraction and spectrophotometric determination of Cu(II) ions. Methods: Application in Extraction and Detection: The ability of the dye to selectively extract Cu(II) ions from aqueous solutions was investigated via liquid-liquid extraction experiments. The dye-Cu(II) complex formation was monitored by UV-Vis spectrophotometry, with systematic optimization of experimental conditions, including pH, solvent system, and extraction duration. Results: The synthesized azocalix[4]resorcinarene dye exhibited a pronounced selectivity towards Cu(II) ions, forming a stable, colored complex. The complexation induced a distinct bathochromic shift in the absorption spectrum, allowing for precise spectrophotometric detection. Optimal extraction was achieved at a specific pH and solvent combination, with the method demonstrating a low detection limit and high sensitivity. The dye showed minimal interference from other metal ions, confirming its selectivity for Cu(II). Conclusion: The tetrafunctionalized azocalix[4]resorcinarene dye is a highly effective chromogenic agent for the selective extraction and detection of Cu(II) ions. Its robust performance in both extraction efficiency and spectrophotometric detection underscores its potential utility in environmental analysis and industrial applications where trace metal detection is crucial.

MODERNIZING CLASSICAL OPTIMAL CONTROL: HARNESSING DIRECT AND INDIRECT OPTIMIZATION

An introduction to optimal control, a fundamental concept in engineering and science disciplines, is a process of finding ways of controlling dynamic systems in such a way that they achieve certain goals while being subjected to given state limitations. The conventional approaches developed in the past have been integral to solving optimal control problems, including the maximum principle belonging to Pontryagin and the dynamic programming method. While relatively straightforward, these methods are not always amenable to higher-dimensional scenarios, interacting forces, or other non-trivial constraints. This paper presents a new methodology to extend classical optimal control, considering both direct and indirect optimization techniques. The direct methods, Euler and Runge-Kutta, Trapezoidal, and Hermite-Simpson, do not require the explicit derivation of the analytical control laws to execute control trajectories. Semi-analytical techniques, such as the shooting method, are based the control laws on proposed adjoint differential equations. This paper presents the basic outline of the classical optimal control problem and discusses the direct and indirect optimization methods. It is illustrated by referencing various examples, like the fixed-rate royalty payment approach. After outlining each framework, we identify the positive and negative aspects of the approach in questions of consistency and performance. Finally, the problems on the synergy of direct and indirect methods are considered further, and areas of further development of the presented methods and their integration with the more sophisticated tools, such as machine learning are identified. It can be concluded that the approach of using both direct and indirect optimization methods presents great potential in modernizing the classical optimal control, which challenges the conventional techniques and indicates potential for further development of the control system optimization.

Evaluation of influential factors on energy system optimisation

Energy system analysis is gaining more importance in political decisions, science, and society, thus, the basis of interpreting the results is increasingly being questioned. Different approaches have been used in energy system models: Scenario variations, changing base assumptions or sensitivity analyses. A direct comparison of these approaches aims to show that the influence on the results is comparable for all and thus needs to be considered alongside each other. Therefore, this paper uses energy system optimisation within a case study on Germany. It confirms that all approaches greatly influence the results and are thus suitable for discussing uncertainties. Among other things, the sole change of the reference weather year can have a comparable influence on the emissions just like the specification of an entirely different mobility scenario (+44% and +56% respectively). However, the target function (total costs) is significantly more influenced by scenarios. Among the evaluated parameters, the investment costs of renewable technologies and the potential of renewable feedstock affect the system the most. In summary, all three methods provide important insights into the optimisation model and should therefore be used together for validation, discussion and design of energy system models.

Advancing Solar Power Efficiency: Innovations in Material Science and System Optimization for Enhanced Solar Energy Conversion

This study aims to advance solar power efficiency through innovations in material science, surface engineering, and system optimization. The research integrates experimental testing and computational modeling to assess improvements in energy conversion efficiency. The methodology includes testing photovoltaic materials such as perovskite and multi-junction cells, anti-reflective coatings, self-cleaning surfaces, and optimizing panel orientation, cooling systems, and Maximum Power Point Tracking (MPPT) algorithms. Perovskite cells demonstrated a 22% increase in efficiency, while multi-junction cells achieved up to 35% efficiency. Anti-reflective coatings and self-cleaning surfaces improved energy capture by 4% and 7%, respectively. System optimization, including dual-axis trackers and AI-enhanced MPPT algorithms, provided additional efficiency gains of up to 25% and 10%, respectively. These findings demonstrate significant advancements in solar panel performance, providing a clear path for enhancing scalability and commercial viability in diverse environmental conditions. The integration of innovative materials and system optimizations presents a promising solution for making solar energy more sustainable and cost-effective. Future research should focus on improving the durability and reducing the costs of these technologies for widespread adoption.

Optimizing a hybrid wind-solar-biomass system with battery and hydrogen storage using generic algorithm-particle swarm optimization for performance assessment

This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8 %, achieved an availability index of 99.2 %, a renewable energy fraction of 88.87 %, GHGs emission of 953,615 kg, land use of 3,842,875 m2, and water consumption 528,678 L respectively. GAPSO achieved a 2.17 % and 0.01 % improvement in cost-effectiveness and 11.11 % increase in reliability compared to GA and PSO.

Influence of Aqueous Phase Salt and Oil Phase Surfactants and Viscosity on the Dynamic Interfacial Tension and Coalescence Timescales.

Liquid-liquid separation is a critically important process in the treatment of emulsions that can occur in our environment, such as oily stormwater, shipboard bilgewater, or off-shore oil spill treatment. Effective filtration systems, including coalescing filters, are essential for mitigating these environmental pollutants. Achieving this requires a comprehensive understanding of liquid-liquid interface dynamics influenced by additives and surfactants. Furthermore, understanding the impact of surfactants on emulsion stability in saline environments is vital for optimizing filtration processes and ensuring the protection of marine and freshwater ecosystems. In this work, these effects are highlighted using measurements performed across a range of droplet size, surfactant concentration, viscosity ratios, and saline presence. Dynamic IFT measurements are conducted using the pendant drop method for water in light mineral oil, with and without salt in the water phase. The effect of salt addition is also highlighted by using microfluidic coalescence experiments, in which it was found that the addition of salt increases the dimensionless drainage time below the critical micelle concentration. The second focus of this work is to study the effect of bulk phase viscosity on the stability. Dynamic IFT measurements are performed at both millimeter and micrometer scales using pendant drop experiments and microfluidic tensiometry, respectively, involving light and heavy mineral oils with varying SPAN80 surfactant concentrations. The surfactant diffusivity and interfacial adsorption and desorption rates are then extracted by fitting a surfactant diffusion and equation of state equations to the dynamic IFT measurements. The results of the IFT decay, surfactant diffusivity, and adsorption rates are compared at two different viscosity ratios. This study also compares the times required for IFT relaxation with the film drainage times in water-in-oil systems. The comparison aids in comprehending the impact of competing timescales during film drainage. The findings presented in this paper offer valuable insights into the design and optimization of liquid-liquid filtration systems, especially when operating under challenging environmental conditions, such as in saline environments. The principles explored here can be applied to improving industrial water treatment and in the design of advanced filtration technologies for chemical and petrochemical industries, particularly those involving flow, contributing to more sustainable and efficient practices in handling emulsified waste streams.

Performance and economic analysis of various geothermal power plant systems: Case study for the Chingshui geothermal field in Taiwan

Geothermal power plants are a source of sustainable energy. However, their performance is influenced by various factors, including production temperature and well locations, and few studies have investigated the relationships between these factors. To address this research gap, the present study developed integrated models of a geothermal reservoir and a variety of power plant systems. By using COMSOL and MATLAB, we conducted simulations for the Chingshui geothermal field in Taiwan to investigate the performance and economic feasibility of five common types of geothermal power plant systems over their operational lifetime under different well configurations. The key findings of this investigation are as follows. First, the locations of production wells are a primary factor influencing production temperature. Constructing wells in low-permeability zones results in considerable increases in the pressure differential in the injection pump; thus, wells should not be constructed in such zones. Second, as system flow rate and well depth decrease, production temperature remains relatively stable, net power output decreases, and system efficiency increases. This efficiency increase is caused by decreases in pumping power requirements at lower flow rates and shallower well depths. Finally, among the investigated systems, the organic Rankine cycle system had the lowest electricity production cost and shortest payback period within the enthalpy range of 500–1000 kJ/kg. Double-flash (DF) and DF–binary systems are also viable options when the mass flow rate is 10–20 kg/s and the enthalpy is 700–800 kJ/kg. Overall, the results of this study offer valuable insights for the design of geothermal power plants, thereby contributing to the optimization of renewable energy systems.

Simulation and Experiments on Optimization of Vortex-Induced Vibration Power Generation System Based on Side-by-Side Double Blunt Bodies

In order to improve the utilization efficiency of converting low-flow current energy into electric energy for Reynolds number 10,000 ≤ Re ≤ 40,000, this paper proposes a vortex-induced vibration power generation system based on a side-by-side double blunt body. In this system, the side-by-side double blunt body structure is used in the current energy capture part to enhance the collection of low-flow current energy; the permanent magnet linear motor is used in the electric energy conversion part to improve the efficiency of electric energy conversion; and a laboratory device is constructed for testing. The effects of the blunt body structure parameters and the center spacing ratio on the energy harvesting performance of the system are qualitatively explained by constructing a simulation model. Compared with the single blunt body energy capture structure, the side-by-side double blunt body structure increases the vibration amplitude by 1.04 times and the lift by 1.14 times at the center spacing S/D = 2.4. Meanwhile, energy harvesting can be realized at a lower flow velocity, increasing the vortex-induced vibration’s energy capture range. Finally, the power generation system was experimentally verified in the laboratory, and the results showed that the vibration amplitude of the double blunt body structure was increased by 1.12 times compared to the single blunt body. The maximum output power of the generator is 10.55 W when the water velocity is 0.7 m/s. The energy conversion efficiency of the power generation system can reach a maximum of 52.93%, which is 12.33% higher than that of a single blunt body structure, which proves that the system has a higher power conversion efficiency than that of a conventional single conversion system.

Mitigating local minima in extracting optimal parameters for photovoltaic models: An optimizer leveraging multiple initial populations (OLMIP)

The accurate identification of parameters in photovoltaic models is of paramount importance to accurately predict the electrical behavior of photovoltaic systems and improve their performance. Nowadays, this task poses a multimodal optimization problem, aiming to find the best combination of parameters that fit the model to real data. This article presents a proposal for an Optimizer Leveraging Multiple Initial Populations (OLMIP) that aims to achieve optimal solutions while effectively avoiding undesirable local optima. By utilizing a separate evolution strategy involving four distinct initial populations, followed by the construction of an elite population, the algorithm can explore multiple regions of the search space and escape local minima. Experiments were conducted with four models: the single diode, double diode, triple diode from RTC France cell, and Photowatt-PWP201 module. The mean squared errors obtained are 9.860219E-04, 9.824849E-04, 9.824849E-04, and 2.425075 E−03, respectively. These results indicate that the algorithm achieves superior or comparable accuracy to that of six competitors. Furthermore, the statistical analysis of the results, including the Wilcoxon and Friedman tests, confirms the robustness and effectiveness of the approach used for parameter estimation in photovoltaic systems. These findings open up new prospects for the improvement and optimization of photovoltaic systems.

Optimization of combustion chamber geometry for a two-stroke spark-ignited direct-injection opposed-piston rod-less aviation kerosene engine

Spark-ignited direct-injection opposed-piston rod-less kerosene engines are highly suitable for small and medium-size unmanned aerial vehicles (UAVs) because of the high power-to-weight ratio, self-balancing ability, and safety of such engines. The design and optimization of combustion systems are crucial for power, fuel economy, and knock control. To meet the increasing demand for UAV power systems and fill the research void in this area, reported here for the first time are the design and optimization of a swirl-wall-guided combustion system including the spray pattern and pit structure on the top surface of the piston, with the dynamic, fuel economy, and knock intensity as the evaluation indicators. The results indicate that the spray pattern mainly affects the fuel escape rate and inhomogeneity. After it is injected into the pit, the fuel is guided to the combustion chamber by the steering role of the guide arc. If the spray drop point is located away from the pit, it leads to increased fuel escape rate and inhomogeneity. The turbulent kinetic energy in the spark-plug zone decreases as the pit diameter decreases, fluctuating by 48 %, while the average value remains relatively constant. The distribution is influenced primarily by the enveloping effect of the pit structure on the airflow. Under low-speed conditions, as the pit diameter decreases, the ignition delay period increases and then decreases, and the combustion duration decreases monotonically. The fluctuation of indicated power is relatively small (2.64 %), and the fuel escape rate decreases monotonically, resulting in the same variation pattern of indicated specific fuel consumption (ISFC). When the pit diameter is 43 mm, the fuel escape rate and ISFC reach their lowest values. Knock combustion near the wall is influenced mainly by two factors: the temperature rise rate and the flame propagation speed. The temperature rise rate increases significantly with decreasing pit diameter. When the pit diameter is less than 50 mm, knock combustion begins to occur. Under high-speed equivalent combustion conditions, as the pit diameter decreases, the indicated power changes by 5.21 %, and the ISFC decreases monotonically.

Dynamic liquid level prediction for multiple oil wells based on transfer learning and multidimensional feature fusion network

Abstract Accurate prediction of the dynamic liquid level (DLL) in oil wells is crucial for the intelligent optimization of pumping systems. It not only provides real-time insights into the operational conditions of the pumping system but also supports the optimization of operational parameters with data. However, due to the long-term operation of oil wells and their complex internal environments, direct measurement of the DLL is challenging, leading to low reliability of the obtained data. Therefore, this paper conducts an in-depth analysis of the parameters involved in the pumping process, identifies the model's input features, and develops a DLL prediction model for multiple wells based on multidimensional feature fusion (MFF). This model captures the characteristics of DLL changes and the diversity of input features. To address the issues of slow model training and low prediction accuracy caused by insufficient datasets in practical applications, this paper integrates transfer learning techniques and proposes a new model, the DLL model for multiple wells based on Transfer Learning and Multidimensional Feature Fusion (TMFF). Initially, the Euclidean distance and Maximum mean discrepancy methods are employed to verify the feature similarity between the source and target domains, using highly similar DLL data as experimental data. By combining transfer learning techniques with the MFF model, the TMFF model is established. The model's capabilities are validated using field-collected data with broad representativeness. Experimental results demonstrate that the proposed MFF model possesses high accuracy and generalization capability. Additionally, the TMFF model effectively resolves the issue of insufficient data during model training. In summary, the methods proposed in this paper can provide accurate DLL data for practical applications in intelligent oilfields.

Optical turbulence simulations over the Tibetan Plateau based on a single-column model with high-order closure

Optical turbulence limits the maximum resolution of ground-based telescopes and leads to image degradation. The use of atmospheric numerical techniques to forecast optical turbulence is crucial for observation scheduling and optimization of adaptive optic systems. Current research methods for forecasting optical turbulence are primarily based on mesoscale models with turbulence closure techniques to parameterize the key terms required for C n 2 calculations under the assumption of atmospheric quasi-steady-state balance, and then the integrated astroclimatic parameters related to C n 2 profile can be obtained. In this study, we propose what we believe to be a novel approach to forecast C n 2 using a boundary layer parameterization based on higher-order turbulence closure in the single-column framework of the CLUBB model. Compared to mesoscale models, the CLUBB model serves as a single-column model, which simplifies modifications and reduces compilation time, and is more conducive to testing the performance of C n 2 parameterization scheme. In the design of C n 2 parameterization scheme, we consider a more complete physical process rather than omitting certain terms to obtain a steady-state solution. The performance of the model is evaluated using measurements obtained during a field campaign conducted at Da Qaidam site above the Tibetan Plateau. The results show that the model is able to capture typical features of the C n 2 profile evolution under convective conditions. Comparison of the model with contemporaneous sounding measurements and quantification of the model’s performance using statistical operators demonstrate the statistical agreement between simulations and measurements. In terms of atmospheric seeing, we can observe a bias of 0.01 and a root-mean-square error (RMSE) of 0.31 without any model calibration, which outperforms the results of previous mesoscale modeling studies. In addition, the new parameterization scheme is also compared with two representative C n 2 algorithms previously used in the mesoscale models, with some improvements observed. The potential demonstrated by this approach is expected to bring greater value and advancement to the research of three-dimensional forecasting of optical turbulence in the future by coupling with the mesoscale model.

Selection of design parameters of a spacecraft electric propulsion engine ensuring the geostationary orbit raising mission

The problem of selecting suboptimal parameters of an electric propulsion system as part of a spacecraft, ensuring a geostationary orbit raising mission using a Soyuz-5 launch vehicle at the initial launch stage, is investigated. An expression is obtained for the main optimality criterion – the payload relative mass. An algorithm is obtained for solving the problem of parametric optimization of an electric propulsion system as part of a hybrid transportation system. The task of selecting the optimal electric propulsion engine for launching a spacecraft with an electric propulsion system to a geostationary orbit using the launch vehicle under consideration is solved. A design ballistic calculation and a comparative analysis of the obtained data are performed.

Performance assessment of a 30.26 kW grid-connected photovoltaic plant in Egypt

Abstract This paper presents an experimental analysis and performance evaluation of a grid-connected photovoltaic plant installed on the rooftop of the Electronics Research Institute in Cairo, Egypt. Cairo is classified as a hot-desert climate region according to the standard Koppen-Geiger climate classification system. Over a year, we monitored real-time data to assess key system performance metrics, such as energy yield, efficiencies, performance ratio, capacity factor, and losses. Based on the obtained experimental results, the highest final yield of 5.2498 hr/day was observed in the summer, whereas the lowest yield of 3.439 hr/day occurred in the winter months. The photovoltaic plant had an average annual system efficiency of 15.8%, while the photovoltaic and inverter had mean yearly efficiencies of 17.1% and 97.2%, respectively. The average annual performance ratio is 83.03%, and the capacity factor is 18.72%. The monthly total loss exhibited a linear rise alongside increasing ambient temperature and solar irradiance. The ambient temperature affected the system efficiency, photovoltaic efficiency, and performance ratio. The findings can help strengthen forecasts of future large-scale photovoltaic plants in hot desert climates. Moreover, they can guide the design, optimization, operation, and maintenance of new grid-connected photovoltaic systems.

Optimal Operation of the Power-to-Gas Storage System Considering Energy Optimization in the Independent Electrical System

Optimal Operation of the Power-to-Gas Storage System Considering Energy Optimization in the Independent Electrical System