Energy Extraction Research Articles

π-Conjugation-Induced In Situ Nanoscale Ordering of Spiro-OMeTAD Boosts the Efficiency and Stability of Perovskite Solar Cells.

Spiro-OMeTAD hole transport materials typically exhibit an amorphous state in perovskite solar cells. However, the lack of structural ordering leads to weak intermolecular interaction, inferior carrier transfer, and poor stability in devices. Herein, we developed a π-conjugation-induced short-range ordering strategy to modulate the stacking order of spiro-OMeTAD during film formation. A clear molecular ordering at the nanoscale is observed, which enhances intermolecular π-π stacking in spiro-OMeTAD and enables effective carrier extraction and favorable energy level alignment. The nanoscale-ordered spiro-OMeTAD allows the achievement of perovskite solar cells with a champion efficiency of 25.37%, surpassing devices utilizing amorphous spiro-OMeTAD (23.52%). The unencapsulated device demonstrates enhanced operational stability by retaining 98% of its initial efficiency under continuous 1 sun equivalent illumination at 60 °C for 840 h. This work establishes a significant and valid modulation concept for the stacking order of organic transport materials, paving the way for the development of efficient and stable perovskite solar cells.

Artificial intelligence assisted prediction of optimum operating conditions of shell and tube heat exchangers: A grey‐box approach

AbstractIn this study, a Grey‐box (GB) model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger (STHE) under varying process conditions. Aspen Exchanger Design and Rating (Aspen‐EDR) was initially used to construct a first principle model (FP) of the STHE using industrial data. The Genetic Algorithm (GA) was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions. A dataset comprised of optimum process conditions was generated through FP‐GA integration and was utilised to develop an Artificial Neural Networks (ANN) model. Subsequently, the ANN model was merged with the FP model by substituting the GA, to form a GB model. The developed GB model, that is, ANN and FP integration, achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP model. Performance of the GB framework was also comparable to the FP‐GA approach but it significantly reduced the computation time required for estimating the optimum process conditions. The proposed GB‐based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.

High‐Gain Boost Converter Based Energy Harvesting Method for the Ultra‐Low Voltage Difference of HVDC Transmission Line

ABSTRACTOnline monitoring sensors are crucial for ensuring the long‐term stable operation of high‐voltage direct current (HVDC) transmission systems. However, ensuring a sustainable and reliable energy supply for these sensors in the HVDC transmission line is extremely challenging. Energy harvesting is an efficient solution to meet the energy supply needs of self‐powered online monitoring sensors. This work proposes a novel method for harvesting energy from the ultra‐low voltage difference of HVDC transmission line using a high‐gain boost converter. Firstly, the structure of the energy harvesting system is proposed. Then, the mechanism by which the gain of the boost converter is easily affected by parasitic parameters of the circuit and device under ultra‐low input voltage is analyzed. A method for automatically stabilizing the output voltage of the boost converter using a post‐stage energy management circuit is proposed. This method avoids the use of closed‐loop control circuits, which would otherwise increase losses. Finally, the boost converter with high gain is designed to elevate the ultra‐low voltage difference to a higher voltage, providing sufficient power for the online monitoring sensors. The effectiveness of the proposed method has been verified through simulations and experiments, demonstrating that the energy harvesting system can extract energy from a voltage difference as low as 35 mV. Additionally, the system can produce a stable output power of up to 128 mW when the voltage difference is 100 mV.

Assessing the design of integrated methane sensing networks

Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom-up (i.e., activity level) or top-down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom-up and top-down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom-up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom-up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.

Application of improved Jellyfish search algorithm for 9-parameters cell extraction and GMPPT in PV systems

Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions.

Multi-Objective Optimization of an Inertial Wave Energy Converter for Multi-Directional Wave Scatter

To advance wave energy devices towards commercialization, it is essential to optimize their design to enhance system performance. Additionally, a thorough economic evaluation is crucial for making these technologies competitive with other renewable energy sources. This study focuses on the techno-economic optimization of an innovative inertial system, the so-called SWINGO system, which is based on gyropendulum technology. SWINGO stands out due to its high energy efficiency in multi-directional installation sites, where wave directions vary significantly throughout the year. The study introduces the application of a multi-objective Evolutionary Algorithm (EA), specifically the Non-dominated Sorting Genetic Algorithm II (NSGA-II), to optimize the techno-economic performance of the SWINGO system. This approach aims to identify optimal design parameters that maximize energy extraction while considering economic viability. By deriving a Pareto frontier, a set of optimal devices is selected for further analysis. The performance of the SWINGO system is also compared to an alternative (mono-directional) inertial wave energy converter, the Inertial Sea Wave Energy Converter (ISWEC), to highlight the differences in techno-economic outcomes. Both systems are evaluated at two different installation sites: Pantelleria island and the North Sea in Denmark, with a focus on the directional wave scatter at each location.

Optimization of deposition time on structural, morphological, and optical properties of Cd1-xZnxS nanocrystalline thin films

In this work, Cd1-xZnxS nanocrystalline thin films have been synthesized over a glass substrate by a simple and cost-effective Chemical Bath Deposition (CBD) technique at various deposition time keeping other deposition parameters constant. The structural, morphological, and optical properties are studied by incorporating a range of characterization techniques, including X-ray diffraction (XRD), Field Emission Scanning Electron Microscopes (FESEM), Energy Dispersive X-rays (EDX), and UV–Visible spectrophotometers (UV–Vis). The films have been deposited at five different deposition time in the range from 30 mins to 150 mins to record the changes which address in depth the structural, morphological and optical properties. Empirical fitting equations have also been developed to obtain a more quantitative analysis of the effect of deposition time. The results obtained in this work are compared with some recent reported works for performance analysis of the buffer layer. The proposed buffer layer deposited at 30 mins not only attains all the desirable qualities required for maximum extraction of solar energy but also a low-cost and environment friendly option, making it a potential candidate for solar cell and optoelectronic applications.

Numerical simulation for subsidence control in CO2 storage and methane hydrate extraction

Gas hydrates are increasingly viewed as a promising alternative to traditional fossil fuels. However, their extraction process poses risks to structural integrity, potentially causing significant subsidence. In this study, we developed a Thermo-Hydro-Mechanical-Chemical (THMC) model to analyze the impact of gas hydrate extraction on seabed subsidence. Our investigation focused on the influence of bottom hole flowing pressure, initial hydrate concentration, gas saturation, permeability, porosity, and rock thermal conductivity on subsidence during gas hydrate extraction via depressurization.The results show that seabed subsidence is affected by various factors such as bottom hole flowing pressure, initial hydrate concentration, gas saturation, permeability, porosity, and rock thermal conductivity. It was noted that significant subsidence is associated with low initial hydrate concentration, high permeability, porosity, low gas saturation, low rock thermal conductivity, and a notable pressure drop of 79.31%.To address this issue, we propose a seabed subsidence mitigation strategy involving CO2 injection. This approach not only safeguards offshore infrastructure and coastal communities but also helps reduce CO2 emissions, aligning with global climate change mitigation efforts. In our model, CO2 injection occurs in the subsurface reservoir at the interface between the free water zone and hydrate-bearing formations. The CO2 hydrates formation process releases heat, which dissociates methane hydrates, allowing the methane to be replaced by CO2 molecules and move towards the production well.Our analysis reveals that increasing injection temperature and rate significantly reduces subsidence. Additionally, the range of investigated injection pressures, which included pressures equal to and more than double the initial reservoir pressure, showed inconsequential impacts on seabed subsidence.The effectiveness of subsidence reduction is significantly enhanced by injecting a CO2/N2 mixture compared to pure CO2 injection. The most substantial reduction in subsidence occurred when a mixture of CO2 and N2 in a 50/50 vol/vol ratio was injected at a high rate.These findings offer crucial insights for optimizing the efficiency and control of gas hydrate extraction methods. They emphasize the importance of employing balanced injection strategies to minimize environmental risks and ensure sustainable energy extraction.

Dynamics and energy harvesting of a flow-induced snapping sheet with nonuniform stiffness distribution

Energy harvesting through periodic snap-through of a buckled sheet has recently gained considerable attention because of its potential applications in energy harvesting in low incoming flow. Although the snapping dynamics of uniform buckled sheets has been extensively studied, the present work focuses on the energy harvesting and dynamics of a nonuniform snapping sheet with both of its ends clamped in a channel flow. The analysis reveals that the sheet undergoes periodic snap-through oscillations, with its rear half consistently serving as the main contributor to effective energy harvesting, and the potential energy contributing significantly more than the kinetic energy. Varying the stiffness difference ΔEI* shows that increasing the stiffness of the rear part and decreasing that of the fore part shifts the deformation wave toward upstream and enhances the snapping amplitude of the fore part, optimizing energy extraction. At a length compression ratio ΔL* = 0.3, the maximum potential energy is observed for ΔEI* = 1, and the total energy peaks at ΔEI* = 2. The study also identifies an optimal ΔL* = 0.4 that maximizes both total and potential energies, and triples the potential energy in comparison with ΔL* = 0.1. However, the enhancement of nonuniformity disappears at ΔL* > 0.3 for the total energy and ΔL* > 0.2 for the potential energy. These findings provide insights to aid optimization of the design and performance of snapping sheet energy harvesters.

Near-Complete Stored Energy Extraction From Large Core Fibers Using Coherent Pulse Stacking Amplification of Femtosecond Pulses

Near-Complete Stored Energy Extraction From Large Core Fibers Using Coherent Pulse Stacking Amplification of Femtosecond Pulses

Nonlinear Model Predictive Control of Heaving Wave Energy Converter with Nonlinear Froude–Krylov Forces

Wave energy holds significant promise as a renewable energy source due to the consistent and predictable nature of ocean waves. However, optimizing wave energy devices is essential for achieving competitive viability in the energy market. This paper presents the application of a nonlinear model predictive controller (MPC) to enhance the energy extraction of a heaving point absorber. The wave energy converter (WEC) model accounts for the nonlinear dynamics and static Froude–Krylov forces, which are essential in accurately representing the system’s behavior. The nonlinear MPC is tested under irregular wave conditions within the power production region, where constraints on displacement and the power take-off (PTO) force are enforced to ensure the WEC’s safety while maximizing energy absorption. A comparison is made with a linear MPC, which uses a linear approximation of the Froude–Krylov forces. The study comprehensively compares power performance and computational costs between the linear and nonlinear MPC approaches. Both MPC variants determine the optimal PTO force to maximize energy absorption, utilizing (1) a linear WEC model (LMPC) for state predictions and (2) a nonlinear model (NLMPC) incorporating exact Froude–Krylov forces. Additionally, the study analyzes four controller configurations, varying the MPC prediction horizon and re-optimization time. The results indicate that, in general, the NLMPC achieves higher energy absorption than the LMPC. The nonlinear model also better adheres to system constraints, with the linear model showing some displacement violations. This paper further discusses the computational load and power generation implications of adjusting the prediction horizon and re-optimization time parameters in the NLMPC.

Sustainable solution to the critical challenges of an oceanic wave farm for maximizing power generation

In this paper, a general wave farm design procedure has been presented. For example, the design procedure is applied to the Bay of Bengal, Bangladesh. Firstly, the most effective site is selected to construct a wave farm considering various techno-economic factors. A coordinated drawing for the proposed site selection procedure is presented, which includes a coastal area map and sea, grid map, wave height, and different assorted factors. Transmission line cost is analyzed for nearly equipotential locations. Then wave height, time period, wave power, and vertical speed are observed for the selected location. In the next step, a wave farm layout has been designed considering point absorber type device and the oceanic wave dataset with statistical analysis. Further, a linear electrical generator (LEG) is designed and analyzed for the wave farm. The generator is simulated in the ANSYS/Maxwell environment. Since the wave parameters vary from time to time, the LEG is simulated for several vertical speeds. To find the highest output power, the load profiles are analyzed for each speed separately. In the next step, the LEG is optimized. Simulation result shows that the flux distribution of optimized LEG is better than its initially designed counterpart. Efficiency of the optimized LEG is 7 % greater than its initial design. The proposed method of site selection, statistical analysis of the wave dataset, and the optimized LEG design are combined in this paper for maximum wave energy extraction.

Self-Powered Flexible Sensors: From Fundamental Mechanisms toward Diverse Applications

Abstract Today, human life is inseparable from energy for clothing, food, housing, use and travelling, however, traditional resources can no longer meet our energy needs today. Self-powered sensing devices that can run consistently and sustainably without a source of power from the outside are top choices for new energy sources with their unique advantages. Based on advances in the field of materials and manufacturing techniques, it is able to autonomously extract energy from different sources. In this review, we focus on reviewing the current state of research on self-powered wearable sensors, providing a concise overview of energy harvesting technologies, energy conversion mechanisms, structural or material innovations, energy storage and processing platforms. Then，experimental advances in different energy sources are presented, showing their underlying mechanisms, and the potential for energy acquisition. In addition, we discuss applications of self-powered flexible sensors in medicine, sports and food. Despite considerable achievements in this field, widespread commercialization will necessitate enhanced sensor detection abilities, improved design factors for adaptable devices, and a harmony between sensitivity and standardisation.

Experimental Implementation of Reinforcement Learning Applied to Maximise Energy from a Wave Energy Converter

Wave energy has the potential to provide a sustainable solution for global energy demands, particularly in coastal regions. This study explores the use of reinforcement learning (RL), specifically the Q-learning algorithm, to optimise the energy extraction capabilities of a wave energy converter (WEC) using a single-body point absorber with resistive control. Experimental validation demonstrated that Q-learning effectively optimises the power take-off (PTO) damping coefficient, leading to an energy output that closely aligns with theoretical predictions. The stability observed after approximately 40 episodes highlights the capability of Q-learning for real-time optimisation, even under irregular wave conditions. The results also showed an improvement in efficiency of 12% for the theoretical case and 11.3% for the experimental case from the initial to the optimised state, underscoring the effectiveness of the RL strategy. The simplicity of the resistive control strategy makes it a viable solution for practical engineering applications, reducing the complexity and cost of deployment. This study provides a significant step towards bridging the gap between the theoretical modelling and experimental implementation of RL-based WEC systems, contributing to the advancement of sustainable ocean energy technologies.

Propulsion and energy extraction performance of wave-powered mechanism considering wave and current

Wave-powered mechanism can extract wave energy as propulsion, enabling long-term ocean observation. The kinematic properties of wave propulsion mechanisms are susceptible to ocean current loads in addition to wave effects, presenting complex dynamic behaviors. In this paper, to understand the effects of ocean currents on the kinematic performances of wave propulsion mechanism, the dynamic performance of a wave propulsion mechanism in the combined condition of wave and current is analyzed based on numerical simulation method for fluid-rigid body coupling and experimental methods. The performance of the propulsion mechanism against the current under counter-current conditions and the contribution of flow to the propulsion performance under co-current conditions are discussed. The intrinsic relationship between wave height, current velocity and current direction is investigated, and the energy extraction performance of wave propulsion mechanism is analyzed. According to the results, the ability of propulsion mechanism to resist counter-current is obtained, and it is found that as the counter-current velocity increases, the hydrofoil will stall and the propulsion force and efficiency decreases significantly. Whilst, the co-current has a promoting effect on the forward of the mechanism, but within a certain range of co-current speed, the mechanism will be negatively affected by the shedding vortex. And the discrimination mechanisms for wave-dominated propulsion and current-dominated propulsion are obtained. This work reveals the combined effect of waves and currents on the motion performance of wave propulsion mechanism, providing a reference for the development of surface vehicles that utilize multiple ocean energies for synergistic propulsion.

Thermo-hydro-mechanical fully coupled model for enhanced geothermal system and numerical solution method based on finite volume method

Enhanced geothermal system (EGS) has been widely used to improve the production performance of geothermal reservoirs. Extracting the heat energy from geothermal reservoirs involves complex multi-physical process, referred to as the thermal–hydro–mechanical (T–H–M) coupling. To optimize the production schedule, numerical simulator is an important tool for EGS development. However, the numerical solution method based on FVM is rare in the literature. To address this gap, in this study, we derive the mathematical equations of fully coupled T–H–M model. Then, the solution method based on FVM is established. Next, the its accuracy is verified against two cases. Last, the proposed method is applied to an EGS model to simulate the heat extraction process. The evolutions of pressure, temperature, and fracture aperture are analyzed. The effects of controlling parameters of the extraction efficiency are discussed. Results indicate that increasing the injection rate will reduce the lifespan of EGS but accelerate the energy extraction. Increasing the injection temperature can promote the heat extraction to limited extent. Increasing the matrix permeability can significantly improve the extraction efficiency. Enlarging the fracture spacing can enlarge the heat recovery rate. In addition, reducing the fracture permeability can avoid thermal shortcut and improve the production performance.

Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model

The application of floating offshore wind turbines means better exploitation of offshore wind resources. However, the six-degree of freedom motion characteristics of floating platforms bring greater challenges to control system design. Based on the dynamic characteristics of semi-submersible floating offshore wind turbines, this paper establishes a simplified nonlinear dynamic model for control system design. On this basis, a complete framework for nonlinear model predictive control of maximum wind energy extraction for semi-submersible floating offshore wind turbines considering wind and wave disturbances is developed. Based on the previewed wind and wave, a dynamic optimization problem with both state and control constraints is constructed, considering maximum wind energy extraction and torque fluctuation. Then, an improved equilibrium optimizer is proposed to address the nonlinear non-convex dynamic optimization problems, which achieves a better trade-off between exploitation and exploration. Simulation results verify the superiority of the proposed nonlinear model predictive control framework via the improved equilibrium optimizer, and the influences of different control algorithms on the platform motion state, power coefficient, and equivalent wind speed are analyzed.

Use of data science in the development of concrete based on construction and demolition waste: a brazilian view

The growth in urbanization and construction activity has led to a growing increase in construction and demolition waste and the extraction of natural resources, energy, and CO2. The projection is that these figures will expand even more in the coming years, making it worrying for a sustainable balance in construction, especially in the manufacture of concrete, which is one of the most consumed materials globally. In this context, recycled concrete has emerged as a sustainable alternative to traditional concrete. Concepts such as urban mining combined with data mining and machine learning techniques are emerging as alternatives that serve as a basis for producing recycled concrete with the required qualities. Therefore, this study aims to map the existing research in the areas of materials and technology, focusing on the recycling and reuse of construction and demolition waste (CDW) and the manufacture of recycled concrete. Surveying this research will provide guidance for future research and encourage more sustainable standards in the construction sector.

Tapering-enhanced high-efficiency THz waveguide oscillator

Using a waveguide in a THz FEL has been shown to maximize the coupling and enable efficient extraction of relativistic electron beam energy from a single passage through a tapered helical undulator. An oscillator configuration can further boost energy extraction above the single-pass limits and open the door toward very high average power THz sources. Embedding the undulator in an oscillator cavity is particularly useful in combination with high repetition rate electron sources, even if at reduced peak brightness, since recycling a fraction of the radiation as an intense seed can compensate for lower single-pass gain. In this paper, we investigate the efficiency scaling of a tapering-enhanced waveguide oscillator, showcasing its capability for frequency-tuning operation and high-efficiency operation at different wavelengths. Using a thermionic-driven beamline equipped with compression elements, numerical start-to-end simulation results indicate a 16% efficiency at 200 GHz and a 2.1% efficiency at 1.5 THz, resulting in kW-level average power out-coupled in radiation pulses with few hundred μJ energy and tens of MW peak power. Published by the American Physical Society 2024

A High-Efficiency Piezoelectric Energy Harvesting and Management Circuit Based on Full-Bridge Rectification

This paper presents a high-efficiency piezoelectric energy harvesting and management circuit utilizing a full-bridge rectifier (FBR) designed for powering wireless sensor nodes. The circuit comprises a rectifier bridge, a fully CMOS-based reference source, and an energy management system. The rectifier bridge uses a PMOS cross-coupled structure to greatly reduce the conduction voltage drop. The CMOS reference source provides the necessary reference voltage and current. The energy management system delivers a stable 1.8 V to the load and controls its operation in intermittent bursts. Fabricated with a 110 nm CMOS process, the circuit occupies an area of 0.6 mm2, and is housed in a QFN32 package. Test results indicate that under 40 Hz frequency and 4 g acceleration vibrations, the chip’s energy extraction power reaches 234 μW, with the load operating every 3 s at a supply voltage of 1.8 V. Thus, this FBR interface circuit efficiently harnesses the energy output from the piezoelectric energy harvester.