Load Operating Conditions Research Articles

The Influence of Structure Optimization on Vortex Suppression and Energy Dissipation in the Draft Tube of Francis Turbine

Under partial load operating conditions, vortex rope generation in the draft tube of a Francis turbine is considered one of the main reasons for hydro unit vibration. In this paper, a Francis turbine HLA551-LJ-43 in the laboratory was taken as a prototype. Numerical simulations of the entire flow passage were carried out. Four different hydro-turbines were chosen to analyze the effect of vortex suppression, which were named the prototype turbine (N-J), the turbine with J-grooves installed on its conical section (W-J), the one with extending runner cone (C), and the one that considered the J-grooves and the extending runner cone at the same time (J+C). Under the part load conditions in which the vortex rope is easily generated (0.4–0.8 times design flow QBEP), the spectrum characteristics of pressure fluctuation, the morphology of vortex rope, and the energy dissipation based on the entropy production theory in the draft tube were studied. The results show that the three optimized structures W-J, C, and J+C could reduce the pressure pulsation in the conical section of the draft tube, weaken the eccentricity of the vortex rope, and decrease the energy losses in the runner and draft tube. It is worth mentioning that the turbine with a J+C optimized structure had the most potent effect on vortex suppression and energy dissipation. Primarily when operating in deep partial load (DPL) conditions, the efficiency of the turbine with a J+C optimized structure was increased by 13.7% compared to the prototype turbine, and the main frequency amplitude of the pressure pulsation in the draft tube was reduced to 32% of the prototype.

Physics-informed machine learning approach for reduced-order modeling of integrally bladed rotors: Theory and application

Integrally bladed rotors are commonly used in aircraft and rocket turbomachinery and known to exhibit complex dynamics when subject to operational loading conditions. Though nominally cyclic symmetric structures, in practice, cyclic symmetry is destroyed due to mistuning caused by random sector-to-sector imperfections in material properties and geometry. Simulating mistuned blisk dynamics using high-fidelity models can be computationally expensive, thus, a variety of physics-based reduced-order models have been previously developed. However, these models cannot easily incorporate experimental data nor leverage potential benefits of data-driven and machine-learning-based approaches. Here, we present a novel first-of-its-kind physics-informed machine learning modeling approach that incorporates physical laws directly into a novel network architecture while maintaining a sector-level viewpoint. The approach is combined with an assembly procedure resulting in a significantly smaller linear system based on blade-alone response data, and can directly incorporate physical response data like that measured with blade tip timing and/or traveling-wave excitation. Validation is shown using a large-scale finite-element model, with multiple traveling-wave forced-response predictions and response selection cases considered. Using only as little as a single degree of freedom per sector from the blade tip, this approach shows high accuracy relative to high-fidelity simulations.

Study on intelligent tyre force estimation algorithm based on strain analysis

The objective of this paper is to propose a novel approach to develop an algorithm for estimating the grounding force between a tyre and the road surface. First, a finite element model of a heavy-duty tyre is developed in this paper, and the validity of the model is verified by load application tests and mechanical vibration tests. When the tyres are operated under various operating conditions, that is, load, side deflection, side tilt and longitudinal slip conditions, the sensitivity analysis is performed by combining the three-directional forces based on the strain curves measured when the tyres are rolling, respectively. In order to achieve accurate estimation of the three-directional forces, this paper establishes a three-directional force estimation model using the support vector machine (SVM) technique, with the tyre force-sensitive eigenvalues as inputs and the tyre forces as outputs, so as to achieve the three-directional force estimation of the tyre-grounded three-directional forces. joint estimation of tyre-road contact force. The results show that the estimation error of vertical force is 1.79%, the estimation error of lateral force is 3%, the estimation error of longitudinal force is 4.65% and in general, the estimation error of grounding force is kept within 5%, and the estimation effect is good.

Box-Behnken assisted RSM and ANN modelling for biodiesel production over titanium supported zinc-oxide catalyst

Global utilization of Green fuels mitigates the harmful effects of climate change caused by greenhouse gas emissions from fossil fuels. Biodiesel was identified as an alternative fuel to satisfy the growing need for energy. Waste cooking oil is applied as a feedstock due to legitimate worries about using food crops to produce fuel. In this study, optimization of biodiesel synthesized from waste cooking soybean oil in the presence of a novel enhanced titanium-supported zinc oxide (ZnO/TiO2) catalyst had been reported. The aim was to use DOE to correlate relationships between optimal biodiesel productivity and the operating parameters. The influence of catalyst loading, methanol-to-oil ratio, and reaction temperature were investigated using a time-efficient Box-Behnken design of response surface methodology and artificial neural network. The predicted and experimental yield was comparable with 94.04 % (BBD-RSM), 93.99 % (ANN), and 94.42 % respectively. A significant biodiesel yield of 94.93 % was obtained at optimal operating conditions of catalyst loading (21.9 wt%), reaction temperature of 55 OC, and methanol oil ratios of 8:1. Comparative analysis indicates higher prediction capabilities for RSM than the ANN model in terms of lowest error functionality and highest correlation coefficient. However, the obtained FAME has properties within the standard limits set for biodiesel.

Analysis of the Cyclic Strength of Technical Systems under Complex Operating Loading Conditions

Analysis of the Cyclic Strength of Technical Systems under Complex Operating Loading Conditions

Data-driven MPPT techniques for optimizing vehicular fuel cell performance in hybrid DC microgrid

This paper aims to apply data-driven maximum power point tracking (MPPT) techniques specifically tailored for fuel cell vehicle (FCV) supported hybrid DC microgrids to enhance the power harvesting capability of fuel cell (FC) stacks. Compared to existing MPPT techniques, the current study focuses on developing and evaluating data-driven approaches for maximum power extraction by dynamically determining the operating point of FC power sources through a Zeta converter. An in-depth analysis is conducted by considering parameters such as efficiency, tracking accuracy, response time, and robustness to variations in load demand and operating conditions. The performance results validate that the developed three-layer neural network (TNN)-based MPPT gives better performance findings than Gaussian process regression (GPR), support vector regression (SVR), decision tree regression (DTR), and bagging ensemble decision tree (BEDT). In the performance evaluation phase, a vehicular FC with a rating of 1.26 kW is designed and operated within the temperature range of 320 K to 343 K for hydrogen pressure values ranging from 1 bar to 1.8 bar. For these operational conditions, the prediction accuracy value of the proposed TNN method is 99.6% while the performance values GPR, SVR, DTR, and BEDT are 99%, 98.6%, 97.2%, and 96%. In addition, system efficiency is increased by 0.98%, 1.25%, 2.51%, and 3.02% compared to GPR, SVR, DTR, and BEDT, respectively.

Analyzing the effect of carbon nanoparticles on the combustion performance and emissions of a DI diesel engine fueled with the diesel-methanol blend

With the energy crisis and worsening environmental pollution, methanol, as a carbon-neutral fuel, has become a research hotspot in the field of internal combustion engines. This study discusses the influence of different kinds (graphite oxide, GO; multilayer graphene, MG; carbon nanotubes, CNT) and concentrations of carbon nanoparticles, which do not introduce any new elements, added to the diesel-methanol blend on combustion performance and exhaust emissions in a direct injection (DI) diesel engine across diverse operating load conditions. The results showed that the nano-fuels with carbon nano-additives had a better combustion process, with higher peak cylinder pressure (Pmax) and peak heat release rate (HRRmax), and lower ignition delay (ID) and combustion duration (CD). Moreover, the presence of carbon nano-additives resulted in an improvement in brake thermal efficiency (BTE) and a decrement in the break-specific fuel consumption (BSFC). The nano-fuels with GO showed the best optimization effect on the combustion process, followed by MG and CNT. As for emission, the nano-fuels had fewer emissions of CO, HC, and smoke opacity. The nano-fuels with CNT achieved the most significant decrease in emissions of CO and HC. While the presence of GO had the best reduction effect in smoke opacity. However, there was a slight increase in NOx emission. The experimental findings demonstrate that the utilization of carbon nanoparticles in nano-fuels can effectively enhance the application of methanol in internal combustion engines.

Sustainable energy storage solutions for coal-fired power plants: A comparative study on the integration of liquid air energy storage and hydrogen energy storage systems

With the majority of the world's energy demand still reliant on fossil fuels, particularly coal, mitigating the substantial carbon dioxide (CO2) emissions from coal-fired power plants is imperative for achieving a net-zero carbon future. Energy storage technologies offer a viable solution to provide better flexibility against load fluctuations and reduce the carbon footprint of coal-fired power plants by minimizing exergy losses, thereby achieving better energy efficiency. This work focuses on developing two such energy storage technologies: Liquid Air Energy Storage (LAES) and Hydrogen Energy Storage (HES), and their integration strategies with a sub-critical coal-fired power plant. The performance of the integrated systems is analyzed based on key parameters like energy storage capacity, net unit power output during charging and discharging, round-trip efficiencies, and net unit efficiencies at different operating load conditions of the plant. Our results show that during the charging cycles, by increasing the air flow rate in LAES and the hydrogen production rate in HES, the net unit efficiency of the integrated plant decreases by 3–10% at different load conditions, while the energy-based round-trip efficiency increases with increasing liquid air and hydrogen flow rates during the discharge cycles. For the HES system, utilization of the produced hydrogen in the fuel cells gives 40–50% better roundtrip efficiencies than co-firing hydrogen in the boiler at full and minimum load and it increases with increasing hydrogen flow rates. While HES has shown a better overall net unit efficiency (41.5%, in fuel cell scenario) of the integrated unit than the LAES system (40%) for the base case operation, LAES shows superior round-trip efficiencies when operated at higher liquid air discharge flow rates (>80 kg/s). The results provide insights into the system modeling of LAES and HES integrated with a sub-critical coal power plant, contributing to the advancement of sustainable energy storage solutions.

Performance optimization of an integrated solar combined cycle for the cogeneration of electricity and fresh water

Many Arabic countries suffer water scarcity while having an energy surplus. The present work investigates and optimizes a proposed plant for the cogeneration of electricity and fresh water. This plant comprises an Integrated Solar Combined Cycle (ISCC) power plant and a Multi Stage Flash (MSF) desalination unit. The steam condenser of the ISCC acts as a brine heater of the MSF desalination unit. Thus, the waste thermal energy from the ISCC is used to desalinate the seawater. The proposed cycle produces fresh water during its part load operating conditions. Simulation programs were developed and validated using real data from the ISCC power plant in Kuraymat-Egypt. The present results show that the proposed plant is very promising for the cogeneration of electricity and fresh water. An optimum top brine temperature (To) of 80 °C was estimated based on the amount of fuel required to desalinate the seawater. The proposed plant produces up to 1140 m3/h of fresh water under the typical operating conditions of Hurghada-Egypt, 50 % of the rated output power, and To = 80 °C. It saves up to 84.7 % of the fuel required for desalination compared to a standalone MSF desalination unit that operates under the same conditions.

Buckling behaviour of the stiffened hygrothermally stable composite plates with interfacial debond subjected to environmental and operational loading conditions

Buckling behaviour of the stiffened hygrothermally stable composite plates with interfacial debond subjected to environmental and operational loading conditions

Flow Field Analysis of Francis Turbine Draft Tube using POD at Design and Part Load Operating Conditions

Flow Field Analysis of Francis Turbine Draft Tube using POD at Design and Part Load Operating Conditions

Determination of the Strength of a Container with Sandwich Panel Walls under Operational Loading Conditions

Purpose. The main purpose of this work is to present the results of determining the strength of a container with sandwich panel walls under the main operating modes of loading. Methodology. To ensure the strength of the bearing structure of the container, it is proposed to make its end and side walls from sandwich panels. This involves the creation of sandwich panels from two metal sheets, between which there is a filler in the form of an energy-absorbing material. The thickness of the sandwich panel sheets is determined by the Bubnov-Galerkin method. The sheet is considered as a thin-walled plate with the corresponding width and height parameters. To determine the strength of the load-bearing structure of a container with sandwich panel walls, the corresponding calculations were performed using the finite element method in the SolidWorks Simulation software package. The Mises criterion (IV theory of strength) was used as a calculation criterion. The spatial model of the container was created in SolidWorks. Findings. Taking into account the calculations, the rational thickness of the sheets, in terms of strength, of the side and end walls was established, which was 1.6 and about 3.0 mm, respectively. It is important that the use of rectangular corrugations makes it possible to reduce the thickness of the end wall sheet to 1.0 mm. The thickness of the side wall sheet is the same. The maximum stresses in the container in the case of its longitudinal loading occur in the fittings and amount to 268.3 MPa, which is 13.6 % lower than the permissible ones. The maximum stresses in the container under transverse loading were recorded in the areas of interaction between the side wall and the corner posts. The numerical value of these stresses was 178 MPa, which is 15.2 % lower than the permissible ones. Originality. The paper scientifically substantiates the use of sandwich panels as the walls of a uni-versal container. Practical value. The research will contribute to the creation of recommendations for the design of modern modular-type vehicle structures and improve the efficiency of the transport industry.

Operating condition optimization of liquid metal heat pipe using deep learning based genetic algorithm: Heat transfer performance

Liquid metal heat pipes play a critical role in various high-temperature applications, with their optimization being pivotal to achieving optimal thermal performance. In this study, a deep learning based genetic algorithm is suggested to optimize the operating conditions of liquid metal heat pipes. The optimization performance was investigated in both single and multi-variable optimization schemes, considering the operating conditions of heat load, inclination angle, and filling ratio. The single-variable optimization indicated reasonable performance for various conditions, reinforcing the potential applicability of the optimization method across a broad spectrum of high-temperature industries. The multi-variable optimization revealed an almost congruent performance level to single-variable optimization, suggesting that the robustness of optimization method is not compromised with additional variables. Furthermore, the generalization performance of the optimization method was investigated by conducting an experimental investigation, proving a similar performance. This study underlines the potential of optimizing the operating condition of heat pipes, with significant consequences in sectors such as high temperature field, thereby offering a pathway to more efficient, cost-effective thermal solutions.

Multichannel energy monitoring based on the sliding window method in an industrial environment

The research on nonintrusive load monitoring (NILM) typically focuses on residential and commercial users. However, high-energy industrial users also require load monitoring analysis to understand their electricity consumption patterns and load operating conditions. Currently used load monitoring methods and parameters are not applicable to industrial environments. In this paper, we propose an innovative multichannel low window sequence-to-point (MLSP) method based on deep neural networks for NILM of high-energy industrial users. First, the data from each channel of the main line terminal are normalized and then aggregated into a time series, which is several times longer than the original data to expand its data volume. Then, the multichannel low window sequence-to-point method is used to select the window data of several times the length, corresponding to the active power of the target device. Finally, the discriminative features of the data are learned using a deep neural network model. The analysis of actual industrial user data shows that the lowest mean absolute error (MAE) of the original sequence-to-point method for related branches is 20.38, and the highest correlation coefficient (CC) is 90.23%. Our proposed method reduces the MAE to 11.02 and increases the CC to 97.42%. The MAE and CC indicators of all branches decreased by 25.8% and increased by 22.4%, respectively. According to Mean Absolute Percentage Error (MAPE), the performance of this model is improved by 39.6% compared with Seq2point. Compared with the original sequence-to-point method, our proposed method achieves better load monitoring results and can more accurately decompose load fluctuations, which are closer to the actual load curve.

Flow Characteristics Analysis of a 1 GW Hydraulic Turbine at Rated Condition and Overload Operation Condition

Flow stability is extremely important for hydraulic turbines, especially for 1 GW hydraulic turbines, and has a strong impact on mesh stability. However, turbines often operate under non-design conditions, and current research on this aspect is still lacking. So a model of the fluid domains of a high-quality installed 1 GW Francis turbine was established to investigate the flow characteristics of the turbine and fluid domains. CFD simulations of a 1 GW Francis turbine under rated load and overload operation conditions were performed. According to simulation results, when the turbine is under the overload operation condition, the internal flow stability of the 1 GW hydraulic turbine can be obviously different from that of the rated load. In the overload condition, the flow field is more turbulent and a large number of vortices are generated in the draft tube, resulting in significant changes in pressure, flow rate, and output. In order to improve calculation accuracy, a pure clearance model containing only clearances and pressure balance pipes was established. The results of the full flow channel and pure clearance were compared. It was found that under the rated operating condition and the overload condition, compared with the pure clearance model, the axial force of the runner calculated by the full flow channel model is approximately 2–7% biased, the radial force is biased by approximately 7–8%, and the leakage flow is smaller.

Heat and mass transfer characteristics of vertical falling film cylindrical plastic surfaces under partial wetting conditions for liquid desiccant regeneration

The regenerator plays a dominant role in the overall energy-economic performance of liquid desiccant systems. Nevertheless, very few studies have been reported on falling film liquid desiccant regenerators. Most of the previous studies targeted experimental/numerical modelling needs of regeneration process under design conditions. In the current research workexperimental investigation of regeneration process is carried out to study the coupled heat and mass transfer process for adiabatic regeneration of LiCl liquid desiccant over outer surface of vertical Plain and Modified polypropylene cylindrical surfaces under design (complete wetting) and part load (partial wetting) operating conditions. Modified cylindrical surfaces are developed to compensate the poor wetting of the plastic surfaces. New generalised one-dimensional numerical philosophy is proposed to accommodate the need of part load operating conditions by inclusion of actual wetting of the working surface as well as convective heat transfer happening from dry patches of solid surface to the flowing air. The heat and mass transfer coefficients are determined by solving the governing coupled heat and mass balance non-linear differential equation using the numerical finite difference method. It is found that the Modified surface offered an average improvement of 35.8% in the mass transfer coefficient under partial wetting conditions. New Sh and Nu number correlations are proposed by incorporating heat and mass transfer driving potentials and new parameter wetting factor. The developed correlations predicted the change in humidity and temperature across the regenerator with an average error of 6.2% and 7.5%, whereas from the existing two correlations found in the literature, the first one predicted the same experimental observations with an average error of 54.6% and 36.2%, while second one predicted with an average error of 77.3% and 22.0%, respectively. The results highlighted that the assumption of complete wetting of working surface under part load operating conditions leads to significant error in prediction of air outlet conditions. The proposed correlations will be helpful for numerical modelling and simulation of falling film regenerators under wide range of liquid loading conditions.

A Theoretical Methodology and Measurement of Dynamic Characteristics of Wind Turbines with Composite Bucket Foundations

A composite bucket foundation (CBF) is a new type of supporting structure in offshore wind engineering. Its huge transition part is the key difference compared to other offshore foundations. Firstly, the vibration measurement system of a wind turbine with the CBF is introduced. A finite element method (FEM) was developed, and the rigid deformation performance of the transition part was characterized. Then, to clarify the influence of the transition part brings to wind turbines with CBFs, a three-DOF theoretical model was established by simplifying the transition part as a rigid body. Horizontal and rotational foundation stiffness were considered to present the constraint effect below the mudline. Sensitivity studies were conducted on the parameters (including mass, moment of inertia and mass center height) of the transition part. Further, the vibration properties of the CBF structures under different operation load conditions were compared through the theoretical model and the in situ data. The results show that the relative errors between the theoretical model and FEM model are 3.78% to 5.03%, satisfying the accuracy requirements. The parameters of the transition part have varying degrees of influence on the natural frequency, foundation stiffness and vibration response of the wind turbines with CBFs. Compared to wind and 1P loads, the 3P load has a greater influence if the 3P frequency is close to the natural frequency of the wind turbine.

The novel strategy of electrical arc furnace design and control approach for voltage flicker investigation

Voltage flickers and harmonics are power quality (PQ) problems in the electric system during a variation and arc furnace (AF) adaptability. AF creations must determine a harmonic and flicker. This article evaluates complex AF systems. This article presents a newly established time domain (TD) static become to on an AF's V-I attributes (VIA). Static arc configurations are useful for harmonic analyses, but dynamic methods are needed for PQ studies, especially voltage flicker analysis. The MATLAB-based dynamic AF configuration is simulated for four different configurations. A response with configurations 1 and 4 varies from the real AF outcomes. The simulation results and numerical finding shows that configurations 2 and 3 are much more appropriate and produce better results for minimum 3rd harmonics for arc current, arc voltage, and point of common coupling (PCC) voltage. The novelty of this configuration is that the energy transferred to the load by the AF during the cycle of operation has been identified, making the developed scheme more reliable and dependent on the load's operational conditions. After that, effective applications of these configurations and other configurations' accuracy should be clarified.

Finite element analysis on failure-yielding stresses in the wheel bolt of automobile vehicle due to operational loading conditions

Finite element analysis on failure-yielding stresses in the wheel bolt of automobile vehicle due to operational loading conditions

Hierarchical Bayesian calibration of deck deflection models using distributed fiber optic strain data

A naval hovercraft was outfitted with a distributed fiber optic sensing system (FOSS) on its cargo deck in which two fibers were placed along orthogonal axes. A series of trucks were loaded onto the cargo deck to simulate operational loading conditions and the resulting strain profiles were measured by the distributed FOSS. The aim of this study is to quantify the deck deflections that correspond to the measured strains under different vehicular loads. This is accomplished by idealizing the cargo deck as a rectangular plate and using the data to estimate the associated plate model parameters. In order to account for measurement noise and model prediction error, as well as the inherent variability of inferences made with different data sets, a hierarchical Bayesian scheme that considers both the model parameter uncertainty and the prediction error variance is utilized to derive the posterior distributions of the model hyper-parameters, i.e., their mean and variance. Given the volume of FOSS strain data available, different approaches were taken for incorporating the data into the hierarchical approach in order to determine whether the inclusion of all available strain data meaningfully reduced the uncertainty compared to using only time-averaged strain. Comparisons between the different approaches are explored in the context of 95% confidence intervals for strain profiles, deck deflection envelopes, and reliability indices based on allowable deflections.