Basis Of Experimental Data Research Articles

Comparative study of real-time photovoltaic fault diagnosis using artificial intelligence: Fuzzy logic and neural network approaches

ABSTRACT Solar photovoltaic (PV) systems are becoming increasingly popular for renewable energy production. However, due to environmental and operational conditions, various faults can occur in PV modules, which can cause a significant reduction in system performance. This study suggests employing two distinct approaches, Fuzzy Logic (FL) and Artificial Neural Networks (ANN), to diagnose defects in photovoltaic (PV) systems. Both the simulation and measuring are used to compare the performance of various strategies. The simulation was performed using MATLAB/Simulink, while the experiment was carried out using the dSPACE DS1104 platform to apply the diagnostic model built in the Matlab/Simulink® program. The suggested approaches have been verified using an experimental database of climatic and electrical attributes obtained from a photovoltaic (PV) panel situated at the LGEB Laboratory of the University of Biskra in Algeria. For six single- and multi-fault types, partial shading, soiling, SC of one by-pass diode, SC of two by-pass diodes, by-pass diode shunted and shading & by-pass diode disconnected. A dSPACE DS1104 platform which shows the results and informs the user about the state of the PV panel has also been developed. The results of the simulation show that both FL and ANN methods can accurately detect and diagnose the six types of faults by 100% and 99.7%, respectively. The study demonstrates the effectiveness of both FL and ANN methods for PV fault diagnosis. However, in the experimental tests, the ANN method shows better performance in terms of accuracy compared to FL by 99.6% and 99.2%, respectively, and speed takes only 1.04s, while FL consumes 3.02s and could be a more practical solution for real-time surveillance PV fault diagnosis

HyStor: An experimental database of hydrogen storage properties for various metal alloy classes

In this work, we introduce the HyStor database, consisting of 1282 metal alloys along with their maximum hydrogen storage capacity (H2wt%) at a given absorption temperature. The curated HydPark database consist of 831 entries. We sourced compositions from research articles and various patent documents, resulting in addition of 451 compositions to the HydPark database. The addition is reflected in the data across all existing classes of alloys. Further, low entropy alloys (LEA), medium entropy alloys (MEA) and high entropy alloys (HEA) have been newly included classes. This has broadened the scope of the database to encompass the latest materials of interest for hydrogen storage. HyStor contains representation of 54 elements, with a temperature range of 200–800 K, and H2wt% ranging from 0.1 to 7.19. We conducted thorough checks for duplicate entries, erroneous data, and conflicting compositions within the database to ensure data quality. Furthermore, we conducted multiple tests to identify potential outlier compositions. The data curation and updation reflects into slight improved error metrics of the HYST model, reducing the Mean Absolute Error (MAE) from 0.31 to 0.29 and increasing the R2 score from 0.77 to 0.79.

Development and distribution of an in situ experimental wind turbine noise database

The PIBE project is the first French collaborative research project on wind turbine noise. One of its objectives is to quantify the experimental dispersion observed in situ on acoustic quantities/metrics and influencing parameters, and to compare it with the numerical dispersion estimated using acoustic propagation models. A long-term experimental campaign was carried out over consecutive 410 days around a wind farm comprising eight 80-metre-high and 90-metre diameter turbines. Acoustic sensors (five Class I sound level meters) were deployed on site, recording third-octave spectra and overall A- and Z-weighted values of the Leq indicator and fractile indices (L10, L50 and L90), at distances from 325 m to 1400 m from the wind turbines row. In addition, three 3D ultrasonic anemometers and a Lidar system were used to measure meteorological variables influencing long-range sound propagation (e.g. wind and temperature verical profiles). Besides, train and aircraft passing close to the site were inventoried to identify periods of high background noise. All experimental data have been processed into an ElasticSearch database. An interactive web application has been developed in R Shiny to facilitate processing, visualization and analysis of the experimental database.

Advancing source reactor-type discrimination using machine learning techniques and SFCOMPO-2.0 experimental database

In recent years, nuclear forensic analysis has become crucial due to the growing global threat of nuclear terrorism and smuggling. Since 2005, extensive research has been conducted on identifying the origin of spent nuclear fuel, focusing on the source reactor-type discrimination, 235U enrichment of the fresh fuel, and the fuel exposure in the reactor (known as burnup). However, the majority of research relies on computed databases, which may lead to tracing discrepancies compared with actual situations. The present study employs the isotopic measurements from the experimental SFCOMPO-2.0 database to predict nuclear reactor types using Factor Analysis (FA) and various machine learning classification algorithms. The results reveal that FA is an effective method for dimension reduction and visualization. The FA-KNN, Random Forest (RF), and Multilayer Perceptron (MLP) algorithms are applied using a consistent dataset partition to ensure unbiased comparisons. The prediction results based on 10-fold stratified cross-validation are quite promising and the Receiver Operating Characteristic (ROC) curves for multi-class classification confirm the excellent generalization ability of models. Therefore, the application of machine learning techniques is highly effective for reactor-type forensics analysis, especially for RF and MLP.

Simulation and Performance of 99Mo/99mTc Radioisotope Separation by Modified Extraction Method

Abstract Technetium-99m (99mTc) is a nuclide in nuclear medicine, commonly derived as a decay product from molybdenum-99 (99Mo). Unfortunately, Japan and Indonesia rely entirely on imports for their 99Mo/99mTc supply. This study aims to determine the distribution coefficient (Kd) for the 99Mo/99mTc production process and assess the efficiency of Methyl Ethyl Ketone (MEK) as a solvent in the extraction process. The method Initial tests using Rhenium (Re) as a substitute for 99mTc, conducted in collaboration with the Japan Atomic Energy Agency (JAEA), revealed optimal conditions: a standing time of 5 to 15 minutes, a mixing time of 3 minutes with a motor speed of 250 rpm, and a MEK volume exceeding 20 ml. These conditions yielded a Re extraction efficiency exceeding 85.14%, with a Re elution yield of up to 98.73%. These findings served as a database for experiments involving 99Mo/99mTc conducted at the National Research and Innovation Agency (BRIN). In hot experiments, the 99mTc recovery rate through MEK exceeded 100%, while the elution process achieved a recovery rate of 97.81%. Through simulation and facility enhancements in the hot cell, the 99mTc extraction recovery increased to 44.86%, and elution recovery ranged from 90.62%. From the calculation results, the distribution coefficient value database (Kd-99mTc) was 0.45. Radionuclide and radiochemistry purity of the 99mTc solution were assessed using the Gamma Spectrometer and TLC Scanner, resulting in purity of 99.90% and 99.80%, respectively. These results underscore the potential for domestic production 99mTc, reducing reliance on imports.

Evaluation and Research on Correlation for Spray Cooling

Abstract Spray cooling is influenced by many related parameters, making analysis of heat transfer mechanisms and prediction of cooling performance challenging. After summarizing and analyzing fifteen different forms of correlation in spray cooling literature, it appears that there are four expressions for spray cooling correlation. 2989 experimental data under different experimental conditions in twelve studies were collected to form an experimental database to evaluate the prediction of correlations. The prediction accuracy of Nusselt number correlations based on droplet diameter is considered the best, while that of correlations based on dimensionless heat flux is considered the worst. The factor analysis method was used to analyze the prediction differences of correlations and investigate the correlation between parameters and the heat transfer process. We found that the correlation with the best evaluation accuracy must contain more parameters with high correlation. A new correlation was derived based on this conclusion to predict the data better.

Decoding burst swimming performance: a scaling perspective on time-to-fatigue.

Fatigue curves quantify fish swimming performance, providing information about the time ([Formula: see text]) fish can swim against a steady flow velocity (Uf) before fatiguing. Such curves represent a key tool for many applications in ecological engineering, especially for fish pass design and management. Despite years of research, though, our current ability to model fatigue curves still lacks theoretical foundations and relies primarily on fitting empirical data, as obtained from time-consuming and costly experiments. In the present article, we address this shortcoming by proposing a theoretical analysis that builds upon concepts of fish hydrodynamics to derive scaling laws linking statistical properties of [Formula: see text] to velocities Uf, pertaining to the so-called burst range. Theoretical arguments, in the present study, suggest that the proposed scaling laws may hold true for all fish species and sizes. A new experimental database obtained from over 800 trials and five small-sized Cypriniformes support theoretical predictions satisfactorily and calls for further experiments on more fish species and sizes to confirm their general validity.

Design-oriented stress-strain model for short concrete columns wrapped with FRP sheets

Previous studies have presented models aimed at predicting the axial stress-strain behavior of circular concrete columns wrapped with carbon fiber reinforced polymer (CFRP). The majority of these models were developed based on normal strength concrete (NSC) with strengths below 50 MPa. Moreover, several of these models relied on a limited experimental database. In this study, a large collection of experimental data was gathered, including 310 concrete columns confined with CFRP wraps. This database comprised test results for unconfined concrete with a compressive strength ranging from 19.7 to 169.7 MPa, and a height-to-diameter (H/D) ratio of two. From these data, two new expressions were developed to estimate the ultimate compressive strength and the corresponding strain. To model the stress-strain behavior, new relationship was suggested. A comparison between the proposed model and the experimental results revealed good agreement, outperforming several existing models. Additionally, the expressions for ultimate strength and corresponding strain are simple and accurate, making them easy to apply in design. Furthermore, the proposed model demonstrates superior performance compared to other existing models.

Talking about diseases; developing a model of patient and public-prioritised disease phenotypes.

Deep phenotyping describes the use of standardised terminologies to create comprehensive phenotypic descriptions of biomedical phenomena. These characterisations facilitate secondary analysis, evidence synthesis, and practitioner awareness, thereby guiding patient care. The vast majority of this knowledge is derived from sources that describe an academic understanding of disease, including academic literature and experimental databases. Previous work indicates a gulf between the priorities, perspectives, and perceptions held by different healthcare stakeholders. Using social media data, we develop a phenotype model that represents a public perspective on disease and compare this with a model derived from a combination of existing academic phenotype databases. We identified 52,198 positive disease-phenotype associations from social media across 311 diseases. We further identified 24,618 novel phenotype associations not shared by the biomedical and literature-derived phenotype model across 304 diseases, of which we considered 14,531 significant. Manifestations of disease affecting quality of life, and concerning endocrine, digestive, and reproductive diseases were over-represented in the social media phenotype model. An expert clinical review found that social media-derived associations were considered similarly well-established to those derived from literature, and were seen significantly more in patient clinical encounters. The phenotype model recovered from social media presents a significantly different perspective than existing resources derived from biomedical databases and literature, providing a large number of associations novel to the latter dataset. We propose that the integration and interrogation of these public perspectives on the disease can inform clinical awareness, improve secondary analysis, and bridge understanding and priorities across healthcare stakeholders.

Harnessing explainable Artificial Intelligence (XAI) for enhanced geopolymer concrete mix optimization

Geopolymer concrete (GC) emerges as a sustainable alternative yet faces challenges in achieving optimal resource utilization for strength development. Balancing these aspects is crucial for its large-scale adoption as a sustainable material. The type and dosage of precursors, activator, curing, and mixing conditions influence compressive strength, setting time, and workability. Moreover, multiple experimental trials are required for a desirable geopolymer blend. Even the experimental parameters alone do not meet the design principles concerning sustainable construction. This paper presents a study on the mix design and interpretation of machine learning techniques (MLT) with XAI. To train the model, extensive experimental databases using the shapley additive explanations (SHAP) technique rank input factors that impact the strength aspect. The prediction models' performance was compared using coefficient of determination (R2) and root mean square error (RMSE). SHAP interpretations reveal that temperature, Na to Al ratio, and NaOH molarity are the main factors influencing the compressive strength of GC. Further, these parameters were crucial in developing the dense geopolymer matrix. By integrating XAI into the MLT approach, we have also opened new criteria for understanding the complex relationships between geopolymer concrete potential parameters and their compressive strength.

Machine learning based prediction of compressive and flexural strength of recycled plastic waste aggregate concrete

In the last 50 years, the use of plastics has increased significantly due to advances in technology, population growth and increasing needs. However, this trend has led to the accumulation of large amounts of plastic waste. Numerous studies have been conducted on the use of recycled plastic materials in concrete production as a means of recycling and environmental protection. This study aims to predict the 28-day cylinder compressive and flexural strength of concretes using Polyethylene Terephthalate (PET), Phenolic Formaldehyde (PF), Polypropylene (PP), Polycarbonate (PC), Polyvinyl Chloride (PCV), High-Impact Polystyrene (HIPS) and High-Density Polyethylene (HDPE) plastic waste as aggregates. In this scope, a comprehensive database was created using experimental results obtained from the literature. Then, this study used six ML models, including Support Vector Regressor (SVR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), XGBoost Regressor (XGR), LightGBM Regressor (LGR), Bagged Decision Trees Regressor (BDTR), to predict the compressive and flexural strength of recycled plastic waste aggregate concrete (RPWAC). From the experimental database, 253 data points with strength between 6.00–70.05 MPa were identified to estimate the compressive strength (CS) and 175 data points with strength between 1.21–7.96 MPa were identified for flexural strength (FS). Cross-validation was used for six ML models and hyperparameter fine-tuning was performed. The results showed that six different ML models predicted the compressive and flexural strengths of RPWAC with high accuracy. The XGR model predicted CS and FS better than the other five different models with R2 values of 0.931 and 0.999, respectively. The three main properties effecting the CS of RPWAC were plastic waste content, water/cement ratio and concrete specific gravity, while the three main properties affecting the FS of RPWAC were cement specific gravity, plastic waste content and cement CS.

SCIENTIFICLY BASED CALCULATION METHODS AND EXPERIMENTAL DATA BASES FOR THE PREVENTION OF PREMIUM FIRE EXPLOSIONS AND UNSAFE EXPLOSIONS OF PYROTECHNIC MULTICOMPONENT NITRATE-METALLIZED MIXTURES UNDER EXTERNAL THERMAL

Problem statement. Methods of preventing the occurrence of forced fire-hazardous destruction of products in the event of exposure to external thermal actions are of great practical importance. At the same time, they should be based on scientifically based methods for determining the critical values of parameters of thermal effects on products and technological parameters of mixture charges, the excess of which leads to premature fire-hazardous destruction of products. To develop such methods, it is necessary to have the results of theoretical and experimental studies of the processes of heating the shells of mixture charges for various external heat flows and their thermoreaction times, as well as the processes of ignition and development of their combustion under different external conditions. Purpose of the article. To form the results of theoretical studies in the form of scientifically based methods that allow preventing and controlling the premature ignition of mixture charges, the explosive development of their combustion process and the fire-hazardous destruction of products under conditions of external thermal effects, as well as the compilation of the results of experimental studies into a single database with determination of the influence of the technological parameters of the mixtures on the characteristics of their ignition processes and the development of combustion, the use of which allows reducing the number of fire-explosive destruction of products in conditions of elevated heating temperatures and external pressures. Conclusion. Scientifically based methods of determining the critical values of the parameters of external thermal actions for the quantitative assessment of the level of fire safety of pyrotechnic products based on multi-component nitrate-metallized mixtures have been developed. A scientific and technical base of experimental data was created to determine the regularities of the influence of a wide class of technological parameters of mixtures on the main characteristics of the processes of ignition and development of combustion of mixtures (ignition temperature, induction time, speed of combustion development) under conditions of external thermal actions (elevated heating temperatures, external pressures, etc.), which makes it possible to determine the probability of fires occurring at facilities where products are stored.

Validation of prediction capability of operating space for plasma initiation in MAST-U

Abstract DYON is a plasma initiation modelling code that solves the differential equation system of the full circuit equations (plasma current, active coil currents and eddy currents in full passive structures) and 0D global energy and particle balance equations[1]. In order to test the capability of the full electromagnetic plasma initiation model to predict individual discharges in experiments and thus the operating space in the device, a dedicated experimental database was built in MAST-U by scanning the prefilled gas pressure p0 and the induced loop voltage Vloop . In the experimental operating space of p0 and Vloop the lower and the upper limits of p0 are determined by the plasma breakdown failure and the plasma burn-through failure, respectively. The lower limit of Vloop is determined by the plasma burn-through failure. By directly reading the control room data used in each discharge (i.e. currents in the solenoid, poloidal field coils, and toroidal field coils, p0 , and gas puffing rate), the full electromagnetic DYON consistently predicted the failed breakdown, failed burn-through, and successful plasma initiation discharges in the experimental database, demonstrating its capability to predict the operating space for inductive plasma initiation. The Paschen curve calculated with the effective connection length in MAST-U indicates a much higher p0 required for plasma breakdown than the experimental data, indicating that individual field line evaluation is necessary to calculate the quantitative requirements for Townsend breakdown. The demonstration in this paper shows that the full electromagnetic DYON could be a useful simulation tool to assess the feasibility of inductive plasma initiation and to optimise operating scenarios in future devices. [1] Hyun-Tae Kim 2022 Nuclear Fusion 62 126012

Integration of a hybrid vibration prediction model for railways into noise mapping software: methodology, assumptions and demonstration

AbstractWithin the SILVARSTAR project, a user-friendly frequency-based hybrid prediction tool has been developed to assess the environmental impact of railway-induced vibration. This tool is integrated in existing noise mapping software. Following modern vibration standards and guidelines, the vibration velocity level in a building in each frequency band is expressed as the sum of a force density (source term), line source transfer mobility (propagation term) and building correction factor (receiver term). A hybrid approach is used that allows for a combination of experimental data and numerical predictions, providing increased flexibility and applicability. The train and track properties can be selected from a database or entered as numerical values. The user can select soil impedance and transfer functions from a database, pre-computed for a wide range of parameters with state-of-the-art models. An experimental database of force densities, transfer functions, free field vibration and input parameters is also provided. The building response is estimated by means of building correction factors. Assumptions within the modelling approach are made to reduce computation time but these can influence prediction accuracy; this is quantified for the case of a nominal intercity train running at different speeds on a ballasted track supported by homogeneous soil of varying stiffness. The paper focuses on the influence of these parameters on the compliance of the track–soil system and the free field response. We also demonstrate the use and discuss the validation of the vibration prediction tool for the case of a high-speed train running on a ballasted track in Lincent (Belgium).

Shear capacity of slender FRP-RC beams utilizing a hybrid ANN with the firefly optimizer

The popularity of fiber-reinforced polymer (FRP) bars as a structural element has soared due to their advantageous mechanical and physical properties. Despite an abundance of code requirements and heuristic equations, engineers specializing in structural retrofitting and analysis often struggle to utilize a suitable yet precise equation. This study introduces a novel approach by presenting a firefly optimization algorithm (FOA) combined with an artificial neural network (ANN)—termed as FOA-ANN—as an advanced hybrid machine learning model. The primary objective is to predict the shear capacity of slender FRP reinforced concrete (FRP-RC) beams without stirrup. An extensive experimental database of slender FRP-RC beams without stirrup was compiled. Leveraging this database and the proposed hybrid method, a simple yet accurate closed-form equation for determining the shear capacity of slender FRP-RC beams without stirrup was formulated. Additionally, a selection of pre-existing equations was provided for comparison of accuracy. Results indicate that the suggested FOA-ANN equation offers a more accurate alternative, outperforming equations derived from CSA S806-12 and AASHTO LRFD. The FOA-ANN hybrid technique proves to be highly effective in predicting the shear capacity of slender FRP-RC beams without stirrup.

Hybrid rehabilitation of 3D RC beam-column joints using UHP-HFRC and FRP wrapping: Experimental evaluation and theoretical analysis

Beam-column joints are crucial elements in reinforced concrete (RC) structures subjected to seismic loading, as they are vulnerable elements prone to significant damage during earthquakes. Due to extensive structural damage, various methods have been developed to strengthen beam-column joints. However, research on rehabilitation and repair methods has been limited, with most studies focusing on 2-D joints and overlooking the confinement effect of corner beams on the joint core. This study aims to investigate novel hybrid rehabilitation systems, including ultra-high performance hybrid fiber-reinforced concrete (UHP-HFRC) and fiber-reinforced polymer (FRP) systems, alongside embedding reinforcement into heavily damaged 3-D beam-column joints subjected to pre-cyclic loading and subsequent cyclic loading. Parameters related to hysteresis response were obtained and discussed, such as ductility, lateral stiffness, dissipated energy, equivalent hysteresis damping, and pinching ratio of the tested specimens. The experimental findings revealed that utilizing an FRP and UHP-HFRC hybrid rehabilitation system enhanced ductility, dissipated energy, and lateral stiffness of heavily damaged joints by 25 %, 73 %, and 81 %, respectively, compared to the undamaged reference specimen. Additionally, a simplified design model was developed to predict the shear resistance of beam-column joints, incorporating principles of strut-and-tie. The theoretical design model was also validated against the experimental database, demonstrating a good accuracy with MV and COV of 0.97 and 13 %, respectively.

SOX7 inhibits the malignant progression of bladder cancer via the DNMT3B/CYGB axis

Bladder cancer (BCa) stands out as a highly prevalent malignant tumor affecting the urinary system. The Sex determining region Y-box protein family is recognized for its crucial role in BCa progression. However, the effect of Sex determining region Y-box 7 (SOX7) on BCa progression has not been fully elucidated. Herein, RNA-sequencing, western blot (WB), immunohistochemistry (IHC), immunofluorescence (IF) and tissue microarray were utilized to assess SOX7 expression in vitro and in vivo. Additionally, SOX7 expression, prognosis, and SOX7 + cytoglobin (CYGB) score were analyzed using R software. In vitro and vivo experiments were performed with BCa cell lines to validate the effect of SOX7 knockdown and overexpression on the malignant progression of BCa. The results showed that SOX7 exhibits low expression in BCa. It functions in diverse capacities, inhibiting the proliferative, migratory, and invasive capabilities of BCa. In addition, the experimental database demonstrated that SOX7 binds to the promoter of DNA methyltransferase 3 beta (DNMT3B), leading to the transcriptional inhibition of DNMT3B. This subsequently results in a reduced methylation of CYGB promoter, ultimately inhibiting the tumor progression of BCa. SOX7 + CYGB scores were significantly linked to patient prognosis. In conclusion, SOX7 inhibits the malignant progression of BCa via the DNMT3B/CYGB axis. Additionally, the SOX7 + CYGB score is capable of predicting the prognostic outcomes of BCa patients. Therefore, SOX7 and CYGB may play an important role in the progression of bladder cancer, and they can be used as prognostic markers of bladder cancer patients.

Passing-through I-plates-to-SHS moment resisting joints subjected to symmetric bending moments

When using hollow structural section (HSS) members in multi-storey buildings, beam-to-column moment resisting connections’ design raises critical questions to tackle. With conventional welded and bolted joints’ response being generally very flexible, the design resistance becomes governed by a deformation criterion rather than a strength criterion. This underscores the necessity for smart reinforcement techniques. A widespread solution is the diaphragm approach, in which the stiffeners usually protrude over the tubular column. The solution of plates passing through the HSS column is another stiffening option which was studied recently for CHS, yet, SHS columns have not been subject to comparable scrutiny. The objective of this paper is to study experimentally, numerically, and analytically the mechanical behaviour of I-beam-to-SHS column moment resisting joints using passing through I-plates under symmetric bending moments. The testing of two full-scale specimens corroborates previous findings with CHS columns in the elastic regime. However, significant deviations were observed in the post-peak response, revealing new insights into the behaviour of SHS columns. Failure was due to progressive buckling of passing through plates and yielding of tube-wall in transverse compression. A finite element model using solid and contact elements is developed and validated against experimental data. This model underscores how loads are redistributed between the tube wall and passing plates. A simplified version of the FE model is used to perform a thorough parametric numerical analysis on 101 configurations expanding upon the experimental test database by varying geometrical parameters such as passing plate thickness, tube, and beam dimensions. Finally, an analytical model integrating the different components of the joint is proposed to evaluate the initial rotational stiffness and the bending resistance.

Experimental Research and Improved Neural Network Optimization Based on the Ocean Thermal Energy Conversion Experimental Platform

With the progress of research on ocean thermal energy conversion, the stabI have checked and revised all. le operation of ocean thermal energy conversion experiments has become a problem that cannot be ignored. The control foundation for stable operation is the accurate prediction of operational performance. In order to achieve accurate prediction and optimization of the performance of the ocean thermal energy conversion experimental platform, this article analyzes the experimental parameters of the turbine based on the basic experimental data obtained from the 50 kW OTEC experimental platform. Through the selection and training of experimental data, a GA-BP-OTE (GBO) model that can automatically select the number of hidden layer nodes was established using seven input parameters. Bayesian optimization was used to complete the optimization of hyperparameters, greatly reducing the training time of the surrogate model. Analyzing the prediction results of the GBO model, it is concluded that the GBO model has better prediction accuracy and has a very low prediction error in the prediction of small temperature changes in ocean thermal energy, proving the progressiveness of the model proposed in this article. The dual-objective optimization problem of turbine grid-connected power and isentropic efficiency is solved. The results show that the change in isentropic efficiency of the permeable device is affected by the combined influence of the seven parameters selected in this study, with the mass flow rate of the working fluid having the greatest impact. The MAPE of the GBO model turbine grid-connected power is 0.24547%, the MAPE of the turbine isentropic efficiency is 0.04%, and the MAPE of the turbine speed is 0.33%. The Pareto-optimal solution for the turbine grid-connected power is 40.1792 kW, with an isentropic efficiency of 0.837439.

Development of mechanical properties of concrete in vacuum tunnel of vacuum-based maglev train

The vacuum-based maglev train is an emerging transportation system currently under development worldwide. While concrete is a technically viable material for constructing vacuum tunnels, its mechanical performance in this unique environment remains unclear. This paper investigates the behavior of concrete in vacuum using a large customized vacuum tube. The results show that concrete undergoes significant water loss in vacuum, resulting in impeded cement hydration and porous hydration products with a foam structure. Vacuum exposure during the early ages of concrete has negative effects on mechanical properties. However, once the cement hydration reaches a high level of maturity, further vacuum exposure has little influence. Compared with atmospheric conditions, the shrinkage of concrete in vacuum is significantly increased. These findings provide a valuable experimental database and new insights for the use of concrete in vacuum-based maglev trains.