Relevant Parameters Research Articles

Conductive panel cooling by using coupled effects of nano-jet impingement, double rotating cylinders and magnetic field under cross-flow

Purpose This study aims to examine the effects of cross-flow and multiple jet impingement on conductive panel cooling performance when subjected to uniform magnetic field effects. The cooling system has double rotating cylinders. Design/methodology/approach Cross-flow ratios (CFR) ranging from 0.1 to 1, magnetic field strength (Ha) ranging from 0 to 50 and cylinder rotation speed (Rew) ranging from −5,000 to 5,000 are the relevant parameters that are included in the numerical analysis. Finite element method is used as solution technique. Radial basis networks are used for the prediction of average Nusselt number (Nu), average surface temperature of the panel and temperature uniformity effects when varying the impacts of cross-flow, magnetic field and rotations of the double cylinder in the cooling channel. Findings The effect of CFR on cooling efficiency and temperature uniformity is favorable. By raising the CFR to the highest value under the magnetic field, the average Nu can rise by up to 18.6%, while the temperature drop and temperature difference are obtained as 1.87°C and 3.72°C. Without cylinders, magnetic field improves the cooling performance, while average Nu increases to 4.5% and 8.8% at CR = 0.1 and CR = 1, respectively. When the magnetic field is the strongest with cylinders in channel at CFR = 1, temperature difference (ΔT) is obtained as 2.5 °C. The rotational impacts on thermal performance are more significant when the cross-flow effects are weak (CFR = 0.1) compared to when they are substantial (CFR = 1). Cases without a cylinder have the worst performance for both weak and severe cross-flow effects, whereas using two rotating cylinders increases cooling performance and temperature uniformity for the conductive panel. The average surface temperature lowers by 1.2°C at CFR = 0.1 and 0.5°C at CFR = 1 when the worst and best situations are compared. Originality/value The outcomes are relevant in the design and optimization-based studies for electric cooling, photo-voltaic cooling and battery thermal management.

Automatically discovering ordinary differential equations from data with sparse regression

Discovering nonlinear differential equations that describe system dynamics from empirical data is a fundamental challenge in contemporary science. While current methods can identify such equations, they often require extensive manual hyperparameter tuning, limiting their applicability. Here, we propose a methodology to identify dynamical laws by integrating denoising techniques to smooth the signal, sparse regression to identify the relevant parameters, and bootstrap confidence intervals to quantify the uncertainty of the estimates. We evaluate our method on well-known ordinary differential equations with an ensemble of random initial conditions, time series of increasing length, and varying signal-to-noise ratios. Our algorithm consistently identifies three-dimensional systems, given moderately-sized time series and high levels of signal quality relative to background noise. By accurately discovering dynamical systems automatically, our methodology has the potential to impact the understanding of complex systems, especially in fields where data are abundant, but developing mathematical models demands considerable effort.

Using sprouting behaviour to quantify physiological ageing of seed tubers of potato (Solanum tuberosum L.)

The physiological age of potato seed tubers evolves with chronological age, influenced by growth history and storage conditions. Effective quantification of physiological ageing requires a dynamic and flexible modelling approach. In this study, we conducted three cycles of field production and storage of seed tubers, using contrasting and widely grown cultivars. Different storage temperatures were employed as the sole treatment to introduce variation in the physiological age of the same cultivar. Throughout the storage seasons, periodic assessments of sprouting behaviour were conducted, both at the onset and at the end of a sprouting test. The total sprout dry weight per tuber produced in the sprouting test emerged as the best measure of seed tuber vigour, exhibiting cultivar-specific responses to storage temperatures. To capture these diverse sprouting behaviours, we devised a piecewise sprouting behaviour function. This is the first mathematical model that effectively describes the evolving sprouting patterns and provides biologically relevant parameters for the analysis of cultivar-specific sprouting trends. The temporal parameters of the function displayed sensitivity to storage temperature and year-to-year variation in growth conditions, highlighting distinct physiological phases. Consequently, the function facilitated insightful analysis of these key factors, enabling interpretation of underlying processes involved in the physiological ageing of potato seed tubers.

Potentialities of the combined use of underwater fluorescence imagery and photogrammetry for the detection of fine-scale changes in marine bioconstructors

Marine communities are facing both natural disturbances and anthropogenic stressors. Bioconstructor species are endangered by multiple large-scale and local pressures and the early identification of impacts and damages is a primary goal for preserving coral reefs. Taking advantage of the recent development in underwater photogrammetry, the use of photogrammetry and fluorimetry was coupled to design, test and validate in laboratory a multi-sensor measuring system that could be potentially exploited in open water by SCUBA divers for assessing the health status of corals and detecting relevant biometric parameters with high accuracy and resolution. The approach was tested with fragments of the endemic coral Cladocora caespitosa, the sole zooxanthellate scleractinian reef-builder in the Mediterranean. The most significant results contributing to the scientific advancement of knowledge were: 1) the development of a cost-effective, flexible and easy-to-use approach based on emerging technologies; 2) the achievement of a sub-centimetric resolution for measuring relevant biometric parameters (polyp counting, colony surface areas and volumes); 3) set up of a reliable and repeatable strategy for multi-temporal analyses capable of quantifying changes in coral morphology with sub-centimeter accuracy; 4) detect changes in coral health status at a fine scale and under natural lighting through autofluorescence analysis. The novelty of the present research lies in the coupling of emerging techniques that could be applied to a wide range of 3D morphometrics, different habitats and species, thus paving the way to innovative opportunities in ecological research and more effective results than traditional in-situ measurements. Moreover, the possibility to easily modify the developed system to be installed on an underwater remotely operated vehicle further highlights the possible concrete impact of the research for ecological monitoring and protection purposes.

Seasonal Dynamics of Phytoplankton in Integrated Paddy-cum-Fish Culture

Background: An experimental study was conducted during June-November 2020 at Tungjoy village of Manipur, India, to assess the assemblage pattern, abundance and seasonal dynamics of phytoplankton in simultaneously integrated paddy-cum-fish culture (IPFC). Methods: The experimental IPFC fields were prepared by enhancing width and height of dykes and constructing central refuge pond. Rice saplings were manually transplanted into the prepared fields in June 2020 while the common carp fingerlings were introduced 30 days after rice-transplantation. Both rice and fish were harvested in November 2020. Assemblage pattern, abundance and dynamics of phytoplankton, soil nutrients and water quality were analysed for three seasons (monsoon, post-monsoon and winter) in paddy fields without fish (PF) compared with IPFC fields. Relevant fish growth parameters were analysed on monthly basis. Result: The study recorded 37 genera of phytoplankton belonging to 25 families and 18 orders falling under class Bacillariophyceae, Chlorophyceae and Cyanophyceae. Bacillariophyceae was the most diverse class. Phytoplankton density ranges from 14.00±1.57 × 103 to 33.25±3.60 × 103 µL-1 and 9.05±2.27 × 103 to 33.06±3.45 × 103 µL-1 in PF and IPFC, respectively, wherein Chlorophyceae dominated during monsoon and post-monsoon. Phytoplankton in IPFC was lower than PF, indicating effective utilisation by the reared fish. Fish production from IPFC fields was 326 kg ha-1 with 83% survival rate while rice production was enhanced by 7.4% as compared to rice monoculture.

Study on the fracture propagation law of deep shale reservoir under the influence of different number of fracturing clusters

Deep reservoirs have a large difference in geo-stress, and compared to shallow reservoirs, multiple clusters of fracturing are usually required to effectively improve the quality of reservoir reconstruction. In this paper, considering the relevant geological parameters of a certain reservoir in the southwest, multi-cluster reservoir fracturing models under three-dimensional conditions based on the cohesive element modelling method are established. Then, the quantitative rules of fluid pressure, fracture length, fracture aperture, fracture area, tensile failure rate, and the fractal dimension of fracture morphology under different fracturing cluster numbers were revealed. The results show that compared to conventional fracturing, multi-cluster fracturing can significantly increase the number of main fractures and improve the effectiveness of reservoir reconstruction. As the number of clusters increases, the number of main fractures in the reservoir increases, but it can also lead to the increase of small opening fractures, which may be unfavourable for the pumping of proppant and subsequent mining. Meanwhile, based on the fractal dimension results of fracture morphology, it was found that under this simulation condition, the number of fracturing clusters had a significant impact on the fractal dimension of fracturing fractures before the fracturing of six clusters, while after the fracturing of six clusters, the impact of the number of fracturing clusters on the fractal dimension of fracturing fractures decreased. Therefore, when considering factors such as the complexity of fractures, multi-cluster fracturing does not necessarily result in more fracturing clusters being better but should be comprehensively considered for optimization. This study has certain reference significance for selecting the spacing between multiple fracturing clusters.

Study on mechanical properties and self-sensing properties of sprayed high-performance concrete containing glass aggregate

Using glass sand as a substitute for sand in high-performance concrete not only conserves resources but also helps protect the environment. This study replaced manufactured sand with glass sand and added carbon fiber to prepare high-performance shotcrete. Its mechanical and self-sensing properties were then tested separately. The findings suggest that with an increase in glass sand content, the compressive strength of high-performance shotcrete initially rises and subsequently declines. When the replacement ratio of glass sand is 75%, the compressive strength exhibits its maximum value, whereas an increase in the glass sand content leads to a reduction in both splitting tensile strength and flexural strength. Although increasing carbon fiber content does not significantly contribute to compressive strength, it significantly improves flexural strength and splitting tensile strength. In addition, when testing the self-sensing performance of the material, it was found that its self-sensing performance increased significantly with increasing carbon fiber content but decreased with increasing glass sand content. Finally, an Ansys APDL simulation using the relevant parameters of the optimal group of mechanical properties was performed to model a tunnel and verify its feasibility and validity. This study provides a theoretical and practical reference for future engineering applications.

Design of ion-imprinted amino-modified chitosan for selective chromium (Ⅲ) ion removal

Green ion-imprinting technique was applied to construct a Cr(III) ion-imprinted absorbent material from an amino functionalized chitosan derivative. First, nitrogenous organic compounds (GACS) as functional monomer is synthesized by a Schiff base reaction followed by a grafting reaction. In the end, imprinting was completed by cross-linking with epichlorohydrin (ECH) in aqueous media and eluting the coordinated ions with hydrochloric acid to obtain chromium-imprinted polymers (Cr(III)-IIP) with pores. The relevant parameters of the imprinting system were explored, and the superior polymer (Cr(III)-IIP9) has an excellent adsorption capacity 385.89 mg/g, wide pH range adsorption, good adsorption rates and equilibrium times (≤120 min) for Cr(III) ion. Additionally, the kinetic study is best defined in terms of pseudo-second order, while the Freundlich isotherm model fits the data well. Competitive adsorption of the quaternary system of Cr(III)/ Cd(II)/ Ni(II)/ Cu(II) were also investigated, it demonstrated a greater affinity for the Cr(III) ion than other ions. Hence, the imprinting materials prepared by amino functionalized chitosan enhances the adsorption of Cr(III) ions in the simulation of actual water sample. The adsorption mechanism accords with the hard and soft acid and base theory (HSAB), which is expected to be is utilized for the separation of Cr(III) ions in the environment.

Study of an unsteady magnetohydrodynamic‐free convective flow under parabolic ramped temperature and concentration in the presence of Soret and heat sink

AbstractThe purpose of this paper is to investigate the effects of Soret, thermal radiation, and chemical reaction on an unsteady magnetohydrodynamic free convective flow past an impulsively initiated semi‐infinite vertical plate with heat sink under parabolic ramped temperature and parabolic ramped concentration. Using some nondimensional parameters, the flow boundary equations in this case are first converted to dimensionless equations. The closed‐form Laplace transform technique is employed here to solve the partial differential equations and get the solutions for fluid velocity, temperature, and concentration. The velocity, temperature, and concentration of the fluid tend to vary with the effect of various flow factors. These changes are graphically represented and analyzed. Differences in skin friction, Nusselt number, and Sherwood number for the different relevant parameters are also recorded. The Soret number hikes the fluid velocity and concentration. The rate of heat transfer, mass transfer, and momentum transfer improves due to the application of parabolic ramped conditions.

Entropy optimization of chemically reactive bioconvective Powell-Eyring nanofluid stratified flow over a Riga plate: A non-Fourier heat and mass flux modeling

This work examines the effects of Riga plate flow in a Powell-Eyring bioconvection Powell-Forchheimer medium. For the Powell-Eyring base fluid, mobile microorganisms are taken into account in addition to nanoparticles. Additionally taken into account are chemical reactions and the effects of linear radiation. The governing PDEs with boundary conditions are transformed into an ODE by using similarity transformation. The resulting equations are solved using the homotopy analysis approach. A detailed description is given of the link between the relevant parameters and the density of dynamic microorganisms, motion, fluid temperature, skin friction rates, local Sherwood and Nusselt numbers, and nanocomposite volume. Gyrotactic microorganism-containing nanoparticles have potential applications in biotechnology, microfluidic devices, microbial fuel cells, and enzyme biosensors. Various features of several physical characteristics are addressed through the use of tables and graphs.

The Implementation of Neural Networks for Polymer Mold Surface Evaluation.

This paper presents the measurement and evaluation of the surfaces of molds produced using additive technologies. This is an emerging trend in mold production. The surfaces of such molds must be treated, usually using laser-based alternative machining methods. Regular evaluation is necessary because of the gradually deteriorating quality of the mold surface. However, owing to the difficulty in scanning the original surface of the injection mold, it is necessary to perform surface replication. Therefore, this study aims to describe the production of surface replicas for in-house developed polymer molds together with the determination of suitable descriptive parameters, the method of comparing variances, and the mean values for the surface evaluation. Overall, this study presents a new summary of the evaluation process of replicas of the surfaces of polymer molds. The nonlinear regression methodology provides the corresponding functional dependencies between the relevant parameters. The statistical significance of a neural network with two hidden layers based on the principle of Rosenblatt's perceptron has been proposed and verified. Additionally, machine learning was utilized to better compare the original surface and its replica.

Modeling the Conduction Mechanism in Chemoresistive Gas Sensor Based on Single-Crystalline Sn3O4 Nanobelts: A Phenomenological In Operando Investigation.

Investigating the sensing mechanisms in semiconducting metal oxide (SMOx) gas sensors is essential for optimizing their performance across a wide range of potential applications. Despite significant progress in the field, there are still many gaps in comprehending the phenomenological processes occurring in one-dimensional (1D) nanostructures. This article presents the first insights into the conduction mechanism of chemoresistive gas sensors based on single-crystalline Sn3O4 nanobelts using the operando Kelvin Probe technique. From this approach, direct current (DC) electrical resistance and work function changes were simultaneously measured in different working conditions, and a correlation between the conductance and the surface band bending was established. Appropriate modeling was proposed, and the results revealed that the conduction mechanism in the single-crystalline one-dimensional nanostructures closely aligns with the behavior observed in single-crystalline epitaxial layers rather than in polycrystalline grains. Based on this assumption, relevant parameters were further estimated, including Debye length, concentration of free charge carriers, effective density of states in the conduction band, and position of the Fermi level. Overall, this study provides an effective contribution to understanding the role of surface chemistry in the transduction of the electrical signal generated from gas adsorption in single-crystalline one-dimensional nanostructures.

Applying Resampling and Visualization Methods in Factor Analysis to Model Human Spatial Vision.

Studies have reported different numbers of spatial frequency channels for chromatic and achromatic vision. To resolve the difference, we performed factor analysis, a multivariate modeling technique, on population data of achromatic and chromatic sensitivity. In addition, we included resampling and visualization methods to evaluate models from factor analysis. These routines are complex but widely useful. Therefore we have archived our analysis routines by building smCSF, an open-source software package in R (https://smin95.github.io/dataviz/). Data of 103 normally-sighted adults were analyzed. They included blue-yellow, red-green, and achromatic contrast sensitivity. To obtain the confidence interval of relevant statistical parameters, factor analysis was performed using a resampling method. Then exploratory models were developed. We then performed model selections by fitting them against the empirical data and quantifying the quality of the fits. During the exploratory factor analysis, different statistical tests supported different factor models. These could partially be reasons for why there have been conflicting reports. However, after the confirmatory analysis, we found that a model that included two spatial channels was adequate to approximate the chromatic sensitivity data, whereas that with three channels was so for the achromatic sensitivity data. Our findings provide novel insights about the spatial channels for chromatic and achromatic contrast sensitivity from population data. Also, the analysis and visualization routines have been archived in a computational package to boost the transparency and replicability of science.

Study on multi-cluster fracturing simulation of deep reservoir based on cohesive element modeling

With the depletion of conventional reservoir development, reservoir fracturing under deep high geo-stress and high geo-stress difference conditions is receiving increasing attention. Deep reservoirs typically require multi-cluster fracturing to achieve efficient reservoir transformation and development. In this paper, considering the relevant geological parameters of a certain reservoir in the southwest, three-dimensional multi-cluster reservoir fracturing models were established based on cohesive element modeling. Then, the propagation law of artificial fractures in reservoirs under the influence of the different number of fracturing clusters, injection displacement, and Young’s modulus in different regions of the 60 m fracturing well section is analyzed, and the quantitative law of parameters such as fracture length, maximum fracture width, injection point fracture width, fracture area, and tensile failure ratio during multi-cluster fracturing construction, as well as the propagation law of fracture morphology are revealed. The simulation results show that using multi-cluster fracturing can significantly improve the effectiveness of reservoir reconstruction, but as the number of fracturing clusters increases, it is also easy to form some small opening artificial fractures. These small opening artificial fractures may not be conducive to the transportation of proppants and fluids. During single cluster fracturing, the interface stiffness and rock Young’s modulus have a significant impact on the propagation of artificial fractures in the reservoir. As the number of fracturing clusters increases, the competition between artificial main fractures expands significantly, which may reduce the impact of interface stiffness and rock Young’s modulus. The fluid injection rate has a significant impact on reservoir fracturing, and in the same area, using high displacement injection can significantly increase the volume of reservoir reconstruction. This study can provide some reference for multi-cluster fracturing construction in deep reservoirs.

Mixed H∞‐based finite‐time passive filtering for a class of uncertain nonlinear singular systems

AbstractThis article discusses the finite‐time mixed and passive filtering design problem for uncertain nonlinear singular systems. The parameter uncertainties are constrained by well‐defined upper bounds on their magnitudes. The objective is to design a full‐order filter to ensure that the augmented singular system to be finite‐time boundedness(FTB) with and passivity performance. Firstly, a sufficient condition for achieving singular finite‐time boundedness with mixed and passivity performance for uncertain nonlinear singular system is derived by introducing a free matrix. Secondly, a novel criterion is established for analyzing finite‐time and passivity problem of filtering error singular system based on augmented matrix technique. Thirdly, the filtering design conditions are presented and the relevant parameters of the desired filter are determined using linear matrix inequality decoupling principle. Finally, a practical circuit system and a numerical example are provided to demonstrate the feasibility of the proposed scheme.

Effect of granular structure and initial suction on shear strength of GMZ bentonite for deep geological disposal

Granular bentonites have recently attracted increasing attention to serve as buffering/backfilling material alternatives in deep geological repository for disposing high-level radioactive waste (HLRW). In this paper, GMZ bentonite was prepared in the forms of compacted powder (CP), powder/pellet mixture (PPM) and pellet mixture (PM), and a series of direct shear tests, compression tests, soil-water retention tests and microstructural observation were conducted. Results show that the water retention capacity is independent of granular structure and dry density at suction s > 10 MPa. Below this suction, variation in water content should be related to the difference in microstructure. As s decreased, the specimen became more compressible and its shear resistance decreased while showing unimportant changes at s > 100 MPa. Besides, differences in compression and shear behavior between three types of specimens became gradually less obvious with decreasing suction. This indicates that wetting-induced comparable fabric could gradually cause similar compression and shearing response. Both high-density pellets and gradation are considered to affect the internal friction mode and mechanical interlocking phenomenon, so that the peak shear strength and relevant parameters varied. The unified effective stress equation considering both capillary and adsorptive mechanisms was used to predict the peak shear strength, and the predicted values agreed well with test data mostly for the high suction range. The results and ideas obtained in this study are conceived to provide helpful reference for material selection and assessment of bentonite barriers for deep geological disposal.

Utilizing advanced machine learning approaches to assess the seismic fragility of non-engineered masonry structures

Seismic fragility assessment provides a substantial tool for assessing the seismic resilience of these buildings. However, using traditional numerical methods to derive fragility curves poses significant challenges. These methods often overlook the diverse range of buildings found in different regions, as they rely on standardized assumptions and parameters. Consequently, they may not accurately capture the seismic response of various building types. Alternatively, extensive data collection becomes essential to address this knowledge gap by understanding local construction techniques and identifying the relevant parameters. This data is crucial for developing reliable analytical approaches that can accurately derive fragility curves. To overcome these challenges, this research employs four Machine Learning (ML) techniques, namely Support Vector Regression (SVR), Stochastic Gradient Descent (SGD), Random Forest (RF), and Linear Regression (LR), to derive fragility curves for probability of collapse in terms of Peak Ground Acceleration (PGA). To achieve the research objective, a comprehensive input/output dataset consisting of on-site data collected from 646 masonry walls in Malawi is used. Adopted ML models are trained and tested using the entire dataset and then again using only the most highly correlated features. The study includes a comparative analysis of the efficiency and accuracy of each ML approach and the influence of the data used in the analyses. Random Forest (RF) technique emerges as the most efficient ML approach for deriving fragility curves for the surveyed dataset in terms of achieved lowest values for evaluation metrics of the ML methods. This technique scored the lowest Mean Absolute Percentage Error (MAPE) of 16.8 %, and the lowest Root Mean Square Error (RMSE) of 0.0547. These results highlight the potential of ML techniques, particularly RF, in derivation of fragility curves with proper levels of accuracy.

An evolutionary game study on the collaborative governance of environmental pollution: from the perspective of regulatory capture.

Local governments have been captured by enterprises and, thus, have relaxed environmental regulations. This phenomenon has occurred repeatedly and has resulted in serious environmental pollution, posing an enormous threat to public health. To solve this problem, this study introduces central environmental protection inspection and media supervision and considers the economic preferences and environmental preferences of local governments. A four-party evolutionary game model composed of enterprises, local governments, the central government and the media is constructed, and the equilibrium solution of four-party replicator dynamics equations is obtained. The influence of relevant parameters on the choice of strategies of the four main bodies is simulated by using MATLAB software to explore the paths and measures for overcoming regulatory capture and to further improve the modern environmental governance system. The results show the following: First, local governments are easily captured by large enterprises. Second, the central government can improve the environmental behavior of local governments by reducing their economic preferences and strengthening punishment. Third, compared to the penalties imposed by the central government, those imposed by local governments have a more significant impact on the environmental behaviors of enterprises. Fourth, compared to the use of an environmental protection tax policy or a tax relief policy alone, the combination of the two has a more significant impact on the environmental behaviors of enterprises. Fifth, central environmental protection inspection and media supervision can improve the environmental behaviors of both local governments and enterprises, and the effect of media supervision is better than that of central environmental protection inspection. This study recommends improving the performance evaluation system for local governments to coordinate economic development and environmental protection, ensuring that local governments assume the main responsibility, using a combination of incentive and constraint policies for enterprises, and increasing the environmental protection inspection and media supervision of local governments and enterprises to resolve the dilemma of regulatory capture in environmental pollution through the simultaneous enhancement of the environmental behavior of local governments and enterprises.

A Fractional Study of MHD Casson Fluid Motion With Thermal Radiative Flux and Heat Injection/Suction Mechanism Under Ramped Wall Condition: Application of Rabotnov Exponential Kernel

Abstract The primary objective of this research is to extend the concept of fractionalized Casson fluid flow. In this study, a comprehensive analysis of magnetohydrodynamic (MHD) natural convective flow of Casson fluid is conducted, focusing on obtaining analytical solutions using the non-integer-order derivative known as the Yang–Abdel-Aty–Cattani (YAC) operator. The YAC operator utilized in this research possesses a more generalized exponential kernel. The fluid flow is examined in the vicinity of an infinitely vertical plate with a characteristic velocity denoted as u 0. The mathematical modelling of the problem incorporates partial differential equations, incorporating Newtonian heating and ramped conditions. To facilitate the analysis, a suitable set of variables is introduced to transform the governing equations into a dimensionless form. The Laplace transform (LT) is then applied to the fractional system of equations, and the obtained results are presented in series form and also expressed in terms of special functions. The study further investigates the influence of relevant parameters, such as α, β, Pr , Q, Gr, M, Nr and K, on the fluid flow to reveal interesting findings. A comparison of different approaches reveals that the YAC method yields superior results compared to existing operators found in the literature. Graphs are generated to illustrate the outcomes effectively. Additionally, the research explores the limiting cases of the Casson and viscous fluid models to derive the classical form from the YAC fractionalized Casson fluid model.

Study of the fault diagnosis method for gas turbine sensors based on inter-parameter coupling information

Correct and reliable measurement data are crucial for state monitoring, safe operations, health assessment, and life prediction of integrated energy systems (IESs). Sensors are often installed in harsh environments and prone to all kinds of faults; therefore, it is necessary to diagnose sensor faults. A diagnostic method for sensor faults based on gradient histogram distribution (GHD) combined with light gradient boosting machine (LightGBM) is presented in this paper. This proposed method effectively utilizes the coupling information between the relevant parameters. The GHD efficiently extracted the time-domain characteristics of sensor faults and reduced the dimension of eigenvectors. This is beneficial to increasing the diagnostic speed. The kernel density estimation distributions of the gradient and eigenvectors for the sensor with strong correlation are similar, but that for the sensor with weak correlation are completely different. A LightGBM classifier trained based on the feature vectors was utilized to diagnose and classify the sensor faults. The diagnosis accuracy and the diagnosis time of this developed method were examined using the multiple-condition practical operation data of gas turbines in the IES. The experiment results demonstrate that the diagnostic accuracy of five sensor faults using this developed method is all above 90%. The diagnostic time is about 0.47–1.34 s, and is less than 2 s for the gradual faults.