Set Of Parameters Research Articles

Automatic calibration of SWMM parameters based on multi-objective optimisation model

Abstract To address the issue of low accuracy and inefficiency in the traditional parameter calibration methods for the SWMM model, this paper constructs an automatic parameter calibration model based on multi-objective optimisation algorithms. Firstly, the Sobol method and GLUE method are utilised to determine sensitive parameters and their ranges, aiming to narrow down the solution space and expedite the model-solving speed. Secondly, the NSGA-3 multi-objective optimisation algorithm based on the Pareto theory is applied for the optimisation and calibration of sensitive parameter sets. The model is validated in the rainwater drainage system with independent runoff in a residential area in a northwestern city in China. The results show that parameters such as N-Imperv and KSlope are highly sensitive to the model output under the land-use conditions of the study area. The simulation accuracy of the multi-objective continuous optimisation algorithm is significantly better than that of the single-objective genetic algorithm. The simulation results of the SWMM model under multi-objective optimisation demonstrate a certain level of reliability and stability. The research findings can provide technical support for the automatic calibration of SWMM model parameters, accurate model simulation, and application.

Fractional PID with Genetic Algorithm Approach for Industrial Tank Level Control Process

Process control is used in various types of industries to boost production while using current resources, product quality, and safety, with level control being one of the most important schemes. It is suggested that study be conducted on nonlinear continuous processes, particularly in process control applications such as conical tank level control. Initially, each process receives one set of Proportional-Integral (PI) controller settings and a First Order Plus Dead-Time (FOPDT) model using the Ziegler Nicholas (ZN) step response tuning method. The numerous FOPDT models and parameter sets are also obtained. Responses obtained with a single set of parameters and responses obtained with many sets of parameters are compared. By creating a Fractional PID Controller (FPID) with Genetic Algorithm (GA), a simulation-based tuning strategy is proposed. The step responses are acquired through experimental research using fine-tuned settings. At most operational points, the Fractional PID Controller with Genetic Algorithm-based tuning approach outperforms, with lowest oscillation, extremely tiny overshoot, minimum average ISE, and minimum average IAE. The proposed FPID with GA method provides better setpoint and regulatory tracking performance compared to ZN-PID.

Розвиток інтелектуальних транспортних систем

The purpose of the article is to develop a refined methodology for the general design of a container terminal. The difference between the given design methodology and the existing ones is that a decision is proposed to change its throughput (annual container flow passing through it), without changing the dimensions of the container yard and the way containers are stored, that is, for the same capacity of the container yard. Such cases occur, for example, when it is necessary to reconstruct a container terminal. Cargo terminals for containers are a specific field of logistics and cargo delivery system, where cargo is processed in consolidated transport modules - containers.
 Considered the main stages of designing a container terminal as a complex stochastic technical system: setting the task (it should be a single one); selection and determination of elements of technological zones and the structure of the container terminal, as various connections between system elements; analysis of system functioning (including its modeling); account of interaction of the system with the environment; consideration of the results of the container transport and technological system and operation. It is determined that this general methodology can be used during the creation or analysis of any other transformation of a technical or social system that receives a stream of some objects with one set of parameters, processes it through its structure and outputs them from other parameters. This method of formalizing the functioning of the container terminal can be used for its calculations, modeling, research and optimization. In the processes of interaction with environmental systems, the container terminal passes from one state to another, which is characterized by the types and volume of technological operations that must be performed at a certain moment, cargo that must be stored, delivered to and from the terminal, assembled, sorted, business employee processes, forklifts, cranes and other equipment in use. The functioning of the container terminal (part of the warehouse system) as a procedure of transitions from one state to another can be represented in the form of a graph or transition probability matrix.

Удосконалення методики проєктування контейнерного терміналу

The purpose of the article is to develop a refined methodology for the general design of a container terminal. The difference between the proposed design methodology and the existing ones is that a solution is proposed to change its throughput (annual container flow passing through it), without changing the dimensions of the container yard and the way containers are stored, that is, with the same capacity of the container yard. Such cases occur, for example, when it is necessary to reconstruct a container terminal. Cargo terminals for containers are a specific area of logistics and cargo delivery systems, where cargo is processed in consolidated transport modules – containers. The main stages of analyzing or projecting a warehouse as a complex stochastic technical system should be considered: objective setting (it should be single); selection and determination of elements of technological areas and warehouse structure, as various connections between system elements; analyzing of system functioning (including its simulation); consideration of system interaction with surrounding environment; consideration of results of the warehouse system performance, and operation. It is interesting to point out that this general methodology can be used while creating or analyzing any other transformation of technical or social system, which receives a flow of some objects with one set of parameters, processes it through its structure, and gives them out with other values of parameters. 
 This method of formalizing the functioning of the container terminal can be used for its calculations, modeling, research and optimization. In the processes of interaction with environmental systems, the container terminal passes from one state to another, which are characterized by the types and volume of technological operations that must be performed at the moment, cargo that must be stored, delivered to and from the terminal, assembled, sorted, business employee processes, forklifts, cranes and other equipment in use. The functioning of the container terminal (part of the warehouse system) as a procedure of transitions from one state to another can be represented in the form of a graph or transition probability matrix.

CRDS line-shape study of the (7–0) band of CO

We present the results of the spectral line-shape study of the first measurement of the extremely weak (7–0) band of the 12C16O molecule. Measurements were done with a highly sensitive cavity ring-down spectrometer. Collisional narrowing, analyzed in terms of speed-dependent effects, was observed for the first time for transitions with line intensities below 2⋅10−29 cm/molecule at 296 K. We provide a full set of line-shape parameters of the speed-dependent and regular Voigt profile analysis for 14 transitions from P and R branches. Experimental verification of a strong vibrational dependence of the pressure shifting described by the Hartmann model (Hartmann, 2009) is extended up to the sixth overtone highly sensitive to the model parameter.

Pre-trained models for detection and severity level classification of dysarthria from speech

Automatic detection and severity level classification of dysarthria from speech enables non-invasive and effective diagnosis that helps clinical decisions about medication and therapy of patients. In this work, three pre-trained models (wav2vec2-BASE, wav2vec2-LARGE, and HuBERT) are studied to extract features to build automatic detection and severity level classification systems for dysarthric speech. The experiments were conducted using two publicly available databases (UA-Speech and TORGO). One machine learning-based model (support vector machine, SVM) and one deep learning-based model (convolutional neural network, CNN) was used as the classifier. In order to compare the performance of the wav2vec2-BASE, wav2vec2-LARGE, and HuBERT features, three popular acoustic feature sets, namely, mel-frequency cepstral coefficients (MFCCs), openSMILE and extended Geneva minimalistic acoustic parameter set (eGeMAPS) were considered. Experimental results revealed that the features derived from the pre-trained models outperformed the three baseline features. It was also found that the HuBERT features performed better than the wav2vec2-BASE and wav2vec2-LARGE features. In particular, when compared to the best-performing baseline feature (openSMILE), the HuBERT features showed in the detection problem absolute accuracy improvements that varied between 1.33% (the SVM classifier, the TORGO database) and 2.86% (the SVM classifier, the UA-Speech database). In the severity level classification problem, the HuBERT features showed absolute accuracy improvements that varied between 6.54% (the SVM classifier, the TORGO database) and 10.46% (the SVM classifier, the UA-Speech database) compared to the best-performing baseline feature (eGeMAPS).

Inferring from an imprecise Plackett–Luce model: Application to label ranking

The Plackett–Luce model is a popular parametric probabilistic model to define distributions between rankings of objects, modelling for instance observed preferences of users or ranked performances of algorithms. Since such observations may be scarce (users may provide partial preferences, or not all algorithms are run for a given experiment), it may be useful to consider the case where the parameters of the Plackett–Luce model are imprecisely known. In this paper, we first introduce the imprecise Plackett–Luce model, induced by a set of parameters (for instance, parameters with a high relative likelihood). Given a set of possible parameters for the model, we then provide an efficient algorithm to make cautious inferences, returning sets of possible optimal rankings (for instance in the form of partial orders). We illustrate the use of our imprecise model on label ranking, a specific kind of supervised learning.

Processing-Microstructure-Properties of Columns in Thermal Barrier Coatings: A Study of Thermo-Chemico-Mechanical Durability.

Contemporary gas turbine engines rely on thermal barrier coatings (TBCs), which protect the structural components of the engine against degradation at extremely high operating temperatures (1300-1500 °C). The operational efficiencies of aircraft engines have seen significant improvement in recent years, primarily through the increase in operating temperatures; however, the longevity of TBCs can be potentially impacted by several types of degradation mechanisms. In this comprehensive study, a wide range of novel columnar suspension plasma sprayed (SPS) coatings were developed for their erosion, calcium-magnesium-aluminum-silicate (CMAS), and furnace cycling test (FCT) performance. Through a comprehensive investigation, the first of its kind, we achieved a range of SPS microstructures by modifying the spray parameters and measuring their microhardness, fracture toughness, column densities, and residual stresses using Raman spectroscopy. We were able to produce dendritic, lateral, branched, and columnar microstructures with a unique set of processing parameters. Coatings enhanced with a refined columnar microstructure, achieved by modulating the distance from the plasma torch, exhibited superior thermal cycling resilience. Conversely, the development of a columnar microstructure with dendritic branches, obtained by decreasing the robot's traversal speed during deposition, bolstered resistance to erosion and minimized damage from molten CMAS infiltration, thereby notably augmenting the coating's lifespan and robustness. The pursuit of the optimal columnar microstructure led to the conclusion that for each SPS coating, a general framework of optimization needs to be conducted to achieve their desired thermo-chemico-mechanical resistance as the properties required for TBCs are intertwined.

Orbit of the Patroclus–Menoetius Binary System and Predictions for the 2024/2025 Mutual Events Season

We report on the ephemeris development for Menoetius, the satellite of Patroclus. Our data set consisted of ground–based and Hubble Space Telescope relative astrometry, as well as 42 lightcurves from the mutual events seasons in 2007, 2012, and 2017/2018. Our dynamical model included the effects of oblate, nonspherical shapes of the components, and we assumed that Menoetius contained ∼22% of the system’s mass. We numerically integrated the equations of motion and obtained a set of dynamical parameters that fit the data. We report the fit results in terms of residuals, state vectors, orbital elements and their 1σ uncertainties. The mean osculating semimajor axis is a = 692.5 ± 4.0 km, the mean eccentricity is e = 0.004 ± 0.004, and the International Celestial Reference Frame pole direction in R.A. and decl. is R. A. = 178.0 ± 0.5 deg, δ = −74.1 ± 0.2 deg. We determined the siderial orbital period of P = 4.282753 ± 0.000023 days. The fit yielded the system GM = 0.0950 ± 0.0012 km3 s−2, which, in combination with the system volume determined from the stellar occultation and the assumed volume uncertainty of 20%, suggests a system bulk density of 1.05 ± 0.21 g cm−3(1σ). The next season of mutual events starts in February of 2024 and lasts until January of 2025. The Patroclus system is in opposition for the observers on Earth in late September and is suitable for observations of the mutual events with an edge-on geometry in October.

Experimental investigation and CALPHAD modeling of the Mo-Nb-Zr system

To accelerate the design of novel nuclear fuel cladding materials for applications, the phase equilibria of the Mo-Nb-Zr system at 1223 K and 1773 K have been experimentally investigated by a combination of scanning electron microscopy, energy dispersive spectroscopy, electron probe microanalysis with wavelength dispersive spectrometer. In addition, the liquidus of the vertical sections at x(Mo) + x(Nb) = 0.67, x(Zr) + x(Nb) = 0.60 were also studied in this work utilizing an in-situ high-speed pyrometer. The thermodynamic assessment of the Mo-Nb-Zr system was carried out by the CALPHAD method based on the experimental data available in the present work and literature. A set of self-consistent thermodynamic parameters of the Mo-Nb-Zr system was obtained. The calculated liquidus projection of the Mo-Nb-Zr system over the whole composition was also presented.

Self-focusing/defocusing of skew-cosh-Gaussian laser beam for collisional plasma

The inverse relationship between the linear increase in skewness parameter s and the domain’s width of the order of skewness n plays a vital role in both critical beam radius and propagation dynamics of skew-cosh-Gaussian (skew-chG) laser beams. The interplay between the skewness parameter s and the order of skewness n is explored analytically and graphically in the current study to unveil the complexity of the propagation dynamics of the skew-chG laser beam. Naturally, the intensity’s complexity considerably affects the dielectric constant of the medium. Basically, the nonlinearity in the dielectric function of collisional plasma is attributed to the non-uniform heating of energy carriers along the wavefront of the laser beam, which becomes important and is used in the current study. By following Akhmanov’s parabolic wave equation approach under Wentzel–Kramers–Brillouin and paraxial approximations, the nonlinear differential equations are set up for the beam width parameters f 1 and f 2 and solved numerically. The present work analytically investigates the domains of the order n of skew-chG laser beams for a given set of skewness parameter s to investigate their effects on the self-focusing and defocusing of skew-chG laser beams. The critical curve also gets affected significantly due to the choice of domains for n. Finally, the numerical results are presented in the form of graphs and discussed in this study.

Simulation study of electron beam optics for a distributed X-ray source toward stationary CT architecture

For computed tomography (CT) imaging to be considered “real time”, one set of tomographic projections are to be acquired in less than 30 ms. Current conventional CT systems are limited to approximately 300 ms because of mechanical and material limitations. To bypass the mechanical limitations of a conventional gantry system, there is an open design challenge to develop a distributed X-ray source that is tightly packed and bright. The work presented here reports a design for a distributed X-ray source based on a rotating cylindrical anode. In particular, this work focuses on designing the electron beam optics for said X-ray source and refining these optics via multi-physics simulation studies. We designed these studies to investigate the electron beam behavior for switching, steering, and focusing. We demonstrated that the high-energy electron beam could be turned off and on via the grid-switching technique, could be steered, and could be dynamically focused for distinct positions along the cylindrical anode. We also report the optimal set of parameters that result in the desired electronic focal spot size (1 mm × 7 mm) and shape at each source position for our system.

Selecting effluent outfall location in small rivers using proper water quality models based on effluent and local characteristics

ABSTRACT This study presents a methodology to determine the appropriate location for effluent disposal in a low-flow river using numerical modelling, via a case study of a river located in Southern Brazil, considering disposal of landfill leachate with known characteristics. There are four key steps: 1) a principal component analysis (PCA) for a set of representative parameters; 2) a decision flowchart for proper water quality models; 3) a modified Water Quality Index (WQI) using the resulting concentrations and 4) simulation on 1D and 2D models and WQI calculation and evaluation. The PCA highlights eight relevant parameters: ammonia (NH3), chloride (Cl−), biochemical oxygen demand (BOD), total phosp horus, nitrite (NO2 −), nitrate (NO3 −), dissolved oxygen (DO) and total dissolved solids. Two models were considered suitable: HEC-RAS (1D) and SisBaHiA (2DH). After simulating the parameters in both models, a WQI was calculated for each location. Both HEC-RAS and SisBaHiA lead to the same effluent outfall location, showing that spatial discretisation does not affect the results.

Assessment of Cleaning and Disinfection Practices on Pig Farms across Ten European Countries.

This study delves into the assessment of cleaning and disinfection (C&D) measures within the context of European pig farming, employing the Biocheck.UGent™ tool as an effective instrument for evaluation. A comprehensive set of relevant parameters was examined to enable meaningful comparisons across farms from 10 European countries during four years (2019-2022). Findings indicate a notable increase in C&D measure implementation in select countries (Belgium, Finland, Italy, and Spain), reflecting heightened awareness and responsibility among farmers. Additionally, the overall score for the C&D subcategory highlights variation across countries, with Italy (75), Poland (74), and Belgium (72) displaying the highest scores, while Ireland (56), Slovenia (55), and Serbia (50) reported the lowest scores. However, the considerable variation in the number of participating farms necessitates cautious comparisons. The study identifies well-implemented C&D measures in the frame of external biosecurity but underscores gaps in the application of C&D measures for the material introduction practices across farms (22% of farms), which are attributed to awareness gaps and resource limitations. In the areas of internal biosecurity, strong points include C&D procedures after each production cycle (79%), implementing different stages in the C&D process (65%) and sufficient sanitary break (82%), while gaps are evident in the presence and using of hand hygiene stations (19% of farms) and boots disinfection equipment (40% of farms) between compartments/units. Notably, the study reveals a lack of evaluation of hygiene after C&D procedures (only 1% of farms), signaling critical knowledge gaps among farmers regarding proper assessment tools and methods. In conclusion, this comprehensive analysis sheds light on the implementation status of C&D measures in European pig farming, offering insights into both areas of progress and those requiring improvement. The findings emphasize the need for targeted awareness campaigns and training initiatives to bolster biosecurity practices within the industry.

ADMM-TGV image restoration for scientific applications with unbiased parameter choice

AbstractImage restoration via alternating direction method of multipliers (ADMM) has gained large interest within the last decade. Solving standard problems of Gaussian and Poisson noise, the set of “Total Variation” (TV)-based regularizers proved to be efficient and versatile. In the last few years, the “Total Generalized Variation” (TGV) approach combined TV regularizers of different orders adaptively to better suit local regions in the image. This improved the technique significantly. The approach solved the staircase problem inherent of the first-order TV while keeping the beneficial edge preservation. The iterative minimization for the augmented Lagrangian of TGV problems requires four important parameters: two penalty parameters $${\rho }$$ ρ and $${\eta }$$ η and two regularization parameters $${\lambda _{0}}$$ λ 0 and $${\lambda _{1}}$$ λ 1 . The choice of penalty parameters decides on the convergence speed, and the regularization parameters decide on the impact of the respective regularizer and are determined by the noise level in the image. For scientific applications of such algorithms, an automated and thus objective method to determine these parameters is essential to receive unbiased results independent of the user. Obviously, both sets of parameters are to be well chosen to achieve optimal results, too. In this paper, a method is proposed to adaptively choose optimal $${\rho }$$ ρ and $${\eta }$$ η values for the iteration to converge faster, based on the primal and dual residuals arising from the optimality conditions of the augmented Lagrangian. Further, we show how to choose $${\lambda _{0}}$$ λ 0 and $${\lambda _{1}}$$ λ 1 based on the inherent noise in the image.

Prediction of Electricity Generation Using Onshore Wind and Solar Energy in Germany

Renewable energy production is one of the most important strategies to reduce the emission of greenhouse gases. However, wind and solar energy especially depend on time-varying properties of the environment, such as weather. Hence, for the control and stabilization of electricity grids, the accurate forecasting of energy production from renewable energy sources is essential. This study provides an empirical comparison of the forecasting accuracy of electricity generation from renewable energy sources by different deep learning methods, including five different Transformer-based forecasting models based on weather data. The models are compared with the long short-term memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as a baseline. The accuracy of these models is evaluated across diverse forecast periods, and the impact of utilizing selected weather data versus all available data on predictive performance is investigated. Distinct performance patterns emerge among the Transformer-based models, with Autoformer and FEDformer exhibiting suboptimal results for this task, especially when utilizing a comprehensive set of weather parameters. In contrast, the Informer model demonstrates superior predictive capabilities for onshore wind power and photovoltaic (PV) power production. The Informer model consistently performs well in predicting both onshore wind and PV energy. Notably, the LSTM model outperforms all other models across various categories. This research emphasizes the significance of selectively using weather parameters for improved performance compared to employing all parameters and a time reference. We show that the suitability and performance of a prediction model can vary significantly, depending on the specific forecasting task and the data that are provided to the model.

Organic Challenge: The Organic Challenge: Cultivating Conscious Design for Biodigital Tectonics within AI’s Prompt-to-Pixel Process

The fusion of digital advancements and biological systems is transforming modern-day architecture, and this bio-digital approach, when paired with AI image generation models, promises novel design possibilities. The major drawback of this merger is the dismal performance of AI text-to-image models in translating organic tectonic details into architecture. This study examines the complexity of processes, materials, and techniques necessary in a bio-digital architectural approach. Through a series of digital trials, it identifies the need for sophisticated computational models that can capture the complex intricacies and subtle nuances present in living organisms. Before the testing, a set of parameters considered the limitation of how much tectonic information an image could portray. The Nautilus Shell, ferns, mushrooms, seahorses, and grasshoppers were taken as inspiration models because of varying biological configurations. Next, two AI image-generating tools, Midjourney and Stable Diffusion, were used with three different prompt types, each with varying degrees of complexity drawn from five organic systems. A critical analysis of AI-generated images led to the conclusion that, despite AI's exceptional abilities in creating visual content, the complex comprehension of biological systems and their conversion into architectural designs faced significant challenges.

Bending analysis of functionally graded plates resting on elastic foundation: A Rayleigh-Ritz approach and ANN method

This study presents a comprehensive investigation into the static bending characteristics of rectangular plates constructed from functionally graded materials resting on an elastic foundation. The research examines numerical factors that include central deflection, bending, and normal stresses, in functionally graded rectangular plates (FGRP) subjected to mixed edge constraints under various loading conditions. Classical plate theory is employed, and a computationally efficient mathematical expression for bending analysis is proposed, utilizing the energy principle. The Rayleigh-Ritz method with an algebraic function is applied to solve the governing equation, and the computation is performed using MATLAB. The study investigates the impact of aspect ratios, material property exponents, and the moduli of the Winkler and Pasternak foundations on the numerical factors of embedded FGRPs. The findings reveal that material property exponents and aspect ratios significantly affect numerical factors. The Pasternak foundation moduli demonstrate a more pronounced influence compared to the Winkler foundation moduli. This research provides valuable insights about the static analysis of FGRPs on Winkler-Pasternak elastic foundations, offering practical applications in the design and analysis of structures with similar configurations. Additionally, a salient aspect of this research is in the implementation of an artificial neural network (ANN) for the purpose of predicting the central deformation of FGRP while manipulating the input parameters. This approach is especially advantageous in engineering applications as it facilitates precise predictions for any set of input parameters without using complex computational work.

ALR_Sim_tracks — Trajectory Simulator Software to Assist the Search for Favourable Trajectories for the Exploration of the Triple Asteroid 2001-SN263 From the Laser Altimeter Point of View

This paper presents a simulator tool, named _ALR_Sim_tracks_, dedicated to the preliminary analysis of trajectories planned for a space mission to explore the surface of celestial bodies using optical sensors, such as a laser altimeter. The software offers a prediction of the coverage results obtainable in a simulated exploration campaign carried out along a selected/simulated trajectory, for the set of target and instrument parameters used (in qualitative and quantitative terms, including 2D/3D visualizations). The software was created to perform an analysis that would allow the identification of favourable trajectories for conducting the intended exploration of the triple asteroid 2001-SN263 during Brazil's first deep space mission (ASTER mission) from the point of view of the Laser Altimeter being designed to fly in this mission, which was named ALR. Because there are few tools described and available for this type of analysis in the literature, and these are generally complex, proprietary and of restricted use, this work aims to help broaden the understanding of the process involved and the access to this type of tool. The paper contains the description of the _ALR_Sim_tracks_ software and everything involved in its operation. The illustrative examples presented in this text involve the exploration of an asteroid from the point of view of a laser altimeter, because this was the initial motivation for creating the simulator. However, other optical instruments with similar operation, used to explore other types of targets (e.g. planets or moons), are also considered when suitable modelling is available.

Numerical Simulation Study on the Mechanics and Pore Characteristics of Tectonically Deformed Coal under Multi-Level and Multi-Cycle Loading and Unloading Conditions

Horizontal well cavern completion and stress release is considered a potential technique for efficient development of coalbed methane in tectonically deformed coal (TDC). Pulsating loading and unloading is a key technique for the controlled expansion of caverns and broader stress release within the reservoir. However, current understanding of the mechanical characteristics and pore network structure evolution of TDC under cyclic loading and unloading conditions is still limited. This paper employs numerical simulation methods to study the mechanical behavior and damage characteristics of TDC under cyclic loading and unloading. After obtaining a set of micromechanical parameters reflecting the behavior of TDC samples under triaxial compression in high-stress states, the effects of different stress gradients and cyclic amplitudes on the stress–strain curve, porosity changes, and crack propagation in TDC samples were analyzed. The study results indicate that under various cyclic loading and unloading conditions, the mechanical response characteristics of TDC samples are broadly similar, primarily divided into compression, slow expansion, and accelerated expansion phases. Under low unloading level conditions, the volume expansion of TDC samples is minimal. Also, at the same unloading level, the strain increment decreases with an increasing number of cycles. Correspondingly, under these conditions, the porosity and microcrack expansion in TDC are less than in high-stress gradient scenarios. Under the same unloading level but different amplitudes, the volume expansion rate at 50% unloading amplitude is higher than at 1 MPa unloading amplitude for TDC, with an increased number of crack expansions. Therefore, under cyclic loading conditions, the sensitivity of crack propagation within TDC samples to amplitude is greater than that to unloading level. Under actual pulsating excitation conditions, a low-amplitude, low-stress gradient pulsation method should be used to maintain the stability of horizontal well caverns, and gradually increase the cyclic amplitude to achieve the efficient extraction of coalbed methane in TDC reservoirs. The findings of this study can serve as an important reference for optimizing process parameters in cyclic pulsating stress release engineering for TDC.