Calibration Parameters Research Articles

Three-Dimensional Micromechanical Simulation and Evaluation of High-Toughness Ultra-Thin Friction Course with X-Ray Computed Tomography

As a novel pavement wear layer material, the micromechanical mechanisms of High-toughness Ultra-thin Friction Course (HUFC) have not been fully elucidated. This paper presents a new method for the three-dimensional micromechanical simulation of high-toughness asphalt mixtures based on a viscoelastic parameter calibration model. X-ray Computerized Tomography (CT) was employed to scan samples of high-toughness asphalt mixtures to obtain detailed information on the internal structure (aggregate, fine aggregate matrix FAM and voids), and a three-dimensional micromechanical model was constructed based on the real-scale distribution of these components. Aggregates in the high-toughness asphalt mixture were modeled as elastic bodies, while FAM was treated as a viscoelastic material characterized by the Burgers model. Using the Boltzmann linear superposition principle and Laplace transform theory, the viscoelastic properties of FAM were converted into Prony parameters recognizable by finite element software, and the viscoelastic parameters were calibrated. Micromechanical simulations were conducted for three different gradings of high-toughness asphalt mixtures, and the results show that the predicted deformation closely matched the measured deformation. This method accurately reflects the deformation characteristics of different gradings of high-toughness asphalt mixtures, overcoming the limitations of traditional numerical simulations based on homogeneous material models. It represents an advancement and refinement of micromechanical simulation methods for high-toughness asphalt mixtures.

Comprehensive study on statistical methods for optimization of process parameters and material properties of AlSi10Mg in laser powder bed fusion

This study provides a systematic investigation of the effects of process parameters and heat treatments on the material properties of AlSi10Mg, produced by laser powder bed fusion (L-PBF). Using a central composite design (CCD) with 106 test specimens (49 cubes, 57 tensile), the samples were studied for key properties: density (up to 99.96%), hardness (up to 154.6 HV1), surface roughness (as low as 1.9 Ra), tensile strength (up to 487.5 MPa), and elongation at break (up to 16.6%). Statistical analysis (ANOVA) identified laser power and scanning speed as the most influential parameters on these properties. Additionally, heat treatment was shown to reduce hardness and tensile strength but increase elongation at break, demonstrating the ability to modify mechanical properties based on the desired outcome. Process parameter optimization yielded properties comparable to some of the highest reported values for AlSi10Mg in the literature. The study also discusses the transferability and reproducibility of L-PBF results across different machines, highlighting challenges related to machine-to-machine variations, lack of calibration and standardization and parameter consistency. The results demonstrate the potential of L-PBF to produce AlSi10Mg parts with tailored properties for industrial applications.

Calibration of Dual-Polarised Antennas for Air-Coupled Ground Penetrating Radar Applications

Radar polarimetry is a technique that can be used to enhance target detection, identification and classification; however, the quality of these measurements can be significantly influenced by the characteristics of the radar antenna. For an accurate and reliable system, the calibration of the antenna is vitally important to mitigate these effects. This study presents a methodology to calibrate Ultra-Wideband (UWB) dual-polarised antennas in the near-field using a thin elongated metallic cylinder as the calibration object. The calibration process involves measuring the scattering matrix of the metallic cylinder as it is rotated, in this case producing 100 distinct scattering matrices from which the calibration parameters are derived, facilitating a robust and stable solution. The calibration procedure was tested and validated using a Vector Network Analyser (VNA) and two quad-ridged antennas, which presented different performance levels. The calibration methodology demonstrated notable improvements, aligning the performance of both functioning and under-performing antennas to equivalent specifications. Mid-band validation measurements indicated minimal co-polar channel imbalance (<0.3 dB), low phase error (<0.8°) and improved cross-polar isolation (≈48 dB).

Research on coal rock parameter calibration based on discrete element method

To ensure that the discrete element model of the coal wall accurately reflects the actual cutting process of coal and rock during virtual prototype simulation of mining equipment, this study aims to expedite the development of intelligent mining machinery. Using coal samples from the 4602 working face of Yangcun Coal Mine, operated by Yanzhou Coal Mining Group, we conducted coal rock packing experiments and uniaxial compression tests to obtain the packing angle and compressive strength of the coal samples. Based on the experimental results, we designed Plackett–Burman experiments (PB experiments), steepest ascent experiments, and Box-Behnken experiments to study the influence of particle contact mechanics parameters and bonding mechanics parameters on the packing angle and compressive strength, using the packing angle and compressive strength as response variables. Our objective is to minimize the relative error between the simulated packing angle and the measured packing angle. We solved and optimized the parameter calibration model and conducted simulation calibration experiments based on the optimization results. A comparative analysis with the actual test results revealed that the maximum relative errors between the simulated and measured values for the packing angle and compressive strength were only 2.9% and 5.0%, respectively. Additionally, a discrete element model of a typical working face coal wall was established based on the parameters obtained from this calibration method. A bidirectional coupling model of the cutting process between the coal and rock was created using EDEM-RecurDyn to simulate the rigid-flexible coupling of the coal cutter. An experimental coal wall model was constructed based on similarity theory, and both simulation and physical experiments were conducted. The evaluation metrics for comparison were the time-domain and frequency-domain characteristics of the drum’s vibration signals. The maximum relative error for the time-domain signal characteristics between the two experimental setups was only 4.19%, while the maximum relative error for the frequency-domain signal characteristics was 3.75%. This validates the feasibility of the proposed calibration method for the discrete element coal wall model and the accuracy of the calibration results. Furthermore, it demonstrates that the constructed discrete element coal wall accurately represents the actual coal and rock properties. The virtual simulation based on this model effectively replicates the interaction process between mining machinery and coal, providing a safe, efficient, and low-cost technological approach for performance analysis of mining equipment and intelligent control of supporting devices.

A Filter Calibration Method for Laser-Scanned Weld Toe Geometries

The scanning of weld seams can be used to evaluate the local weld toe geometry for fatigue assessments. Laser scanned weld seam profiles often contain noise which complicates the accurate measurement of the weld toe geometry. For that reason, filtering of the scanned data is necessary. The issue at hand is that a filtering method can significantly affect the measurement results. Therefore, a calibration of the filter input parameters is needed. In this study, a calibration method for filtered laser-scanned weld profiles is presented by using artificial weld toe geometries. The adjustment of different filter functions is achieved by using an optimization method on predefined weld toes with an artificial noise. The resulting input data for the filter functions is tested on a real specimen to verify the method. Through the calibration method it is possible to achieve satisfactory measurement results with precisely set input parameters for the filter functions. The most suitable filter functions for the measurement of the weld toe are the Gaussian and the Lowpass filter. Both functions are adequate as a universally applicable filter. For the evaluation of the measurement results of the radii and angles, a tolerance range is introduced, which is defined by the theoretically minimum measurable radii and angles. Using an adjusted Lowpass filter and a point distance of 0.07 mm set by the laser scanner, a measurement within the tolerance range of 0.2 mm is achievable for the weld toe radius. For the weld toe angle, the tolerance range of 1.5° is achieved for the majority of measurements.

A calibration-informed deep learning model for three-dimensional particle reconstruction of volumetric particle image velocimetry

Accurately reconstructing three-dimensional particle fields is essential in fluid velocity measurement research. This study addresses the limitations of current three-dimensional (3D) particle reconstruction methods, such as computational efficiency, precision at high particle density, and particle morphology issues, by introducing a calibration-informed deep learning model named the calibrated pixel to voxel convolutional neural network (CPV-CNN) for 3D Particle Reconstruction. This innovative neural network framework employs a unique Gaussian attention mechanism that bridges pixels and voxels, enabling the mapping of pixel features from two-dimensional (2D) particle images to 3D voxel features. This approach eliminates the need for an initial particle field for particle reconstruction, while significantly enhancing reconstruction efficiency. Additionally, the neural network incorporates camera calibration parameters and the physical coordinates of the reconstructed domain, thereby improving the model's generalization capability and flexibility. Numerical experiments demonstrate that CPV-CNN delivers superior results in terms of accuracy, generalization, and robustness in 3D particle reconstruction. The reconstructed particles exhibit favorable morphology, without the elongation issues commonly observed with conventional methods. This achievement illustrates a practical particle reconstruction algorithm based on artificial intelligence (AI) techniques and represents an important step toward developing an end-to-end AI-based particle reconstruction method in the future.

Mechanical properties and damage plastic model for cement-soil mortar: laboratory tests and numerical analysis

Cement-soil mortar is a composite material that provides an efficient and cost-effective solution for a wide range of construction applications. This study analyzes the mechanical properties of cement-soil mortar through experimental investigation and explores the application and parameter calibration of the Concrete Damage Plasticity (CDP) model in the finite element simulation of cement-soil mortar. Additionally, an innovative Initial Defect Generation (IDG) method is proposed to enhance the accuracy in simulation of failure mode. The research findings provide a simulation framework that balances simplicity and accuracy for cement-soil mortar. Uniaxial compressive tests are first conducted on cement-soil mortar specimens with water contents ranging from 40% to 70%, and cement-to-soil proportions from 30% to 300%. Based on the experimental results, the regression relationships correlating unconfined compressive strength (UCS) with elastic modulus, peak strain and stress-strain curves are established. Then, the simplified equations for calculating CDP model parameters from the UCS of cement-soil mortar are further proposed following damage mechanics. A parameter table summarizing the calculation methods for all relevant CDP parameters is provided to streamline the model calibration process. Simulations incorporating the simplified calibration method and IDG method successfully replicated the stress-strain responses and failure modes observed in uniaxial compressive strength tests of cement-soil specimens with varied strength. The results demonstrate the reliability and broad applicability of this simulation framework in predicting the mechanical performance of cement-soil mortar.

Using calibrated minimum input models to predict retrofit performance on residential dwellings

Accurate energy models for existing buildings depend on the appropriate calibration of a large number of unknown parameters against detailed monitoring data. This process typically requires significant computational effort and often produces an underdetermined model. Simplifying the model by reducing unknown parameters can alleviate these challenges. This study assesses whether a previously introduced minimum input method, which uses a subset of significant parameters for simplified calibration, can predict post-retrofit performance. The paper compares the predictive power of this new method with conventional calibration, using post-retrofit monitored data from three social housing dwellings. Results indicate that minimum input models achieve significant improvements in retrofit prediction from uncalibrated models and can outperform conventionally calibrated models. On average, minimum input models reduced CVRMSE by 5.6% from baseline, slightly less than the 6.3% improvement from full calibration. Our results support simplified calibration as a viable alternative for predicting thermal performance from residential retrofits.

Study on the impact of bowtie filter on photon-counting CT imaging

Objective.The aim of this study was to investigate the impact of the bowtie filter on the image quality of the photon-counting detector (PCD) based CT imaging.Approach.Numerical simulations were conducted to investigate the impact of bowtie filters on image uniformity using two water phantoms, with tube potentials ranging from 60 to 140 kVp with a step of 5 kVp. Subsequently, benchtop PCD-CT imaging experiments were performed to verify the observations from the numerical simulations. Additionally, various correction methods were validated through these experiments.Main results.It was found that the use of a bowtie filter significantly alters the uniformity of PCD-CT images, depending on the size of the object and the x-ray spectrum. Two notable effects were observed: the capping effect and the flattening effect. Furthermore, it was demonstrated that the conventional beam hardening correction method could effectively mitigate such non-uniformity in PCD-CT images, provided that dedicated calibration parameters were used.Significance.It was demonstrated that the incorporation of a bowtie filter results in varied image artifacts in PCD-CT imaging under different conditions. Certain image correction methods can effectively mitigate and reduce these artifacts, thereby enhancing the overall quality of PCD-CT images.

Autonomous Landing Strategy for Micro-UAV with Mirrored Field-of-View Expansion

Positioning and autonomous landing are key technologies for implementing autonomous flight missions across various fields in unmanned aerial vehicle (UAV) systems. This research proposes a visual positioning method based on mirrored field-of-view expansion, providing a visual-based autonomous landing strategy for quadrotor micro-UAVs (MAVs). The forward-facing camera of the MAV obtains a top view through a view transformation lens while retaining the original forward view. Subsequently, the MAV camera captures the ground landing markers in real-time, and the pose of the MAV camera relative to the landing marker is obtained through a virtual-real image conversion technique and the R-PnP pose estimation algorithm. Then, using a camera-IMU external parameter calibration method, the pose transformation relationship between the UAV camera and the MAV body IMU is determined, thereby obtaining the position of the landing marker’s center point relative to the MAV’s body coordinate system. Finally, the ground station sends guidance commands to the UAV based on the position information to execute the autonomous landing task. The indoor and outdoor landing experiments with the DJI Tello MAV demonstrate that the proposed forward-facing camera mirrored field-of-view expansion method and landing marker detection and guidance algorithm successfully enable autonomous landing with an average accuracy of 0.06 m. The results show that this strategy meets the high-precision landing requirements of MAVs.

Impact of stochastic vehicle generation on traffic microsimulation model output

One of the most important characteristics of microsimulation is the ability to model the temporal and spatial nature of traffic demand. Every traffic microsimulation has a vehicle generation model that determines how and when the driver–vehicle units are introduced in the simulation. While microsimulation use has become increasingly popular, it is unclear how the vehicle generation options affect the final result. The purpose of this paper is to examine the stochastic component of the vehicle generation model and how it may affect the simulation output. Three scenarios, including the passenger car equivalent (PCE) model of the Highway Capacity Manual (HCM-7), were used for assessing stochastic volumes and their impacts on performance measures. Results indicate a statistically significant impact of the variability of stochastic volumes on the estimation of performance measures at the breakdown flow phase and synchronized flow phase for interrupted and uninterrupted flow conditions, respectively. This finding highlights the importance of reporting vehicle generation as a calibration parameter, enabling others to replicate the experiments. When utilizing microsimulations for the assessment of roadway/system performance, it is crucial for users to have a thorough understanding of demand generation.

Assessment of the Choked Flow Model of RELAP5 for Application of Inverse Quantification Methods

In the framework of deterministic safety analysis, best estimate plus uncertainty methodologies can provide a more informative detailed analysis of transients than conservative approaches. However, one important step in the application of such methodologies is derivation of uncertainty of the physical models. Probability density functions of the physical model parameters are obtained by applying inverse uncertainty quantification (IUQ) methods to experimental data. The present work deals with evaluation of the experimental database and assessment of the simulation model, which are required steps prior to the application of IUQ methods. The U.S. Nuclear Regulatory Commission system code RELAP5 has been applied for simulation of three experimental databases on choked/critical flow: Sozzi-Sutherland, Super MobyDick, and Marviken Critical Flow Tests. First, the adequacy of the experimental data to the specific target domain is carried out, to then proceed to calibration of the input model parameters by combining the use of Gaussian Process metamodels and optimization schemes. The assessment of the simulation models is performed by evaluating independently accuracy, precision, and consistency through four statistical indicators, including a novel indicator to evaluate the consistency of the results. The final results indicate that the model is capable of reproducing the selected experimental database and overall average accuracy, precision, and consistency below the 10% threshold and can be used for application of IUQ methods.

Prediction of Scour Depth for Diverse Pier Shapes Utilizing Two-Dimensional Hydraulic Engineering Center’s River Analysis System Sediment Model

Examining scouring around bridge piers is crucial for ensuring water-related infrastructure’s long-term safety and stability. Accurate forecasting models are essential for addressing scour, especially in complex water systems where traditional methods fall short. This study investigates the application of the HEC-RAS 2D sedimentation model, which has recently become available for detailed sediment analysis, to evaluate its effectiveness in predicting scoring around various pier shapes and under different water conditions. This study offers a comprehensive assessment of the model’s predictive capabilities by focusing on variables such as water velocity, shear stress, and riverbed changes. Particular attention was paid to the influence of factors like floating debris and different pier geometries on scour predictions. The results demonstrate that while the HEC-RAS 2D model generally provides accurate predictions for simpler pier shapes—achieving up to 85% precision—it shows varied performance for more complex designs and debris-influenced scenarios. Specifically, the model overpredicted scouring depths by approximately 20% for diamond-shaped piers and underpredicted by 15% for square piers in debris conditions. Elliptical piers, in contrast, experienced significantly less erosion, with scour depths up to 30% shallower compared to other shapes. This study highlights the novel application of the HEC-RAS 2D model in this context and underscores its strengths and limitations. Identified issues include difficulties in modeling water flow and debris-induced bottlenecks. This research points to the improved calibration of sediment movement parameters and the development of advanced computational techniques to enhance scour prediction accuracy in complex environments. This work contributes valuable insights for future research and practical applications in civil engineering, especially where traditional scour mitigation methods, such as apron coverings, are not feasible.

Lateral Shear Stress Calculation Model Based on Flow Velocity Field Distribution from Experimental Debris Flows

AbstractDebris flows continuously erode the channel downward and sideways during formation and development, which changes channel topography, enlarges debris flow extent, and increases the potential for downstream damage. Previous studies have focused on debris flow channel bed erosion, with relatively little research on lateral erosion, which greatly limits understanding of flow generation mechanisms and compromises calibration of engineering parameters for prevention and control. Sidewall resistance and sidewall shear stress are key to the study of lateral erosion, and the distribution of the flow field directly reflects sidewall resistance characteristics. Therefore, this study has focused on three aspects: flow field distribution, sidewall resistance, and sidewall shear stress. First, the flow velocity distribution and sidewall resistance were characterized using laboratory debris flow experiments, then a debris flow velocity distribution model was established, and a method for calculating sidewall resistance was developed based on models of flow velocity distribution and rheology. A calculation method for the sidewall shear stress of debris flow was then developed using the quantitative relationship between sidewall shear stress and sidewall resistance. Finally, the experiment was validated and supplemented through numerical simulations, enhancing the reliability and scientific validity of the research results. The study provides a theoretical basis for the calculation of the lateral erosion rate of debris flows.

Test-and-FE-based method for obtaining complete stress-strain curves of structural steels including fracture

Ductile fracture is a common limit state of frameworks of steel structures and can be predicted by incorporating models for fracture initiation and propagation in finite element (FE) analyses. However, accurate prediction of fracture relies on unique fracture parameters for a given material, requiring precise predictions of fracture strain and stress states obtained through coupon tests and accompanying FE data analysis. In recent decades, various methods have been proposed to predict ductile fracture initiation, including various design geometries of coupons, test setups and FE analyses, which may lead to inconsistent fracture strains and stress states. Hence, there is a need for proposing a standard procedure for the design, test and accompanying FE-based calibration of fracture parameters, as pursued in this paper. Given that ductile fracture initiates under significant plastic strain, a constitutive model for the full strain range is required, as also proposed in this paper. Moreover, to reduce the experimental and data analytical efforts, an empirical method is proposed that uses only a small number of coupon tests to calibrate the fracture parameters. The method encompasses three levels wherein one, two, or three coupon tests are required. All in all, the paper presents a methodology for determining the full-range true stress-strain curve and the initiation of fracture, including a new post-necking constitutive model and methods for determining the free parameters of the post-necking and fracture initiation models. While applied to steel in this paper, the proposed methodology is potentially also applicable to other metals.

Design of Exterior Orientation Parameters Variation Real-Time Monitoring System in Remote Sensing Cameras

The positional accuracy of satellite imagery is essential for remote sensing cameras. However, vibrations and temperature changes during launch and operation can alter the exterior orientation parameters of remote sensing cameras, significantly reducing image positional accuracy. To address this issue, this article proposes an exterior orientation parameter variation real-time monitoring system (EOPV-RTMS). This system employs lasers to establish a full-link active optical monitoring path, which is free from time and space constraints. By simultaneously receiving star and laser signals with the star tracker, the system monitors changes in the exterior orientation parameters of the remote sensing camera in real time. Based on the in-orbit calibration geometric model, a new theoretical model and process for the calibration of exterior orientation parameters are proposed, and the accuracy and effectiveness of the system design are verified by ground experiments. The results indicate that, under the condition of a centroid extraction error of 0.1 pixel for the star tracker, the EOPV-RTMS achieves a measurement accuracy of up to 0.6″(3σ) for a single image. Displacement variation experiments validate that the measurement error of the system deviates by at most 0.05″ from the theoretical calculation results. The proposed EOPV-RTMS provides a new design solution for improving in-orbit calibration technology and image positional accuracy.

Total Least Squares In-Field Identification for MEMS-Based Inertial Measurement Units

Inertial Measurement Units are widely used in various applications and, hardware-wise, they primarily consist of a tri-axial accelerometer and a tri-axial gyroscope. For low-end commercial employments, the low cost of the device is crucial: this makes MEMS-based sensors a popular choice in this context. However, MEMS-based transducers are prone to significant, non-uniform and environmental-condition-dependent systematic errors, that require frequent re-calibration to be eliminated. To this end, identification methods that can be performed in-field by non-expert users, without the need for high-precision or costly equipment, are of particular interest. In this paper, we propose an in-field identification procedure based on the Total Least Squares method for both tri-axial accelerometers and gyroscopes. The proposed identification model is linear and requires no prior knowledge of the parameters to be identified. It enables accelerometer calibration without the need for specific reference surface orientation relative to Earth’s gravity and allows gyroscope calibration to be performed independently of accelerometer data, without requiring the sensor’s sensitive axes to be aligned with the rotation axes during calibration. Experiments conducted on NXP sensors FXOS8700CQ and FXAS21002 demonstrated that using parameters identified by our method reduced cross-validation standard deviations by about two orders of magnitude compared to those obtained using manufacturer-provided parameters. This result indicates that our method enables the effective calibration of IMU sensor parameters, relying only on simple 3D-printed equipment and significantly improving IMU performance at minimal cost.

Calibration of discrete meta-parameters of bamboo flour based on magnitude analysis and BP neural network.

In the research and development of technology and equipment for bamboo products deep processing, such as filling, drying, and medicinal use of bamboo flour (BF), the poor compaction and fluidity of BF materials entails the need for accurate discrete element model (DEM) and BF parameters to provide a reference for the simulation of BF processing operationsand the development of related equipment. The average particle size of the 5 types of BFs ranges from 0.136 mm to 0.293 mm, and the small particle size of BF particles causes to the number of BF particles in bamboo processing equipment to reach tens of millions or even billions. When conventional methods are used for simulation, ordinary computers cannot provide the required computing power. To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. Using particle scaling theory and dimensional analysis methods, the average particle size of the BF was increased to 1 mm, and the main discrete element contact parameters of the five types of BF to be tested were used as input layers. The injection method and sidewall collapse method were used to obtain the angle of repose (AR) as the output layer. Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. The relative error between the simulated AR obtained through calibration parameters and the physical experimental values is less than 2.3%. Through BF parameter validity verification, the simulated maximum compression displacement and compression ratio after stabilization were 34.81 mm and 0.477, which were close to the actual experimental results of 34.77 mm and 0.461, respectively, verifying the accuracy of the neural network prediction model. The research results provide a reference for the simulation of BF processing operations and the development of related equipment.

A New LC-MS/MS-Based Method for the Simultaneous Detection of α-Tocopherol and Its Long-Chain Metabolites in Plasma Samples Using Stable Isotope Dilution Analysis

Background: Our study presented a novel LC-MS/MS method for the simultaneous quantification of α-tocopherol (α-TOH) and its phase II metabolites, α-13′-COOH and α-13′-OH, in human serum using deuterium-labeled internal standards (d6-α-TOH, d6-α-13′-COOH, d6-α-13′-OH). Methods: The method addresses the analytical challenge posed by the significantly different concentration ranges of α-TOH (µmol/L) and its metabolites (nmol/L). Previous methods quantified these analytes separately, which caused an increase in workflow complexity. Results: Key features include the synthesis of stable isotope-labeled standards and the use of a pentafluorophenyl-based core-shell chromatography column for baseline separation of both α-TOH and its metabolites. Additionally, solid phase extraction (SPE) with a HybridSPE® material provides a streamlined sample preparation, enhancing analyte recovery and improving sensitivity. By utilizing deuterium-labeled standards, the method compensates for matrix effects and ion suppression. This new approach achieves precise and accurate measurements with limits of detection (LOD) and quantification (LOQ), similar to previous studies. Calibration, accuracy, and precision parameters align well with the existing literature. Conclusions: Our method offers significant advantages in the simultaneous analysis of tocopherol and its metabolites despite concentration differences spanning up to three orders of magnitude. In contrast to earlier studies, which required separate sample preparations and analytical techniques for tocopherol and its metabolites, our approach streamlines this process. The use of a solid-phase extraction procedure allows for parallel sample preparation. This not only enhances efficiency but also significantly accelerates pre-analytical workflows, making the method highly suitable for large-scale studies.

Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization

In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.