Calibration Of Model Parameters Research Articles

A generative car-following model conditioned on driving styles

Car-following (CF) modeling, an essential component in simulating human CF behaviors, has attracted increasing research interest in the past decades. This paper pushes the state of the art by proposing a novel generative hybrid CF model, which achieves high accuracy in characterizing dynamic human CF behaviors and is able to generate realistic human CF behaviors for any given observed or even unobserved driving style. Specifically, the ability of accurately capturing human CF behaviors is ensured by designing and calibrating an Intelligent Driver Model (IDM) with time-varying parameters. The reason behind is that such time-varying parameters can express both the inter-driver heterogeneity, i.e., diverse driving styles of different drivers, and the intra-driver heterogeneity, i.e., changing driving styles of the same driver. The ability of generating realistic human CF behaviors of any given observed driving style is achieved by applying a neural process (NP) based model. The ability of inferring CF behaviors of unobserved driving styles is supported by exploring the relationship between the calibrated time-varying IDM parameters and an intermediate variable of NP. To demonstrate the effectiveness of our proposed models, we conduct extensive experiments and comparisons, including CF model parameter calibration, CF behavior prediction, and trajectory simulation for different driving styles.

Maintaining the long-term accuracy of water distribution models with data assimilation methods: A comparative study

The upgrading of water supply services is calling for more accurate and adaptive numerical models to give insight into actual water distribution systems (WDSs), which underlines the importance of carefully calibrating model parameters. Due to unavoidable uncertainties in the calibration process such as measurement errors, errors in model parameters assumed to be known, and local-optimum of calibration algorithms, calibrated parameters could still contain non-negligible latent errors, and the calibrated model may not able to maintain its long-term accuracy when operating conditions change. To solve this problem, there is growing interest in adopting data assimilation (DA) methods to utilize more comprehensive information in long-term measurements to reduce the impact of uncertainties and maintain the accuracy and stability of calibrated models. In this study, two traditional calibration methods and four DA methods were tested and compared in two WDSs with different structures, which aims to form a general understanding of the behavior and applicability of different methods. The calibration results show that DA methods perform better than traditional methods and are more robust to different types of uncertainties, which provide an effective way to maintain the long-term accuracy of WDS models to enable better management of WDSs. Ensemble-based DA methods such as Particle Filter (PF) and Inferential-Measurement Kalman Filter (IMKF) performed well in the real-life system. They avoid linear approximation and can better estimate the impact of uncertainties to assimilate accurate correction information of the parameters. Gradient-based DA methods such as Extended Kalman Filter (EKF) and Variational Bayesian Adaptive Kalman Filter (VBAKF) have lower computational demand, but they are found to be less robust when dealing with large system uncertainties and nonlinearities.

Prediction of solid solubility in supercritical carbon dioxide using a pairwise surface contact equation of state — COSMO-SAC-Phi

This study compares the performance of COSMO-SAC-Phi (CSP) and other predictive models from literature when calculating the solubility for 2450 experimental data points with pressures ranging from 45 to 500 bar and temperatures varying from 298 to 473 K. For the calibration of the pure compound CSP model parameters, pure compound saturation pressure and liquid volume were used, no binary parameters were adjusted. The average logarithm deviation in mole fraction was found to be 0.46 and 0.51 for the CSP (2021) and CSP (2019) versions, respectively. The CSP (2021) results represent an overall improvement of 27 %, 48 %, and 64 % when compared to PSRK, PRCS, and SRK-MC+vdW results, respectively. This indicates that CSP can give useful solubility estimates, especially when no experimental solubility data are available.

Bayesian calibration of continuum damage model parameters for an oxide-oxide ceramic matrix composite using inhomogeneous experimental data

The calibration of continuum damage mechanics (CDM) models is often performed by least-squares regression through the design of specifically crafted experiments to identify a deterministic solution of model parameters minimizing the squared error between the model prediction and the corresponding experimental result. Specifically, this work demonstrates a successful application of Bayesian inference for the simultaneous estimation of eleven material parameters of a viscous multimode CDM model conditioned upon a small inhomogeneous multiaxial experimental dataset. The stochastic treatment of CDM model parameters provides uncertainty estimates, enables the propagation of uncertainty into further analyses, and provides for principled decision making regarding informative subsequent experimental tests of value. The methodology presented in this work is also broadly applicable to various mechanical models with high-dimensional parameter sets.

Solute transport in dual conduit structure: Effects of aperture and flow rate

• This study provides experiment material to enhance understanding of transport processes in dual-conduit structures. • For dual-conduit structures, the influence of aperture combination and flow rate are investigated. • Experimental BTCs were well simulated by the DRMIM transport model. A typical karst aquifer configuration is the multiple conduit structure. However, it remains to be investigated how the aperture distributions and the flow rate should influence the transport process in the multiple conduit structures. To better understand the transport process in the multiple conduit structures,11 lab-scale dual-conduit structures are manufactured by varying the apertures of the two conduits ( h 1 and h 2 denote the aperture width of the shorter conduit and the longer conduit respectively). Solute transport experiments of three different flow rates are conducted on these structures. As the flow rate increases, the dual-conduit structures are more likely to present dual-peaked BTCs. The 11 structures make one exhaustive representation of the possible aperture combination of the dual-conduit structures and the transport experiments have been conducted by three flow rates (varying by 2 degrees of magnitude), so the experimental results constitute a detailed material that should improve the understanding of transport processes in such structures. Two numerical models, Weighted Sum Advection–Dispersion Equation (WSADE) and Dual Region Mobile Immobile Model (DRMIM), are applied to fit the experimental BTCs in order to obtain some insight into the actual solute-transport processes by exploring the calibrated model parameters. Considering the possible effect of solute detention, we initially applied the DRMIM model. This DRMIM better replicated the experimental BTCs than the WSADE. This study suggests the karst community shall take the DRMIM as one candidate transport model for characterizing the dual-peaked BTCs obtained in karst aquifers.

Yield estimation of Lycium barbarum L. based on the WOFOST model

Previous research about crop growth simulation and yield prediction mainly focused on annual crops with very few such studies relating to perennial fruit tree crops. In this study, field experiments were conducted in different growth periods of Lycium barbarum L. in Gansu province, China to determine and improve the initial WOFOST model parameters with combination of the time-series leaf area index (LAI) obtained from Sentinel-2 satellite and predict yields for L. barbarum shrubs at a field scale for summer fruit (SF) and autumn fruit (AF). The results showed that the performance of initial parameters established by previous research and partial measured data was relatively poor, with a relative error (RE) of 20.95% for annual yield. The simulation after model parameter calibration significantly improved model prediction accuracy. The predicted SF was 2588 kg/ha and AF was 601 kg/ha with a RE of -5.51%. This study provided a strategy to improve modelled yield for fruit tree crops through utilization of remote sensed and field measured data in model parameter calibration, which on the other hand provides useful information for regional development planning and crops marketing.

Water Purification Effect of Ecological Floating Bed Combination Based on the Numerical Simulation

The Wuqing urban section of the North Canal Basin, Tianjin, is a significant gathering place for multisource pollution, showing the characteristics of a stagnant water body supplied by unconventional water sources. With the development of the economy and society, the water quality of the Wuqing urban section of the North Canal Basin, Tianjin, has been seriously polluted due to the discharge of sewage outlets and the influx of nonpoint source pollution from farmland. In this study, based on the results of special water experiments, a two-dimensional hydrodynamic water quality model was constructed. The concentrations of ammonia nitrogen (NH3-N), total phosphorus (TP), and chemical oxygen demand (COD) in the study area were simulated, and the model parameters were calibrated and verified with the measured values. Based on the model verification, the water quality improvement scheme of the ecological floating bed with different plant ratios was set up to simulate the water quality. The research results showed that the average concentrations of NH3-N, TP, and COD decreased by 10.4%, 15.7%, and of d 26.3%, respectively, after the ecological floating bed was arranged. During model parameter calibration and validation, the RMSE ranges of NH3-N, TP, and COD were 0.09~0.22 mg/L, 0.00~0.02 mg/L, and 0.37~2.42 mg/L, respectively. Other statistical indicators are also within a reasonable range, and the model accuracy and reliability are high. The simulation results of different scenarios showed that the optimal ratio of ecological floating bed plants was 700 m2 of Scirpus validus Vahl and 700 m2 of Canna in zone 1 of the floating bed combination, 430 m2 of Scirpus validus Vahl, and 170 m2 of Iris in zone 2 of the floating bed combination, and 200 m2 of Iris and 200 m2 of Lythrum salicaria in zone 3 of the floating bed combination. This study can provide a theoretical basis for the sustainable development of water purification in the North Canal. It can also provide a model approach for the implementation of river water purification schemes, exemplified by the North Canal.

Characterising driver heterogeneity within stochastic traffic simulation

ABSTRACT Drivers’ heterogeneity and the broad range of vehicle characteristics on public roads are primarily responsible for the stochasticity observed in road traffic dynamics. Understanding the behavioural differences in drivers (human or automated systems) and reproducing observed behaviours in microsimulation has lately attracted significant attention. Calibration of car-following model parameters is the prevalent way to chracterize different driving behaviours. However, most car-following models do not realistically reproduce free-flow accelerations and therefore, model parameters are usually mainly the result of over-fitting with limited possibility to reproduce realistic drivers’ heterogeneity in simulation. To solve this problem, the present study proposes a novel framework to identify individual driver fingerprints based on their acceleration behaviours and reproduce them in microsimulation. The paper also discusses the unsuitability of vehicle acceleration to properly characterise alone the aggressiveness of a driver. A large experimental campaign and simulation results demonstrate the robustness of the proposed method.

Hierarchical Bayesian support vector regression with model parameter calibration for reliability modeling and prediction

Support vector regression (SVR) has been widely used for reliability modeling and prediction in various engineering practices. In order to improve the accuracy and robustness of SVR models, this paper proposes a hierarchical Bayesian support vector regression (HBSVR) model, which can be used for dynamic high-dimensional reliability modeling with small data sets. First, a hierarchical Bayesian model is developed to obtain the posterior inference of HBSVR model parameters. Then, a step-size adaptive accelerated Markov Chain Monte Carlo (SAA-MCMC) method is proposed and combined with Sequential Minimal Optimization (SMO) for model parameter calibration. In addition, a HSBVR dynamic update algorithm is proposed to readjust the current parameters of HBSVR by using the updated dataset and SAA-MCMC to avoid repeated modeling during online prediction. Finally, an active learning algorithm is applied to guide the selection of new samples to improve the model accuracy continuously. Eight numerical examples and two real engineering cases are conducted. The results show that the proposed HBSVR outperforms other methods in terms of prediction accuracy and robustness in small sample reliability prediction tasks.

Calibration of Discrete Element Model Parameters of Soil around Tubers during Potato Harvesting Period

To address the fact that existing studies may not be able to accurately describe the discrete element parameters of the soil during the potato harvesting period and to improve the accuracy of the potato harvesting equipment simulation studies, this study was conducted on the soil around the harvested potato tubers in the field. The simulation parameters of the discrete element model of soil were determined by the soil stacking angle test and soil block shear crushing test with the actual test measurements as the target. Based on a series of experimental designs, the factors affecting the stacking angle were the static friction coefficient and the rolling friction coefficient between soil particles; and the rolling friction coefficient between soil and steel. The factors affecting the maximum shear damage force were normal stiffness and shear stiffness per unit area. The quadratic regression equations were established and solved to obtain the optimal discrete element simulation parameters. The results can provide more realistic and reliable parameters for the construction of soil simulation models of potato fields during harvesting and for the discrete element simulation of soil-touching components of potato harvesting equipment. It can also further enrich the parameter data of soil discrete element simulation models and provide a reference for related research

Modelling water and energy fluxes with an explicit representation of irrigation under mulch in a maize field

Globally, water-saving irrigation plays a vital role in agricultural ecosystems to achieve sustainable food production under climate change. Irrigation under mulch (IUM) system has been widely used in modern agricultural ecosystems due to its high water use efficiency, but it remains unclear how each component of the water and energy processes responds to this agricultural management practice. Current modeling approaches are inadequate in investigating the impacts of IUM management on water-energy balance, which have shown more complicated than non-mulched management. Therefore, this study provided an explicit simulation of water and energy fluxes in IUM system using a process-oriented ecosystem model—CoupModel and the three years of the eddy covariance (EC) measurements. Based on Monte Carlo and the multiple model performance evaluation criteria, most of the model sensitive parameters were well constrained and 32 potentially important parameters, e.g., iscovevap, the fraction of mulch coverage, were identified to characterize the impacts of plastic mulching on energy balance and water transport. After proper calibration, the coefficient of determination (R2) for measured and simulated soil temperature (T) and soil water content (SWC) was 0.79 and 0.60, respectively, and the R2 for T and SWC during the validation period were 0.91 and 0.71, respectively. Furthermore, we found that there was a strong coupling between the parameters of the water and energy processes, which would restrict the simulation results due to the correlation between the parameters and the evaluation indices. This study presented a systematic model parameters calibration in the agricultural ecosystem implemented with IUM and provided with a more comprehensive understanding of the water and energy balance in cropland. These results would help agricultural model development with more detailed considerations of the water-saving management.

Finite element model updating through derivative-free optimization algorithm

Finite element (FE) model of updating is the process of calibrating model parameters to improve the accuracy of numerical prediction. This goal is usually achieved by solving an optimization problem with the objective function measuring the misfit between simulated responses and experimental data. The sensitivity method is the predominate class of algorithms to FE model updating problems, and has opened a wide range of applications. However, this method often suffers from large errors due to linearization. To address this challenge, this paper proposes to utilize the unscented Kalman inversion (UKI) method to solve the FE model updating problems in a derivative-free manner. As an iterative optimization method, the UKI determines the new iterate using function values at a set of sample points rather than the derivative information. Implementation details, such as handling constraints during the optimization process, are presented in the paper. To validate the proposed UKI, model updating of a pedestrian bridge is conducted using the simulated and experimental data. Both validation examples show that the UKI is a competitive method for solving FE model updating problems.

Study of radiative heat transfer and flow physics from medium-scale methanol pool fire simulations

A medium-scale (30 cm diameter) methanol pool fire was simulated using Sandia National Laboratories’ Sierra/Fuego low-Mach number multi-physics turbulent reacting flow code. Large Eddy Simulation (LES) with subgrid turbulent kinetic energy closure was used as the turbulence model. Combustion was modeled using a strained laminar flamelet library approach. Radiative heat transfer was modeled using the gray-gas approximation. This paper details analysis done to support a validation study for the fire model. In this analysis, integral quantities were primarily examined. The radiant fraction was computed and used as a model calibration parameter. Integrated buoyancy flux was calculated and compared to an engineering correlation. Entrainment rate was computed with and without a mixture fraction threshold filter and compared to engineering correlations. Turbulent kinetic energy was computed and the effect of mesh size on the subgrid and total turbulent kinetic energy was examined. Flame height was calculated using an intermittency definition with two input parameters. A sensitivity study was then conducted to determine the sensitivity of the estimated flame height to the input parameters. This analysis aided in achieving the primary validation study objectives by providing model calibration and expanding the scope of the validation effort. In addition, the range of physics examined was increased, enhancing the understanding of the model's overall performance and of the relationship between phenomena.

Oil dependence and business cycles in Algeria: new keynesian DSGE framework analysis

This study aims to investigate the macroeconomic effect of oil price shock in Algeria at different levels of oil dependency. To do this, an extensive version of small open DSGE model developed by Medina and Soto (2005) has been used. This version of DSGE model is attentive to the fact that the Algerian economic structure is characterized by oil dependency. After we calibrate model parameters, this paper presents simulation results for positive oil price shock to illustrate how the structure of the model and its theoretical underpinnings shape the transmission of the shock to real variables of the domestic economy. In particular, the paper shows 3D impulse responses for oil price shock and characterizes their impact on real GDP, CPI inflation, external position and real effective exchange rate. The results show that the effect of the real oil price shock on the main macroeconomic variables varies according to the level of oil dependency. High oil dependence makes the economy more vulnerable to oil price fluctuations compared to low oil dependence. Therefore, structural transformation is unavoidable necessity for Algerian economy.

Evaluation of Hydrological Rainfall Loss Methods Using Small-Scale Physical Landslide Model

An adequate representation of the relationship between effective rainfall and rainfall losses is required in hydrological rainfall–runoff models to reduce the uncertainty of the modelling results. This study evaluates the performance of several hydrological rainfall loss methods using the experimental data obtained from a laboratory small-scale physical landslide model with variable slope inclination, homogenous material and no vegetation effects. Three different experiments were selected and five rainfall loss methods were tested to evaluate their performance in reproducing the experimental results from the perspective of the surface runoff formation on the experimental slope. Initial and calibrated parameters were used to test the performance of these hydrological rainfall loss methods. The results indicate that the initial parameters of the rainfall loss model can satisfactorily reproduce the experimental results in some cases. Despite the fact that the slope material characteristics used in the laboratory experiments were relatively homogenous, some well-known methods yielded inaccurate results. Hence, calibration of the rainfall loss model proved to be essential. It should also be noted that, in some cases, the calibrated model parameters were relatively different from the initial model parameters estimated from the literature. None of the tested hydrological rainfall loss methods proved to be superior to the others. Therefore, in the case of natural environments with heterogeneous soil characteristics, multiple rainfall loss methods should be tested and the most suitable method should be selected only after cross-validation or a similar evaluation of the tested methods.

Application of QUAL2K for Water Quality Modeling and Management for the Euphrates River in Fallujah City as a Case Study

The main objective of this research is an examination of the applicability of the QUAL2K mathematical model on large rivers like the Euphrates, and it is the first study of its kind conducted on this important section within Fallujah Euphrates Reach (FER), using this model for the purpose of evaluating the quality of water and creating a database to predict the quality of river. The tests were conducted during January and April of 2022, where they were calibrated and verified for DO and CBOD5 at a distance of 12.86 km. Google Earth pro and Arc-GIS were utilized to provide certain data as input into the QUAL2K model. The model was calibrated in January using the trial-and-error technique, and the verification was completed in April using the model calibration parameters. For the purposes of evaluating the model's performance, statistical measures such as mean absolute error (MAE), root mean square error (RMSE), and relative error (RE) were used. In addition, three different scenarios were applied (pollution load modification, flow augmentation for point sources, and flow augmentation for headwater) to improve the water quality of Euphrates river. The results show that the difference between observed and anticipated values had a relatively small error percentage, which means that this mathematical model can be applied to this important section of the river. While the concentration of DO was within the permissible limits to perpetuate aquatic life and maintain the ecological health of the river, the concentration of CBOD5 was also within the limits in some locations. In addition, the scenario represented by modifying the pollution loads represented by BOD5 for sewage water were effective in raising the concentration of DO in the river, while increasing point sources discharge and headwater flow does not yield meaningful results.

Development of a calibration approach using DNDC and PEST for improving estimates of management impacts on water and nutrient dynamics in an agricultural system

Calibration and validation are standardized practices to establish the credibility of biogeochemical models for understanding agroecosystem nutrient dynamics. We evaluated three automatic calibration approaches, including simultaneous, sequential, and separate, for the calibration of model parameters of the biogeochemical DeNitrification DeComposition (DNDC) model through inverse modeling using PEST, open-source parameter estimation, and uncertainty analysis software. While manual calibration by experts performed the best during calibration period, followed by simultaneous calibration, sequential calibration had the best model performance during the validation period. Model sensitivity analyses demonstrated water leaching to be sensitive to curve number and drain spacing, nitrate leaching to be sensitive to porosity and clay content, and corn yield to be sensitive to accumulative temperature and grain C/N ratio. While some level of expertise is required to inform the automated calibration procedure, it represents a more efficient and robust approach toward increasing the performance of biogeochemical models.

Ecosystem Service Value Estimation of Paddy Field Ecosystems Based on Multi-Source Remote Sensing Data

A paddy field ecosystem (PFE) is an important component of an agricultural land ecosystem and is also a special artificial wetland with extremely high value. Taking Tianjin (a municipality city in China) as the research area and using multi-source remote sensing data, we improved the accounting framework of the ecosystem service value (ESV) of PFEs and the calibration of model parameters. The ESV of PFEs was mapped at medium-high resolution and fine-grain at the provincial scale. The results showed that: (1) the net ESV of PFEs in Tianjin in 2019 was RMB 29.68 × 108, accounting for 0.21% of GDP. The positive ESV was RMB 35.53 × 108, the negative ESV was RMB 5.84 × 108, and the average ESV per unit area was RMB 5.47 × 104/ha; (2) as a proportion of the ESV of PFE, the value of climate regulation (61.27%) was greater than the value of carbon fixation and oxygen release (15.29%), which was greater than the value of primary products supply (8.08%). The production value of PFEs is far lower than their ESV; (3) the total net ESV in Baodi District was RMB 16.85 × 108, accounting for 56.77% of Tianjin’s ESV, and the net ESV per unit area was RMB 5.72 × 104/ha, both of which were higher than in other districts; (4) the pixel-based hot spots analysis showed that the number of hot spots (high-value ESV) and cold spots (low-value ESV) reached 98.00% (hot spots 56.9%, cold spots 41.1%) with a significant cluster distribution. The hot spots were mostly distributed in Baodi District (37.8%) and the cold spots were mostly distributed in Ninghe District (17.2%). The research results can support agricultural development, improve countermeasures according to local conditions, and provide theoretical support for regional land use planning, ecological compensation policy formulation and ecological sustainable development. Our methodology can be used to assess the impact of land use change on ESV.

An Algebraic Evaluation Framework for a Class of Car-Following Models

Car-following models describe how a driver follows the leading vehicle in the same lane. They serve as the cornerstone of microscopic traffic-flow simulations and play an essential role in analyzing human factors in traffic casualty, congestion, efficiency, and emissions. An extensive and continuously growing number of car-following models in the literature raises the requirement to evaluate and compare different models objectively. Generally, a car-following model is evaluated after model parameter calibration: the optimal residual between the calibrated model output and the measured counterpart is used as a metric to assess a car-following model’s performance. However, model parameter calibration, usually formed as a numerical optimization problem, suffers from several issues, such as local optimality and heavy computational burden. More importantly, different formulations of the cost function can lead to distinct calibration outcomes and contradictory conclusions of the model evaluation results. This paper proposes instead a purely algebraic framework for evaluating a class of car-following models whose parameters can be linearly identified. Car-following models with nonlinear relationships among parameters, e.g., the behavioral car-following models, are out of the scope of analysis in this paper. Algebraic manipulations performed on a model finally produce a system error index, which is a uniform metric for evaluating and comparing different car-following models. During the whole process, no cost function needs to be designed a priori, and no computationally expensive numerical optimization is involved. Three car-following models are evaluated and compared under the proposed algebraic framework.

Bayesian updating of modal parameters for modeling chatter in turning

Variations in the mechanics and dynamics of the machining process under operational conditions cause inaccuracies in chatter model predictions. The parameters of chatter models therefore require re-calibration based on experimental observations during machining. This paper presents a new Bayesian method for the in-process calibration of chatter model parameters in turning. The new method comprises two stages. First, we identify the dominant closed-loop poles of the machining system from in-process vibration signals; then, we use those poles in a Bayesian model updating method to determine the probability distributions of the model parameters. Compared to existing methods, which require experimental observations under both stable and unstable conditions, the presented method requires a limited set of vibration measurements during stable conditions only. Moreover, the updated probability distributions are used to establish credibility bounds around the Stability Lobe Diagrams (SLD). An experimental example is presented to demonstrate the efficiency and effectiveness of the presented method in enhancing the accuracy of chatter predictions in turning.