Velocity Prediction Model Research Articles

ST-TDCN: A two-channel tree-structure spatial–temporal convolutional network model for traffic velocity prediction

In the development of intelligent transport systems which aims to provide safe, convenient, and comfortable transport and effectively addresses issues with traffic congestion and pollution, accurate collection of spatial–temporal data is of great significance. Based on the study of the topology and spatial characteristics of the transportation network, data with spatial–temporal attributed can be predicted by relevant models. Thus, the purpose of providing additional information for decision making and labor cost saving is achieved. This study proposed a more accurate traffic velocity prediction model, named Spatial-Temporal Tree-structure Dual-channel Convolution Network. The model designs a spatial tree convolution module for capturing the spatial features of nodes in the traffic network represented by the tree structure and applies a dual-channel temporal convolutional network module to capture the temporal information of velocity data more accurately. Comparative tests have demonstrated that the model proposed in this study has the advantages of higher accuracy, better convergence and more flexibility in responding to dynamic changes of traffic velocity.

Development of laminar burning velocity prediction model and correlation of iso-octane air mixtures using artificial neural network

Surrogate fuels provide an economical alternative for forecasting the combustion properties of transportation fuels like diesel, gasoline, and kerosene. Iso-octane (2,2,4-trimethylpentane), a primary reference fuel for gasoline, is extensively used as a surrogate component. This study utilizes a Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) to predict the laminar burning velocity (LBV) of iso-octane/air mixtures. The ANN model was developed using a dataset of 6339 data points, including 3788 experimental data points from literature since 2004 and 2551 simulation data points from reduced kinetic reaction mechanism (RKM) CHEMKIN simulations. The grid search cross-validation (CV) method was employed to optimize the ANN hyperparameters. Implemented in Python using Keras, the ANN model addresses research gaps by forecasting LBV for hydrocarbons beyond n-heptane and modelling LBV under conditions reflective of actual engine operations. Unlike prior studies, we optimized various ML models, their complexity, size, and hyperparameters to achieve high prediction accuracy. We also integrated a hybrid model combining Particle Swarm Optimization (PSO) with Genetic Algorithms (GA) for hyperparameter optimization, ensuring efficient configuration of the ANN. The constructed ANN model was compared to other ML models, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), and XGBoost regression. For the testing set (20 % of the total dataset), the ANN model outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R2) of 0.9903, root mean square error (RMSE) of 1.911, and mean absolute error (MAE) of 1.206. Optimizing the ANN model with PSO further enhanced prediction accuracy, achieving a correlation coefficient of 0.9973 and a reduced mean absolute error of 0.753. To evaluate the ANN model's predictive ability, a reduced mechanism from the Lawrence Livermore National Laboratory (LLNL) was used. Compared to the LLNL RKM, the ANN predicted values showed lower percentage deviation from experimental data. Additionally, computation cost comparisons revealed that while RKM simulations in CHEMKIN took 244.2 s per case, the PSO-optimized ANN model required only 900 s for 150 cases. A 16-term correlation for laminar burning velocity (LBV) was derived from ANN predictions, serving as a practical tool to predict LBV based on pressure, temperature, and equivalence ratio. Unlike existing literature correlations, this newly developed correlation is valid across a wide range of operating conditions, offering a fundamental database for real-time combustion applications.

An energy management strategy for fuel cell hybrid electric vehicle based on HHO-BiLSTM-TCN-self attention speed prediction

This research aims to improve the performance and economics of fuel cell hybrid electric vehicles (FCHEVs), validated and established by introducing an innovative energy management strategy (EMS) based on a speed-predictive fusion model. Firstly, a mixed prediction model was built based on BiLSTM, TCN, and Self-attention (SA) mechanism to accurately search, capture and fuse multi-granularity features in time series. Then, Harris-Hawk Optimization (HHO) was used to optimize the dropout rate and model learning rate of the combined BiLSTM-TCN-SA time series model to improve the prediction accuracy and generalization ability of the model. Finally, stochastic model predictive control was combined with BiLSTM-TCN-SA to form SMPC-NSGA III algorithm, which was used for multi-objective optimization of fuel economy, fuel cell durability and battery durability. In this study, the effectiveness of the proposed strategy was verified under the condition of CLTC-P driving cycle. The experimental results showed that RMSE and R2 of HHO-BiLSTM-TCN-SA velocity prediction model are 1.169 and 0.998, respectively. In addition, the output of the model is within the confidence interval of 97.5 % of the real speed, and there is no significant difference, which is statistically significant. Under the SMPC-NSGA III strategy, the average efficiency of the fuel cell was increased by 12 % and 1 % respectively.

Physics Constrained High-Precision Data-Driven Modeling for Multi-Path Ultrasonic Flow Meter in Natural Gas Measurement.

Ultrasonic flow meters are crucial measuring instruments in natural gas transportation pipeline scenarios. The collected flow velocity data, along with the operational conditions data, are vital for the analysis of the metering performance of ultrasonic flow meters and analysis of the flow process. In practical applications, high requirements are placed on the modeling accuracy of ultrasonic flow meters. In response, this paper proposes an ultrasonic flow meter modeling method based on a combination of data learning and industrial physics knowledge. This paper builds ultrasonic flow meter flow velocity prediction models under different working conditions, combining pipeline flow field velocity distribution knowledge for data preprocessing and loss function design. By making full use of the characteristics of the physics and data learning, the prediction results are close to the real acoustic path flow velocity distribution; thus, the model has high accuracy and interpretability. Experiments are conducted to prove that the prediction error of the proposed method can be controlled within 1%, which can meet the needs of ultrasonic flow meter modeling and subsequent performance analysis in actual production.

Modelling the Smoke Flow Characteristics of a Comprehensive Pipe Gallery Fire with Rectangular Section

In this study, a numerical model of the cable cabin of a comprehensive pipe gallery was established to study the smoke flow diffusion behaviour of a comprehensive pipe gallery fire under a rectangular cross-section. The effects of fire source power (Q = 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 MW) and fire source location (D = 10, 20, 40, 50, 60, 80, 100 m) on the smoke flow characteristics—such as smoke layer height and thickness, longitudinal airflow velocity, and ceiling temperature distribution—were analysed, and the corresponding prediction model was fitted. The results show the following: (1) The height of the smoke layer decreases with increasing fire power, and the predictive model of the smoke layer thickness obtained from the fitting is proportional to the smoke mass flow rate and inversely proportional to the aspect ratio of the pipe gallery. (2) Longitudinal air velocity prediction models of D < 50 m and D ≥ 50 m are fitted, and the average error between them and the numerical simulation values is 9.611%. (3) The temperature decay gradient of the smoke decreases gradually with increasing distance from the fire source, while there is a significant temperature difference between the two sides of the fire source. The average relative errors of the dimensionless temperature rise models fitted upstream and downstream of the fire source in the form of ΔTT0=AeBD−XH+C exponentials with respect to the numerical simulations were 11.688% and 7.296%, respectively. The results of the study can provide a reference for smoke flow and fire prevention and control in comprehensive pipe galleries.

Shear wave velocity prediction for fractured limestone reservoirs based on artificial neural network

AbstractShear wave velocity is an essential parameter in reservoir characterization and evaluation, fluid identification and prestack inversion. However, conventional data‐driven or model‐driven shear wave velocity prediction methods exhibit several limitations, such as lack of training data sets, poor model generalization and weak model robustness. In this study, a model‐ and data‐driven approach is presented to facilitate the solution of these problems. We develop a theoretical rock physics model for fractured limestone reservoirs and then use the model to generate synthetic data that incorporates geological and geophysical knowledge. The synthetic data with random noise is utilized as the training data set for the artificial neural network, and a well‐trained shear wave velocity prediction model, random noise shear wave velocity prediction neural network, is established by parameter tuning, which fits the synthetic data with noise well. The neural network is applied directly to the real field area. Compared with conventional shear wave prediction methods, such as empirical formulas and the improved Xu–White model, the prediction results show that the random noise shear wave velocity prediction neural network has better prediction performance and generalization. Furthermore, the prediction results demonstrate the efficacy of the proposed approach, and the approach has the potential to perform shear wave velocity prediction in real areas where training data sets are unavailable.

Fragment dispersion characteristics of uncoupled charge structures with internal explosive loading

The demand for uncoupled charge structures has risen alongside advancements in warheads and protective design technologies in defense and public security sectors. Consequently, this work investigates the fragment dispersion characteristics of uncoupled charge structures and the impact of different structural parameters through numerical simulations. A modified equation derived from the Gurney formula is proposed using numerical simulation and the smoothed particle hydrodynamics method, tailored to the uncoupled charge concept and the foundational theory of the Gurney formula. To validate the fragment velocity prediction model for uncoupled charge structures, explosion experiments and other testing methods were utilized. By examining the theoretical underpinnings and the application of numerical simulation methods, this paper provides a robust foundation for the uncoupled charge structure, offering a crucial reference for designing protective measures against improvised explosive devices.

A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon

Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%.

Establishment of critical non-depositing velocity prediction model for sediment in drip irrigation laterals based on PSO-SVM

Accurately determining the critical non-depositing velocity for sediment (CNDVS) in drip irrigation laterals is essential to address issues of sediment deposition and clogging in drip irrigation pipes caused by the use of muddy water. This paper focuses on three main factors: the percentage of intermediate-sized sediment particles (P), pipe diameter (D), and sediment concentration (S), all of which significantly influence the CNDVS in drip irrigation laterals. To predict the CNDVS, a comprehensive gradient test was developed and the Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm was applied to establish a response surface for predicting the CNDVS. Additionally, the PSO algorithm optimized the penalty parameter (C), loss parameter (ε), and kernel parameter (δ) in the SVM, resulting in a prediction model for the CNDVS. The proposed model was trained and tested using experimental data, and its prediction performance and accuracy were compared with the regression fitting model. The results indicate that within a specific range, the CNDVS increases with higher values of the three factors. The PSO-SVM model, with optimal parameters combination values of C = 486.37, ε = 0.04, and δ = 68.49, provided the most prediction accurate. Compared with the regression fitting model, the PSO-SVM model has the smaller values of Mean Absolute Error (MAE) = 0.0049, Root Mean Square Error (RMSE) = 0.0057, and Percent Bias (PBIAS) = 0.0004, and the larger value of Nash–Sutcliffe Efficiency (NSE) = 0.9874, Willmott index (WI) = 0.9401, and Coefficient of Determination (R2) = 0.9875. These results demonstrate that the PSO-SVM model established in this study exhibits high prediction performance and accuracy. The findings of this research can serve as a foundation for developing optimal operating and flushing flow velocity within the laterals in muddy water drip irrigation systems.

Experimental study of decompression wave characteristics and steel pipe applicability of supercritical CO2 pipeline

High-strength pipeline steel is the optimal choice for continuously transporting large amounts of carbon dioxide (CO2) in the future Carbon capture, utilization and storage (CCUS) industrial chain, and supercritical CO2 (S–CO2) is the ideal phase state from an emitter to an onshore storage site. In CCUS projects, it is very indispensable to carry out the suitability of high steel-grade pipelines to ensure the safe operation of pipelines. It is considered a catastrophic incident for creatures and equipment once pipeline failure when fractures or propagation occurs. In this paper, the fracture propagation velocity of X70, X80, and X90 pipeline materials is calculated base on the high strength line pipe (HLP). Meanwhile, three sets of full-bore fracture (FBR) experiments of S–CO2 pipelines were conducted with similar initial status on an industrial-scale setup, and the decompression wave velocity was obtained with the data acquisition system. The result shows that the initial decompression wave velocity of S–CO2 is about 270 m/s, and the pressure plateau is between 6.8 and 7.3 MPa, corresponding to the decompression wave velocity, which is about in the range of 100–220 m/s. After that, the decompression wave velocity prediction model was established based on the homogeneous equilibrium method (HEM) and the equation of state (Eos), and the prediction results are more conservative than the experimental results. To ensure that the pipeline will not ductilely fracture in the event of a leakage or rupture, X70, X80 and X90 grade steel pipeline design wall thickness and minimum arrest toughness values are determined based on the two-curve method (TCM), respectively. Furthermore, a CO2 pipeline design model is developed based on the experimental data and decompression wave calculation method. The significant experimental data could be used for safety assessment and theoretical model validation in the CO2 transport pipeline and is useful in developing better fracture control models.

Research and prediction of pipeline inspection gauges velocity based on simulation and neural network

Pipeline transportation is a highly efficient transportation mode, and pipeline inspection gauge (PIG) is a widely used pipeline cleaning, detection and maintenance device. The PIG's ability to fulfill its intended purpose hinges heavily on its motion velocity. And it is vital to predict and control the velocity of PIG to ensure optimal functioning. This paper establishes several models with different rotary valve orifice numbers, orifice diameters, valve openings, and sealing disc compression amounts. The effects of the factors on PIG force state and velocity are analyzed by finite element method (FEM). With these simulation outcomes, the paper establishes velocity prediction models based on polynomial fitting, support vector machine (SVM) and neural network methods. In addition, by combining neural networks with genetic algorithms, the PIG size is optimized. The results demonstrate that compared with polynomial fitting and SVM, neural networks are more convenient and accurate and have a broader application prospect.

Experimental study on the acoustic wave propagation characteristics of bedding shales under changes in temperature and pressure

To determine the acoustic wave propagation characteristics of bedded shales under different confining pressures and temperatures, shales from the Longmaxi Formation in the Sichuan Basin are taken as research objects. Based on ultrasonic experiments, the acoustic wave propagation properties of shales with different bedding angles are investigated. The effects of the confining pressure, temperature, and bedding angle on the acoustic velocity, attenuation coefficient, and acoustic anisotropy coefficient are analyzed. Based on the results, an acoustic velocity prediction model for bedded shales considering the confining pressure, temperature, and bedding angle is established. The experiments show that, for confining pressures from 0 to 50 MPa and temperatures from 20 to 100 °C, the acoustic velocity of the shales increases with increasing confining pressure and decreases with increasing temperature and bedding angle. The attenuation coefficient of the shales exhibits a decreasing trend with increasing confining pressure, but increases with increasing temperature and bedding angle. The acoustic anisotropy coefficient of shale gradually decreases with increasing confining pressure, but increases with increasing temperature and bedding angle. The acoustic velocity prediction model for in-situ bedded shales established in this study has a high level of accuracy. The relationship between the acoustic anisotropy coefficient and the bedding angle is satisfied by a binomial equation. The relationship between the acoustic anisotropy coefficient and the confining pressure and temperature follows a binary linear logarithmic equation.

A Sound Velocity Prediction Model for Seafloor Sediments Based on Deep Neural Networks

The acoustic properties of seafloor sediments have always been important parameters in sound field analyses and exploration for marine resources, and the accurate acquisition of the acoustic properties of sediments is one of the difficulties in the study of underwater acoustics. In this study, sediment cores were taken from the northern South China Sea, and the acoustic properties were analyzed. Since traditional methods (such as regression equations or theoretical models) are difficult to apply in practical engineering applications, we applied remote sensing data to sound velocity prediction models for the first time. Based on the influencing mechanism of the acoustic properties of seafloor sediments, the sediments’ source, type and physical properties have a great influence on the acoustic properties. Therefore, we replaced these influencing factors with easily accessible data and remote sensing data, such as parameters of granularity, distance to the nearest coast, decadal average sea surface productivity, water depth, etc., using deep neural networks (DNN) to develop a sound velocity prediction model. Compared with traditional mathematical analyses, the DNN model improved the accuracy of prediction and can be applied to practical engineering applications.

Centralized model‐predictive cooperative and adaptive cruise control of automated vehicle platoons in urban traffic environments

AbstractRecent research has demonstrated that cooperative and automated driving functions can improve traffic efficiency and safety. Traffic efficiency can be increased and the overall vehicle energy consumption can be reduced through cooperative driving functions not only on the highway, but also in urban areas. To this end, this paper presents a linear model predictive control (MPC) algorithm for the optimization of velocity trajectories of vehicles in a platoon in an urban traffic environment. A novel centralized approach is implemented to optimize the total energy consumption of all vehicles while improving traffic efficiency. To solve the optimal control problem, for the first time, an additional data‐driven velocity prediction model of the vehicle in front of the platoon is used to consider the influence of realistic prediction errors when evaluating the developed control strategy in a vehicle simulation environment. The simulated total energy consumption is compared with a rule‐based cooperative adaptive cruise control (CACC) algorithm. Furthermore, the traffic density and the platoon string stability, the robustness and computer time of the centralized approach are also included in the evaluation. Simulation results indicate that, considering all evaluation criteria, the centralized cooperative control strategy can improve energy efficiency by up to 15% in the investigated urban scenario.

Driver-Centric Velocity Prediction With Multidimensional Fuzzy Granulation

Dear Editor, This letter deals with a real-world problem regarding chaotic time series prediction, where a driver-centric velocity prediction model is presented for vehicle intelligent control and advanced driver assistance, i.e., multi-dimension fuzzy predictor. Inspired by fuzzy granulation technology, a finite-state Markov chain (MC) is reinforced to capture probabilities of the transitions between velocity and acceleration and present signals that vary in a continuous range. The predictability of the multi-dimensional fuzzy predictor is examined by comparing two existing MC-based predictors over the two laboratory cycles and one virtual driving cycle, both of which have high accuracy.

Experimental study on shear wave velocity of sand-gravel mixtures considering the effect of gravel content

Sand-gravel mixtures are special engineering geological materials between soils and fractured rocks. This study performs a series of bending element tests to systematically investigate the shear wave velocity (Vs) of the sand-gravel mixtures, establish an effective evaluation method, and assess the influence of relative density and effective confining pressure on mixtures with a wide range of gravel contents. The results showed that the shear wave velocity increases and then decreases with the increase in gravel content and increases with the rise in relative density and effective confining pressure. Furthermore, a shear wave velocity prediction model is proposed in this study based on the intergranular contact state theory, including the stress parameter (n) and skeleton void ratio. The stress parameter can be described by a power function considering the uniformity coefficient. The model serves as a reference guide for estimating the shear wave velocity of sand-gravel mixtures with a wide range of gravel contents.

Safety assessment for a ballast railway induced by underground subway tunnel blasting: A case study

Modern railroad infrastructure is subject to blast vibrations. The dynamic safety of an operating railroad under the influence of tunnel blasting is a primary problem for metro development in urban areas. In this paper, the blasting excavation of Wuhan Metro Line 5 was selected as a case. The ballast rail- sleeper- ballast bed composite structure numerical model was developed and validated in order to evaluate the ballast railway’s safety. The smoothed particle hydrodynamics element was chosen to replicate the ballast bed due to the instability and unpredictability of the ballast bed constructed from crushed stone. Further analysis was conducted on the dynamic response characteristics of the ballast rail-sleeper-ballast bed composite structure. On the basis of the parameter calculation and analysis, a prediction model of the blast vibration velocity of the ballast rail under blasting conditions was developed. Next, the rail was simulated as a semi-infinite Euler beam and placed on the Kelvin foundation to calculate the rail displacement at the train’s limited operation speed. By substituting the maximum rail displacement when the train is running at maximum speed into the rail velocity prediction model, it is possible to determine the maximum blast velocity that the rail can withstand in this instance. In this case, the ballast bed, sleeper, and ballast rail were also deemed safe.

A new nonlinear seepage model for clay soil considering the initial hydraulic gradient of microscopic seepage channels

There is a clear nonlinear characteristic of the seepage velocity of clay; however, its existing prediction models are mostly empirical, and the prediction accuracy needs to be improved. In this paper, the soil–water retention curve is considered an indirect indicator reflecting the scale distribution of the soil seepage channel to calculate the initial hydraulic gradient of the microscopic seepage channel, based on which a nonlinear seepage velocity prediction model for clay soils was established in combination with the Tao-Kong model. To verify this model, the saturated seepage velocity of three dry densities of Hunan clay under different hydraulic gradients was measured with a GDS permeameter. Using the new model, the saturated seepage velocity under different hydraulic gradients was predicted and compared with experimental results and prediction results from existing models. The comparison showed that the new model has the highest accuracy. Both the prediction and experimental results showed that seepage in Hunan clay exhibits a nonlinear behavior and has an initial hydraulic gradient. When the seepage pressure is small, the permeability coefficient is small; as the seepage pressure increases, there is a steep increase in stair, and the larger the dry density is, the larger the initial hydraulic gradient.

Fractal wave velocity evolution model of ultrasonic propagation and attenuation in water-bearing coal

A fractal wave velocity evolution model of the water-bearing coal is constructed to explore the relationship between the complex pore and fissure structure parameters of the water-bearing coal mass and the ultrasonic P-wave velocity, based on the fractal theory and with consideration of the attenuation of ultrasonic waves propagating in the porous media of coal. In addition, experimental study and numerical analysis of the model are carried out, the following conclusions are reached. (1) The P-wave velocity increases with the increase of the coal sample length, the water content, the wave velocity in the coal matrix and the maximum pore radius, and decreases with the increase of the minimum pore radius. (2) The experimental results also show that the P-wave velocity increases with the increase of the water content and fractal dimension of the coal sample, indicating that the wave velocity prediction model has high accuracy. (3) The fractal dimension, water content, wave velocity in the coal matrix and coal sample length have great effects on the P-wave velocity of coal samples, while the minimum pore radius and the maximum pore radius have relatively little influences on the P-wave velocity.

How macrostructures enhance droplet coalescence jumping: A mechanism study

Macrostructures exhibit excellent performance in coalescence-induced droplet jumping enhancement. Previous velocity prediction models were mainly established for flat plain surface, which usually set up the balance relationship between excess surface energy, kinetic energy and dissipation. But these models are no longer appropriate in macrostructure cases due to great variation of oscillational kinetic energy and gravitational potential energy caused by textures. In this work, coalescence-induced droplet jumping on macrostructures was investigated numerically on flat, sidewall, string, ridge and egg shape substrates. An energy conversion model based on energy efficiency rather than energy balance was established for departure velocity by proposing three sub-efficiencies to evaluate jumping enhancement. In this model, oscillational kinetic energy was distinguished from dissipation, gravitational potential energy induced by macrostructures were considered, and the real droplet morphology were used to calculate surface energy. By dividing the whole droplet jumping process into merge and contact stage, the influence of macrostructures on duration of each stage was closely connected to the macrostructure effects on dissipation and kinetic energy conversion. Pressure and velocity distributions near the macrostructure were also discussed to further explore the mechanism for droplet jumping enhancement. Further, the significant increase of initial surface energy for egg case points out a possible new approach to enhance jumping velocity. This model helps us understand the determinants of jumping enhancement with macrostructures and it also applies to flat plain.