Speed Prediction Research Articles

Numerical Prediction of Tool Rotation Speed for Optimum Mechanical Properties of Weld Joint in Friction Stir Welding of Dissimilar Aluminum Alloys

Numerical Prediction of Tool Rotation Speed for Optimum Mechanical Properties of Weld Joint in Friction Stir Welding of Dissimilar Aluminum Alloys

Analysis of Shield Tunneling Parameters and Research on Prediction Model of Tunneling Excavation Speed in Volcanic Ash Strata of Jakarta–Bandung High-Speed Railway Project

Insufficient investigations have been conducted on the analysis of shield tunneling parameters and the prediction of the tunneling excavation speed in formations composed of volcanic ash strata. To address this issue, we employ a comprehensive approach utilizing literature research, mathematical statistics, and other methodologies, centered on the analysis of the No. 1 Tunnel of the Jakarta–Bandung High-Speed Railway. Our focus is on examining the evolution patterns and inter-relationships of shield tunneling parameters within volcanic ash strata. Subsequently, we propose an optimized strategy for these tunneling parameters. By employing six machine-learning algorithms to construct prediction models, we compare and analyze their performance in predicting the tunneling excavation speed. The results indicate a positive correlation between slurry pressure and tunnel depth in volcanic ash strata, suggesting that the grouting pressure should exceed the slurry pressure by approximately 0.22 MPa. In the composite stratum of “volcanic ash debris + round gravel”, the cutter torque exhibits a strong negative correlation with the total thrust (−0.77). Due to tool wear and ground resistance, the excavation speed and cutter speed are weakly negatively correlated. Compared to other strata, shield tunneling in volcanic ash strata exhibits larger grouting pressure fluctuations, slower tunneling excavation speed, greater total thrust, higher cutter torque, and lower cutter speed. Regarding shield tunneling excavation speed prediction, the ranking of the algorithm performance is RF > DNN > ANN > BPNN > MNR > SVM, with RF achieving a decision coefficient of 0.829. The RF model is well-suited for predicting the shield structure tunneling excavation speed.

A physics-informed learning algorithm in dynamic speed prediction method for series hybrid electric powertrain

Engine-generator set (EGS) is an important energy supply component of high-voltage microgrid in series hybrid electric powertrain (SHEP). Sustained and steady energy supply from EGS is one of the conditions for the balanced energy between supply and demand. In some high-power processes, the balanced energy would be broken and the dynamic speed of EGS would be out of expectation, which can result in unstable working states of EGS. If the unstable working states of EGS can be known prior, it is significant for the research of unstable state identification and avoidance. Predicting rotational speed of EGS can warn of the previous issue in advance, while the insufficient data of unstable states would encounter overfitting problems in common prediction methods, so it is a challenge to improve the prediction effect of dynamic speed and then accurately predict the unstable states. Base on the above problems, a physics-informed learning algorithm with adaptive mechanism is proposed for EGS rotational speed prediction in this paper. First, a prediction problem related to the stability of SHEP running state is studied, which is found from engineering knowledge. Second, a new mechanism is proposed for physics-informed learning algorithm, and the physical information adopted to learning algorithm is more selective. Third, a professional adaptive function is originally formed according to speed characteristics, which bridge the information between physics and learning algorithm. By importing the experimental data, the prediction accuracy of proposed method in one of the test cycles is better than the results of baseline methods, specifically 27.11% and 3.49%, 11.90% and 7.94%, 53.83% and 27.62%. In summary, the proposed method can have better predictions against other baseline methods.

Frequency Regulation Adaptive Control Strategy of Wind Energy Storage System for Wind Speed Uncertainty

In order to reduce the negative influence of wind speed randomness and prediction error on frequency modulation, the reliability of the wind storage system was assessed effectively. In the wind storage frequency modulation system, a state of charge (SOC) adaptive adjustment method for wind speed randomness is proposed. Firstly, through the correlation analysis between the standby capacity of frequency modulation and the output power of wind turbine, the uncertainty of its frequency modulation capacity is revealed. Secondly, in view of the uncertainty of wind turbine frequency modulation, the output power of energy storage frequency modulation is optimized with the goal of minimizing the frequency modulation power deviation of the wind storage front under the framework of model predictive control, and the improved whale optimization algorithm (WOA) is used to solve the problem. Finally, the simulation results show that, under the given 5 min continuous disturbance, the root mean square of frequency regulation of the proposed restoration method is reduced by 56.65% compared to the SOC recovery base point set to 0.5. Under continuous large perturbations, the maximum frequency deviation is reduced by 0.0455 Hz. This effectively shows that this method can not only improve the frequency modulation reliability of wind power system but also improve the continuous frequency modulation capability of energy storage system.

Long, short, and mediumterms wind speed prediction model based on LSTM optimized by improved moth flame optimization algorithm.

Time series prediction of wind speed has been widely used in wind power generation. The volatility and instability of wind speed have a large negative impact on wind turbines and power systems, which can lead to grid collapse in severe cases. Therefore, accurate wind speed prediction is crucial for wind power generation. In this paper, considering the influence of different parameters on algorithm training and prediction, an improved moth flame optimization algorithm is constructed to optimize the LSTM wind energy prediction system to obtain better performance. The system consists of three modules: data preprocessing, optimization, and prediction. The data preprocessing module uses fuzzy information granulation to blur the input data. On this basis, the combination of swarm intelligent optimization algorithm and prediction model can effectively predict wind speed time series. Taking the California wind farm as an example, the MAPE of the experiment in the short-term forecast is 3.15%, the MAPE of the medium-term forecast is 4.38%, and the MAPE of the long-term forecast is 18.28%. The experimental results show that the proposed model has obvious advantages over the previous model.

Short-term wind speed prediction of wind farm based on TSO-VMD-BiLSTM

Aiming at the random and intermittent characteristics of wind speed, a short-term wind speed prediction (SWSP) method based on TSO-VMD-BiLSTM is proposed in this article. Firstly, open-source historical data from a certain region in 2022, including wind speed, direction, pressure, and temperature is analyzed. The data is processed through variational mode decomposition (VMD) to fully extract feature data from historical wind speed records. Secondly, taking historical wind speed, direction, pressure, and temperature as inputs and wind speed as output, a SWSP model based on long short-term memory (LSTM) network is constructed. Thirdly, the tuna swarm optimization (TSO) algorithm is utilized for parameters optimization, and a bi-directional long short-term memory (BiLSTM) network is incorporated to enhance prediction accuracy for micrometeorological parameters. The proposed TSO-VMD-BiLSTM model is validated through comparison with other models, demonstrating its higher accuracy with the maximum absolute error of only 2.52 m/s, the maximum root mean square error of 0.81, the maximum mean absolute error of only 0.54, and the maximum mean absolute percentage error of 6.89%.

Cooperative optimization of energy recovery and braking feel based on vehicle speed prediction under downshifting conditions

Regenerative braking can effectively recover vehicle kinetic energy, but its energy conversion efficiency is low under low-speed conditions, and there is also the problem of premature exit from energy recovery due to insufficient reverse electromotive force. The cooperation of gearbox gears can increase the speed range for the motor to recover energy, but unreasonable shifting will cause fluctuations in braking force and affect the consistency of the braking feel. Therefore, this paper aims to collaboratively optimize energy recovery and braking force fluctuations during gear shifting. Firstly, based on the model of braking system and transmission, the influence of shift strategy on braking impact and energy recovery is studied. In view of the challenge of determining a shift strategy with uncertain target braking speeds, a speed prediction model reconstructed by the support vector regression (SVR) model and the hybrid nonlinear autoregressive neural network (NAR) is proposed. On the basis of NAR-SVR speed prediction, the coupling effect of braking impact force and energy recovery efficiency is considered, and the collaborative optimization of regenerative braking torque and shift time is solved through a multi-objective cuckoo search algorithm. The hardware-in-the-loop test results verified that under high-speed conditions, the braking energy recovery rate of the proposed strategy was increased by 47.06 %, and the peak braking impact was reduced by 61.4 %. This research can provide a reference for the brake downshift optimization strategy and regenerative braking research of vehicles with non-decoupled electro-hydraulic composite braking systems.

Comparative evaluation of meteorological inputs for improved storm surge modeling: A case study of tropical Cyclone Vayu

The selection of meteorological inputs in storm surge is crucial, with wind and pressure fields playing a significant role in energy transfer and the study area's bathymetry. While using observed track information for wind and pressure drop has been the standard approach for the past decade, recent studies have highlighted the need for atmospheric inputs from models like the Weather Research and Forecast model (WRF) for storm surge forecast. This study aims to compare the performance of a DELFT-3D FM storm surge model forced with inputs from IMD (India Meteorological Department) observed the best track and pressure drop (EXP-1) and wind and pressure fields from WRF (EXP-2) for Tropical Cyclone Vayu, which occurred in the southern Arabian Sea basin in June 2019. The study compares the simulated storm water levels and highlights the benefits of using time and space-varying wind and pressure input for improved surge representation. Results show that the WRF-DELFT setup outperforms the IMD-DELFT setup, particularly for tide gauge locations close to the storm eye. The simulated storm surge captures the intensified stage of Vayu and provides a more realistic representation than the model forced with IMD track data. However, biases and limitations, such as inadequate representation of land surface parameterization, are identified. The study suggests further exploring wave-induced effects on total water level and multiple cyclone scenarios to enhance wind speed and track displacement prediction accuracy and improved land-sea parameterization can help address these limitations.

Multistep short‐term wind speed prediction with rank pooling and fast Fourier transformation

AbstractShort‐term wind speed prediction is essential for economical wind power utilization. The real‐world wind speed data are typically intermittent and fluctuating, presenting great challenges to existing shallow models. In this paper, we present a novel deep hybrid model for multistep wind speed prediction, namely, LR‐FFT‐RP‐MLP/LSTM (linear fast Fourier transform rank pooling multiple‐layer perceptron/long short‐term memory). Our hybrid model processes the local and global input features simultaneously. We leverage RP for the local feature extraction to capture the temporal structure while maintaining the temporal order. Besides, to understand the wind periodic patterns, we exploit FFT to extract global features and relevant frequency components in the wind speed data. The resulting local and global features are, respectively, integrated with the original data and are fed into an MLP/LSTM layer for the initial wind speed predictions. Finally, we leverage a linear regression layer to collaborate these initial predictions to produce the final wind speed prediction. The proposed hybrid model is evaluated using real wind speed data collected from 2010 to 2020, demonstrating superior forecasting capabilities when compared with state‐of‐the‐art single and hybrid models. Overall, this study presents a promising approach for improving the accuracy of wind speed forecasting.

Prediction of emission characteristics of diesel/n-hexanol/graphene oxide blended fuels based on fast outlier detection-sparrow search algorithm-bidirectional recurrent neural network

This study aims to utilize deep learning (DL) technology to address the issue of carbon soot emission in traditional internal combustion engines. Spraying and combustion experiments were conducted in a constant volume combustion (CVC) chamber using n-hexanol and graphene oxide nanoparticles as partial substitutes for diesel, with the goal of predicting their emission characteristics based on experimental data. Initially, spray and combustion data under different operating conditions were collected using the CVC experimental setup, and outliers were removed using the fast outlier detection (FOD) algorithm. Subsequently, the learning rate and the number of hidden layer neurons in the Bidirectional Recurrent Neural Network (BiRNN) model were optimized using the Sparrow Search Algorithm (SSA) to enhance the model's accuracy and generalization ability. Unlike previous research methods, the novel FOD-coupled SSA optimization method used in this study ensures the optimal parameter configuration of the speed prediction model while accelerating the convergence speed, which ultimately improves the stability of the model. In addition, the optimal output variables were determined based on the Spearman and Pearson correlation coefficient rules. Finally, the effectiveness of the predictive model was validated using evaluation metrics such as root mean square error (RMSE) and R-squared (R2). The results indicate that the KL coefficient for D80H20GO20 fuel in the CVC device is minimal. The FOD-SSA-BiRNN model achieved R2 values of 0.99409, 0.99529, and 0.99692 for D100, D80H20, and D80H20GO20 fuels, respectively. Therefore, this study provides an effective method for the online accurate prediction of carbon soot emissions in mixed fuels.

Wind speed prediction

With the help of wind farms, wind energy is a vital renewable energy source that contributes significantly to the worlds energy balance. The lifespan and maintenance costs of wind turbines will be reduced with an accurate wind speed prediction. On the other hand, wind speed is highly volatile and unpredictable. Thus, it is essential to do research into creating complex models and algorithms for precise wind speed prediction. So far, some of the most promising models include Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Autoregressive Moving Average (ARMA). Python, as an advanced and versatile programming language, is exceptionally suited for scripting the algorithms of these sophisticated models. This paper will use the data from Austin Texas and apply a Support Vector Machine (SVM) for wind speed prediction involves several stages, including data collection, data preprocessing, model selection, model training, parameter optimization, model validation, and prediction. Wind energy resource optimisation, maintenance cost reduction, and total wind farm efficiency can all be significantly improved by incorporating these models into predictive analytics and continuously improving them against changing data.

Initiation and Carbene Induced Radical Chain Reactions in CH2F2 Pyrolysis.

High temperature dissociations of organic molecules typically involve a competition between radical and molecular processes. In this work, we use a modeling, experiment, theory (MET) framework to characterize the high temperature thermal dissociation of CH2F2, a flammable hydrofluorocarbon (HFC) that finds widespread use as a refrigerant. Initiation in CH2F2 proceeds via a molecular elimination channel; CH2F2→CHF+HF. Here we show that the subsequent self-reactions of the singlet carbene, CHF, are fast multichannel processes and a facile source of radicals that initiate rapid chain propagation reactions. These have a marked influence on the decomposition kinetics of CH2F2. The inclusion of these reactions brings the simulations into better agreement with the present and literature experiments. Additionally, flame simulations indicate that inclusion of the CHF+CHF multichannel reaction leads to a noticeable enhancement in predictions of laminar flame speeds, a key parameter that is used to determine the flammability of a refrigerant.

Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models

Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models

An Enhanced Long-Term Wind Speed Prediction Using Dynamic Unified Ensemble Learning and Data Assimilation Techniques: A Case Study in Tamil Nadu, India

Reliable wind speed prediction is essential for effective grid management since wind energy is a key component of renewable energy generation. However, controlling wind speed just right is no easy feat since the wind flow changes constantly. This paper presents an innovative method for predicting wind speeds in the future. It uses state-of-the-art data assimilation methods and a dynamic unified ensemble learning model. For efficient wind energy planning and monitoring, WSP accuracy is crucial. Data from a single site limited the accuracy of WSPs in previous research. An improved accuracy of long-term wind speed predictions is achieved by the proposed model via the integration of data assimilation and ensemble learning. To increase forecast accuracy, the model uses sophisticated data assimilation methods like the Kalman filter to combine data from many sources. Specifically, the model employs the Stacked CNN + BiLSTM with Data Assimilator (SCBLSTM+DA) technique, which integrates Wind Speed (WS) data from adjacent areas with the CNN + BiLSTM-based Ensemble Learning Model (ELM) and the Four-Dimensional Variational and Ensemble Kalman Filter (4DVar/EnKF) Data Assimilation method. Using real-world wind speed data from nine meteorological stations in Tamil Nadu, India, we find that current prediction models, including both classical statistical and cutting-edge machine learning models, perform better. Further, unlike standalone models, the suggested model shows less susceptibility to changes in prediction time scales. Promising a solution to improve long-term wind speed predictions accuracy, this study has significant consequences for wind energy management and production.

ICEEMDAN-Informer-GWO: a hybrid model for accurate wind speed prediction.

Extensive research has been diligently conducted on wind energy technologies in response to pressing global environmental challenges and the growing demand for energy. Accurate wind speed predictions are crucial for the effective integration of large wind power systems. This study presents a novel and hybrid framework called ICEEMDAN-Informer-GWO, which combines three components to enhance the accuracy of wind speed predictions. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) component improves the decomposition of wind speed data, the Informer model provides computationally efficient wind speed predictions, and the grey wolf optimisation (GWO) algorithm optimises the parameters of the Informer model to achieve superior performance. Three different sets of wind speed prediction (WSP) models and wind farm data from Block Island, Gulf Coast, and Garden City are used to thoroughly assess the proposed hybrid framework. This evaluation focusses on WSP for three specific time horizons: 5minutes, 30minutes, and 1hour ahead. The results obtained from the three conducted experiments conclusively demonstrate that the proposed hybrid framework exhibits superior performance, leading to statistically significant improvements across all three time horizons.

Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines

The work carried out in this paper focused on “Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines”. The aim of this work is to develop and verify the ability of machine learning models to solve the problem of estimating the turbulent flame speed for a spark-ignition internal combustion engine operating with a hydrogen-natural gas mixture, then evaluate the relevance of these models in relation to the usual approaches. The novelty of this work is the possibility of a direct calculation of turbulent combustion speed with a good precision, using only machine learning model. The obtained models are also compared to each other by considering in turn as a comparison criterion: the precision of the result, calculation time, and the ability to assimilate original data (which has not undergone preprocessing). An important particularity of this work is that the input variables of the machine learning models were chosen among the variables directly measurable experimentally, based on the opinion of experts in combustion in internal combustion engines and not on the usual approaches to dimensionality reduction on a dataset. The data used for this work was taken from a MINSEL 380, a 380-cc single-cylinder engine. The results show that all the machine learning models obtained are significantly faster than the usual approach and Random Forest (R2: R-squared = 0.9939 and RMSE: Root Mean Square Error = 0.4274) gives the best results. With a forecasting accuracy of over 90 %, both approaches can make reasonable predictions for most industrial applications such as designing engine monitoring and control systems, firefighting systems, simulation, and prototyping tools.

Driving Intention Recognition and Speed Prediction at Complex Urban Intersections Considering Traffic Environment

Driving Intention Recognition and Speed Prediction at Complex Urban Intersections Considering Traffic Environment

Research on prediction of water target motion status of unmanned surface vehicle based on lidar

To improve the perception ability of unmanned boats on water targets and improve the autonomous collision avoidance function of unmanned boats, the problem of target motion state prediction is studied and a method for predicting the motion state of unmanned water targets based on lidar is proposed. Firstly, a three-dimensional point cloud space model is established and the down-sampling and clutter filtering are completed through various filters. Secondly, based on European clustering target segmentation, a target tracking algorithm combined with 3D-IOU metric is proposed to achieve target tracking. Finally, the Kalman filter is introduced for denoising, combined with the iterative closest point algorithm (Iterative Closest Point, ICP) and the centroid calculation algorithm to realize the prediction of the target speed and course. After the test on the lake, the method can meet the real-time requirements, complete the accurate prediction of the target motion state and control the error at a low level. At the same time, this method can also lay the foundation for the development of subsequent autonomous navigation tasks of unmanned vehicles.

An artificial visual neuron with multiplexed rate and time-to-first-spike coding.

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.

GA-LSTM speed prediction-based DDQN energy management for extended-range vehicles

In this paper, a dual deep Q-network (DDQN) energy management model based on long-short memory neural network (LSTM) speed prediction is proposed under the model predictive control (MPC) framework. The initial learning rate and neuron dropout probability of the LSTM speed prediction model are optimized by the genetic algorithm (GA). The prediction results show that the root-mean-square error of the GA-LSTM speed prediction method is smaller than the SVR method in different speed prediction horizons. The predicted demand power, the state of charge (SOC), and the demand power at the current moment are used as the state input of the agent, and the real-time control of the control strategy is realized by the MPC method. The simulation results show that the proposed control strategy reduces the equivalent fuel consumption by 0.0354 kg compared with DDQN, 0.8439 kg compared with ECMS, and 0.742 kg compared with the power-following control strategy. The difference between the proposed control strategy and the dynamic planning control strategy is only 0.0048 kg, 0.193%, while the SOC of the power battery remains stable. Finally, the hardware-in-the-loop simulation verifies that the proposed control strategy has good real-time performance.