Transfer Function Research Articles

Burner and Flame Transfer Matrices of Jet-Stabilized Flames: Influence of Jet Velocity and Fuel Properties

Abstract To achieve the decarbonization of electrical power generation, gas turbines need to be upgraded to combust high-hydrogen content fuels reliably. One of the main challenges in this upgrade is the burner design. A promising burner concept for a high-hydrogen fuel mixture are jet burners, which are highly flashback resistant thanks to their high bulk velocity. Due to its nonacoustically compact extension and the presence of hydrogen in the fuel mixture, new challenges arise in assessing the (thermo)acoustic response of this burner design. A burner transfer matrix (BTM) and the flame transfer function (FTF) or transfer matrix (FTM) are typically measured with the multimicrophone method (MMM) to assess the performance of new burner types in relation to thermoacoustic stability. With the switch toward hydrogen, the fuel/air mixture is significantly altered in its properties regarding the speed of sound and density, which are of fundamental importance for acoustic waves propagation and their reconstruction via the MMM, as highlighted in recent work. In this work, we extend this discussion by studying the influence of the gas composition within the burner when measuring BTMs, and its indirect effect on the assessment of FTFs. Experimentally, we achieve this by adapting the preheating temperature during the measurement of the BTM with a nonreactive mixture in order to match the speed of sound of the hydrogen–air mixture required to flow in the burner under reactive conditions. Additionally, we present an analytical model for the jet burner transfer matrix, which is validated against the experimental data. Since the BTM is fundamental for the assessment of the FTM and FTF, the propagation of the error of changing fuel mixtures in the burner is evaluated. The influence of the variation in reactant composition of the BTM on the FTM assessment is noticeable, particularly in the gain of the FTFs. Furthermore, the influence of the total mass flow and, thus, the bulk flow velocity on the FTF is analyzed.

Small-Signal Stability Analysis and Voltage Control Parameter Design for DC Microgrids

Small-signal instability issues will occur in the DC microgrid when the high-frequency oscillation peaks of the voltage closed-loop transfer function are not effectively suppressed. To ensure the small-signal stability of DC microgrids, the concept of a small-signal stability domain for voltage control parameters is proposed. Based on the voltage closed-loop transfer function, a small-signal-stability-domain-solving algorithm is proposed. With this stability domain, the impact of voltage-proportional coefficient and voltage-integral coefficient on oscillation frequency (or damping factor) is quantitatively analyzed. In addition, the influence of current control parameters on the small-signal stability boundary is also analyzed. With the introduction of this stability domain, a design method for voltage control parameters has been proposed. The voltage control parameters, which are quantitatively designed by this method, are able to maintain the small-signal stability of the system. Finally, based on the RT-BOX hardware-in-the-loop experimental platform, a switching model for a typical DC microgrid is established. Additionally, the effectiveness of the proposed algorithm and designed control parameters is verified by multiple sets of experimental results.

Impact of different blood pressure targets on cerebral hemodynamics in septic shock: A prospective pilot study protocol-SEPSIS-BRAIN.

Septic shock, a life-threatening condition, can result in cerebral dysfunction and heightened mortality rates. In these patients, disturbances in cerebral hemodynamics, as reflected by impairment of myogenic cerebral autoregulation (CA), metabolic regulation, expressed by critical closing pressure (CrCP) and reductions in intracranial compliance (ICC), can adversely impact septic shock outcomes. The general recommendation is to maintain a target mean arterial pressure (MAP) of 65 mmHg but the effect of different MAP targets on cerebral hemodynamics in these patients is not clear and optimal targets might be dependent on the status of CA. This protocol aims to assess the cerebral hemodynamics profile at different pressure targets in septic shock patients. Prospective, non-randomized, single-center trial, which will study cerebral hemodynamics in patients with septic shock within 48 hours of its onset. Patients will be studied at their baseline MAP and at three MAP targets (T1: 65, T2: 75, T3: 85 mmHg). Cerebral hemodynamics will be assessed by transcranial Doppler (TCD) and a skull micro-deformation sensor (B4C). Dynamic CA will be expressed by the autoregulation index (ARI), calculated by transfer function analysis, using fluctuations of MAP as input and corresponding oscillations in cerebral blood velocity (CBv). The instantaneous relationship between arterial blood pressure and CBv will be used to estimate CrCP and resistance-area product (RAP) for each cardiac cycle using the first harmonic method. The B4C will access ICC by intracranial pressure waveforms (P2/P1). The primary aim is to assess cerebral hemodynamics (ARI, CrCP, RAP, and P2/P1) at different targets of MAP in septic shock patients. Our secondary objective is to assess cerebral hemodynamics at 65mmHg (target recommended by guidelines). In addition, we will assess the correlation between markers of organ dysfunction (such as lactate levels, vasoactive drugs usage, SOFA score, and delirium) and CA. The results of this study may help to understand the effect of the recommended MAP and variations in blood pressure in patients with septic shock and impaired CA and ICC. Furthermore, the results can assist large trials in establishing new hypotheses about neurological management in this group of patients. Approval was obtained from the local Ethics Committee (28134720.1.0000.0048). It is anticipated that the results of this study will be presented at national and international conferences and will be published in peer-reviewed journals in 2024 and 2025. Trial registration number: NCT05833607. https://clinicaltrials.gov/study/NCT05833607.

Real-time ultrasound AR 3D visualization toward better topological structure perception for hepatobiliary surgery.

Ultrasound serves as a crucial intraoperative imaging tool for hepatobiliary surgeons, enabling the identification of complex anatomical structures like blood vessels, bile ducts, and lesions. However, the reliance on manual mental reconstruction of 3D topologies from 2D ultrasound images presents significant challenges, leading to a pressing need for tools to assist surgeons with real-time identification of 3D topological anatomy. We propose a real-time ultrasound AR 3D visualization method for intraoperative 2D ultrasound imaging. Our system leverages backward alpha blending to integrate multi-planar ultrasound data effectively. To ensure continuity between 2D ultrasound planes, we employ spatial smoothing techniques to interpolate the widely spaced ultrasound planes. A dynamic 3D transfer function is also developed to enhance spatial representation through color differentiation. Comparative experiments involving our AR visualization of 3D ultrasound, alongside AR visualization of 2D ultrasound and 2D visualization of 3D ultrasound, demonstrated that the proposed method significantly reduced operational time(110.25 ± 27.83s compared to 292 ± 146.63s and 365.25 ± 131.62s), improved depth perception and comprehension of complex topologies, contributing to reduced pressure and increased personal satisfaction among users. Quantitative experimental results and feedback from both novice and experienced physicians highlight our system's exceptional ability to enhance the understanding of complex topological anatomy. This improvement is crucial for accurate ultrasound diagnosis and informed surgical decision-making, underscoring the system's clinical applicability.

ADAPTATION OF THE FEEDBACK TRANSFER FUNCTION FOR OSICILATION BASED TESTING OF SECOND-ORDER ACTIVE RC FILTERS

This paper, explores possible solutions for the feedback transfer functions applied in active RC filters for oscillation-based testing (OBT). Three types of filter transfer functions, namely low-pass (LP), high-pass (HP) and band-pass (BP) are examined. Active filters are realized with the realistic operational amplifier model targeting the 180nm CMOS technology. To confirm the theoretical insights, three realizations of LP, HP, BP second-order filterers with proposed feedbacks are designed and simulated. One of the solutions, namely the BP filter, is further examined by inserting defects in the circuit. Based on the time domain simulations, the key parameters are extracted from the oscillating filter’s output signal. The fault dictionary has been created and appropriate classification of the defects is performed.

A non‐isolated single‐input dual‐output DC–DC boost converter with high‐voltage gain

AbstractThis paper presents a new structure of a high step‐up DC–DC converter. The proposed converter has one input and two outputs with different voltage levels, suitable for applications that require multiple voltage levels such as photovoltaic systems, electric vehicles and DC microgrids. The proposed converter is expandable to more outputs and has a higher voltage conversion ratio compared to similar converters. The operational modes of the proposed converter are investigated and the conversion ratio of the converter is obtained. Additionally, small‐signal equations of the converter are derived, and using state‐space matrices, transfer functions of the converter are obtained and the controller is designed. Also, the proposed converter is compared with similar converters and power losses equations of the converter are derived. To investigate the converter's performance, simulations are done using MATLAB/Simulink software. Moreover, to validate the theoretical analysis, a laboratory prototype is implemented. The obtained results confirm the operation of the proposed converter.

Engine Mass Flow Estimation through Neural Network Modeling in Semi-Transient Conditions: A New Calibration Approach

Nowadays, engine experimental research represents a very expensive field within the automotive industry, but it remains fundamental for engine and vehicle development. The present work aims to investigate a novel approach for engine control system calibration, by adopting machine learning techniques to model physical parameters of the engine starting from experimental data measured at the test bench. The main goal is to create a methodology which accelerates the calibration process without losing accuracy. A model that estimates air mass flow is created by adopting either a tree ensemble model or an artificial neural network trained on a small dataset, which was previously acquired at the test bench using a random calibration of the volumetric efficiency map. The model’s performance is first validated on a larger, random dataset. Then, the volumetric efficiency calculated from the air mass flow model estimation is used to calibrate the transfer function of the Engine Control Unit. Finally, the sensitivity of the model error correlated with the number of data points acquired is used in order to determine the best practice for a Design Of Experiment, which minimizes data acquisition. The methodology proposed can lead to reduced time and costs of the whole calibration process of the engine, without losing accuracy. The analysis was conducted on the entire vehicle, which is crucial for drivability, especially in motorcycles since they are highly sensitive to air-to-fuel ratio adjustments. This work demonstrates that machine learning models can be adopted for the fine-tuning of the calibration process, which is normally performed manually.

A fast quadrupole-based analytical model for solving transient heat transfer in ventilated double-skin walls and assessing their energy saving potential

This research proposes a fast and accurate method for modeling transient heat transfer in ventilated double-skin façades (VDFs) with forced ventilation to predict their energy-saving potential. Using the thermal quadrupole formalism, the method solves the VDF problem in the Laplace domain while considering the full transient nature of the heat transfer; the only approximation is in space. The solution involves obtaining a general transfer function that predicts heat transfer rates or air temperatures at ventilated cavity's exit. The quadrupole method is validated against computational fluid dynamic numerical simulations conducted under similar meteorological, design and boundary conditions. A very good agreement was found (less than 3 % average deviation) with a significant reduction in computation time (less than 3s against 6h for CFD calculations). The model is computationally efficient and can consider important factors such as the opacity of the walls, construction materials, and air cavity design parameters. Finally, the model allows the assessment of the energy-saving potential of VDFs under various scenarios compared to conventional systems, which helps contributing to more sustainable building design practices. The energy efficiency of a VDF configuration was compared against a conventional wall system. Energy savings of 8.4 and 5.2 % were obtained, in cold and hot climates respectively.

An approach for coherent periodogram averaging of tilt-series data for improved CTF estimation.

Cryo-electron microscopy (cryo-EM) has become an indispensable technique for determining three-dimensional structures of biological macromolecules. A critical aspect of achieving high-resolution cryo-EM reconstructions is accurately determining and correcting for the microscope's contrast transfer function (CTF). The CTF introduces defocus-dependent distortions during imaging; if not properly accounted for, the CTF can distort features in and limit the resolution of 3D reconstructions. For tilt-series data used in cryo-electron tomography (cryo-ET), CTF estimation becomes even more challenging due to the tilt of the specimen, which introduces a defocus gradient across the field of view, as well as the low dose and signal in individual tilt images. Here, we describe a simple algorithm to improve the accuracy of CTF estimation of tilted images by leveraging the tilt-series alignment parameters determined for tomographic reconstruction to explicitly account for the tilted specimen geometry. In brief, each tilt image is divided into patches, each of which are then stretched according to their defocus shift. These are then summed to provide a coherent power spectra at the tilt axis, which can then be used in standard CTF estimation algorithms. This uses all the data in each image to enhance the visibility of Thon rings, thereby improving high-resolution CTF estimation and subsequent enhancements in the resolution of subtomogram averages.

MODELING COOKING OIL PRODUCTION WITH CRUDE PALM OIL (CPO) PRICE USING TRANSFER FUNCTION

Indonesia is the largest palm oil producer in the world. However, Indonesia is currently experiencing a significant scarcity of cooking oil for the basic needs of the community. One of the causes of this problem is the high price of crude palm oil (CPO). The price of CPO affects the production of cooking oil produced by the cooking oil industry. CPO prices are released in the form of daily prices in the market which form a time series pattern that affects the production of cooking oil which is also produced daily in the industry. To forecast cooking oil production from CPO prices can be done with a transfer function model. The stages of analysis are forming an ARIMA model, processing input time series whitening, and output time series whitening. Checking white noise by plotting ACF αt, ACF βt. Make a CCF graph and determine the order (r,s,b). Estimate the first parameter of the disturbance series (nt) and identify the disturbance series (nt) in the ARIMA model. Next, perform a diagnostic check of the transfer function model by checking the cross-correlation between Xt & Yt and checking the autocorrelation of Xt and Yt for model feasibility testing. Then make an ACF graph of the transfer function model's residuals to test the model's suitability. The final stage is to select the best model based on the smallest AIC value, ensuring the accuracy of the selected model using MAPE value, and making predictions. As a result, the model for CPO price is an input series variable, and cooking oil production is an output series variable. However, the MAPE result was 100%, indicating that the model is not very accurate for this data. Nevertheless, by ignoring the result and continuing with the forecasting, this model shows that the forecasted values for cooking oil based on CPO price have increased from the form of the model obtained for the time data from 2013 to 2022.

A new forward‐flyback converter without right half plane zero

AbstractDue to its unique features, the flyback converter is widely used to convert DC to DC power. This converter has a non‐minimum phase (NMP) dynamic behavior, which is one of its most significant disadvantages. By changing the topology of the flyback converter to a forward‐flyback (FF) converter, the dynamic behavior of the converter can be minimum phase (MP) while the main advantages of the flyback converter such as isolation of the output from the input and simple topology are maintained. This article proposes an innovative FF converter topology by doubling the forward path and adding only one diode and one capacitor. In addition to better dynamic behavior, the proposed double FF (DFF) converter has the same advantages as the conventional FF converter. A new method is used to obtain the transfer function of the output voltage to the duty cycle of the proposed converter. The converter is designed in continuous conduction mode (CCM) with minimum phase dynamics. Finally, simulation and experimental results of both conventional FF and the proposed DFF converters are obtained and compared to validate the superiority of the proposed converter.

Key controls on water transit times along a tropical precipitation gradient

The tropics are one of the most diverse and dynamic regions on Earth. Despite their importance, our understanding of tropical hydrological processes remains a significant challenge, mostly due to limited monitoring. Here, we used high-resolution daily input–output isotope data sets from seven tropical catchments ranging in size from 1.6 to 990 km2, situated within a precipitation gradient ranging from on average 958 to 5117 mm year−1, in Australia, Costa Rica, and Ecuador. For these catchments, we estimated and compared streamflow mean transit times (MTT) with potential explanatory hydro-geomorphological and climate variables. Using a simple lumped convolution integral model with a Gamma distribution as transfer function (best-fit Kling Gupta efficiencies up to 0.92), our tropical dataset resulted in short MTTs from 49 to 497 days (0.13 – 1.4 years), together with considerable presence of new water in the stream and low damping ratios in most of the catchments. The gamma distribution alpha parameter was below the previously identified quasi-global pattern of around 0.5 in 5 out of 7 catchments. Principal Component Analysis (PCA) and a relative importance test identified rock type (%), days with zero precipitation and the water storage capacity as the most important controls on TT. In addition, the TT distribution as indicated by the Gamma distribution alpha parameter was best explained by annual evapotranspiration, altitude, and Andosol soil cover (%). Our findings identified the key controls on TT and TT distribution in fast-responding tropical catchments compared to other geomorphic and climate zones, emphasizing the value of TT as a coherent descriptor that can be used for catchment characterization.

Nonideal mixing effect on the input multiplicity and controller performance of CSTR based on Cholette’s model – a simulation study

ABSTRACT The transfer function of the lead-lag process in a CSTR based on Cholette’s model is used to facilitate the input multiplicity analysis. Ideal and nonideal mixing are introduced to investigate the mixing condition effect on the CSTR dynamic behavior. It is verified that the input multiplicity origins include the output multiplicity and the nonideal mixing bypass effect. The former leads to instability in the first-order process, while the latter transforms the first-order process into a lead-lag process and introduces a positive zero transfer function. The start-up diagrams established with both ideal and nonideal mixings are of critical importance to identify the unique and multiple operating regions. We also analyzed the stability conditions based on the pole and the zero of the lead-lag process, which is determined by the nonideal mixing and dominates the behaviors of a real isothermal CSTR with control. The linear and nonlinear integral controller regulatory response simulation results demonstrate that the controller performance could be deteriorated by nonideal mixing.

Direct-bonded diamond membranes for heterogeneous quantum and electronic technologies

Diamond has superlative material properties for a broad range of quantum and electronic technologies. However, heteroepitaxial growth of single crystal diamond remains limited, impeding integration and evolution of diamond-based technologies. Here, we directly bond single-crystal diamond membranes to a wide variety of materials including silicon, fused silica, sapphire, thermal oxide, and lithium niobate. Our bonding process combines customized membrane synthesis, transfer, and dry surface functionalization, allowing for minimal contamination while providing pathways for near unity yield and scalability. We generate bonded crystalline membranes with thickness as low as 10 nm, sub-nm interfacial regions, and nanometer-scale thickness variability over 200 by 200 μm2 areas. We measure spin coherence times T2 for nitrogen vacancy centers in 150 nm-thick bonded membranes of up to 623 ± 21 μs, suitable for advanced quantum applications. We demonstrate multiple methods for integrating high quality factor nanophotonic cavities with the diamond heterostructures, highlighting the platform versatility in quantum photonic applications. Furthermore, we show that our ultra-thin diamond membranes are compatible with total internal reflection fluorescence (TIRF) microscopy, which enables interfacing coherent diamond quantum sensors with living cells while rejecting unwanted background luminescence. The processes demonstrated herein provide a full toolkit to synthesize heterogeneous diamond-based hybrid systems for quantum and electronic technologies.

Study on Photon Beam Imaging Properties of CsPbBr3-PP Scintillator: Monte Carlo Simulation Approach

Perovskite scintillators have garnered significant interest in the realm of gamma-ray imaging in the past few years. Here, a comprehensive investigation into the gamma-ray imaging properties of CsPbBr3-PP composite scintillators is presented, wherein the Monte Carlo simulation approach offered by Geant4 is utilized. The primary focus is on the point spread function and modulation transfer function of the material, elucidating the nuanced interactions between gamma-ray energy, scintillator thickness, and their resultant imaging capabilities. A key aspect of this study is the exploration of the nonlinear and inverse effect of scintillator thickness and the energy of the photon beam on the imaging quality, highlighting the trade-offs between energy deposition and image resolution. This research underscores the importance of optimizing the scintillator design to balance these factors, catering to specific applications in high-energy detection and imaging. This work not only contributes significantly to the field of material sciences and radiographic imaging, but also provides practical insights for the development of more effective scintillator-based detectors. The findings of this study have broad implications for the design and application of perovskite scintillators in various high-tech industries and scientific research.

Modeling and Simulation of Fractional PID Controller for Under-actuated Inverted Pendulum Mechanical System

The stabilization of the non-linear inverted pendulum system requires a robust control strategy, as this system is inherently unstable and sensitive to disturbances. This research utilizes Lagrangian mechanics, a powerful technique in analytical dynamics, to derive the mathematical representation of the system. By applying the principles of Lagrangian dynamics, we can accurately model the energies involved and derive the equations of motion that govern the pendulum’s behavior. Following this, state-space feedback is employed to determine the Proportional, Integral, and Derivative (PID) values essential for effective control. This control strategy is particularly useful due to its ability to minimize error over time and ensure stability. To further enhance the control process, a comprehensive mathematical model is developed to establish the transfer function that correlates the pendulum's angle with the displacement of the cart. This relationship is crucial for understanding how changes in the cart's position affect the pendulum's stability. To validate the proposed control law, extensive simulations are conducted, allowing for comparative analysis against an Integer Order Controller. These simulations not only highlight the effectiveness of the PID controller but also provide insights into the dynamic behavior of the system under various conditions. The results demonstrate significant improvements in settling time and overshoot, showcasing enhanced performance metrics for the selected objective functions. This research contributes to the broader field of control systems engineering, suggesting that advanced control strategies can effectively manage complex, non-linear systems.

Design and analysis of a high-efficiency bi-directional DAB converter for EV charging

The escalating demand for electric vehicles (EVs) arises from apprehensions regarding fossil fuel depletion and the ecological repercussions of conventional combustion engines, propelling the transition towards environmentally sustainable alternatives. EVs are conventionally comprised of a powertrain, battery management system, and communication infrastructure, with the battery playing a pivotal role. Achieving an efficient EV battery charger necessitates the implementation of a proficient charging algorithm and a high-power converter capable of adeptly regulating battery parameters. Among the array of available options, a bi-directional DC/DC converter is often employed for this purpose. This study predominantly focuses on the Bi-Directional Dual Active Bridge Converter with Single-Phase Shift Control. It is renowned for attaining a broad voltage range through transformer turn ratio adjustments. Its controllable parameters, such as phase shift ratio and duty cycle, bolster its versatility. Moreover, due to its input–output isolation and minimal passive elements, this converter exhibits superior efficiency due to its soft-switching capabilities. The analytical framework encompasses steady-state and dynamic assessments, small-signal modeling, and system transfer function analysis, all of which contribute to formulating an optimized controller design. Additionally, the study conducts simulations of various battery charging techniques, including constant current (CC), constant voltage (CV), and CCCV mode, employing a 1.5 kW Dual Active Bridge DAB-based charger through MATLAB/SIMULINK. Furthermore, voltage mode control simulations for a 5 kW DAB converter augment the study's insights into effective EV charging strategies.

Ensemble width estimation in HRTF-convolved binaural music recordings using an auditory model and a gradient-boosted decision trees regressor

Binaural audio recordings become increasingly popular in multimedia repositories, posing new challenges in indexing, searching, and retrieval of such excerpts in terms of their spatial audio scene characteristics. This paper presents a new method for the automatic estimation of one of the most important spatial attributes of binaural recordings of music, namely “ensemble width.” The method has been developed using a repository of 23,040 binaural excerpts synthesized by convolving 192 multi-track music recordings with 30 sets of head-related transfer functions (HRTF). The synthesized excerpts represented various spatial distributions of music sound sources along a frontal semicircle in the horizontal plane. A binaural auditory model was exploited to derive the standard binaural cues from the synthesized excerpts, yielding a dataset representing interaural level and time differences, complemented by interaural cross-correlation coefficients. Subsequently, a regression method, based on gradient-boosted decision trees, was applied to the formerly calculated dataset to estimate ensemble width values. According to the obtained results, the mean absolute error of the ensemble width estimation averaged across experimental conditions amounts to 6.63° (SD 0.12°). The accuracy of the method is the highest for the recordings with ensembles narrower than 30°, yielding the mean absolute error ranging between 0.8° and 10.2°. The performance of the proposed algorithm is relatively uniform regardless of the horizontal position of an ensemble. However, its accuracy deteriorates for wider ensembles, with the error reaching 25.2° for the music ensembles spanning 90°. The developed method exhibits satisfactory generalization properties when evaluated both under music-independent and HRTF-independent conditions. The proposed method outperforms the technique based on “spatiograms” recently introduced in the literature.

A comparative view to H_infinity-norm of transfer functions of linear DAEs

A comparative view to H_infinity-norm of transfer functions of linear DAEs

Multi-objective optimization for connected and automated truck platoon control with improved CACC model

Connected and Automated Truck Platoon (CATP) refers to a group of trucks traveling closely together with minimal spacing to improve fuel economy and safety. However, challenges arise from instability due to internal platoon factors and external traffic disturbances. This research presents an improved Cooperative Adaptive Cruise Control (CACC) model tailored for CATP to address these challenges. The model is designed to enhance safety, fuel efficiency, and traffic efficacy. The improvements of the proposed model are in two aspects: the optimizing of the time headway strategy and the dynamic parameter adjustments of controller based on multi-objectives. The Dynamic Safety Requirement Time Headway (DSRTH) strategy facilitates the timely detection of the accelerations of the leading vehicles within the platoon, enabling quick driving responses. Additionally, Model Predictive Control (MPC) enables dynamic calibration of Proportional-Derivative (PD) control parameters and issuance of velocity commands. Meanwhile, the integration of a second-order time-delay response model has been implemented to adapt to dynamic changes in commands. A transfer function has been established, and stability has been proven. To evaluate the model performance, simulation analysis was performed using real vehicle trajectories as the CATP following vehicles. The results indicate that the DSRTH strategy outperforms both the Constant Time Headway (CTH) and Variable Time Headway (VTH) strategies, allowing rear vehicles to reach the speed trough earlier, with response speeds improved by 3.1 % and 1.5 %, respectively. Compared to the Intelligent Driver Model (IDM) and CACC models, the improved CACC model achieves a steady state of constant acceleration sooner, with recovery times reduced by 17.7 % and 3.2 %. Additionally, compared to the IDM model, the improved CACC model can save 3.23 % in fuel consumption. Furthermore, sensitivity analysis indicates that as the CATP proportion and platoon size increase, there is a positive impact on traffic flow. However, when the platoon size exceeds 5 vehicles, it shows a negative impact on the stability of other vehicles in the traffic flow besides those in the CATP.