Thin non-uniform particle size wicks are essential to improve the maximum heat flux of two-phase thermal management systems by improving the wickability. To understand the enhanced wickability, we examine a pore-scale capillary flow within the thin sintered particle wick using a free-energy-based, single-component, two-phase Lattice Boltzmann Method (LBM) with a minimal parasitic current. The developed LBM approach is validated through the rate-of-rise in the two-parallel plates with parallel plates spacing of W = 48 against analytical Bosanquet equation, achieving the RMS error below 10%. The LBM predicts the rate-of-rise through the uniform and non-uniform particle-size wicks between two-parallel plate, including the capillary meniscus front and dynamic capillary filling. At the same plate spacing and porosity, i.e., W = 48 lu and ε = 0.75, the non-uniform particle size wick achieves enhanced wickability by providing the selective flow pathway through pore networks formed in the smaller pores between the small/large particles, which is in qualitative agreement with previous experimental results. The enhancement of the maximum and minimum dimensionless liquid height and the liquid-filled pore ratio of non-uniform particle size wick is found to be up to 11.1, 27.47, and 26.11%, respectively. The simulation results provide insights into the optimal wick structures for high heat flux two-phase thermal management system by enhancing the wickability through the non-uniform particle (or pore) sizes.

Residential water heaters contain water stratified by temperature-driven density differences. This implies that a water tank can reach a state in which the top and bottom sections have different temperatures, unless mixing happens. A high degree of thermal stratification can improve the efficiency of some water heaters, by saving the amount of energy required for the heat-up process. Studies of stratification became popular in the 1970s and it remains an active research topic today. The research has led to the development of different models and techniques to better predict and define a stratified tanks behavior. By comparing these models and techniques used previously to describe thermal stratification, the phenomenon could be better understood, exploited, and used to increase efficiency and thermal energy capacity in modern water tanks. From the existing models, we found the one-dimensional standard plug-flow and a multi node model to be appropriate for analyzing the processes of the heat up and cool-down in a water tank. These two models are based on energy balances. This work involved comparing the accuracy and computational effort needed to implement these models. To assess accuracy, we compared both types of existing models to experimental data (also collected in this work) which included a heat up process using an external heat pump. This external process included a layering process that has an eddy diffusivity at five times the rate of thermal diffusion. For this project, we implemented the models in MATLAB, the multi-paradigm numerical computing environment. We quantified model accuracy using the root mean squared error between modeled data and experimental data for six measured tank temperatures. Comparing the accuracy and the computational time taken to run the simulation provides a method to contrast the performance of each model and a way to rate it. The multi node model was run using from 6 to 96 spatial nodes; the plug flow model was run using 1 to 0.001 °C temperature bin sizes. Additionally, timesteps were varied from 4 to 236 s. The results quantify the tradeoff between accuracy and computational time, providing guidance for simulations to intelligently select the best model type and simulation parameters. This research can be used to validate the pre-existing models and possibly improve the modern water tank.

This work is a continuation of a previous study (IMECE2019-11449) which sought to explore the feasibility and means of successfully modeling the hydrogen fast filling process of cylinders lined with phase change material (PCM) entirely in CFD software. The first focus of this work was to address the simplistic approach of how the liner temperature was modelled in the previous study. Previously, the entire liner was assigned a single temperature which was obtained and updated through the lumped heat capacity method. This meant that the hotter gas at the end of the cylinder opposite the inlet was in contact with a liner at a temperature lower than could realistically be expected. This was remedied by splitting the liner into four sections. Two sections were used for the curved portions at each end of the cylinder, and the straight wall section was split into two. Each section had its temperature independently calculated through the lumped heat capacity method. A temperature difference on the order of a ten degrees Celsius was observed between the different sections of the liner prior to latent heating beginning. The mass averaged temperature of the hydrogen inside the cylinder obtained with the sectioned wall case matched that obtained with the single wall temperature almost exactly, less than a degree difference. Despite the unexpected findings of the average hydrogen temperature not changing much when the wall is split into sections, this approach was still taken with all the cases completed in this study. The liner could be split into a greater amount of sections than four, but this was considered unnecessary due to the findings regarding the overall hydrogen temperature. Four sections were considered adequate and used to model the temperature gradient along the wall or liner. The effect of gravity on the filling process was also explored based on the orientation of the cylinder. This required completing three-dimensional simulations to accurately simulate buoyancy driven flow in horizontally mounted cylinders. All the simulations were completed with ANSYS Fluent 2019 R1 without the use of additional software to handle the heat transfer involving the PCM. All simulations were completed with the coupled pressure-based solver and K-Omega SST turbulence model. The gas properties were obtained from tables generated from NIST properties (REFPROP) available within ANSYS Fluent to limit the amount of error in the accumulated mass within the cylinder due to inaccurate gas properties. The initial conditions for the gas and liner temperatures were 25°C and 100 bar for the gas pressure. A constant mass flow rate of 0.02174 kg/s at a temperature 0°C were used as the initial conditions for the inlet hydrogen gas.

Microfluidics has shown great promise for the sorting or separation of biological cells such as circulating tumor cells since the first studies came out a few decades ago. With recent advances in high-throughput microfluidics, analysis of massive amounts of data needs to be completed in an iterative, timely manner. However, the majority of analysis is either performed manually or through the use of superimposing multiple images to define the flow of the particles, taking a significant amount of time to complete. The objective of the work is to increase the efficiency and repeatability of particle detection in a high-throughput deterministic lateral displacement (DLD) device. The program proposed is in the early stages of development, but shows promise. The average time it takes to analyze over a gigabyte of video is 24.21 seconds. The average percent error of the total particle detection was 21.42%. The assumptions made for the initial version of the program affect the accuracy of the particle in wall detection, so new techniques that do not follow the assumptions will need to be investigated. More work will be completed to implement machine learning or deep learning to assist in the development of DLD devices.

Material parameters related to deterministic models can have different values due to variation of experiments outcome. From a mathematical point of view, probabilistic modeling can improve this problem. It means that material parameters of constitutive models can be characterized as random variables with a probability distribution. To this end, we propose a constitutive models of rubber-like materials based on uncertainty quantification (UQ) approach. UQ reduces uncertainties in both computational and real-world applications. Constitutive models in elastomers play a crucial role in both science and industry due to their unique hyper-elastic behavior under different loading conditions (uni-axial extension, biaxial, or pure shear). Here our goal is to model the uncertainty in constitutive models of elastomers, and accordingly, identify sensitive parameters that we highly contribute to model uncertainty and error. Modern UQ models can be implemented to use the physics of the problem compared to black-box machine learning approaches that uses data only. In this research, we propagate uncertainty through the model, characterize sensitivity of material behavior to show the importance of each parameter for uncertainty reduction. To this end, we utilized Bayesian rules to develop a model considering uncertainty in the mechanical response of elastomers. As an important assumption, we believe that our measurements are around the model prediction, but it is contaminated by Gaussian noise. We can make the noise by maximizing the posterior. The uni-axial extension experimental data set is used to calibrate the model and propagate uncertainty in this research.

The methodology referred as probabilistic surface damage tolerance for aeroengine rotors is used to evaluate the risk of fractures induced by low-cycle failure problems, and fracture mechanical models are usually used to carry out the analysis, in which stress intensity factors (SIFs) need to be calculated. Weight function method (WFM) can help improve the probabilistic surface damage tolerance methodology. The WFM offers remarkable computational efficiency in calculating SIFs for cracks in aeroengine rotors with complex stress gradients that are mainly induced by a local stress concentration or multiple loads, including centrifugal, thermal, and residual stresses. In this case, the universal weight functions (mode I) for surface cracks in three-dimensional finite plates, including flat plates and plates with holes, are presented. The critical step of WFM is to obtain certain coefficients in the universal weight function. Response surface method (RSM) can effectively derive the coefficients determined by multiple geometric parameters, including the length and thickness of a flat plate and the length, thickness, radius of a plate with a hole. Errors between the WFM results and the finite element results are less than 5 MPa m within the application scope. Furthermore, a surface damage tolerance analysis of the hole features in the aeroengine rotors based on the abovementioned two types of WFM is conducted. The selection of the weight function influences the SIF results, and the probability of failure (POF) calculated by the applied weight function of the plate with a hole is 2.02% higher than that of the flat plate. The relevant difference, which is determined by the fracture threshold crack size, has a negative relationship with the magnitude of stress distributed on the crack surface.

In this paper, a generalized finite element method (GFEM) with local gradient smoothed approximation (LGS-GFEM) using triangular meshes is proposed. The displacement field function of LGS-GFEM consists of the finite element shape function and the node displacement function. In order to obtain the nodal displacement function, the second order Taylor expansion is considered. The derivative term in Taylor expansion is obtained by using gradient smoothed technique in a smoothed domain. The displacement in smoothed operation is interpolated by polynomial basis function and radial basis function. Two kinds of integration schemes are considered, i.e. LGS-GFEM-I and LGS-GFEM-II respectively. The smoothed composite shape function of LGS-GFEM retains the ideal Kronecker property of the finite element shape function. Besides, the proposed LGS-GFEM has some other important properties such as no extra DOFs, linear independent, etc. The superiority of LGS-GFEM including high accuracy, rapid error convergence and temporal stability, is demonstrated by two representative numerical examples of static and free vibration, and compared with the classical finite element of triangular (FEM-T3) and quadrilateral (FEM-Q4) elements.

Advanced classical Probabilistic Risk Assessment (PRA) effectively combines various methods for quantitative risk evaluation, such as event trees, fault trees, and Bayesian networks. PRA methods and tools provide the means for the qualitative reliability evaluation (e.g., cut sets) and the computation of quantitative reliability metrics (e.g., end states probabilities). Modern safety-critical systems from various industrial domains tend toward a high level of autonomy and demand not only reliability but also resilience, the ability to recover from degraded or failed states. The numerical resilience analysis of such dynamic systems requires more flexible methods. These methods shall enable the analysis of the systems with sophisticated software parts and dynamic feedback loops. A suitable candidate is the Dual-graph Error Propagation Model (DEPM) that can capture nontrivial failure scenarios and dynamic fault-tolerance mechanisms. The DEPM exploits the method for the automatic generation of Markov chain models and the application of probabilistic model checking techniques. Moreover, the DEPM enables the analysis of highly-customizable system resilience metrics, e.g., “the probability of system recovery to a particular state after a specified system failure during a defined time interval.” In this paper, we show how DEPM-based resilience analysis can be integrated with the general PRA methodology for resilience evaluations. The proposed methodology is demonstrated on a safety-critical autonomous UAV system.

Due to their superior weight to strength ratio of composites to common metallic structures, composite technology is widely used in aerospace industry. Assessment of damage in composites has gained interest after a large number of accidents caused by unanticipated damages in the composite structures. Many different structural health monitoring applications were developed over the years due to the fact that composite materials may inherit damage from within, not always visible from surface. The most common types of errors encountered in the industry are due to misaligned fibers, a mix-up in ply order, and delaminations: all presenting changes in the vibro-acoustical performance of the composite structure. This paper discusses the change in the dynamic properties of a composite structure contains a manufacturing error such as a ply lay-up error, and a ply angle error. Both plate and cylindrical structure types were considered for the stated error types. Effect of symmetric errors, unsymmetrical and unbalanced errors, and mid-plane errors were considered in the case of ply orientations, and dynamic stiffness matrix was used to identify the error. Identification of the structure’s layup properties and manufacturing error identification is employed. From the measured modal properties of the structure, a back-tracking strategy was used to generate the ply lay-up of the composite structure. Prepreg plates of a single carbon fiber system and filament wound hybrid cylinders consisting of glass and carbon fibers were manufactured for testing. Modal tests on plates and cylindrical composite structures were performed and compared with the analysis. A good match between the finite element model and experiment was shown in natural frequencies and mode shapes.

This study presents a computer simulation for the dynamic design of compact high-contact-ratio spur gear transmissions. High contact ratio gears have the potential to produce lower dynamic tooth loads and minimum root stress but they can be sensitive to tooth profile errors. The analysis presented in this work was performed by using the NASA gear dynamics code DANST (Dynamic Analysis of Spur Gear Transmissions). In the analysis, the addendum ratio (addendum/diametral pitch) was varied over the range 1.30 to 1.40 to obtain a contact ratio of 2.00 or higher. The constraints of bending stress limit and involute interference provide the main criteria for this investigation. Compact design of high-contact-ratio gears with different gear ratios and pressure angles was investigated. Comparison of compact design between low-contact-ratio and high-contact-ratio gears was conducted. With the same operating parameters, high-contact-ratio gears appear to have much more compact design than low-contact-ratio gears. For compact design of high-contact-ratio gears, a diametral pitch of 6.00 appears to be the best choice for an optimal gear set.

Statistics have shown that the main reason for traffic accidents is human error. Modern vehicles are equipped to protect occupants in the event of a crash. The latest advanced vehicles come with driver behavior monitoring systems in recent years, and many have been proven to be effective systems in the prevention of accidents. However, these systems do not provide a complete solution and can only detect driver fatigue or driver distraction. This project aims to build an AI model for sensing the distraction of drivers and identifying the kind of distraction using the Kinect sensor and the Brio camera and reorient driver’s attention on driving. For this, the system is divided into three sub-segments; calling arm position (arms up or down, arms right or left), facial expressions (blinking and mouth), and head orientation. Each segment develops important info for gauging the distraction of a driver based on the depth mapping of data and color from the Kinect sensor and Brio camera respectively. Testing on a driving simulator is completed on 4 different drivers of diverse ethnicity, sex, and age along with over 240 mins of recorded material. Since all the segments were recorded and prepared separately, they can further be taken to build different outcomes and can be implemented for real car systems.

It is well established that the law of conservation of momentum is applicable to all interactions between any two bodies during an impact. The law dictates that the total momentum before a collision is the same as the total momentum after the collision (for elastic collisions). This law has been accepted as a fundamental part of Newtonian physics since Newton incorporated Descartes’ work into his own. Scientists rarely question its applicability to kinematics of bodies in the non-quantum world. The law of conservation of momentum is regularly used in vehicle accident reconstruction to calculate a speed of one of the vehicles before or after the collision, assuming that the masses of the vehicles and sufficient other velocities are known. Collisions of vehicles with highly disparate masses pose an interesting dilemma. In such a collision, the speed of the higher mass vehicle will change very little in a collision with a vehicle of much smaller mass and the speed change of the smaller mass vehicle will be large. This is due to the ratio of the masses of the two vehicles which minimizes the effect of the smaller mass vehicle on the vehicle of larger mass. The lopsided nature of the speed change of this kind of collision can lead one to conclude that momentum is not applicable to collisions between vehicles of dramatically different masses. The objective of this work is to investigate the effect of dissimilar masses of vehicles on the effectiveness of momentum to accurately predict speed change. The mathematical formulation of the error associated with dissimilar mass vehicles is presented and quantified to give the reconstructionist guidance in when momentum can be used reliably and when the error resulting from the use of momentum may be larger than desired.

Site assessments for bifacial Photovoltaic (PV) installation are quite challenging to conduct manually due to the area size and the extreme temperature conditions at desert sites. We designed and built an autonomous Unmanned Ground Vehicle (UGV) fitted with a Global Navigation Satellite Network-System Real-Time Kinematic (GNSS-RTK) positioning device, an Inertial Measurement Unit (IMU), encoder to improve and aid site assessments in desert condition. Sandy terrains deserts are challenging for UGV’s because they increase the likelihood of wheel slippage due to reduced traction. Sensor details such as IMU, GNSS-RTK, and encoder should be taken into consideration to account for the errors that the desert terrains pose. This study compared the Extended Kalman Filter (EKF) for standard GPS & GNSS-RTK to verify which performs better for the UGV’s position estimation. The estimated UGV’s position from the kinematics model and EKF are validated using a drone camera system that uses an image processing technique to verify the UGV’s position with the help of the visible reference cones. Throughout the experiments, the GNSS-RTK performed better than GPS. Also, the EKF performed as well as the GNSS-RTK by trusting it more than the encoder/gyroscope reading.

This paper describes the implementation of various algorithms to control the distance between a lead vehicle and a following (ego) vehicle. The ego robot equipped with a monocular camera and a rotating laser sensor (LDS). The monocular camera used for object detection using the Aggregate Channel Features (ACF) detection algorithm. The width of the bounding box generated by the detection algorithm had used to determine the distance between the lead and the following vehicles. Since this research focused on longitudinal autonomy, the data from the rotating laser sensor downsampled from 360 points to 30 points. These sampled points covered the front view of the vehicle. All data points transformed into a planar world coordinate (two-dimensional plane). The outputs of the camera and laser sensor (LDS) were fused to obtain accurate distance measurements for the lead vehicle. Sensor calibration had achieved by comparing sensor data with the ground truth values. Kalman Filter was used to implementing sensor fusion by combining perception data from the monocular camera and LDS for accurate position and velocity estimation. This calibration provided information about the sensor noise and deviation of sensor data from its ground truth values. These values helped to determine the error covariance matrixes of the Kalman filter. For implementation, the Robot Operating System (ROS)-MATLAB platform used to communicate between robot and host Personal Computer (PC). The experiments evaluated the performance of Proportional Control (P), Proportional-Integral Control (PI), and Model Predictive Control (MPC) in maintaining a minimum distance between the vehicles. For the MPC implementation in MATLAB, Model Predictive Control Quadratic Programming (MPCQP) solver was used to get the optimal solution for control output. The results show that the MPC yields faster response times when compared to P control and PI control. These algorithms evaluated during constant velocity and constant acceleration of the lead vehicle. The steady-state errors of P and PI controllers were around 0.1 meters (m) in both scenarios and 0 to 0.2m for constant velocity and 0 to 0.15m for ramp velocity, respectively. And for MPC, steady-state error varied from −0.05m to 0.05m in both the scenarios. This range in steady-state error was due to varying speed of the ego vehicle with time to maintain the minimum relative distance between the robots, and there was a communication delay in the system that also affected the behavior of the controllers. The MPC was more sensitive to communication delays. However, the effect of this communication delay was negligible to P and PI controllers. This sensitivity resulted in different velocity profiles for the ego vehicle in MPC and P or PI controllers.

In an operating room of a hospital ship, to remotely perform surgery on a patient laid on an operating table utilizing the surgical manipulators attached to the table, the rotation and translation of the operating table must be properly isolated from the wave-induced motion of the floor. Similarly, on a moving vehicle, when a sensitive equipment is transported or a manipulator is utilized to perform precise positioning tasks, it becomes necessary to isolate them from undesirable motion of the vehicle. In response to the need for a motion stabilizer, which isolates a manipulator from undesirable ship or vehicle motion, we present a nonlinear model predictive control (NMPC) of a six degrees-of-freedom, base-moving Stewart platform. Analytical nonlinear equations of motion are utilized for nonlinear model predictive control, wherein an optimization problem in a finite time horizon at each time step is solved adopting C/GMRES algorithm. To predict the future motion caused by a ship or a moving vehicle, we employ an auto regressive moving average model which forecasts future behavior based on past behavior. Furthermore, to incorporate prediction errors as disturbance at each time step, we endow NMPC with the robustness. As a result, even if prediction errors exist, the set of all possible output states are predicted using the equations of motion in a finite time, while the system kinematic constraints are precisely satisfied. In order to assess the performance of the proposed controller, numerical simulations are presented for a base-moving Stewart platform.

Reducing the size of a detail chemical kinetic is necessary in the prospect of numerical computation. In this work a skeleton reduction is done on a detail mechanism of ethanol. The detailed ethanol mechanism used here is developed through reaction mechanism generator (RMG). The generated mechanism is validated at wide range of engine relevant operating conditions using laminar burning speed (LBS), ignition delay time (IDT) and species mole fraction calculation at different reactor conditions. This detail mechanism consists of 67 species and 1031 reactions. Though the mechanism is in a very good agreement at various operating ranges with experimental data, it is costly to use a detail mechanism for 3D computational fluid dynamics (CFD) analysis. To make the mechanism applicable for CFD simulation further reduction of species and reactions is essential. In this work a skeleton mechanism is generated using directed relation graph technique with error propagation and sensitivity analysis (DRGEPSA). The DRGEPSA method, works based on error calculation at user defined condition. This technique is a combination of two methods, directed relation graph with error propagation (DRGEP) and directed relation graph with sensitivity analysis (DRGASA). To ensure the wide range of applicability of the skeleton mechanism, IDT is calculated at temperature, pressure, and equivalence ratio ranges from 700–2000 K, 1–40 atm and 0.6–1.4 respectively. A 10% error estimation is considered during the process. Initially DRGEP is applied on the detail mechanism to eliminate unimportant species. Further, sensitivity analysis helps to identify and reduce more unimportant species from the mechanism. Reactions related to the deleted species are automatically removed from the mechanism in each step. The final skeleton mechanism has 42 species and 464 reactions. This skeleton mechanism is validated and compared with different IDT data for the conditions not used in reduction technique. Results of LBS and different species concentration from reactor conditions is considered for validation. The skeleton mechanism can reduce computational time by 35% for LBS and 25% for IDT calculation. For future work, this skeleton mechanism will be considered in optimum reduction process.

Location of the peak cylinder pressure and the crank angle associated with half of the energy releases during the combustion process are generally used to define the engine combustion phasing and control the engine efficiency. To accelerate the optimization of a natural gas spark ignition internal combustion engine, this study proposes a black box modeling approach that will reduce the experimental or computational time needed to estimate the high efficient operating conditions at a particular engine speed and load via combustion phasing information. Specifically, a k-nearest neighbors (KNN) algorithm applied key engine operating variables such as the spark timing, air-fuel ratio, and engine speed as inputs to predict combustion phasing parameters such as the crank angles associated with peak cylinder pressure and 50% energy release. After training the correlative model, the selected engine variables produced acceptable errors for most operating conditions investigated. The results showed that the KNN algorithm predicted much better the location of the peak pressure than the location of the 50% energy release, as evidenced by the larger R 2 values and smaller prediction errors. In addition, the regression model built in this study produced larger errors in the sparse-distributed region. Therefore, a more uniformly distributed training dataset is suggested for KNN algorithm, at least for the situations investigated in this research.

In the manufacturing industry, it is almost inconceivable to produce a rotating component without a minimal amount of roundness tolerance. The importance of studying roundness form deviations of circular and cylindrical features is to avoid the excessive lateral or axial runout deviations of the rotating and reciprocating parts during dynamic operations. Considering the precision that industries require now and will require in the future, the authors of this article have chosen roundness (also called circularity per ASME Standards) as the measurable parameter. In order to arrive at precise results, the roundness of a near-to-perfect cylinder is measured on an accurate spindle and turn-table type measuring instrument. Roundness profile, when measured, can be filtered in various ways to reduce or eliminate unwanted details, with a cut-off value set in terms of undulations per revolution (UPR), which gives valuable information about how the component may function, under specific conditions. Looking at real-life roundness graphs it is clear that information exists in the data at different frequencies. A classic example is ovality, which indicates an irregularity that occurs two times in one complete revolution. The workpiece would be said to have two lobes or two UPR. Multiple lobes may be present on a component, a condition contributing to either problems of fit with mating components or part functionality. Additionally, usage of recommended or generalized filter, yields data that approximately lies in the range of acceptability. Thus, there is a strong need to thoroughly understand the effect of filter size and type on roundness (form error for fit) and part functionality. Many published articles have investigated novel filters to accurately and efficiently calculate roundness. However, no work was found in literature that would present the filter size and type selection criteria and correlate it with roundness depending on mathematical method of calculating roundness and further to part functionality. This paper focusses on the investigation of filter type and size effect on roundness based on different mathematical methods of roundness error calculations. By varying parameters like the filter type (Gaussian 50%, 75% and RC Filters), the filter sizes (1 through 500 UPR) and the methods of measuring the roundness — (Least Squares Circle (LSC), Minimum Circumscribed Circle (MCC), Maximum Inscribed Circle (MIC) and Minimum Zone Circles or Separation (MZC or MZS)), roundness at different heights of the workpiece is evaluated. A clear trend is observed from the results, which can further help one to choose filters and their respective sizes for the respective design intent or the application in question.

Kolsky Bar systems are subjected to inherent system error as all measurement devices are. This is especially true in that as the bar diameter decreases, the system becomes more sensitive to errors such as friction and misalignment. In this work we present a technique for identifying and quantifying the error of a Kolsky system. We also present a method of generating statistically significant bounds for Kolsky systems so that anomalous or improperly executed experiments can be quantitatively identified. This method does not rely on the intuition of the experimentalist to identify an anomalous experiment. After presenting our method for error identification, a series of tests are performed on 2024Aluminum alloy samples. A method is then presented where the system error, as well as some error contributed by a variance in sample dimension, are removed from the calculated error related to the stress on the samples. The result shows the effective variance of the sample response is quite high in the elastic loading period, but reduces when plasticity dominates. This is attributed to the presence of high frequency content in the travelling elastic waves which cannot be accurately measured currently, but is effectively damped out when plastic deformation dominates.

Parallel manipulators have broad application prospects on hybrid machine tools. Kinematic error modelling and identification are two key processes to improve the accuracy of parallel manipulators. The traditional kinematic error modelling method adopts the partial differentiation of the ideal kinematic model. However, the partial differentiation method is pure mathematical calculation, which ignores physical meaning of error terms corresponding to each link. In the process of error identification, the Jacobian matrix obtained from the partial differentiation method is usually ill-conditioned, which leads to non-convergence of the identification process. In order to solve the above problems, this paper proposes a new kinematic error modelling method and an error identification model. Firstly, the basic error terms for single link are analyzed. Based on basic error terms, the kinematic error model is established by using the practical connection point of two adjacent links. Then, a new error identification model is derived from the kinematic error model. Finally, as a study case, a 3-DOF parallel tool head is used to verify the correctness of the proposed method. The numerical results show that the proposed method is effective and the accuracy of the 3-DOF parallel tool head improves significantly after compensation of error terms.