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The first purpose of this paper is to design more accurate, efficient and robust gradient smoothing methods (GSMs) for spatial derivative approximations for computational fluid dynamics (CFD) application. The second purpose is to design an adaptive GSM-CFD solver for the compressible turbulent flows, with special focus on the shock-wave boundary layer interactions.

A new integration scheme is proposed for the node-associated GSM to improve the accuracy and robustness of the previous versions. A matrix-based algorithm and corresponding data structures are devised to improve the computational efficiency of GSM. The GSM-CFD solver is coupled with a mixed solution-based adaptive mesher to form a functional adaptive GSM-CFD solver.

The improved GSMs are insensitive to mesh qualities, and can achieve high accuracy on all kinds of hybrid meshes. The adaptive GSM-CFD solver can better capture the shock wave.

The matrix-based GSM and its corresponding data structure for improved GSM, and the development of the adaptive GSM-CFD solver for compressible turbulent flows are newly presented in this paper.

Due to the strong reliance on element quality, there exist some inherent shortcomings of the traditional finite element method (FEM). The model of FEM behaves overly stiff, and the solutions of automated generated linear elements are generally of poor accuracy about especially gradient results. The proposed cell-based smoothed point interpolation method (CS-PIM) aims to improve the results accuracy of the thermoelastic problems via properly softening the overly-stiff stiffness.

This novel approach is based on the newly developed G space and weakened weak (w²) formulation, and of which shape functions are created using the point interpolation method and the cell-based gradient smoothing operation is conducted based on the linear triangular background cells.

Owing to the property of softened stiffness, the present method can generally achieve better accuracy and higher convergence results (especially for the temperature gradient and thermal stress solutions) than the FEM does by using the simplest linear triangular background cells, which has been examined by extensive numerical studies.

The CS-PIM is capable of producing more accurate results of temperature gradients as well as thermal stresses with the automated generated and unstructured background cells, which make it a better candidate for solving practical thermoelastic problems.

It is the first time that the novel CS-PIM was further developed for solving thermoelastic problems, which shows its tremendous potential for practical implications.

This article deals with improvement of eigenvalues obtained by finite element analysis of C¹ eigenproblems. The proposed method employs high order gradient smoothing at nodal points to derive improved high order interpolation functions for the single element of each mode. Two different schemes were developed for 1–D C¹ eigenproblems (free vibration of beams) and for 2–D quasi C¹ eigenproblems (transverse vibrations of thin plates). High order Hermitian polynomials are used for the beam problem together with some boundary node corrections, while a combination of high‐order and low‐order approximations are used for the modified formulation of the plate problem. Several smoothing options are proposed for both schemes. Numerical results for both schemes are used as examples to demonstrate the accuracy of the present approach.

This paper presents a new approach for estimating the discretization error of finite element analysis of generalized eigenproblems. The method uses smoothed gradients at nodal points to derive improved element‐by‐element interpolation functions. The improved interpolation functions and their gradients are used in the Rayleigh quotient to obtain an improved eigenvalue. The improved eigenvalue is used to estimate the error of the original solution. The proposed method does not require any re‐solution of the eigenproblem. Results for 1‐D and 2‐D C° eigenproblems in acoustics and elastic vibrations are used as examples to demonstrate the accuracy of the proposed method.

In this work, an SFEM is proposed for solving acoustic problems by redistributing the entries in the mass matrix to “tune” the balance between “stiffness” and “mass” of discrete equation systems, aiming to minimize the dispersion error. The paper aims to discuss this issue.

This is done by simply shifting the four integration points’ locations when computing the entries of the mass matrix in the scheme of SFEM, while ensuring the mass conservation. The proposed method is devised for bilinear quadratic elements.

The balance between “stiffness” and “mass” of discrete equation systems is critically important in simulating wave propagation problems such as acoustics. A formula is also derived for possibly the best mass redistribution in terms of minimizing dispersion error reduction. Both theoretical and numerical examples demonstrate that the present method possesses distinct advantages compared with the conventional SFEM using the same quadrilateral mesh.

After introducing the mass-redistribution technique, the magnitude of the leading relative dispersion error (the quadratic term) of MR-SFEM is bounded by (5/8), which is much smaller than that of original SFEM models with traditional mass matrix (13/4) and consistence mass matrix (2). Owing to properly turning the balancing between stiffness and mass, the MR-SFEM achieves higher accuracy and much better natural eigenfrequencies prediction than the original SFEM does.

The paper presents a method for the more accurate solution of C⁰ acoustic vibration problems in finite element (FE) analysis by postprocessing. For each frequency, the method uses the computed eigenvector and the Helmholtz equation to calculate gradients of dependent variables at element centers. Gradients at element centers are then used as sampling points in a patch recovery technique to obtain gradients at nodes. The nodal primary field and its gradients are used to interpolate the dependent variables over each element. This interpolation yields the potential and kinetic energies of each element, and hence a Rayleigh quotient that provides an accurate eigenvalue. One‐, two‐ and three‐dimensional vibration problems are used as numerical examples.

The purpose of this paper is to investigate the feasibility of solving turbulent flows based on smoothed finite element method (S-FEM). Then, the differences between S-FEM and finite element method (FEM) in dealing with turbulent flows are compared.

The stabilization scheme, the streamline-upwind/Petrov-Galerkin stabilization is coupled with stabilized pressure gradient projection in the fractional step framework. The Reynolds-averaged Navier-Stokes equations with standard k-epsilon model are selected to solve turbulent flows based on S-FEM and FEM. Standard wall functions are applied to predict boundary layer profiles.

This paper explores a completely new application of S-FEM on turbulent flows. The adopted stabilization scheme presents a good performance on stabilizing the flows, especially for very high Reynolds numbers flows. An advantage of S-FEM is found in applying wall functions comparing with FEM. The differences between S-FEM and FEM have been investigated.

The research in this work is limited to the two-dimensional incompressible turbulent flow.

The verification and validation of a new combination are conducted by several numerical examples. The new combination could be used to deal with more complicated turbulent flows.

The applications of the new combination to study basic and complex turbulent flow are also presented, which demonstrates its potential to solve more turbulent flows in nature and engineering.

This work carries out a great extension of S-FEM in simulations of fluid dynamics. The new combination is verified to be very effective in handling turbulent flows. The performances of S-FEM and FEM on turbulent flows were analyzed by several numerical examples. Superior results were found compared with existing results and experiments. Meanwhile, S-FEM has an advantage of accuracy in predicting boundary layer profile.

This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend.

This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database.

The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01.

This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features.

The proposed model performs the user authentication through the extracted features from both the palmprint and the finger knuckle images. The two major processes in the proposed system are feature extraction and classification. The proposed model extracts the features from the palmprint and the finger knuckle with the proposed HE-Co-HOG model after the pre-processing. The proposed HE-Co-HOG model finds the Palmprint HE-Co-HOG vector and the finger knuckle HE-Co-HOG vector. These features from both the palmprint and the finger knuckle are combined with the optimal weight score from the fractional firefly (FFF) algorithm. The layered k-SVM classifier classifies each person's identity from the fused vector.

Two standard data sets with the palmprint and the finger knuckle images were used for the simulation. The simulation results were analyzed in two ways. In the first method, the bin sizes of the HE-Co-HOG vector were varied for the various training of the data set. In the second method, the performance of the proposed model was compared with the existing models for the different training size of the data set. From the simulation results, the proposed model has achieved a maximum accuracy of 0.95 and the lowest false acceptance rate and false rejection rate with a value of 0.1.

In this paper, the multimodal biometric recognition system based on the proposed HE-Co-HOG with the k-SVM and the FFF is developed. The proposed model uses the palmprint and the finger knuckle images as the biometrics. The development of the proposed HE-Co-HOG vector is done by modifying the Co-HOG with the holoentropy weights.

Quantitative understanding of the temperatures, gradients and heating/cooling rates in and around the melt pool in laser powder bed fusion (L-PBF) is essential for simulation, monitoring and controls development. The research presented here aims to detail experiment design and preliminary results of high speed, high magnification, in-situ thermographic monitoring setup on a commercial L-PBF system designed to capture temperatures and dynamic process phenomena.

A custom door with angled viewport was designed for a commercial L-PBF system which allows close access of an infrared camera. Preliminary finite element simulations provided size, speed and scale requirements to design camera and optics setup to capture melt pool region temperatures at high magnification and frame rate speed. A custom thermal calibration allowed maximum measurable temperature range of 500°C to 1,025°C. Raw thermographic image data were converted to temperature assuming an emissivity of 0.5. Quantitative temperature results are provided with qualitative observations with discussion regarding the inherent challenges to future thermographic measurements and process monitoring.

Isotherms around the melt pool change in size depending on the relative location of the laser spot with respect to the stripe edges. Locations near the edges of a stripe are cooled to lower temperatures than the center of a stripe. Temperature gradients are highly localized because of rough or powdery surface. At a specific location, temperatures rise from below the measurable temperature range to above (<550°C to >1100°C) within two frames (<1.11 m/s). Particle ejection is a notable phenomenon with measured ejection speeds >11.7 m/s.

Several works are detailed in the Introduction of this paper that detail high-speed visible imaging (not thermal imaging) of custom or commercial LBPF processes, and lower-speed thermographic measurements for defect detection. However, no work could be found that provides calibrated, high-speed temperature data from a melt-pool monitoring configuration on a commercial L-PBF system. In addition, the paper elucidates several sources of measurement uncertainty (e.g. calibration, emissivity and time and spatial resolution), describes inherent measurement challenges based on observations of the thermal images and discusses on the implications to model validation and process monitoring and control.