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Abstract Weighted compact nonlinear schemes (WCNS) are a family of nonlinear shock capturing schemes that are suitable for solving problems with discontinuous solutions. The schemes are based on grids staggered by flux points and solution points, resulting in algorithms with the nonlinear interpolation step independent of the difference step. Thus, only linear difference operators are needed, such that geometric conservation law can be preserved easily, resulting in the preservation of freestream condition. In recent years, these schemes have attracted a lot of attention in the community of computational fluid dynamics. This paper intends to give a brief review of the basic algorithms of these schemes and present some related recent developments.

Aerodynamic modeling and control law design methods are crucial foundational technologies that enable efficient maneuvering flight of aircraft. Hence, this study focuses on how to construct the aerodynamic model and design control law using machine learning, as well as their differences from classical methods. To conduct this, a modified NACA0012 airfoil with a tail functioning as an elevator is employed as the geometry model. Through the utilization of the rigid dynamic grid method and overlapping grid technology, the pitch of the airfoil and the deflection of the elevator are efficient to execute. Firstly, the pitching moment coefficient is sampled through both steady and unsteady computations. Then, multivariate nonlinear regression (MNR) models and artificial neural network (ANN) models are established based on steady and unsteady sampling data, respectively. Additionally, the obtained model is evaluated using open-loop control laws. Based on the evaluation, the proportional-integral-derivative (PID) control algorithm is used to design the airfoil pitching control law using the MNR model. Meanwhile, deep reinforcement learning (DRL) is used to design the pitching control law using the ANN model. Finally, the PID and DRL controllers are implemented in a CFD environment for airfoil pitching control to verify their effectiveness in application scenarios. The results suggest that models based on both steady and unsteady data can reflect dynamic aerodynamic characteristics. However, using unsteady computation for data sampling significantly reduces time consumption compared to steady computation. Furthermore, the model constructed by ANN may have unexpected excellent characteristics. Both the PID and DRL control laws, designed based on the models, perfectly complete the control process in the CFD environment. This study provides valuable insights for the implementation of controllable maneuvering flight in aircraft.

BackgroundNeoadjuvant chemoradiotherapy (neo-CRT) plus surgery has greatly improved the prognosis of locally advanced esophageal cancer (EC) patients. But which factors may influence the pathological tumor response and long-term survival remains unclear. The purpose of this study was to identify the prognostic biomarkers of locally advanced EC patients receiving neo-CRT.MethodsWe reviewed the data of 72 patients with cT2-4N0-3M0 EC who underwent neo-CRT at our hospital. The patients received intensity-modulated radiation therapy with a total radiation dose of 41.4–60.0 Gy. Most patients received platinum + paclitaxel-based combination regimens every three weeks for 2–4 cycles. The recorded data included age, sex, smoking history, alcohol use, histology, tumor location, clinical TNM stage, tumor length, gross tumor volume (GTV), GTV of primary tumor (GTVp), GTV of lymph nodes (GTVn), radiation dose, and number of chemotherapy cycles. Overall survival (OS), progression-free survival (PFS), and pathological complete response (pCR) were analyzed.ResultsThe 3-year OS and PFS rates of these patients who underwent neo-CRT were 51.14% and 43.28%, respectively. In the univariate analyses, smoking history, clinical stage, GTV, GTVp, and GTVn were significantly associated with OS, whereas alcohol use, GTV, GTVp, and GTVn were significantly associated with PFS. Furthermore, in the multivariate analysis, GTV was an independent prognostic predictor of OS (hazard ratio (HR): 14.14, 95% confidence interval (CI): 3.747–53.33, P < 0.0001) and PFS (HR: 6.090, 95% CI: 2.398–15.47, P < 0.0001). In addition, GTV < 60.50 cm3 compared to > 60.50 cm3 was significantly associated with higher pCR rate (59.3% and 27.8%, respectively, P = 0.038). High dose (> 50 Gy) and increased number of chemotherapy cycles (≥ 3) didn’t improve the OS or PFS in patients with GTV > 60.50 cm3.ConclusionGTV was an independent prognostic factor of long-term survival in EC patients, which may be because GTV is associated with histological response to neo-CRT. Additionally, patients with GTV > 60.50 cm3 didn’t benefit from increased radiation dose or increased number of chemotherapy cycles.

To cope with the soft sensor modeling of unobserved multimode nonlinear processes, this paper proposes a modified kernel partial least squares (KPLS) by integrating latent factor clustering (LFC), called LFC-KPLS. In the proposed method, the process data are first divided into several batches orderly, and then projected onto the latent space by using the nonlinear functional expansion technology. In the latent space, partial least squares method is applied to compute the regression coefficients between the input variables and output variable of each batch. These regression coefficients, called the latent factors, can describe the functional relationships in the unobserved multimode data. Therefore, the latent factors are used for mode clustering so that the process data with similar functional relations can be clustered in one mode together. For each mode, the nonlinear soft sensor is established based on KPLS. To assign the mode of the online query sample, a mode identification strategy based on Bayesian inference is designed for the soft sensor online prediction. Finally, two cases studies are adopted to validate the proposed method.

When all process variables are used to establish one data-based model, some variables have little contributions to the model. When these variables are grouped into a small local variable set to develop a data-based model, their contributions will become large. It is called local variable characteristic. Standard canonical variate analysis (CVA) based process monitoring method doesn't consider local process variable characteristic. To solve this problem, a variable sub-region based canonical variate analysis (V-CVA) is proposed for dynamic process monitoring by enhancing the information of local variables. The proposed method combines variable sub-region division and Bayesian fusion. First, standard CVA is performed by using all process variables. Then, K-means clustering is adopted to divide process variables into sub-regions based on the mutual information of process variables and canonical variables. For each variable sub-region, CVA is conducted to compute local process monitoring statistics. Finally, local statistics are fused to build ensemble statistics based on the idea of Bayesian inference. Compared with the standard CVA method, the proposed V-CVA method can emphasize the information of local variables, and has better monitoring performance in the canonical variable feature subspace and the prediction residual subspace. Two examples demonstrate the effectiveness of the proposed method.

In order to mine the local behavior and dynamic characteristic of batch process data for effective process monitoring, a two-dimensional localized dynamic support vector data description (TLDSVDD) method is proposed in this article. The main contributions of the proposed method include three aspects. Firstly, considering that batch process variables may behave differently at each operation stage, a two-dimensional localization strategy is designed to mine the local behaviors of process data from the perspective of the variable dimension and the sample dimension. Secondly, for each local data segment, the slow feature analysis is applied to build the local dynamic sub-models, which can monitor the static and dynamic process changes simultaneously. Lastly, the model ensemble strategy based on Bayesian inference is employed and two holistic monitoring statistics are developed to indicate the process running status. The proposed method not only extracts the local process behaviors, but also determines whether the process fault belongs to the dynamic or static change. Finally, one case study on the simulated industrial batch process is carried out to exhibit the method performance.

In recent years, with the increase of depth and intensity of coal mining, geological disasters in deep engineering occur frequently. It is essential to study the law of subsidence of overburden after mining for analyzing the mechanism and control of geological disasters in deep engineering. However, the value of the subsurface subsidence factor and other overburden subsidence parameters could not be obtained easily. In this paper, the strata bulking of the caving zone, the bed separation of the fractured zone, and the dilatation of bending zone were considered a kind of expansion in the vertical direction uniformly. An assumption on vertical expansion value distribution of overburden strata above the longwall gob was proposed. Based on the assumption, a formula of the subsurface subsidence factor was deduced according to such parameters as the bulking factor, mining depth, mining height, and surface subsidence factor. Moreover, many field data were used to verify the correctness of the assumption by the data fitting method. The results show that the exponential function could well describe the expansion distribution characteristic of overburden strata and subsurface subsidence in the vertical direction. The parameter a in the deduced formula influences the shape of the subsurface subsidence curve; it is called the subsurface subsidence influence factor (SSIF). The SSIF, maximal vertical expansion value, and the mining depth-to-mining height (MD/MH) ratio influence the subsurface subsidence factor. The expansion distribution (E-D) factor is defined to describe the subsidence characteristic of overburden strata above the longwall gob by the surface subsidence factor and bulking factor of the caving zone. The E-D factor presents the logarithm relation with the MD/MH ratio, and the range of its maximum is 2.84~3.40. The case study demonstrates that the subsurface subsidence factor calculated by the proposed method has higher precision, and the mean errors between the calculated value and the measured value are less than 2%.

Abstract This paper presents an engineering-oriented UGKS solver package developed in China Aerodynamics Research and Development Center (CARDC). The solver is programmed in Fortran language and uses structured body-fitted mesh, aiming for predicting aerodynamic and aerothermodynamics characteristics in flows covering various regimes on complex three-dimensional configurations. The conservative discrete ordinate method and implicit implementation are incorporated. Meanwhile, a local mesh refinement technique in the velocity space is developed. The parallel strategies include MPI and OpenMP. Test cases include a wedge, a cylinder, a 2D blunt cone, a sphere, and a X38-like vehicle. Good agreements with experimental or DSMC results have been achieved.

Slow feature analysis (SFA) has been adopted for control performance monitoring (CPM) recently. However, due to the selection criterion of the dominant slow features (SFs) and the performance monitoring statistics, the traditional SFA-based CPM method has certain limitations in monitoring model predictive control (MPC) performance and fails to distinguish the direction of performance change, i.e., whether the performance becomes better or worse. In order to solve the above problems, an MPC performance monitoring and grading strategy based on improved SFA is proposed in this paper. First, a new criterion for selecting dominant SFs is proposed. On this basis, two combined monitoring indices are built to monitor steady-state and dynamic characteristics of MPC systems, respectively. Besides, an SFA-based predictable performance assessment index is proposed to indicate the direction of performance change. Finally, a performance grading strategy based on improved SFA is established to classify current MPC performance to four levels. Two simulation examples demonstrate the effectiveness and superiority of the proposed method.

Statistical local kernel principal component analysis (SLKPCA) has demonstrated its success in incipient fault detection of nonlinear industrial processes by incorporating the statistical local analysis (SLA) technology. However, the basic SLKPCA method builds the statistical model only based on the normal data and neglects the utilization of the prior fault information, which is often available in many industrial cases. To take full advantage of the prior fault information, this paper proposes an enhanced SLKPCA method, called primary-auxiliary SLKPCA (PA-SLKPCA), for better incipient fault monitoring. The contribution of the proposed method includes three aspects. First, one primary-auxiliary statistical monitoring framework is designed, by which not only the normal training data are applied to develop a primary SLKPCA model, but also the prior fault data are used to build the auxiliary SLKPCA models. Second, a double-block modeling strategy is developed to construct the auxiliary SLKPCA model for each fault case, where a variable grouping strategy based on Kullback-Leibler divergence is applied to divide the process variables into the fault-relevant group and fault-independent variable group, and the sub-model is developed for each group. Third, the Bayesian inference is used to combine the statistical results of each variable group, and one weighted fusion strategy is further designed to integrate the monitoring results from the primary and auxiliary models. Lastly, two case studies including one numerical system and the simulated continuous stirred tank reactor (CSTR) system are used for method evaluation and the simulations show that the proposed method can detect the incipient faults effectively and outperform the traditional SLKPCA method.

State-of-health (SOH) prediction for lithium-ion batteries is a challenging and important topic in the modern industry. With the advent of cloud-connected devices, there are huge amounts of the battery degradation trend data available. How to make full use of these existing degradation data for the SOH prediction is a valuable problem deserving deep research. Aiming at this problem, a multiple Gaussian process regression (MGPR) method is proposed for the SOH prediction of lithium-ion batteries. In this work, the health indicators (HIs) are firstly extracted from the charging process curves of the batteries, and the mutual information analysis is used to select the important HIs which are strongly correlated to the SOH. These selected HIs are applied as the regression model input for describing the aging procedure of the battery effectively. Then, Gaussian process regression modeling is performed on the different batteries to bring multiple GPR models. Lastly, a weighting strategy based on the prediction uncertainty is designed to integrate the predictions from the multiple GPR models. The method validations are executed on the battery datasets from NASA, and the results show that the proposed MGPR method has higher prediction accuracy than the basic GPR method.

Recently, the unsupervised extreme learning machine (UELM) technique as a nonlinear data mining approach has been employed to diagnose nonlinear process faults. However, during the dimensionality reduction of process observation data, UELM only aims at mining the local structure feature information of process data and lacks of the ability of preserving the global structure analysis of process data, which would perform unsatisfactorily for monitoring nonlinear process. Furthermore, the choice of the optimal number of hidden layer nodes in UELM method is still a challenging problem. To handle these two tough problems, a new fault detection approach on the basis of global preserving unsupervised kernel extreme learning machine (GUKELM) technique is proposed to monitor nonlinear process effectively. In GUKELM technique, the data global structure preserving framework is naturally incorporated into standard UELM to capture the local structure information as well as to preserve the global structure feature information of nonlinear process observation data during the dimensionality reduction. Meanwhile, to tackle the strong nonlinearity of process observation data, the kernel trick is utilized to successfully solve the challenging issue of setting the optimal number of hidden layer nodes. Based on extracted process data low dimensional feature information from GUKELM method, support vector data description algorithm is adopted to construct the monitoring statistic to detect process fault. Finally, the feasibility and effectiveness of the proposed process monitoring strategy is illustrated through a numerical nonlinear system and the continuous stirred tank reactor process which is a typical nonlinear process.

To improve the resolution and accuracy of the high-order weighted compact nonlinear scheme (WCNS), a new ϵ-adaptive algorithm based on local smoothness indicators is proposed. The new algorithm introduces a high-order global smoothness indicator to adjust the value of ϵ according to the local flow characteristics. Specifically, the algorithm increases ϵ in smooth regions, which can help cover up the disparity in smoothness indicators of sub-stencils and make the nonlinear scheme approach the background linear scheme. As a result, optimal order accuracy can be achieved in smooth regions, including critical points. While near discontinuities, the algorithm decreases ϵ, thereby strengthening the stencil selection mechanism and further attenuating spurious oscillations. Meanwhile, the new algorithm makes nonlinear schemes scale-invariant of flow variables. The results of approximate dispersion relation (ADR) show that the new algorithm can greatly reduce spectral errors of nonlinear schemes in the medium and low wavenumber range without inducing instability. Numerical results indicate that the new algorithm can significantly improve resolution of small-scale structures and suppress numerical oscillations near discontinuities with only a minor increment in computational cost.

Out-fashioned goaf is the protective structure for mining the upper residual coal, and its stability is the core problem in mining the upper residual coal. According to the upward mining demand for No. 5 coal seam above the out-fashioned goaf in Baizi Coal Mine, a new method is proposed to determine the upward mining safety. According to the analysis of the actual situation of the mine, the coal pillar and suspended roof in the out-fashioned goaf are taken as the objects. Furthermore, a “coal pillar-suspended roof” system model based on the variable load induced by abutment pressure of upper coal seam mining is established. After the mechanical model was solved, the parameter acquisition method of the model was established. The basic parameters of Baizi Coal Mine were considered to determine the feasibility of mining residual coal above out-fashioned goaf. And the effects of variable load on the coal pillar and suspended roof stability were analyzed. The results show that the upper No. 5 coal seam in Baizi Coal Mine can be mined safely. Compared to the traditional method, which simplifies all the upper loads to uniform loads, the new method is safer. The system stability of the suspended roof and coal pillar is influenced by “a/L” and “L.” Axial stress curves of the coal pillar and suspended roof appear nearly parabolic with “a/L” varying. Their maximum values are obtained when the “a/L” value is around 0.5∼0.6. In this situation, the combination structure is most easy to to be damaged. The ratio q′/q has a linear relationship with all stresses of the system model. The failure sequence of the system model is determined by analyzing the relationship between the tensile strength of the suspended roof and compressive strength of the coal pillar. This study provides a reference case for coal resources upward mining under similar conditions.

Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVAkNN, for monitoring the disturbances of power transmission systems. In the proposed method, CVA is first used to extract the dynamic features by analyzing the data correlation and establish a statistical model with two monitoring statistics T2 and Q. Then, in order to handling the periodic oscillation of power data, the two statistics are reconstructed in phase space, and the k-nearest neighbor (kNN) technique is applied to design the statistics nearest neighbor distance DT2 and DQ as the enhanced monitoring indices. Further considering the detection difficulty of weak disturbances with the insignificant symptoms, statistical local analysis (SLA) is integrated to construct the primary and improved residual vectors of the CVA dynamic features, which are capable to prompt the disturbance detection sensitivity. The verification results on the real industrial data show that the SLCVAkNN method can detect the occurrence of power system disturbance more effectively than the traditional data-driven monitoring methods.

Support vector data description (SVDD) has been widely applied to batch process fault detection. However, it often performs poorly, especially when incipient faults occur, because it only considers the shallow data feature and omits the probabilistic information of features. In order to provide better monitoring performance on incipient faults in batch processes, an improved SVDD method, called deep probabilistic SVDD (DPSVDD), is proposed in this work by integrating the convolutional autoencoder and the probability-related monitoring indices. For mining the hidden data features effectively, a deep convolutional features extraction network is designed by a convolutional autoencoder, where the encoder outputs and the reconstruction errors are used as the monitor features. Furthermore, the probability distribution changes of these features are evaluated by the Kullback-Leibler (KL) divergence so that the probability-related monitoring indices are developed for indicating the process status. The applications to the benchmark penicillin fermentation process demonstrate that the proposed method has a better monitoring performance on the incipient faults in comparison to the traditional SVDD methods.

For batch process fault detection, regular data-driven methods cannot distinguish quality-irrelevant faults from quality-relevant faults. To solve such problem, we propose a multiway multisubspace canonical variate analysis (MMCVA) method for the batch processes. First, the combination of batch-wise unfolding and variable-wise unfolding is adopted to unfold the three-way process and quality data in to two-way data. Then, we use CVA to project the process and quality data spaces to three subspaces, a process-quality correlated subspace, a quality-uncorrelated process subspace, and a process-uncorrelated quality subspace. Fault detection statistics are developed based on the three subspaces. The proposed MMCVA method is capable of indicating the normality or abnormality of the quality variables, while detecting a process fault. The simulation results of a fed-batch penicillin fermentation process illustrate the effectiveness of the proposed method.

Kernel principal component analysis (KPCA) has been a state-of-the-art nonlinear process monitoring method. However, KPCA assumes the single operation mode while the real industrial processes often run under multiple operation conditions. In order to monitor the nonlinear multimode processes effectively, this paper proposes a modified KPCA method assisted by the local statistical analysis, referred to as local statistics KPCA (LSKPCA). In the proposed method, two kinds of strategies, including local probability density estimation and statistics pattern analysis, are integrated to improve the traditional KPCA method. To handle the multimode characteristic of industrial processes, local probability density estimation is developed to transform the monitored variables into their probability density values, which follow the unimodal data distribution. For further extracting the statistical information among the process data, statistics pattern analysis technique is applied to capture various orders of statistics, including one-order, second-order, and high-order ones, which constitute the statistics pattern matrix of the monitored data. Furthermore, KPCA modeling is performed on the statistics pattern matrix. The simulations on one numerical example and the continuous stirred tank reactor system demonstrate that the proposed LSKPCA method has the superior fault detection performance compared with the conventional KPCA method.

As an effective nonlinear dynamic data analysis tool, kernel slow feature analysis (KSFA) has achieved great success in continuous process monitoring field during recent years. However, its application to batch process monitoring is unexploited, which is a more challenging task because of the complicated characteristics of batch process data. In this paper, we propose a novel batch process monitoring method based on the modified KSFA method, referred to as multiway global preserving kernel slow feature analysis (MGKSFA), to capture high nonlinearity and inherently time-varying dynamics of process data. In the proposed method, a two-step multiway data unfolding strategy is first utilized to convert the three-way batch process training data set into a two-way matrix. Then, the global structure preserving-based kernel slow feature analysis (GKSFA) is used to build the nonlinear statistical monitoring model, which not only explores the local dynamic data relationships but also considers the mining of global data structure information. Furthermore, a rule based on the cumulative slowness contribution is designed to determine the number of the retained slow features. Last, two monitoring statistics T2 and SPE are built to detect the process faults. Two case studies, including one simple numerical nonlinear system and the benchmark fed-batch penicillin fermentation process, are used to demonstrate that the proposed MGKSFA method has the superior fault detection performance over the traditional batch process monitoring methods.

As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.

Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results.

Due to the wide application of model predictive control (MPC) in industrial processes, the assessment of MPC performance is essential to ensure product quality and improve energy efficiency. Recently, the slow feature analysis (SFA) algorithm has been successfully applied to assess the performance of MPC. However, the disadvantage of the traditional SFA-based predictable index is that it can only extract one-step predictable information in the monitored variables. In order to better mine the predictable information contained in the monitored variables with large lag, an enhanced method to assess MPC performance based on multi-step SFA (MSSFA) is proposed. Based on the relationship between the slowness of slow features (SFs) and data predictability, an MSSFA model SFA(τ) is built through extending the temporal derivatives of the SFs from one step to multiple steps to extract multi-step predictable information in the monitored variables, which is used to construct a multi-step predictable index. Then, the predictable information in the SFs is further extracted for enhancing the multi-step predictable index to improve its sensitivity to performance changes. The effectiveness of the proposed method has been verified through two process simulation examples.