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The lattice Boltzmann method LBM is a widely used computational fluid dynamics method for flow problems with complex geometries and various boundary conditions. Large-scale LBM simulations with increasing resolution and extending temporal range require massive high-performance computing HPC resources, thus motivating us to port it onto modern many-core heterogeneous supercomputers like Tianhe-2. Although many-core accelerators such as graphics processing unit and Intel MIC have a dramatic advantage of floating-point performance and power efficiency over CPUs, they also pose a tough challenge to parallelize and optimize computational fluid dynamics codes on large-scale heterogeneous system.

Traditional principal component analysis (PCA) is a second-order method and lacks the ability to provide higher-order representations for data variables. Recently, a statistics pattern analysis (SPA) framework has been incorporated into PCA model to make full use of various statistics of data variables effectively. However, these methods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis (LGSPA) method, which integrates SPA framework and locality preserving projections within the PCA, is proposed to utilize various statistics and preserve both local and global information in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics, a new contribution of process variables is built to locate fault variables. The simulation results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.

It is well known that grid discontinuities have significant impact on the performance of finite difference schemes (FDSs). The geometric conservation law (GCL) is very important for FDSs on reducing numerical oscillations and ensuring free-stream preservation in curvilinear coordinate system. It is not quite clear how GCL works in finite difference method and how GCL errors affect spatial discretization errors especially in nonsmooth grids. In this paper, a method is developed to analyze the impact of grid discontinuities on the GCL errors and spatial discretization errors. A violation of GCL cause GCL errors which depend on grid smoothness, grid metrics method and finite difference operators. As a result there are more source terms in spatial discretization errors. The analysis shows that the spatial discretization accuracy on non-sufficiently smooth grids is determined by the discontinuity order of grids and can approach one higher order by following GCL. For sufficiently smooth grids, the spatial discretization accuracy is determined by the order of FDSs and FDSs satisfying the GCL can obtain smaller spatial discretization errors. Numerical tests have been done by the second-order and fourth-order FDSs to verify the theoretical results.

Numerical investigations of body-wake interactions were carried out by simulating the flow over a rod–airfoil configuration using high-order implicit large eddy simulation (HILES) for the incoming velocity $U_{\infty }=72~\text{m}~\text{s}^{-1}$ and a Reynolds number based on the airfoil chord $4.8\times 10^{5}$. The flow over five different rod–airfoil configurations with different distances of $L/d=2$, 4, 6, 8 and 10, respectively, were calculated for the analysis of body-wake interaction phenomena. Various fundamental mechanisms dictating the intricate flow phenomena including force varying regulation, flow structures and flow patterns in the interaction region, turbulent fluctuations and their suppression, noise radiation and fluid resonant oscillation, have been studied systematically. Due to the airfoil downstream, a relatively higher base pressure is exerted on the surface of the cylinder upstream, and the pressure fluctuation on the surface of the rod–airfoil configuration with $L/d=2$ is significantly suppressed, resulting in a reduction of the fluctuating lift. Following the distance between the cylinder and airfoil strongly decreases, Kármán-street shedding is suppressed due to the blocking effect. The flow in this interaction region has two opposite tendencies: the influence of the airfoil on the steady flow is to accelerate it and the counter-rotating vortices connecting with the leading edge of the airfoil tend to slow the flow down. There may be two flow patterns associated with the interference region, i.e. the Kármán-street suppressing mode and the Kármán-street shedding mode. The primary vortex shedding behind the cylinder upstream, and the shedding wake impingement onto the airfoil downstream, play a dominant role in the production of turbulent fluctuations. When primary vortex shedding is suppressed, the intensity of impingement is weakened, resulting in a significant suppression of the turbulent fluctuations. Due to these factors, a special broadband noise without a manifestly distinguishable peak is radiated by the rod–airfoil configuration with $L/d=2$. The fluid resonant oscillation within the flow interaction between the turbulent wake and the bodies was further investigated by adopting a feedback model, which confirmed that the effect of fluid resonant oscillation becomes stronger when $L/d=6$ and 10. The results obtained in this study provide physical insight into the understanding of the mechanisms relevant to the body-wake interaction.

Growing evidences show that the Symmetrical Conservative Metric Method (SCMM) is essential in preserving freestream conservation and orders of accuracy for high-order finite difference schemes to simulate flows with complex geometries. In this paper, a new family of Hybrid cell-edge and cell-node Dissipative Compact Schemes (HDCSs) has been developed for geometry-complex flows by fulfilling the SCMM as well as by introducing dissipation according to the concept adopted in the construction of the high-order Dissipative Compact Schemes (DCSs). The resolution and dissipation properties of HDCSs are investigated by the Fourier analysis, and the stability property of HDCSs is also investigated by asymptotic stability analysis and amplification factor analysis. HDCSs are validated by computing several benchmark test cases. The vortex convection test case demonstrates that the orders of accuracy of the HDCSs are preserved unless the GCL is satisfied. Although high resolution of HDCSs is observed in the test of acoustic wave scattering of multiple cylinders, the solutions can be contaminated if the GCL is not satisfied. Moreover, the numerical solutions of flow past a high lift trapezoidal wing demonstrate the promising ability of the newly developed HDCSs in solving complex flow problems.

We propose a soft sensing method using local partial least squares models with adaptive process state partition, referring to as the LPLS-APSP, which is capable of effectively handling time-varying characteristics and nonlinearities of processes, the two major adverse effects of common industrial processes that cause low-performance of soft sensors. In our proposed approach, statistical hypothesis testing is employed to adaptively partition the process state into the unique local model regions each consisting of certain number of consecutive-time data samples, and partial least squares is adopted to construct local models. Advantages of this adaptive strategy are that the number of local models does not need to be pre-defined and the local model set can be augmented online without retraining from scratch. Moreover, to improve the prediction accuracy, a novel online model adaptation criterion is proposed, which not only takes the current process dynamics into account, but also enables mining the information contained in the neighborhood of the query sample. The guidelines for tuning the model parameters are also presented. The LPLS-APSP scheme is applied to develop the dynamic soft sensors for a simulated continuous stirred tank reactor and a real industrial debutanizer column, and the results obtained demonstrate the effectiveness of this proposed approach, in comparison to several existing state-of-the-art methods, for online soft sensor design.

To alleviate the effect caused by the numerical dissipation of the second order upwind schemes in Detached-Eddy Simulation (DES), an improved second order hybrid scheme is established through modifying the dissipation term of the standard Roe's scheme and the numerical dissipation of the scheme can be self-adapted according to the flow field information. To validate the numerical method, DES of subsonic flow past the 65° swept delta wing is performed at high angle of attack on unstructured/hybrid meshes. Comparison with experimental data shows that the accuracy of the turbulence simulation is improved with the use of the hybrid scheme.

Display Omitted A nonlinear dynamic process monitoring method is presented.The proposed method can extract the inherent slow features from the high-dimensional observed data.A statistic index is built based on slow features to carry out process monitoring.The method is more sensitive to process faults than the conventional KPCA-based method. A fault detection method based on dynamic kernel slow feature analysis (DKSFA) is presented in the paper. SFA is a new feature extraction technology which can find a group of slowly varying feature outputs from the high-dimensional inputs. In order to analyze the nonlinear dynamic characteristics of the process data, DKSFA is presented which applies the augmented matrix to consider the dynamic characteristic and uses kernel slow feature analysis (KSFA) to extract the nonlinear slow features hidden in the observed data. For the purpose of fault detection, the D monitoring statistic index is built based on DKSFA model and its confidence limit is computed by kernel density estimation. Simulations on a nonlinear system and Tennessee Eastman (TE) benchmark process show that the proposed method has a better fault detection performance compared with the conventional (kernel principal component analysis) KPCA-based method.

Traditional principal component analysis (PCA) based process monitoring method is unsupervised learning method, which builds a statistical model only based on the normal operating dataset. However, in industrial process database, there are also some faulty operating datasets available which are omitted by PCA but may be helpful for fault detection performance improvement. To better utilize both normal operating data and prior faulty data, a modified PCA method, called multiple component analysis (MCA) is presented for monitoring process faults. MCA statistical modelling involves two kinds of data feature extractions. Firstly, based on normal operating data, MCA applies conventional PCA transformation to obtain the principal component features, which describe normal data distribution directions. Then, for the known fault data, non-local preservation projection technique is used to compute fault discriminant features, which describe the fault data distribution directions. Lastly these features are integrated to construct monitoring statistics for fault detection. Simulations on Tennessee Eastman benchmark process show that the proposed method outperforms traditional PCA method in terms of fault detection performance.

Programming and optimizing complex, real-world CFD codes on current many-core accelerated HPC systems is very challenging, especially when collaborating CPUs and accelerators to fully tap the potential of heterogeneous systems. In this paper, with a tri-level hybrid and heterogeneous programming model using MPI + OpenMP + CUDA, we port and optimize our high-order multi-block structured CFD software HOSTA on the GPU-accelerated TianHe-1A supercomputer. HOSTA adopts two self-developed high-order compact definite difference schemes WCNS and HDCS that can simulate flows with complex geometries. We present a dual-level parallelization scheme for efficient multi-block computation on GPUs and perform particular kernel optimizations for high-order CFD schemes. The GPU-only approach achieves a speedup of about 1.3 when comparing one Tesla M2050 GPU with two Xeon X5670 CPUs. To achieve a greater speedup, we collaborate CPU and GPU for HOSTA instead of using a naive GPU-only approach. We present a novel scheme to balance the loads between the store-poor GPU and the store-rich CPU. Taking CPU and GPU load balance into account, we improve the maximum simulation problem size per TianHe-1A node for HOSTA by 2.3i?, meanwhile the collaborative approach can improve the performance by around 45% compared to the GPU-only approach. Further, to scale HOSTA on TianHe-1A, we propose a gather/scatter optimization to minimize PCI-e data transfer times for ghost and singularity data of 3D grid blocks, and overlap the collaborative computation and communication as far as possible using some advanced CUDA and MPI features. Scalability tests show that HOSTA can achieve a parallel efficiency of above 60% on 1024 TianHe-1A nodes. With our method, we have successfully simulated an EET high-lift airfoil configuration containing 800M cells and China's large civil airplane configuration containing 150M cells. To our best knowledge, those are the largest-scale CPU-GPU collaborative simulations that solve realistic CFD problems with both complex configurations and high-order schemes.

Recently, local outlier factor (LOF)-based process monitoring methods, which can handle multimodality and complex data distribution simultaneously, have been successfully applied to industrial process monitoring. However, local variable information cannot be reflected by tradition LOF method and would be submerged in process monitoring. To emphasize information of local variable, this paper proposes a novel multimode process fault detection method, referred to as variable local outlier factor (VLOF). Firstly, the neighbors of a sample are determined in training dataset using Euclidean distance. Then, Euclidean distance between data pairs is transformed into distance vector, with which vector-form reachability distance and LOF vector can be sequentially calculated. While monitoring multimode process, a fused monitoring statistic is constructed by combining whole LOF values of total variables. Finally, simulation on the Tennessee Eastman (TE) process is used to demonstrate the superiority of the proposed method.

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.

Traditional PCA assumes the unimodal distribution of process data and may not perform well in monitoring the process with multiple operation conditions. In order to detect the faults in multimode process, this paper proposes a novel process monitoring method using local neighborhood standardization strategy based on multi-block principal component analysis (LNS-MBPCA). Firstly, the proposed method applies local neighborhood standardization (LNS) strategy to transform the multimode data into unimodal data. Then, all the monitored process variables are partitioned into three sub-blocks based on their correlation with global principal components. By utilizing the variable division results, global PCA model is divided into three PCA sub-models and the local monitoring statistics are constructed. Lastly, Bayesian inference is applied to integrate the monitoring results of each sub-block and two probability-based monitoring statistics are developed to indicate process status. Simulation results on a simulated continuous stirred tank reactor (CSTR) system show that the proposed method has better process monitoring performance.

Principal component analysis (PCA) and kernel PCA (KPCA) are the state-of-art machine learning methods widely used in industrial process monitoring and fault detection field. However, these methods build shallow statistical models based on single layer of features and may not achieve the best monitoring performance. In order to sufficiently mine the intrinsic data features, a deep learning based nonlinear PCA method, referred to as deep PCA (DePCA), is proposed in this paper. Motivated by the idea of deep learning, a layer-wise statistical model structure is designed to extract multi-layer data features, including both linear and nonlinear principal components. At each layer, two monitoring statistics are constructed to monitor the feature changes. For integrating the monitoring statistics of all feature layers, a Bayesian inference strategy is applied to convert the monitoring statistics into fault probabilities, which are weighted to form two probability-based comprehensive monitoring statistics for process fault detection. A case study using the benchmark Tennessee Eastman process demonstrates the superior performance of the proposed DePCA method over the traditional PCA and KPCA methods.

Based on the mechanism of the cavitation effect in the self-excited oscillation pulsed jet nozzle, a modified cavitation model is proposed for improving the precision of the simulation, which considers the influence of Reynolds shear stress, the viscous shear stress and the turbulent pressure fluctuations existed in the shear layer on the vapor pressure. The modified cavitation model is hooked in the Fluent through the user defined function (UDF) to define the vapor pressure property which plays a role of criterion for cavitation. The fluctuation, frequency and magnitude distribution of the velocity at the outlet of the nozzle are analyzed and compared with the corresponding results obtained by using the original cavitation model. It is observed that the results obtained by the modified model are more consistent with the actual working situation of the nozzle. Finally, the effect of the inlet pressure on the occurrence of the cavitaion is investigated for exploring the relationship of the cavitation chance and the factors considered in the modified cavitation.

Traditional kernel principal component analysis (KPCA) based nonlinear process monitoring method may not perform well because its Gaussian distribution assumption is often violated in the real industrial processes. To overcome this deficiency, this paper proposes a modified KPCA method based on double-weighted local outlier factor (DWLOF-KPCA). In order to avoid the assumption of specific data distribution, local outlier factor (LOF) is introduced to construct two LOF-based monitoring statistics, which are used to substitute for the traditional T2 and SPE statistics, respectively. To provide better online monitoring performance, a double-weighted LOF method is further designed, which assigns the weights for each component to highlight the key components with significant fault information, and uses the moving window to weight the historical statistics for reducing the drastic fluctuations in the monitoring results. Finally, simulations on a numerical example and the Tennessee Eastman (TE) benchmark process are used to demonstrate the superiority of the proposed DWLOF-KPCA method.

Kernel principal component analysis (KPCA) based fault detection method, whose statistical model only utilizes normal operating data and ignores available prior fault information, may not provide the best fault detection performance for nonlinear process monitoring. In order to exploit available prior fault data to enhance fault detection performance, a fault discriminant enhanced KPCA (FDKPCA) method is proposed, which simultaneously monitors two types of data features, nonlinear kernel principal components (KPCs) and fault discriminant components (FDCs). More specifically, based on the normal operating data, KPCs are extracted by usual KPCA modeling, while with the normal operating data and prior fault data, FDCs are obtained by the kernel local-nonlocal preserving discriminant analysis (KLNPDA). Monitoring statistics are constructed for both the KPCA and KLNPDA sub-models. Moreover, Bayesian inference is employed to transform the corresponding monitoring statistics into fault probabilities, and the overall probability-based monitoring statistics are constructed by weighting the results of the two sub-models, which provides more effective on-line fault detection capability. Two case studies involving a simulated nonlinear system and a continuous stirred tank reactor demonstrate the superior fault detection performance of the proposed KLNPDA scheme over the traditional KPCA method.

This work investigates the performance of the high-order implicit large eddy simulation (HILES) on curvilinear meshes. The HILES is developed based on a seventh-order hybrid cell-edge and cell-node dissipative compact scheme (HDCS-E8T7) satisfying the surface conservation law (SCL). Efficiency of implicit subgrid-scale model is tested by three-dimensional Taylor–Green vortex case. According to the test of flow over a cylinder, the influence of the SCL errors has been investigated on curvilinear mesh. Then stall phenomena of thin airfoil NACA64A006 have been simulated by the HILES. The slope of lift curve, the maximum lift and the stall angle are successfully predicted. Moreover, the lift characteristic seems to be satisfactorily captured even after the stall angle. The solutions demonstrate the potential of HILES for simulating complex turbulent flow.

Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis (LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis (PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.

This study assesses the likelihood of rearrest among a cohort of all adults ( N = 1,438) released from the Massachusetts state prison system who received mental health services while they were incarcerated. All individuals were followed for 24 months. The analysis focused on four classes of variables: demographic characteristics, clinical history, criminal justice history, and postrelease supervision. These analyses showed that criminal history factors—a juvenile record and a history of multiple previous incarcerations—were significant risk factors, but that clinical factors, including a history of substance abuse, were not. Overall, the models developed here look much like the ones that would be observed in the general offender population. The implications of these findings for criminal justice and mental health policy are discussed.

Most chemical processes generally exhibit the characteristics of nonlinear variation. In this paper, an improved principal component analysis method using information entropy theory, called entropy principal component analysis (EPCA), is proposed for nonlinear chemical process fault diagnosis. This approach applies information entropy theory to build an explicit nonlinear transformation, which provides a convenient way for nonlinear extension of principal component analysis. The information entropy can capture the Gaussian and non-Gaussian information in the measured variables via probability density function estimation. With the entropy of original measured variables, the entropy principal components are calculated using a simple eigenvalue decomposition procedure, and two monitoring statistics are built for fault detection. Once a fault is detected, EPCA similarity factors between the occurred fault dataset and historical fault pattern datasets are computed for fault recognition. Simulations on a continuous stirred tank reactor system show that EPCA performs well in terms of fault detection and recognition. © 2014 Curtin University of Technology and John Wiley & Sons, Ltd.

Data-driven fault diagnosis technique has exhibited its wide applications in industrial process monitoring. However, how to recognize fault pattern based on process dataset is still a difficult problem in data-driven fault diagnosis field. In this paper, a novel nonlinear fault recognition method is proposed based on statistics kernel principal component analysis similarity factor (SKPCASF), which combines nonlinear similarity factor and statistics pattern analysis. Principal component analysis similarity factor (PCASF) is firstly reviewed which measures the similar degree of two datasets by comparing their principal component subspaces. In order to deal with nonlinear characteristics of process dataset, kernel principal component analysis (KPCA) is applied to build a nonlinear similarity factor, referred to as KPCA similarity factor (KPCASF). Moreover, for well utilizing the statistical information of data variables, statistics pattern analysis is used to compute data statistics for substituting for the original measured variables in fault recognition. Based on the statistics, a new similarity factor method called as statistics KPCA similarity factor (SKPCASF) is lastly built for fault pattern recognition. Simulations on a simple nonlinear system and the benchmark Tennessee Eastman process show that the proposed SKPCASF method is more effective than PCASF and KPCASF in terms of fault pattern recognition performance.

Developing high resolution finite difference scheme and enabling the use of this scheme on complex geometry are the aims of this study. High resolution has been achieved by Dissipative Compact Schemes (DCS), however, according to the recent research, applications of DCS on complex geometry may have serious problem for that the Geometric Conservation Law (GCL) is not satisfied, and this may cause numerical instability. To cope with this problem, a new scheme named Hybrid cell-edge and cell-node Dissipative Compact Scheme (HDCS) has been formulated. The formulation of the HDCS contains two steps. First, a new central compact scheme is formulated for the purpose of conveniently fulfilling the GCL, and then dissipation is added on the central scheme by high-order dissipative interpolation of cell-edge variables. The solutions of Euler and Navier-Stokes equations show that the HDCS can be applied successfully on complex geometry, while the DCS may suffer numerical instabilities. Moreover, high resolution of the HDCS may be observed in the test of scattering of acoustic waves by multiple cylinders.

Prior research indicates that probation programs that include efforts to change cognitive orientations and social patterns can enhance their effectiveness. This article reports an evaluation of an ...

The metrics and Jacobian in the fluid motion governing equations under curvilinear coordinate system have a variety of equivalent differential forms, which may have different discretization errors with the same difference scheme. The discretization errors of metrics and Jacobian may cause serious computational instability and inaccuracy in numerical results, especially for high-order finite difference schemes. It has been demonstrated by many researchers that the Geometric Conservation Law (GCL) is very important for high-order Finite Difference Methods (FDMs), and a proper form of metrics and Jacobian, which can satisfy the GCL, can considerably reduce discretization errors and computational instability. In order to satisfy the GCL for FDM, we have previously developed a Conservative Metric Method (CMM) to calculate the metrics [1] and the difference scheme @d^3 in the CMM is determined with the suggestion @d^[email protected]^2. In this paper, a Symmetrical Conservative Metric Method (SCMM) is newly proposed based on the discussions of the metrics and Jacobian in FDM from geometry viewpoint by following the concept of vectorized surface and cell volume in Finite Volume Methods (FVMs). Interestingly, the expressions of metrics and Jacobian obtained by using the SCMM with second-order central finite difference scheme are equivalent to the vectorized surfaces and cell volumes, respectively. The main advantage of SCMM is that it makes the calculations based on high-order WCNS schemes aroud complex geometry flows possible and somewhat easy. Numerical tests on linear and nonlinear problems indicate that the quality of numerical results may be largely enhanced by utilizing the SCMM, and the advantage of the SCMM over other forms of metrics and Jacobian may be more evident on highly nonuniform grids.

Weighted Compact Nonlinear Schemes (WCNSs) possess the merits of high-order accuracy, high resolution, and high endurance in low quality grids. In order to implement WCNS on patched grids, high-order interpolations are generally needed for the purpose of ensuring overall high-order accuracy. However, high-order linear interpolations are susceptible to numerical oscillations in the vicinity of discontinuities and large gradient regions. In order to prevent this kind of oscillations, a new weighted interpolation is developed following the idea of Deng and Zhang (J Comput Phys, 2000). The new interpolation contains three fourth-order sub-interpolations which are weighted together according their smoothness. The weights are designed in such a way that in smooth regions they could approach to the optimal weights to achieve sixth-order accuracy, whereas in regions near discontinuities, the weights of the stencils which contain discontinuities are assigned to be nearly zero. The fifth-order WCNS is combined with the new weighted interpolation to solve flow problems on patched grids. Several benchmark problems, including shock waves, vortex, and shock/vortex interaction, are simulated to test the method. The results indicate that the new interpolation has similar performance with the sixth-order Lagrange interpolation in smooth regions, and is superior to the Lagrange interpolation in preventing interpolation oscillations in the vicinity of discontinuities.

A modified transition model is given based on the shear stress transport (SST) turbulence model and an intermittency transport equation. The energy gradient term in the original model is replaced by flow strain rate to saving computational costs. The model employs local variables only, and then it can be conveniently implemented in modern computational fluid dynamics codes. The fifth-order weighted compact nonlinear scheme and the fourth-order staggered scheme are applied to discrete the governing equations for the purpose of minimizing discretization errors, so as to mitigate the confusion between numerical errors and transition model errors. The high-order package is compared with a second-order TVD method on simulating the transitional flow of a flat plate. Numerical results indicate that the high-order package give better grid convergence property than that of the second-order method. Validation of the transition model is performed for transitional flows ranging from low speed to hypersonic speed.

Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.

In the context of an increasing correctional population and corresponding rates of mental illness and substance abuse among this population, this study focuses on describing the predictors of substance abuse service utilization for ex-inmates with dual disorders. Our aim is to assess the likelihood and characteristics of ex-inmates with mental disorders who access substance abuse treatment services within two years of correctional release.Using merged administrative data on all ex-inmates with open mental health cases released from Massachusetts Department of Corrections and two County Houses of Corrections from 2007 to 2009 (The prevalence of a history of substance use disorders is high in this population (69%;Substance abuse is a chronic relapsing disorder and dual diagnosis is common among individuals with mental disorders involved with the criminal justice system. Their service needs and contacts across substance abuse, mental health, and criminal justice systems highlight individuals caught up in the institutional circuit. Study results point to the need for expanded and targeted dual diagnosis treatment approaches and relapse prevention for ex-inmates with mental disorders post correctional release.

In order to effectively identify industrial process faults, an improved Fisher discriminant analysis (FDA) method, referred to as the statistics local Fisher discriminant analysis (SLFDA), is proposed for fault classification. For mining statistics information hidden in process data, statistics pattern analysis is firstly applied to transform the original measured variables into the corresponding statistics, including second-order and higher-order ones. Furthermore, considering the local structure characteristics of fault data, local FDA (LFDA) is performed which computes the discriminant vectors by modifying the optimization objective with local weighting factor. Simulation results on the benchmark Tennessee Eastman process show that the proposed SLFDA has a better fault classification performance than the FDA and LFDA methods.

Conventional rotation forest does not consider process dynamics. In order to use process dynamics and nonlinearity for classification, a canonical rotation forest (Canonical RF) is proposed and used for process fault diagnosis. For each time instant, a past vector and a future vector are formed. Canonical variate analysis is used for features extraction to obtain dynamic and uncorrelated features at each node of rotation forest. The improved rotation forest is used as a classifier to diagnose process fault class. Simulations on Tennessee Eastman process show that the proposed Canonical RF outperforms the conventional rotation forest in terms of fault diagnosis accuracy.

Aiming at the multimode industrial process monitoring problem, this paper proposes a fault detection method based on local entropy principal component analysis (LEPCA) algorithm. Firstly, in order to deal with the multimode characteristic of operating data, k nearest neighbor Parzen window (kNN-Parzen) method is used to estimate each sample's local probability density. Then, a local relative density estimate function is constructed to decrease the sensitivity to window width parameter. Lastly, the local entropies of process data are calculated by using information entropy theory to extract the feature information effectively, and principal component analysis (PCA) is applied to establish the local entropy component statistical model for fault detection. Simulation results on one continuous stirred tank reactor (CSTR) system indicate that LEPCA can provide superior performance in process monitoring.

Our study is the first-ever initiative to merge administrative databases in Massachusetts to evaluate an important public mental health program. It examines post-incarceration outcomes of adults with serious mental illness (SMI) enrolled in the Massachusetts Department of Mental Health (DMH) Forensic Transition Team (FTT) program. The program began in 1998 with the goal of transitioning offenders with SMI released from state and local correctional facilities utilizing a core set of transition activities. In this study we evaluate the program's effectiveness using merged administrative data from various state agencies for the years 2007–2011, comparing FTT clients to released prisoners who, despite having serious mental health disorders, did not meet the criterion for DMH services. By systematically describing our original study design and the barriers we encountered, this report will inform future efforts to evaluate public programs using merged administrative databases and electronic health records.

A concept of “static reconstruction” and “dynamic reconstruction” was introduced for higher-order (third-order or more) numerical methods in our previous work. Based on this concept, a class of hybrid DG/FV methods had been developed for one-dimensional conservation law using a “hybrid reconstruction” approach, and extended to two-dimensional scalar equations on triangular and Cartesian/triangular hybrid grids. In the hybrid DG/FV schemes, the lower-order derivatives of the piece-wise polynomial are computed locally in a cell by the traditional DG method (called as “dynamic reconstruction”), while the higher-order derivatives are re-constructed by the “static reconstruction” of the FV method, using the known lower-order derivatives in the cell itself and in its adjacent neighboring cells. In this paper, the hybrid DG/FV schemes are extended to two-dimensional Euler equations on triangular and Cartesian/triangular hybrid grids. Some typical test cases are presented to demonstrate the performance of the hybrid DG/FV methods, including the standard vortex evolution problem with exact solution, isentropic vortex/weak shock wave interaction, subsonic flows past a circular cylinder and a three-element airfoil (30P30N), transonic flow past a NACA0012 airfoil. The accuracy study shows that the hybrid DG/FV method achieves the desired third-order accuracy, and the applications demonstrate that they can capture the flow structure accurately, and can reduce the CPU time and memory requirement greatly than the traditional DG method with the same order of accuracy.

The purpose of this article is to summarize our recent progress in high-order and high accurate CFD methods for flow problems with complex grids as well as to discuss the engineering prospects in using these methods. Despite the rapid development of high-order algorithms in CFD, the applications of high-order and high accurate methods on complex configurations are still limited. One of the main reasons which hinder the widely applications of these methods is the complexity of grids. Many aspects which can be neglected for low-order schemes must be treated carefully for high-order ones when the configurations are complex. In order to implement high-order finite difference schemes on complex multi-block grids, the geometric conservation law and block-interface conditions are discussed. A conservative metric method is applied to calculate the grid derivatives, and a characteristic-based interface condition is employed to fulfil high-order multi-block computing. The fifth-order WCNS-E-5 proposed by Deng is applied to simulate flows with complex grids, including a double-delta wing, a transonic airplane configuration, and a hypersonic X-38 configuration. The results in this paper and the references show pleasant prospects in engineering-oriented applications of high-order schemes.

The Spalart-Allmaras (S-A) turbulence model, the shear-stress transport (SST) turbulence model and their compressibility corrections are revaluated for hypersonic compression corner flows by using high-order difference schemes. The compressibility effect of density gradient, pressure dilatation and turbulent Mach number is accounted. In order to reduce confusions between model uncertainties and discretization errors, the formally fifth-order explicit weighted compact nonlinear scheme (WCNS-E-5) is adopted for convection terms, and a fourth-order staggered central difference scheme is applied for viscous terms. The 15° and 34° compression corners at Mach number 9.22 are investigated. Numerical results show that the original SST model is superior to the original S-A model in the resolution of separated regions and predictions of wall pressures and wall heat-flux rates. The capability of the S-A model can be largely improved by blending Catris' and Shur's compressibility corrections. Among the three corrections of the SST model listed in the present paper, Catris' modification brings the best results. However, the dissipation and pressure dilatation corrections result in much larger separated regions than that of the experiment, and are much worse than the original SST model as well as the other two corrections. The correction of turbulent Mach number makes the separated region slightly smaller than that of the original SST model. Some results of low-order schemes are also presented. When compared to the results of the high-order schemes, the separated regions are smaller, and the peak wall pressures and peak heat-flux rates are lower in the region of the reattachment points.

Neurolaw (also known as law and neuroscience) is a newly established interdisciplinary field that explores the neural mechanisms underlying mental phenomena and behavior relevant to the law, utilizing the methods and techniques of cognitive neuroscience. This field focuses on how legal systems address issues brought forward by advances in cognitive neuroscience. The present article reviews research from four aspects of neurolaw, including: (1) evaluating the reliability of testimony of persons such as witnesses, e.g. assessing truth-telling and false memories; (2) exploring the influence of cognitive neuroscience on legal practice and theories of criminal law; (3) predicting a particular individual’s future risk of recidivism to facilitate legal intervention and decision-making; and (4) regulating the application of drugs and techniques that manipulate cognition. The study of neurolaw not only deepens our understanding of psychological and neural mechanisms related to legal behaviors, but also influences legal practice. However, the emerging field of neurolaw needs to address differences in culture and legal systems between China and western countries.

The high-order compact finite difference technique is introduced to solve the Boltzmann model equation, and the gas-kinetic high-order schemes are developed to simulate the different kinetic model equations such as the BGK model, the Shakhov model and the Ellipsoidal Statistical (ES) model in this paper. The methods are tested for the one-dimensional unsteady shock-tube problems with various Knudsen numbers, the inner flows of normal shock wave for different Mach numbers, and the two-dimensional flows past a circular cylinder and a NACA 002 airfoil to verify the reliability of the present high-order algorithm and simulate gas transport phenomena covering various flow regimes. The computed results are found in good agreement both with the theoretical prediction from continuum to rarefied gas dynamics, the related DSMC solutions, and with the experimental results. The numerical effect of the schemes with the different precision and the different types of Boltzmann collision models on the computational efficiency and computed results is investigated and analyzed. The numerical experience indicates that an approach developing and applying the gas-kinetic high-order algorithm is feasible for directly solving the Boltzmann model equation.

The geometric conservation law (GCL) includes the volume conservation law (VCL) and the surface conservation law (SCL). Though the VCL is widely discussed for time-depending grids, in the cases of stationary grids the SCL also works as a very important role for high-order accurate numerical simulations. The SCL is usually not satisfied on discretized grid meshes because of discretization errors, and the violation of the SCL can lead to numerical instabilities especially when high-order schemes are applied. In order to fulfill the SCL in high-order finite difference schemes, a conservative metric method (CMM) is presented. This method is achieved by computing grid metric derivatives through a conservative form with the same scheme applied for fluxes. The CMM is proven to be a sufficient condition for the SCL, and can ensure the SCL for interior schemes as well as boundary and near boundary schemes. Though the first-level difference operators ?3 have no effects on the SCL, no extra errors can be introduced as ?3=?2. The generally used high-order finite difference schemes are categorized as central schemes (CS) and upwind schemes (UPW) based on the difference operator ?1 which are used to solve the governing equations. The CMM can be applied to CS and is difficult to be satisfied by UPW. Thus, it is critical to select the difference operator ?1 to reduce the SCL-related errors. Numerical tests based on WCNS-E-5 show that the SCL plays a very important role in ensuring free-stream conservation, suppressing numerical oscillations, and enhancing the robustness of the high-order scheme in complex grids.

There are still some challenges, such as grid quality, numerical stability, and boundary schemes, in the practical application of high-order finite difference schemes for complex configurations. This study presents some improved strategies that indicate potential engineering applications of high-order schemes. The formally fifth-order weighted compact nonlinear scheme developed by the authors is implemented on point-matched multiblock structured grids, which are generated over complex configurations to ensure the grid quality of each component block. The information transmission between neighboring blocks is carried out by new characteristic-based interface conditions that directly exchange the spatial derivatives on each side of an interface by means of a characteristic-based projection to keep the high-order accuracy and high resolution of a spatial difference scheme. The high-order scheme combined with the interface conditions is shown to be asymptotically stable. The engineering-oriented applications of the high-order strategy are demonstrated by solving several two- and three-dimensional problems with complex grid systems.

Using available information and data, this study examines the historical patterns and characteristics of official corruption in contemporary China. The study identifies four waves of official corruption associated with different stages of the nation’s economic reform. Each wave of corruption has different forms and targets. In our analysis, the general trend is that as the nation’s economic reforms move forward, official corruption in China is becoming more serious and pervasive with more money and higher ranked officials involved in corruption cases. This is raising a serious challenge to Chinese authorities who have shown a willingness and effort to control official corruption.We conclude that without fundamental political reform, official corruption is not likely to be controlled in China.

Cotruption is a persistent problem all over the world (Rose-Ackerman 2004): As the pace of international economic integration has quickened, corruption has increasingly carried transnational dimensions. Major multinational firms use their extensive networks of connections for economic gains and frequently disregard the moral implications of their behaviors. Transnational corruption has become a global issue with significant negative impact on the world economy. Rose-Ackerman (2004) estimated that in 2003 US$1 trillion was paid in bribes out of a $30 trillion world economy. However, sociological understanding of transnational corruption and the role of multinational firms in corruption is rather limited. We know little about interconnections between major multinational firms and theit corruptive practices in various countries, especially in Asian countries (Elliot 1997). It is necessary to extend our knowledge base through both quantitative and qualitative studies, especially in-depth case studies that can reveal what major causal factors affect the cortuption process. The timely study by Professor Sung begins to fill the current gap in the sociological literature. Sung listed some major factors that contributed to the transnational corruption case in Taiwan. First, China's constant push to deprive Taiwan of breathing space in the international arena and Taiwan's urgent need for advanced weapons to protect itself created an opportunity for arms sales and corruption. Second, the prolonged authoritarian rule by the Nationalists in Taiwan and its secretive unmonitored weapon procurement system were important contributing factors. Third, the emergence of the opposition party (the Democratic Progress Party) and lessening media control contributed to Taiwan's gradual progress toward democracy and made the revelation of cotruption cases possible. Finally, the judicial system in Taiwan was not fully independent and affected the outcome of the criminal investigation. Sung also examined the causal factors in France. He concluded that transnational corruption may occur but can certainly be reduced and controlled, and that Taiwan "is now better equipped to prevent the repetition of a similar scandal" because of a more transparent weapons procurement process and the Bush administration's support of Taiwan's need for advanced weapons systems. The study has contributed to the literature on transnational corruption in the following ways. First, it is a challenging task for researchers to conduct solid studies of cross-country corruption, because they have to overcome language and cultural barriers to obtain necessary information (Gupta, De Mello, and Sharan 2001). In addition, access to the appropriate data is a major stumbling block for researchers. Secretive transactions between corrupt officials and businessmen are unlikely to be open for public view, unless the involved parties are exposed by the media or are under criminal investigation. Even if the case is under investigation, cultural contexts may affect how much information is open to the public. Due to the gradual relaxation of media control and constituents' desire for more political participation in Taiwan, Sung was able to obtain relevant information from various sources in Taiwan. However, some Chinese officials were also actively involved in the scandal, but we have no information about their involvement. China's state-controlled media and nontransparent legal system are unlikely to reveal any detailed corruption information that may negatively affect the party's image. The author has overcome many obstacles to get necessaty information. Second, the case study is methodologically appropriate. It allows the researcher to examine the complicated case in great detail. The author screened a large amount of information, placing it in context to examine the complicated incident of transnational corruption and to assess the impact of various social, international, and political factors on transnational corruption. …

OBJECTIVE To decrease radiation injury of the esophagus and lungs by utilizing a CT scan in combination with PET tumor imaging in order to minimize the clinical target area of locally advanced non small cell lung can-cer, without preventive radiation on the lymphatic drainage area. METHODS Of 76 patients with locally advanced non-small cell lung cancer (NSCLC), 32 received a PET examination before radiotherapy. Preventive radiation was not conducted in the mediastinum area without lymphatic metastasis, which was confirmed by CT and PET. For the other 44 patients, preventive radiation was performed in the lymphatic drainage area. PET examinations showed that the clinical target volume of the patients was decreased on average to about one third. The radiation therapy for patients of the two groups was the same, i.e. the dose for accelerated fractionated irradiation was 3 Gy/time and 5 time/week. The preventive dose was 42 to 45 Gy/time, 14 to 15 time/week, with 3-week treatment, and the therapeutic dose was 60 to 63 Gy/time, 20 to 21 time/week, with a period of 4 to 5 weeks. RESULTS The rate of missed lymph nodes beyond the irradiation field was 6.3 ％ and 4.5％ respectively in the group with and without PET exami -nation ( P = 0.831). The incidence of acute radioactive esophagitis was 15.6％and 45.5 ％ in the two groups respectively ( P = 0.006). The incidence of acute radiation pneumonia and long-term pulmonary fibrosis in the two groups was 6.3 ％ and 9.1%, and 68.8% and 75.0%, respectively ( P = 0.982 and P = 0.547). CONCLUSION The recurrence rate in the lymph nodes beyond the tar-get area was not increased after minimizing the clinical target volume (CTV), whereas radioactive injury to the lungs and esophageal injury was reduced, and especially with a significant decrease in the rate of acute radioactive esophagitis. The method of CT in combination with PET for minimizing the mediastinal CTV is superior to the conventional preventive radiation of the mediastinum.

This study examined a range of demographic, clinical, and criminal history factors as they relate to the intensity of offending for up to two years postrelease.This study drew on data from 1,438 individuals released from Massachusetts state prisons between 2007 and 2009 who, while incarcerated, received treatment from the prisons' mental health services and were followed for 24 months postrelease. These data were used to explore predictive factors related to the intensity of criminal justice involvement, defined as number of arrests in the two-year follow-up period.Predictors of subsequent arrests included number of previous incarcerations and black race. Protective factors included older age, supervision by parole, and a drug-related or person-related governing offense on previous arrest. Clinical symptoms were not related to incidence of postrelease arrests.This study identified factors related to criminal history, such as type of charge, that were associated with the intensity of subsequent criminal justice involvement. These findings have not been reported in previous studies, perhaps because intensity of offending as opposed to a different dependent variable was used to measure criminal justice involvement. Further investigation should focus on whether the type of previous offense is related to postrelease risk factors for recidivism.

A monitoring method based on multi-scale kernel principal component analysis (MSKPCA) is proposed for nonlinear dynamic processes, by combining wavelet analysis with nonlinear transformation using kernel principal component analysis. Wavelet analysis is used to analyze dynamic characteristic of process data, while the kernel principal component analysis is to capture nonlinear principal components by kernel functions. This method can simultaneously extract cross correlation, auto correlation, and nonlinearities from the data. Furthermore, a multi-scale principal component analysis similarity factor is proposed for fault identification. Simulation of CSTR process shows that the proposed method outperforms the traditional PCA method in fault detection and diagnosis.

HOSTA is an in-house high-order CFD software that can simulate complex flows with complex geometries. Large scale high-order CFD simulations using HOSTA require massive HPC resources, thus motivating us to port it onto modern GPU accelerated supercomputers like Tianhe-1A. To achieve a greater speedup and fully tap the potential of Tianhe-1A, we collaborate CPU and GPU for HOSTA instead of using a naive GPU-only approach. We present multiple novel techniques to balance the loads between the store-poor GPU and the store-rich CPU, and overlap the collaborative computation and communication as far as possible. Taking CPU and GPU load balance into account, we improve the maximum simulation problem size per Tianhe-1A node for HOSTA by 2.3X, meanwhile the collaborative approach can improve the performance by around 45% compared to the GPU-only approach. Scalability tests show that HOSTA can achieve a parallel efficiency of above 60% on 1024 Tianhe-1A nodes. With our method, we have successfully simulated China's large civil airplane configuration C919 containing 150M grid cells. To our best knowledge, this is the first paper that reports a CPUGPU collaborative high-order accurate aerodynamic simulation result with such a complex grid geometry.