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Feature selection is a widely used data pre‐processing method. However, the research on feature selection stability is very rare. Although there are some related studies that mainly focus on filters rather than wrappers, especially wrappers based on evolutionary algorithms. In this paper, a stable feature selection method called stable brain storm optimisation (SBSO) is proposed. It puts forward a new population initialisation strategy, which treats stable feature selection results as guide information for initialisation and utilises such chaos to initialise population. SBSO also sets up an information archive to store the historical optimal individuals dynamically. A large number of experiments show that SBSO gives excellent classification performance when compared with other methods, and superior feature selection stability when compared with other wrappers.

The rhythmic regulation of transcriptional processes is intimately linked to lipid homeostasis, to anticipate daily changes in energy access. The Rev-erbα-HDAC3 complex was previously discovered to execute the rhythmic repression of lipid genes; however, the epigenetic switch that turns on these genes is less clear. Here, we show that genomic recruitment of MRG15, which is encoded by the mortality factor on chromosome 4 (MORF4)-related gene on chromosome 15, displays a significant diurnal rhythm and activates lipid genes in the mouse liver. RNA polymerase II (Pol II) recruitment and histone acetylation correspond to MRG15 binding, and the rhythm is impaired upon MRG15 depletion, establishing MRG15 as a key modulator in global rhythmic transcriptional regulation. MRG15 interacts with the nuclear receptor LRH-1, rather than with known core clock proteins, and is recruited to genomic loci near lipid genes via LRH-1. Blocking of MRG15 by CRISPR targeting or by the FDA-approved drug argatroban, which is an antagonist to MRG15, attenuates liver steatosis. This work highlights MRG15 as a targetable master regulator in the rhythmic regulation of hepatic lipid metabolism.

With the continuous development of unmanned aerial vehicle (UAV) technology and its falling cost, UAV has been widely used in important fields, such as agriculture, industry, security, and military. In operation, UAVs often need to maintain communication connectivity with ground stations. However, in the case of long distance or shadowing, the quality of the wireless channels will be seriously degraded, which will affect the communication connectivity. To solve this problem, this paper considers deploying multiple UAVs as communication relays. In this paper, we select the long-term channel capacity as the optimization objective and propose two planning methods for UAV relays based on dynamic programming (DP): CMMP-AT and CMMP-OBO. The CMMP-AT method emphasizes the deployment of all relay UAVs at the beginning of the task. This method is simpler to implement, but the computational complexity is intolerable. The CMMP-OBO method proposes to deploy relay UAVs one by one according to the task requirements. Although the CMMP-OBO method may lose performance, it has a lower computational complexity and better scalability and can save more motion energy. The experimental results reveal that the CMMP-AT method requires 40.03 h to obtain the planning results for a two-relay scenario, whereas the CMMP-OBO method only needs 57.66 s and can save 3.87% motion energy. In addition, to precisely control the relay UAVs to follow the planned trajectory, this paper implements trajectory tracking, based on model predictive control (MPC), and realizes the simulation of the multi-relay UAV scenario in the VREP environment. The simulation results reveal that compared with the PID control method, the MPC method can track the planned trajectory more accurately. In two simulation scenarios, the tracking error of MPC is only 43.97% and 41.42% of the PID control method.

Wireless communications and networking are playing an important role in coordination and cooperation of multi-robot systems (MRS). However, it is challenging to keep a reliable and stable wireless connection in practical applications. Especially, robots acting in electromagnetic adversarial (EA) environments may encounter more serious situations including scarce spectrum, active interference, adversarial competition, etc. In this survey, we firstly analyze the challenges faced by MRS in EA environments, and provide a categorization according to the “sense-decide-act” robot control procedure. Secondly, enabling techniques for each challenge are introduced. Finally, typical robotics software architectures are introduced, as frameworks for efficient arrangement of the above mentioned enabling techniques.

Intelligent unmanned aerial vehicles (UAVs) have been applied for civil and military uses. Relative localization (RL) is crucial for multi-UAVs to accomplish complex tasks successfully and safely. In global positioning system (GPS) denied environments, where accurate or meaningful location information is hard to obtain, persistent excitation based RL is a promising approach for multi-UAVs to achieve RL without any needs of external infrastructures. However, for many cases, existing persistent excitation based RL method suffers precision loss, error accumulation and divergence. This article tackles these issues, and proposes an enhanced approach to ensure the practical usage of persistent excitation based RL. Synchronized sensor sample prediction is introduced to confine and reduce RL error, and RL estimation is redesigned to avoid RL error accumulation. We evaluated our solution by simulating various scenarios. The results show that the proposed approach can effectively decrease RL error and prevent RL error accumulation.

Lung cancer is the leading cause of cancer-related deaths worldwide. Cytologic examination is the current “gold standard” for lung cancer diagnosis, however, this has low sensitivity. Here, we identified a typical methylation signature of histone genes in lung cancer by whole-genome DNA methylation analysis, which was validated by The Cancer Genome Atlas (TCGA) lung cancer cohort (n = 907) and was further confirmed in 265 bronchoalveolar lavage fluid samples with specificity and sensitivity of 96.7% and 87.0%, respectively. More importantly, HIST1H4F was universally hypermethylated in all 17 tumor types from TCGA datasets (n = 7,344), which was further validated in nine different types of cancer (n = 243). These results demonstrate that HIST1H4F can function as a universal-cancer-only methylation (UCOM) marker, which may aid in understanding general tumorigenesis and improve screening for early cancer diagnosis. Significance: These findings identify a new biomarker for cancer detection and show that hypermethylation of histone-related genes seems to persist across cancers.

Multiobjective subset selection problems widely exist in many real-world applications. Multiobjective ant colony optimisation (MOACO) is a strong and kind instrument for settling those issues. However, it still has two shortages which the authors must resolve. One, its solution construction process is inconsistent with the disorder characteristics of solutions, which prevent it from getting better solutions. Two, conventional MOACOs which most deal with biobjective optimisation problems are difficult to figure out high-dimensional objectives optimisation problems. In this study, the authors propose a new MOACO to resolve those two disadvantages. They give its detailed descriptions and an exhaust experiment. And the results on C measure, spacing measure and inverted generational distance (IGD) show that the proposed algorithm has a powerful convergence ability and gets a better balance between convergence and diversity compared with other state of art approaches.

The numerical nonreproducibility in parallel molecular dynamics (MD) simulations, which relates to the non-associate accumulation of float point data, leads to great challenges for development, debugging and validation. The most common solutions to this problem are using a high-precision data type or operation sorting, but these solutions are accompanied by significant computational overhead. This paper analyzes the sources of nonreproducibility in parallel MD simulations in detail. Two general solutions, namely, sorting by force component value and using an 80-bit long double data type, are implemented and evaluated in LAMMPS. To optimize the computational cost, a full-list based method with operation order sorted by particle distance is proposed, which is inspired by the spatial characteristics of MD simulations. An experiment on a system with constant energy dynamics shows that the new method can ensure reproducibility at any parallelism with an extra 50% computational overhead.

In the welding process of metal materials, defects often occur due to various factors, which not only affect the welding quality, but also adversely affect the properties of materials. In order to control these defects, it is necessary to take corresponding measures according to the actual situation in the welding process. With the development of society, ultrasonic nondestructive testing technology is widely used in industrial field. It can detect the internal damage of the tested object with simple operation and good performance. Ultrasonic nondestructive testing technology is used to detect hidden defects in welding of metal materials, which can effectively ensure the service performance of metal materials. In this paper, the main defects and preventive measures of metal welding based on ultrasonic nondestructive testing technology are discussed.

The well-established functions of UHRF1 converge to DNA biological processes, as exemplified by DNA methylation maintenance and DNA damage repair during cell cycles. However, the potential effect of UHRF1 on RNA metabolism is largely unexplored. Here, we revealed that UHRF1 serves as a novel alternative RNA splicing regulator. The protein interactome of UHRF1 identified various splicing factors. Among them, SF3B3 could interact with UHRF1 directly and participate in UHRF1-regulated alternative splicing events. Furthermore, we interrogated the RNA interactome of UHRF1, and surprisingly, we identified U snRNAs, the canonical spliceosome components, in the purified UHRF1 complex. Unexpectedly, we found H3R2 methylation status determines the binding preference of U snRNAs, especially U2 snRNAs. The involvement of U snRNAs in UHRF1-containing complex and their binding preference to specific chromatin configuration imply a finely orchestrated mechanism at play. Our results provided the resources and pinpointed the molecular basis of UHRF1-mediated alternative RNA splicing, which will help us better our understanding of the physiological and pathological roles of UHRF1 in disease development.

In a multi-agent game, the complexity of the environment increases exponentially as the number of agents increases. Learning becomes difficult when there are so many agents. Mean field multi-agent reinforcement learning (MFRL) uses the average action of the neighbors to increase the input of the value network, which can be applied in the environment with hundreds of agents. However, inefficient exploration and slow convergence speed limit the performance of the algorithm. In this article, we propose a new Knowledge-Guided Reinforcement Learning (KG-RL) method, which can be divided into rule-mix and plan-extend. We use the rule-mix to encode knowledge into plans which can reduce redundant information and invalid actions in the state. And the plan-extend can combine the result of rule-mix with reinforcement learning to achieve more efficient joint exploration. Through experiments in Magent environment, we prove that the win rate of our proposed KG-RL is 22% higher than that of knowledge-based decision tree and 39% higher than that of MFRL. Thus, the KG-RL can perform well in massive battle games due to its high exploration efficiency and fast convergence.

In this paper, we study a fully decentralized multi-agent pursuit problem in a non-communication environment. Fully decentralized (decentralized training and decentralized execution) has stronger robustness and scalability compared with centralized training and decentralized execution (CTDE), which is the current popular multi-agent reinforcement learning method. Both centralized training and communication mechanism require a large amount of information exchange between agents, which are strong assumptions that are difficult to meet in reality. However, traditional fully decentralized multi-agent reinforcement learning methods (e.g., IQL) are difficult to converge stably due to the dynamic changes of other agents’ strategies. Therefore, we extend actor-critic to actor-critic-N framework, and propose Potential-Field-Guided Deep Deterministic Policy Gradient (PGDDPG) method on this basis. The agent uses the unified artificial potential field to guide the agent’s strategy updating, which reduces the uncertainty of multi-agent’s decision making in the complex and dynamic changing environment. Thus, PGDDPG which we proposed can converge fast and stably. Finally, through the pursuit experiments in MPE and CARLA, we prove that our method achieves higher success rate and more stable performance than DDPG and MADDPG.

The study of multi-agent reinforcement learning can solve many problems in real life. The current research can be divided into two aspects: one is adding the information of other agents into the critic-network to form a global critic-network, as MADDPG; the other is putting them into the actor-network, like CommNet, which takes the actions or observations from other agents into consideration. However, the two methods are faced with these problems: the action space is huge when the number of agents increases; In reality, due to the limitation of bandwidth and delay, communication often cannot perform well or even work normally. Inspired by MFRL, we design our algorithm MFPRL to solve this problem. The neighbors’ average action is predicted by a separate MFP network. The experiment shows that our method achieves better results than MFRL.

To obtain pavement type information on the basis of related theoretical knowledge on deep learning, this study constructs a deep convolutional neural network model that uses video information on ice, snow, asphalt, and cement pavements collected by a vehicle camera. The data are preprocessed to obtain training and test sets. The training set is used for neural network training, and the accuracy of the model is evaluated with the test set. Results show that the constructed network model can accurately classify four kinds of pavements with an accuracy of 99.5%.

OpenFOAM is a widely used open source framework for simulation in several areas of computational fluid dynamics and engineering. As a partial differential equation (PDE)-based framework, OpenFOAM suffers from a performance bottleneck in solving large-scale sparse linear systems of equations. To address the problem, this article proposes a novel OpenFOAM-PETSc framework by inserting PETSc, a dedicated numerical solving package, into the OpenFOAM to speed up the process of solving linear equation systems. The design of the OpenFOAM-PETSc framework is described, and the implementation of an efficient matrix conversion algorithm is given as a case study. Validation tests on a high-performance computing cluster show that OpenFOAM-PETSc reduces the time of solving PDEs by about 27% in the lid-driven cavity flow case and by more than 50% in flow around the cylinder case in comparison with OpenFOAM, without compromising the scalability. In addition, this article also gives a preliminary performance analysis of different numerical solution methods, which may provide guidelines for further optimizations.

Different configurations of coupling strategies influence greatly the accuracy and convergence of the simulation results in the hybrid atomistic-continuum method. This study aims to quantitatively investigate this effect and offer the guidance on how to choose the proper configuration of coupling strategies in the hybrid atomistic-continuum method. We first propose a hybrid molecular dynamics- (MD-) continuum solver in LAMMPS and OpenFOAM that exchanges state variables between the atomistic region and the continuum region and evaluate different configurations of coupling strategies using the sudden start Couette flow, aiming to find the preferable configuration that delivers better accuracy and efficiency. The major findings are as follows:(1)theC→Aregion plays the most important role in the overlap region and the “4-layer-1” combination achieves the best precision with a fixed width of the overlap region;(2)the data exchanging operation only needs a few sampling points closer to the occasions of interactions and decreasing the coupling exchange operations can reduce the computational load with acceptable errors;(3)the nonperiodic boundary force model with a smoothing parameter of 0.1 and a finer parameter of 20 can not only achieve the minimum disturbance near the MD-continuum interface but also keep the simulation precision.

Lung cancer is the leading cause of cancer-related deaths worldwide. Cytologic examination is the current "gold standard" for lung cancer diagnosis, however, this has low sensitivity. Here, we identified a typical methylation signature of histone genes in lung cancer by whole-genome DNA methylation analysis, which was validated by The Cancer Genome Atlas (TCGA) lung cancer cohort (

Fluid simulation is an important application of High Performance Computing. The hybrid atomistic-continuum coupling fluid simulation can effectively handle the contradictory of the reduction of the simulation scale and the increase of the computation load when trying to improve the accuracy. The pre-processing procedure of the coupling simulation is quite important in the whole simulation process and requires the support of an efficient, user-friendly interface. Under the demand of efficient coupling fluid simulation, in this paper we design and implement a visualized pre-processing framework HACP2 based on SALOME, for the unified molecular dynamic-computational fluid dynamics modeling and apply it into the coupling simulation process. The experimental verification indicates that our HACP2 framework can offer efficient, easy-to-use preprocessing for the hybrid atomistic-continuum coupling simulation, and effectively improve the efficiency of the coupling simulation.

In CFD simulations, parameters in the mathematical models wil affect the accuracy of the simulation results. Both manual and automatic approaches have been proposed to optimize the parameters for more accurate simulation results. Open-FOAM, which is an open-source CFD software, has been widely used in recent years. In this paper, we focused on the cavitating flow problems and designed a parameter optimization framework based on OpenFOAM with HPC and database support. We implemented the framework with SAO approach, and carried out tests considering the multi-phase solver interPhaseChangeFoam in OpenFOAM. The overall results show the framework with HPC and database support is effective in parameter optimization.

Followed by our previous study, an OpenFOAM-based viscoelastic flow solver has been further validated through simulation of viscoelastic flow past a cylinder. The drag coefficients calculated by the Oldroyd-B model under the creeping flow in a range of Weissenberg (Wi) number are in good agreements with those reported in the literature. Using the linear Phan-Thien Tanner (L-PTT) model, time-dependent two-dimensional simulations of flow past cylinder have been carried out in a range of Wi number and Reynolds (Re) number, and revealed interesting cooperative effects of inertia and elasticity on the structural evolution of the wake behind the cylinder. The details of parallel computing strategy are analysed and discussed. The codes are evaluated for large scale parallel simulation of two-dimensional and three-dimensional contraction flow as well as two-dimensional flow past a cylinder. The key bottlenecks, which affect the scalability of parallel computing, are discussed.

Free surface flows arise in a variety of engineering applications. To predict the dynamic characteristics of such problems, specific numerical methods are required to accurately capture the shape of free surface. This paper proposed a new method which combined the Arbitrary Lagrangian-Eulerian (ALE) technique with the Finite Volume Method (FVM) to simulate the time-dependent viscoelastic free surface flows. Based on an open source CFD toolbox called OpenFOAM, we designed an ALE-FVM free surface simulation platform. In the meantime, the die-swell flow had been investigated with our proposed platform to make a further analysis of free surface phenomenon. The results validated the correctness and effectiveness of the proposed method for free surface simulation in both Newtonian fluid and viscoelastic fluid.

The phase transition of complex fluids is intrinsically a multi-scale problem. In this paper we propose a multi-scale two-fluid model that couples a coarse-grained microscopic method to the two-fluid framework for studying multi-phase fluids under shear flow. In this model the macroscopic viscoelastic stress is calculated by tracking the massive microscopic Brownian configuration fields in the simulation box. Both the macroscopic and microscopic equations are solved using a modified PISO iterative algorithm based on a finite volume discretization scheme. Our 2D numerical results reproduce numerous dynamic phenomena reported in literature and show that the theoretical model presented here could be a promising multi-scale approach to numerically study multi-phase viscoelastic fluids under flow.

The understanding of flow instability of viscoelastic fluid is very important but far from sufficient. By combining the constitutive equation with Navier-Stoke governing equation, we implement a viscoelastic solver based on OpenFOAM for Rolie-Poly model, which is a single-mode constitutive model derived from the tube-based molecular theory. We numerically investigate two-dimensional viscoelastic flow around a circular cylinder with this solver. A second order parallelized finite volume method (FVM) is employed in the simulation in both spatial and the temporal domain. The numerical results illustrate the significant effects of the polymer elasticity on the flow structure. The vortex shedding and recirculation region exist behind the cylinder under low Reynolds numbers due to the elastic effect. Both drag reduction and enhancement have been observed in the simulation. Results are consistent with other literature. And the solver shows good parallel efficiency and scalability.

A matlab toolbox for modelling viscoelastic constitutive equation has been implemented. It can be used to analysis the rheology properties of various viscoelastic constitutive equations by solving a time dependent PDE. The toolbox also provides support to calculate the optimized parameter of parameters to give a fine approximation with experiment data. We carefully designed the framework to make sure its extensibility and efficiency. Matlab parallel technology has been add to make the toolbox more computational efficient. The tool box does well on the rheology analysis of Rolie-Poly model and parameter optimization. This toolbox is significant to the study of viscoelastic models and provide a template for relative research areas.

Mesh partitioning is significant to the efficiency of parallel computational fluid dynamics simulations. The most time-consuming parts of parallel computational fluid dynamics simulations are iteratively solving linear systems derived from partial differential equation discretizations. This article aims at mesh partitioning for better iterative convergence feature of this procedure. For typical computational fluid dynamics simulations in which partial differential equations are discretized and solved after the mesh is partitioned, numerical information of the linear systems is not available yet during mesh partitioning. We propose to construct approximations for matrix elements and theoretically find out that for finite-volume-based problems, the face area can approximate the corresponding matrix element well. A mesh partitioning scheme using the matrix value approximations for better iterative convergence behavior is implemented and numerically testified. The results show that our method can capture the most important factor influencing the matrix values and achieve partitions with good performance throughout the simulations with non-uniform meshes. The novel partitioning strategy is general and easy to implement in various partitioning packages.

This paper discusses the performance model for practical computational fluid dynamics (CFD) simulation. Based on the analysis of the conventional qualitative model and empirical knowledge in the simulation, a set of practical models for the linear solution in high-order finite element method (FEM) simulation are established: a linear function model for the storage consumption, and a power function model for the solution time. The unknown parameters in the models are estimated by linear fitting and nonlinear fitting, respectively. Tests on a typical high-order FEM platform show that the proposed models are highly accurate and practical. In addition, it has been tested that nonlinear fitting is better than linear fitting in our case.

High order discretization method is one of the most popular research topics on Computational Fluid Dynamics (CFD). However, the development of visualization tools suited for high order methods obtains not as much attention. Most of the software used to visualize numerical solutions do not support high order data format. To visualize the high order solution, the most common method is to tessellate the solution with linear subdivision and then generate the values by resampling. This paper describes an efficient approach for Discontinuous Galerkin Method (DGM) based on OpenFOAM and VTK. We design a method to estimate the visualization error and introduce gaussian quadrature method to calculate it. Under the limit of visualization error set by user, the high order DGM solution is tessellated into a set of quadratic elements and converted into VTK data. By implementing the interpolation interface, this tools can support other discretization methods. The tool is tested with a series of cases. Accurately visualization of geometric information and field attributes are obtained. Comparing to linear methods, less computation and space cost are needed to reach the same visualization error limit.

The hybrid atomistic–continuum coupling method based on domain decomposition serves as an important tool for the microfluidic simulation. However, major modifications to existing codes are often required to enable such simulations, which pose significant difficulties. In this article, in order to provide an efficient and easy-to-use software framework for field users, we propose a hybrid atomistic–continuum parallel coupling framework, named HACPar, based on open-source software platforms. We abstract the software architecture of the hybrid atomistic–continuum coupling framework based on geometric decomposition for the first time, demonstrate the detailed implementation of the framework, and present deep research on the coupling-oriented parallel issues which may improve the flexibility and efficiency of other multiscale parallel applications. The benchmark cases verify the correctness and efficiency of our HACPar framework. The benchmark results show that the scalability of the hybrid simulations is reached up to 1536 cores.

Radial Basis Function(RBF) mesh deformation method has been widely used in CFD simulations with moving boundaries due to its high robustness and accuracy. The original implementation of the RBF mesh deformation method in OpenFOAM(a widely used CFD software) is purely serial with relatively low computational performance. To reduce the time cost of the mesh motion in large-scale simulations, this paper proposes a parallel RBF mesh deformation method with multi-greedy algorithm in OpenFOAM. The proposed multi- greedy method could reduce the control points used by the RBF interpolation on both the moving boundary and the static boundary, which is more applicable than the previous typical greedy algorithm. Based on a master-worker algorithm, the computation of the mesh deformation is highly parallelized. Tests on the benchmark of a three-dimensional moving fish show that with an error tolerance of 1e-4, the interpolation time of the internal mesh motion using our multi-greedy method is about 10.2 times faster than the original one, and with a parallelism of 132, the time cost of the whole mesh motion is greatly reduced with a speedup of 37.

A large amount of CO contained in calcium carbide furnace off-gas can be recycled and used as an important raw material for C1 (one carbon) industrial chemical manufacturing. The key to the recovery is the removal of dust and gaseous impurities from the off-gas. For this purpose, an industrial device has been used for removing the dust. This consists of a laboratory-scale fixed-bed reactor with two kinds of adsorbents, prepared in our laboratory, for removing HCN and COS. The results show that the dust content after the dedusting process was less than 20 mg/m 3 . The dust particles are composed of microspheres, with an obvious tendency to agglomerate, and with an average diameter of about 6.71 μm. After adsorption, the outlet concentrations of both HCN and COS were less than 1 mg/m 3 . In order to remove HCN, activated carbon (AC) was used, impregnated with 0.15 mol/L Cu(CH 3 COO) 2 solution. The optimum calcination temperature was 350 °C. The AC, modified with Cu(CH 3 COO) 2 , was examined by means of N 2 -BET and X-ray photoelectron spectroscopy (XPS) before and after off-gas treatment. The results indicate that the chemisorption products were mainly present in the AC micropores and mesopores, with radius 0.81–1.51 nm. After dust removal and purification, the calcium carbide furnace off-gas is fit for use as a raw material in the C1 chemical manufacturing industry.

Activated carbon sorbents impregnated with KOH, Fe(NO3)3, Cu(NO3)2, Zn(NO3)2 or Co(NO3)2 and their applications in catalytic oxidation reaction of COS were investigated. The results showed that the activated carbon modified with 10 % (mass percentage) KOH enhanced the adsorption ability significantly. And it was also found that the oxygen content and temperature were the two most important factors in the COS adsorption. Further investigation on the pore structures of the samples with X-ray photoelectron spectroscopy indicated that an adsorption/oxidation process happened in the KOH modified activated carbon in which the major existing forms of sulfur were SO4 2− and S species. The oxidation of COS suggested that KOH in the micropores may play a catalytic role during the adsorption. On the other hand, we found that the desorption activation energy from KOHW was higher than that from AC by the CO2-TPD spectra, which indicated the adsorption of CO2 on KOH impregnated activated carbon was stronger. The strong adsorption could be attributed to the basic groups on the activated carbon surface. In conclusion, the activated carbon impregnated with KOH promises a good candidate for COS adsorbent.

In this work, we deal with high-order solver for incompressible flow based on velocity correction scheme with discontinuous Galerkin discretized velocity and standard continuous approximated pressure. Recently, small time step instabilities have been reported for pure discontinuous Galerkin method, in which both velocity and pressure are discretized by discontinuous Galerkin. It is interesting to examine these instabilities in the context of mixed discontinuous Galerkin–continuous Galerkin method. By means of numerical investigation, we find that the discontinuous Galerkin–continuous Galerkin method shows great stability at the same configuration. The consistent velocity divergence discretization scheme helps to achieve more accurate results at small time step size. Since the equal order discontinuous Galerkin–continuous Galerkin method does not satisfy inf-sup stability requirement, the instability for high Reynolds number flow is investigated. We numerically demonstrate that fine mesh resolution and high polynomial order are required to obtain a robust system. With these conclusions, discontinuous Galerkin–continuous Galerkin method is able to achieve high-order spatial convergence rate and accurately simulate high Reynolds flow. The solver is tested through a series of classical benchmark problems, and efficiency improvement is proved against pure discontinuous Galerkin scheme.

For parallel dam break simulations in OpenFOAM (Open Source Field Operation and Manipulation), the core procedure is solving linear systems using iterative methods and the iterative convergence rate is significant to the overall efficiency. A dynamic mesh repartitioning scheme DMRPar (Dynamic Mesh Re-Partitioning) considering the iterative convergence feature is implemented in OpenFOAM. Given that the numerical characteristics of linear systems change a lot along with the complex flow field, DMRPar takes linear system information from the previous timestep into account for the repartitioning at the current timestep. The implementation reuses current mesh topology in OpenFOAM and calculates distributed adjacency graph structure for the mesh. The repartitioning heuristic is based on a general multi-level parallel graph partitioning package called ParMetis. Numerical results on two typical dam break simulations show that DMRPar outperforms the traditional static partitioning method significantly in the total simulation time.

This paper focuses on mesh-partitioning metrics in large-scale parallel computational fluid dynamics (CFD) simulations. Mesh partitioning has a significant influence on the efficiency of parallel preconditioned conjugated gradient (PCG) solving procedure, which is the most representative and timeconsuming part in parallel CFD. As the efficiency of parallel PCG depends on load balancing, communication overhead and iterative convergence rate comprehensively, we present a detailed review of mesh-partitioning metrics on these three aspects respectively. Three typical large-scale CFD applications are built to numerically testify the validity of all those metrics.

The method of Brownian configuration fields (BCF) is a promising multi-scale approach for the simulationof viscoelastic fluids, however, it is a computationally expensive method, which restricts its application in complex scenarios. Therefore, it is of great importance to optimize the parallel implementation in order to improve computational efficiency. In this paper we propose a hybrid decomposition parallel algorithm named: MCDPar, whichenables the simulation problem to bedecomposed simultaneously over mesh cells andthe Brownian configuration fields. Compution processes are split into multiple groups and Brownian configuration fields are equally associated with these groups. Meanwhile, within each group the processes are concurrently executed based on the traditional mesh decomposition approach. Finally we implemented the MCDPar algorithm in a micro-macro numerical solver based on OpenFOAM. Experimental results show that the micro-macro simulation time of viscoelastic fluids issignificantly reduced with improved scalability and parallel efficiency. In the test case with Nf = 2000 and Ncell = 262144, the speedup of the MCDParis up to 9.23x with a 7.5x increase in number of cores compared to the original parallel algorithm.

Purpose – CaCO3 solution was chosen as the research environment in boiling pool heat transfer system using an automatic data collection system and scaling experiments. In this experiment, different concentrations of solution were made and ultrasonic excitation was applied under differing conditions of salt concentration and heat flux, such that the ultrasonics could reduce scale accumulation on the metal surface. The effectiveness of ultrasonics in inhabiting CaCO3 scale formation was evaluated. The paper aims to discuss these issues. Design/methodology/approach – Different concentration solution was made. Under the condition of different concentration and heat flux, the ultrasonic was introduced, so that it can reduce the scale accumulation on the metal surface. Ultrasonic function in inhabiting CaCO3 scale can be evaluated. Findings – The results showed that the ultrasonic not only enhanced the heat transfer rate under boiling conditions but also prevented the accumulation of the scale on the metal surface. Originality/value – The ultrasonic can enhance the heat transfer on the heater surface both in distilled water and scaled solution. In the earlier period, the heat transfer coefficient increases with the heat flux increases.

A series of hexaaluminates, LaMnFexAl11−xO19−δ samples (x = 1, 2, 4, 6, 8) as new catalysts were prepared by carbonate precipitation and calcined at high temperature. Fe and Mn ions were used as active components to replace part of aluminum ions in the hexaaluminate lattices. The structures and properties of these samples were characterized by XRD, BET, and XPS. The series of hexaaluminates exhibited significant catalytic activity and stability at high temperature. The LaMnFe2Al9O19−δ retains a larger surface area and shows a good activity in methane combustion.

Manganese substituted hexaaluminates were prepared either by carbonate precipitation or sol–gel method and calcined at high temperature. The structure and property of these samples were characterized by XRD, BET, SEM and TG–DTA. The series of hexaaluminates exhibited significant catalytic activity at high temperature. Upon calcination at 1200 °C K 2 MnAl 11 O 19− δ retained a specific surface area of 27.6 m 2 /g and showed an excellent activity for methane combustion, which temperatures at 10% and 90% conversion were 490 and 670 °C, respectively.

In order to enhance heat transfer and mitigate contamination in the boiling processes, a new type of vapor-liquid-solid (3-phase) circulating fluidized bed boiling system has been designed, combining a circulating fluidized bed with boiling heat transfer. Experimental results show an enhancement of the boiling curve. Flow visualization studies concerning flow hydrodynamics within the riser column are also conducted whose results are presented and discussed.

A series of Fe and Mn ions modified lanthanum hexaaluminates, LaFeMn x Al 11−x O 19−δ ( x = 0, 0.5, 1, 2, 3, 4, 5) as new catalysts for combustion of methane, was prepared by sol–gel method and calcined at high temperature. Fe and Mn ions were used as active components to replace part of aluminum ions in the hexaaluminates lattices. The structure and properties of these samples were characterized by XRD, BET, TG-DTA and XPS. The series of hexaaluminates exhibited significant catalytic activity and stability at high temperature. The LaFeMnAl 10 O 19− δ catalyst possesses a larger specific surface area and shows a higher activity than the others in methane combustion.

A series of hexaaluminates, La0.8A0.2MnAl11O19−δ samples (A = Ba, Ca, Sr and Y) as new catalysts were prepared by carbonate precipitation and calcined at high temperature. The structure and properties of these samples were characterized by XRD, BET and XPS techniques. Upon calcination at 1200°C, the hexaaluminate structure was formed and it retained the specific surface area of 17∼20 m2g−1. The La0.8Ca0.2MnAl11O19−δ catalyst has a surface area of 19.3 m2g−1 and shows a good activity in CH4 combustion.

Manganese-substituted lanthanum hexaaluminates were prepared by the sol-gel method. The effect of water content on the catalyst preparation was examined. Large water content caused a rapid decrease in the surface area. When the R (R is the mole ratio of H2O/ Al(OC3H7)3) is equal to 1.5, the catalyst retains a specific surface area of 13.5 m2 g−1 and shows higher activity in CH4 combustion.

Fluid simulation is an important application of High Performance Computing. The micro-macro coupling simulation can effectively handle the contradictory of the reduction of the simulation scale and the increase of computation load when try to improve the accuracy. The pre-processing of the coupling simulation is quite important in the whole simulation and requires the support of an efficient, user-friendly interface. Under the demand of efficient coupling simulation, we, based on SALOME, develop a visual pre-processing framework, M2P2, for unified micro-macro and apply into the coupling simulation process. The experimental verification indicates that our M2P2 framework can offer efficient, easy-to-use pre-processing for the micro-macro coupling simulation, and effectively improve the efficiency of the simulation.

Attenuated strains of invasive enteric bacteria, such as Salmonella, represent promising gene delivery agents for nucleic acid-based vaccines as they can be administrated orally. In this study, we constructed a novel attenuated strain of Salmonella for the delivery and expression of the hemagglutinin (HA) and neuraminidase (NA) of a highly pathogenic H5N1 influenza virus. We showed that the constructed Salmonella strain exhibited efficient gene transfer activity for HA and NA expression and little cytotoxicity and pathogenicity in mice. Using BALB/c mice as the model, we evaluated the immune responses and protection induced by the constructed Salmonella-based vaccine. Our study showed that the Salmonella-based vaccine induced significant production of anti-HA serum IgG and mucosal IgA, and of anti-HA interferon-γ producing T cells in orally vaccinated mice. Furthermore, mice orally vaccinated with the Salmonella vaccine expressing viral HA and NA proteins were completely protected from lethal challenge of highly pathogenic H5N1 as well as H1N1 influenza viruses while none of the animals treated with the Salmonella vaccine carrying the empty expression vector with no viral antigen expression was protected. These results suggest that the Salmonella-based vaccine elicits strong antigen-specific humoral and cellular immune responses and provides effective immune protection against multiple strains of influenza viruses. Furthermore, our study demonstrates the feasibility of developing novel attenuated Salmonella strains as new oral vaccine vectors against influenza viruses.