• Journal of Microbiology and Biotechnology
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• v.23 no.2
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• pp.137-143
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• 2013

The diversity and distribution of methanogenic archaea in a four-compartment anaerobic baffled reactor (ABR) treating sugar refinery wastewater were investigated by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). At an organic loading rate of 5.33 kg $COD/m^3{\cdot}day$, the ABR could perform steadily with the mean chemical oxygen demand (COD) removal of 94.8% and the specific $CH_4$ yield of 0.21 l/g $COD_{removed}$. The $CH_4$ content in the biogas was increased along the compartments, whereas the percentage of $H_2$ was decreased, indicating the distribution characteristics of the methanogens occurred longitudinally down the ABR. A high phylogenetic and ecological diversity of methanogens was found in the ABR, and all the detected methanogens were classified into six groups, including Methanomicrobiales, Methanosarcinales, Methanobacteriales, Crenarchaeota, Arc I, and Unidentified. Among the methanogenic population, the acid-tolerant hydrogenotrophic methanogens including Methanoregula and Methanosphaerula dominated the first two compartments. In the last two compartments, the dominant methanogenic population was Methanosaeta, which was the major acetate oxidizer under methanogenic conditions and could promote the formation of granular sludge. The distribution of the hydrogenotrophic (acid-tolerant) and acetotrophic methanogens in sequence along the compartments allowed the ABR to perform more efficiently and steadily.

• Structural Engineering and Mechanics
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• v.57 no.5
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• pp.951-968
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• 2016

Long-span cross-strait bridges extending into deep-sea waters are exposed to complex marine environments. During the construction stage, the flexible freestanding bridge towers are more vulnerable to environmental loads imposed by wind and wave loads. This paper presents an experimental investigation on the dynamic responses of a 389-m-high freestanding bridge tower model in a test facility with a wind tunnel and a wave flume. An elastic bridge model with a geometric scale of 1:150 was designed based on Froude similarity and was tested under wind-only, wave-only and wind-wave combined conditions. The dynamic responses obtained from the tests indicate that large deformation under resonant sea states could be a structural challenge. The dominant role of the wind loads and the wave loads change according to the sea states. The joint wind and wave loads have complex effects on the dynamic responses of the structure, depending on the approaching direction angle and the fluid-induced vibration mechanisms of the waves and wind.

• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.305-317
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• 2021

An AMD control system is usually built based on the original model of a target building. As a result, the fact leads a large calculation workload exists. Therefore, the orders of a structural model should be reduced appropriately. Among various model-reduction methods, a suitable reduced-order model is important to high-rise buildings. Meanwhile, a partial structural information is discarded directly in the model-reduction process, which leads to the accuracy reduction of its controller design. In this paper, an optimal technique is selected through comparing several common model-reduction methods. Then, considering the dynamic characteristics of a high-rise building, an improved balanced truncation (BT) method is proposed for establishing its reduced-order model. The abandoned structural information, including natural frequencies, damping ratios and modal information of the original model, is reconsidered. Based on the improved reduced-order model, a new reduced-order controller is designed by a regional pole-placement method. A high-rise building with an AMD system is regarded as an example, in which the energy distribution, the control effects and the control parameters are used as the indexes to analyze the performance of the improved reduced-order controller. To verify its effectiveness, the proposed methodology is also applied to a four-storey experimental frame. The results demonstrate that the new controller has a stable control performance and a relatively short calculation time, which provides good potential for structural vibration control of high-rise buildings.

• Wind and Structures
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• v.28 no.2
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• pp.129-140
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• 2019

Accurately simulating the wind field of large-scale region, for instant urban areas, the locations of large span bridges, wind farms and so on, is very difficult, due to the complicated terrains or land surfaces. Currently, the regional wind field can be simulated through the combination of observation data and numerical model using observation-nudging in the Weather Research and Forecasting model (WRF). However, the main drawback of original observation-nudging method in WRF is the effects of observation on the surrounding field is fully mathematical express in terms of temporal and spatial, and it ignores the effects of terrain, wind direction and atmospheric circulation, while these are physically unreasonable for the turbulence. For these reasons, a spatial correlation-based observation-nudging method, which can take account the influence of complicated terrain, is proposed in the paper. The validation and comparation results show that proposed method can obtain more reasonable and accurate result than original observation-nudging method. Finally, the discussion of wind field along bridge span obtained from the simulation with spatial correlation-based observation-nudging method was carried out.

• Nano
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• v.13 no.11
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• pp.1850134.1-1850134.10
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• 2018

Increasing active sites and enhancing electric conductivity are critical factors to improve sensing performance toward phenol. Herein, Ni nanoparticle was successfully anchored on acidified multiwalled carbon nanotube (a-MWCNT) surface by electroless plating technique to avoid Ni nanoparticle agglomeration and guarantee high conductivity. The crystal structure, phase composition and surface morphology were characterized by XRD, SEM and TEM measurement. The as-prepared Ni/a-MWCNT nanohybrid was immobilized onto glassy carbon electrode (GCE) surface for constructing phenol sensor. The phenol sensing performance indicated that Ni/a-MWCNT/GCE exhibited an amazing detection performance with rapid response time of 4 s, a relatively wide detection range from 0.01 mM to 0.48 mM, a detection limit of $7.07{\mu}M$ and high sensitivity of $566.2{\mu}A\;mM^{-1}\;cm^{-2}$. The superior selectivity, reproducibility, stability and applicability in real sample of Ni/a-MWCNT/GCE endowed it with potential application in discharged wastewater.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.987-995
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• 2019

Steam jet condensation is of great importance to pressure suppression containment and automatic depressurization system in nuclear power plant. In this paper, the condensation processes of sonic steam jet in a quiescent subcooled pool are recorded and analyzed, more precise understanding are got in direct contact condensation. Experiments are conducted at atmospheric pressure, and the steam is injected into the subcooled water pool through a vertical nozzle with the inner diameter of 10 mm, water temperature in the range of $25-60^{\circ}C$ and mass velocity in the range of $320-1080kg/m^2s$. Richardson number is calculated based on the conservation of momentum for single water jet and its values are in the range of 0.16-2.67. There is no thermal stratification observed in the water pool. Four condensation regimes are observed, including condensation oscillation, contraction, expansion-contraction and double expansion-contraction shapes. A condensation regime map is present based on steam mass velocity and water temperature. The dimensionless steam plume length increase with the increase of steam mass velocity and water temperature, and its values are in the range of 1.4-9.0. Condensation heat transfer coefficient decreases with the increase of steam mass velocity and water temperature, and its values are in the range of $1.44-3.65MW/m^2^{\circ}C$. New more accurate semi-empirical correlations for prediction of the dimensionless steam plume length and condensation heat transfer coefficient are proposed respectively. The discrepancy of predicted plume length is within ${\pm}10%$ for present experimental results and ${\pm}25%$ for previous researchers. The discrepancy of predicted condensation heat transfer coefficient is with ${\pm}12%$.

• Wind and Structures
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• v.28 no.5
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• pp.285-298
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• 2019

For super long-span bridges, the aerodynamic forces induced by the flow passing the box girder should be considered carefully. And the Reynolds number sensitively of aerodynamic characteristics is one of considerable issue. In the study, a numerical study on the Reynolds number sensitivity of aerodynamic characteristic (flow pattern, pressure distribution and aerodynamic forces) of a twin-box girder were carried out using large eddy simulation (LES) with the dynamic Smagorinsky-Lilly subgrid model. The results show that the aerodynamic characteristics have strong correlation with the Reynolds number. At the leading edge, the flow experiences attachment, departure, and reattachment stages accompanying by the laminar transition into turbulence, causing pressure plateaus to form on the surface, and the pressure plateaus gradually shrinks. Around the gap, attributing that the flow experiences stages of laminar cavity flow, the wake with alternate shedding vortices, and turbulent cavity flow in sequence with an increase in the Reynolds number, the pressures around the gap vary greatly with the Reynold number. At the trailing edge, the pressure gradually recovers as the flow transits to turbulence (the flow undergoes wake instability, shear layer transition-reattachment station), In addition, at relative high Reynolds numbers, the drag force almost does not change, however, the lift force coefficient gradually decreases with an increase in Reynolds number.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.