•Helical strip inserts promote flow turbulence without flow separation and generate swirl.•Overall heat transfer performance of insert fitted annuli is higher than that of plain annuli.•Figure of ...Merit of the insert is better at lower Reynolds number.•Flow physics parameters such as velocity, temperature, pressure, eddy viscosity, and turbulent kinetic energy are inter-related.
Helical baffles, wire coils, and rectangular ribs are used to improve the annular fluid flow turbulence and heat transfer performance. Among the different insert types, loose-fitted helical baffles or strips are of major interest because of their better performance. Tight-fitted helical strips can show better performance in the annular flow because they can act as a fin besides acting as a swirl generator compared to loose-fitted ones. For that reason, a three-dimensional (3D) computational fluid dynamics (CFD) study is performed to investigate the heat transfer and pressure drop characteristics of turbulent flow (4000 ≤ Re ≤ 10000) at annuli with and without tight-fitted helical strip inserts. Annuli with 0.6, 0.7, and 0.8 annuli diameter ratio (ADR) are studied for plain and helical strip inserts with pitch ratios (PRs) of 1, 2, and 3. Result showed that higher heat transfer coefficient (HTC) and friction factor were found for insert-fitted annuli compared to those of plain annuli. HTC increased with increase in Re and ADR and decrease in insert PR. The best performance was for 0.8-ADR insert-fitted annuli: 171%–207% (1 PR), 82%–105% (2 PR), and 56%–75% (3 PR) compared to 0.8-ADR plain annuli. Although HTC increased with increase in ADR, the Nusselt number (Nu) decreased because of the smaller hydraulic diameter. Nu increased with increase in Re but decreased with higher ADR and insert PR. The friction factor increased with decrease in insert PR and ADR and decreased with increase in Re. A figure of merit (FoM) was used to combine the benefit of heat transfer enhancement and the drawback of higher pressure drop. The FoM ranged from 0.9 to 1.3, 0.94 to 1.22, and 0.92 to 1.24 for ADR values of 0.6, 0.7, and 0.8, respectively, for a specific Re range. The heat transfer performance obtained was in the order of 3 PR < 2 PR < 1 PR for insert-fitted annuli, but the FoM followed 1 PR < 2 PR < 3 PR because of a much higher pressure drop for lower PR. This study confirms that inserts improve heat transfer, which is better at lower Re.
Abstract
The coiling stable roll of hot-dip galvanized is a defect formed in the galvanizing process. The zinc slags and floating foreign matters in the zinc pot will directly or indirectly adhere to ...the roll surface of the stabilizer roll with the flow of zinc liquid. When the strip steel contacts the stabilizer roll, the zinc slag will extrude and deform the iron base plate and form pits. In this paper. The causes of the defects were analyzed. There were two methods to eliminate the coiling stable roll, that is, to reduce the generation and accumulation of zinc slags in the zinc pot. The main measures are first to control the temperature of the strip entering the zinc can plate and the temperature of the molten zinc, and then control the amount of residual bottom residue before the zinc pot goes online. Another is to ensure the cleanliness of the steel strip before it enters the molten zinc. Based on this, the main measures implemented are: improving the cleaning ability of the cleaning section and improving the reducing ability during annealing to inhibit the formation and accumulation of zinc powder in the furnace head area.
•A novel thermohydraulic characteristics analysis has been carried out.•Double strip compared with single strip helical screw tape in heat exchanger tube.•CuO/water nanofluid used as a working fluid ...and compared with water.•Nusselt number and Thermal performance factor are higher with CuO in double strip.•Correlations for Nusselt number and friction factor developed.•Double strip helical screw tape inserts are suitable to reduce heat exchanger size.
This paper presents the experimental analysis to evaluate thermo-hydraulic characteristics of single and double strip helical screw tape inserts in the copper tube with water and CuO/water nanofluid at constant heat flux conditions at twist ratio of 1.5, 2.5 and 3. Results show that Nusselt number and friction factor with double strip helical screw tape inserts achieves substantial enhancement as compared to single strip helical screw tape insert. The Nusselts number with nano-fluid increases and achieves the increment by 182% and 170% for double strip and single strip helical screw tape inserts respectively, at a twist ratio of 1.5. Whereas at twist ratio of 2.5 and 3, thermal performance factor shows excellent performance for double strip helical screw tape inserts, that makes it suitable for heat exchanger to reduce the size of it for thermal applications. Correlations for Nusselt number and friction factor are also developed within the range of Reynolds number, twist ratio and Number of strips.
0.3 mm‐thick grain‐oriented silicon steel sheets with varying Y contents are produced via twin‐roll strip‐casting and two‐stage cold rolling process. This study primarily investigates the evolution ...of microstructure, texture, and precipitation along the processing. Specifically, the effect of rare earth Y on second‐phase particle precipitation in ultralow carbon grain‐oriented silicon steel is examined. Results indicate that higher Y content will consume beneficial inhibitor elements such as S and N, leading to the formation of coarse rare earth inclusions (≈10 μm) in the cast strip. This significantly diminishes inhibition ability and magnetic induction (B8 = 1.58 T≈1.71 T). On the contrary, the addition of trace Y can accelerate the precipitation of inhibitors. During the intermediate annealing stage, steel with trace Y exhibits a significant enhancement in precipitate distribution density (from 2.8 μm−2 to 13.6 μm−2), and the final magnetic induction B8 increases from 1.86 T to 1.91 T compared to steel without Y. In addition, the results of first‐principle calculations based on density functional theory reveal that the doped Y atom prefers to segregate at the Al–N interface, and the interface energy reduces from 1.871 J m−2 to 1.024 J m−2, thereby promoting the precipitation of AlN.
The addition of trace rare earth Y to the strip‐cast grain‐oriented silicon steel can promote the rapid precipitation of AlN inhibitors. The doped Y atoms tend to segregate at the Al–N interface and reduce the precipitated interface energy.
To comprehensively comprehend variation of strip shape (strip feeding depth and maximum steel sheath cross‐sectional area) under different process parameters (strip feeding speed, strip oscillation ...frequency, strip amplitude, molten steel superheat, strip width, and strip thickness), a mathematical model of feeding strip process has been developed herein. Flow near steel strip is analyzed. Meanwhile, the qualitative relationship between strip shape and process parameters is derived based on energy conservation within the system of molten steel and steel strip. The results show that strip feeding depth is positively correlated with strip feeding speed, strip width, and strip thickness, while it's negatively correlated with strip oscillation frequency, strip amplitude, and molten steel superheat; maximum steel sheath cross‐sectional area is positively correlated with strip width and strip thickness, while it's negatively correlated with strip feeding speed, strip oscillation frequency, strip amplitude, and molten steel superheat. Based on the calculated results, the quantitative relationships are proposed to predict the strip feeding depth and maximum steel sheath cross‐sectional area with aforementioned process parameters, which are suitable for the situations that the strip tip is located between the outlet of submerged entry nozzle and the end of the liquid core.
Herein, a mathematical model has been developed to obtain the strip feeding depth and maximum steel sheath cross‐sectional area under different process parameters including strip feeding speed, strip oscillation frequency, strip amplitude, molten steel superheat, strip width, and strip thickness. This study reveals the comprehensive influence of various process parameters on the shape of steel strip
•Deep learning is applied to segment the hot-rolled steep strip surface defects.•A DSUNet, which can precisely and efficiently segment defects, is proposed.•The results indicate that the DSUNet can ...achieve state-of-the-art performance.
Accurate and efficient image segmentation can contribute to improving the recognition rate of surface defects for hot-rolled steel strips. However, due to its variances in shape, position, defect type and fuzzy boundary, surface defect segmentation is a challenging task. To address this issue, a depth-wise separable U-shape network (DSUNet) is proposed. In order to reduce the computation complexity and accelerate the segmentation performance, depth-wise separable convolution is employed to replace the traditional convolutional layer. In addition, a multi-scale module is proposed to extract multi-scale context and improve the segmentation accuracy. The experimental results indicate that the accuracy and dice of DSUNet reach 95.42% and 80.8%, respectively, and the DSUNet can segment 38.5 images per second, which suggests that the DSUNet can precisely segment surface defects for hot-rolled steel strip with high efficiency.
•Deep learning is applied to recognize the hot-rolled steep strip surface defects.•A DARCNN, which combines channel attention and residual blocks, is proposed.•The results indicate that the DARCNN ...can achieve state-of-the-art performance.
Generally, the existence of surface defects in hot-rolled steel strip can lead to adverse influences on the appearance and quality of industrial products. Therefore, it is significant to timely recognize the surface defects for hot-rolled steel strip. In order to improve the efficiency and accuracy of surface defects, a deep neural network, namely, deep attention residual convolutional neural network (DARCNN), is proposed to automatically distinguish 6 kinds of hot-rolled steep strip surface defects. In this network, a channel attention mechanism is combined with residual blocks so that the network can focus on the significant feature channels without information loss. The experimental results show that the accuracy, precision and area under curve (AUC) of DARCNN reach 99.5%, 99.51% and 99.98%, respectively, and the application of DARCNN can improve the accuracy, precision and AUC for surface defect recognition tasks by 1.17%, 1.03% and 0.58%, respectively, which verifies the applicability of deep learning technologies to materials.
A field paper-based heavy metal strip was designed and implemented for simultaneous detection of the heavy metals Zn, Cr, Cu, Pb and Mn in wastewater samples. The colorimetric paper strip was ...fabricated by drop-casting of chromogenic reagents onto detection zones. When the fabricated paper strip was exposed to Zn, Cr, Cu, Pb and Mn, multiple colors appeared that were then recorded with a smartphone followed by processing in the Color Picker application. After optimizing the analytical parameters, such as the chromogenic concentration, pH and reaction time, the paper strip achieved detection limits of 0.63, 0.07, 0.17, 0.03 and 0.11 mg/L for Zn, Cr, Cu, Pb and Mn, respectively. Five heavy metals analyses were able to be performed within 1 min on one paper strip. This paper strip is accurate with recoveries from 87 to 107%. The results of the proposed paper strip correlated well with those determined by inductively coupled plasma-optical emission spectrometry of wastewater samples. The use of a single paper strip integrated with a smartphone for the detection of five heavy metals in wastewater represents an all-in-one device with on-site detection, leading to cost-effective and rapid assays that show a great application potential for on-site environmental monitoring.
Graphical abstract
This article proposes a novel transferable manifold projection embedded dictionary learning (TMPDL)-based scheme with domain transfer for multimode process (MP) monitoring, where the new modes in ...evolving scenarios can be rapidly modeled. Considering that only new measurements are necessary for updating the model parameters, the proposed method elevates engineering applicability. First, in order to quantitatively analyze the discrepancy between the new and previous modes, the common features are extracted by TMPDL, upon which new modes can be modeled using domain transfer, saving storage resources and ensuring scalability. Then, the corresponding optimization process is fully discussed, which incorporates feature selection and extraction to select specific features for updating while enhancing the interpretability of the model. Concurrently, consistency and independence constraints are imposed on dictionary learning (DL), which makes the features extracted by the proposed method more discriminative. Finally, the monitoring model is developed by feature reconstruction error (FRE), which can derive monitoring results prior to mode identification. Experiments on the real hot strip mill process (HSMP) reveal that the fault detection ability of TMPDL is highly robust against MP, achieving a 94.8% monitoring accuracy rate for the newly arriving mode.
