•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.
•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
Shape setup model (SSM) plays a critical role to achieve satisfactory precision of strip shape in hot strip mill process (HSMP). However, for the design of shape model, the lack of systematic shape ...theory restricts the high accuracy of strip shape. In this paper, the procedure of SSM will be generally introduced and practically demonstrated with a real HSMP producing system. The mechanism of shape modeling and design strategy of SSM is introduced. Special concentration is placed on modeling and calculating the thermal extension and wear of the roll, and mathematical model of roll gap profile is set up on this basis. Then the mechanism of strip profile and flatness is introduced by revealing the shape forming process. Furthermore, the setup strategy of SSM is proposed, whose target is to calculate reference values for shape control actuators. The other focus of this paper concerns on the applicable issue of SSM integrated with the presented design approach. An Ansteel 1,700-mm HSMP line will be employed for the experimental background.
Membrane-based lateral flow immunochromatographic strip (LFICS) is widely used in various fields because of its simplicity, rapidity (detection within 10min), and low cost. However, early designs of ...membrane-based LFICS for preliminary screening only provide qualitative (“yes/no” signal) or semi-quantitative results without quantitative information. These designs often suffer from low-signal intensity and poor sensitivity and are only capable of single analyte detection, not simultaneous multiple detections. The performance of existing techniques used for detection using LFICS has been considerably improved by incorporating different kinds of nanoparticles (NPs) as reporters. NPs can serve as alternative labels and improve analytical sensitivity or limit of detection of LFICS because of their unique properties, such as optical absorption, fluorescence spectra, and magnetic properties. The controlled manipulation of NPs allows simultaneous or multiple detections by using membrane-based LFICS. In this review, we discuss how colored (e.g., colloidal gold, carbon, and colloidal selenium NPs), luminescent (e.g., quantum dots, up-converting phosphor NPs, and dye-doped NPs), and magnetic NPs are integrated into membrane-based LFICS for the detection of target analytes. Gold NPs are also featured because of their wide applications. Different types and unique properties of NPs are briefly explained. This review focuses on examples of NP-based LFICS to illustrate novel concepts in various devices with potential applications as screening tools. This review also highlights the superiority of NP-based approaches over existing conventional strategies for clinical analysis, food safety, and environmental monitoring. This paper is concluded by a short section on future research trends regarding NP-based LFICS.
•This review discusses how various novel nanoparticles improve the performance of traditional LFICS.•This review illustrates some novel concepts in various nanoparticles integrated devices as excellent screening methods.•This review also provides a short future trends section on NPs-based LFICS.
PDF
