Strategy Feasibility Research Articles

Deep learning-based reduced order model for three-dimensional unsteady flow using mesh transformation and stitching

Artificial intelligence-based three-dimensional fluid modeling has gained significant attention recently. However, the accuracy of such models is often limited by the preprocessing of irregular flow data. In order to bolster the credibility of near-wall flow prediction, we present a deep learning-based reduced order model for three-dimensional unsteady flow using the transformation and stitching techniques for multi-block structured meshes. To begin with, full-order flow data is provided by numerical simulations that rely on multi-block structured meshes. The mesh transformation technique is applied to convert each structured grid with data into a corresponding uniform and orthogonal grid, which is subsequently stitched and filled. The resulting snapshots in the transformed domain contain accurate flow information for multiple meshes and can be directly fed into a structured neural network without requiring any interpolation operation. Subsequently, a network model based on a fully convolutional neural network is constructed to predict flow dynamics accurately. To validate the strategy's feasibility, the flow around a sphere with Re=300 was investigated, and the results obtained using traditional Cartesian interpolation were used as the baseline for comparison. All the results demonstrate the preservation and accurate prediction of flow details near the wall, with the pressure correlation coefficient on the wall achieving a remarkable value of 0.9997. The stitching scheme that follows the proposed standard can effectively reduce the accumulation of inferring errors. Moreover, the periodic behavior of flow fields can be faithfully predicted during long-term inference. This work demonstrates that the proposed strategy has the advantage of high efficiency while providing accuracy assurance for downstream tasks.

Design of La3-xYxNi9 (x=0–3) alloys with good cyclic stability considering crystal structure and subunit volume

The poor cyclic stability of AB3 alloys restricts its application in Nickel/Metal hydride batteries. In this work, a new strategy based on phase structure stability and subunit volume regulation is proposed to improve their cyclic stability. The strategy proposes alloys satisfying the three factors may exhibits good cyclic stability: good phase structure stability, [AB5] subunit volume less than 88.3 ų and the [A2B4] subunit volume less than 89.2 ų, and the volumes difference of [A2B4] and [AB5] subunits less than 5.3 ų. Experimental validation with La3-xYxNi9 (x = 2.0, 2.6, 3.0) confirms the strategy's feasibility. While the [AB5] subunit volume less than 88.3 ų and the [A2B4] subunit volume less than 89.2 ų, the smaller the volume difference of subunits, the better the cyclic stability. La0.4Y2.6Ni9 alloy has excellent comprehensive hydrogen storage performance, retaining 80.3% capacity after 130 cycles, much higher than the LaY2Ni9's 78.2% retention after 50 cycles.

Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency

Shaped charge liner (SCL) has been extensively applied in oil recovery and defense industries. Achieving superior penetration capability through optimizing SCL structures presents a substantial challenge due to intricate rate-dependent processes involving detonation-driven liner collapse, high-speed jet stretching, and penetration. This study introduces an innovative optimization strategy for SCL structures that employs jet penetration efficiency as the primary objective function. The strategy combines experimentally validated finite element method with machine learning (FEM-ML). We propose a novel jet penetration efficiency index derived from enhanced cutoff velocity and shape characteristics of the jet via machine learning. This index effectively evaluates the jet penetration performance. Furthermore, a multi-model fusion based on a machine learning optimization method, called XGBOOST-MFO, is put forward to optimize SCL structure over a large input space. The strategy's feasibility is demonstrated through the optimization of copper SCL implemented via the FEM-ML strategy. Finally, this strategy is extended to optimize the structure of the recently emerging CrMnFeCoNi high-entropy alloy conical liners and hemispherical copper liners. Therefore, the strategy can provide helpful guidance for the engineering design of SCL.

Adaptive Hierarchical Fractional-Order Sliding Mode Control Strategy for Multi-terminal VSC-HVDC System with Wind Farm

In this paper, an adaptive hierarchical fractional-order sliding mode controller (AHFSMC) for a multi-terminal Voltage Source Converter (VSC) based High Voltage Direct Current (HVDC) Integrated with a wind farm (WF) system is designed. Multi-terminal VSC-HVDC (MtVDC) connected to wind farms has received several attentions in the power sector because of its numerous benefits. The effectiveness of MtVDC, on the other hand, is dependent on the control scheme used. To achieve this goal, a hierarchical sliding mode control method by fractional-order calculus is used. The controller parameters are adjusted according to a suitable adaptation method to enhance the proposed controller's robustness compared to the system uncertainties coefficient. An appropriate Lyapunov-based approach is used to achieve the adaptation rule. This paper discusses a scheme to design additional controllers in MtVDC systems to damp electromechanical oscillations, one of several features of HVDC presently under active study. Numerical simulations validate the proposed control strategy's feasibility and efficiency. This novel approach is employed for the upgrading of system stability with the dynamic properties of the MtVDC in a variety of operative conditions.

Reverse design of haptens based on antigen spatial conformation to prepare anti-capsaicinoids&gingerols antibodies for monitoring of gutter cooking oil

Rapid simultaneous detection of multi-component adulteration markers can improve the accuracy of identification of gutter cooking oil in edible oil, which is made possible by broad-spectrum antibody (bs-mAb). This study used capsaicinoids (CPCs) and gingerol derivatives (GDs) as adulteration markers, and two broad-spectrum haptens (bs-haptens) were designed and synthesized based on a reverse design strategy of molecular docking. Electrostatic potential (ESP) and monoclonal antibodies (mAbs) preparation verified the strategy's feasibility. To further investigate the recognition mechanism, five other reported antigens and mAbs were also used. Finally, the optimal combination (Hapten 5-OVA/1-F12) and key functional groups (f-groups) were determined. The half maximal inhibitory concentration (IC50) for CPCs-GDs was between 88.13 and 499.16 ng/mL. Meanwhile, a preliminary lateral flow immunoassay (LFIA) study made practical monitoring possible. The study provided a theoretical basis for the virtual screening of bs-haptens and simultaneous immunoassay of multiple exogenous markers to monitor gutter oil rapidly and accurately.

Co-designing a recruitment strategy for lung cancer screening in high-risk individuals: protocol for a mixed-methods study.

Lung cancer is a significant cause of cancer-related mortality globally, with early detection through screening critical to improving patient outcomes. However, recruiting high-risk individuals, particularly in deprived populations, for screening remains a considerable challenge. This study aims to co-design a targeted recruitment strategy for lung cancer screening, tailored to the specific needs and experiences of high-risk individuals, in collaboration with a Patient and Public Involvement (PPI) panel and expert stakeholders in Ireland. We will employ a mixed-methods design guided by the Medical Research Council (MRC) framework for developing complex interventions. Our approach will integrate systematic review findings on screening participation interventions, evaluation of the recruitment strategy's feasibility in an Irish context, and the application of behavioural science frameworks. The target population includes individuals over 55 years, either current smokers or those who quit within the last year, who reside in highly deprived areas. This co-designed recruitment strategy will combine evidence-based research, local context understanding, and stakeholder input to develop a solution that is both scientifically robust and tailored to the target population's needs. This patient-centred approach aims to increase the potential for successful implementation of lung cancer screening programs, thereby improving early detection and patient outcomes.

Biomimetic Catalytic Retro-Aldol Reaction Using a Cation-Binding Catalyst: A Promising Route to Axially Chiral Biaryl Aldehydes.

Here we describe a biomimetic catalytic retro-aldol reaction of racemic α-substituted β-hydroxy ketones utilizing a chiral oligoEG cation-binding catalyst as a type-II aldolase mimic. Our investigation of various aldol substrates has demonstrated that our biomimetic retro-aldol protocol enables rapid access to highly enantiomerically enriched aldols with a selectivity factor (s) of up to 70. Additionally, we have demonstrated the synthetic strategy's feasibility for accessing diverse and valuable axially chiral aldehydes.

A Q-marker screening strategy based on ADME studies and systems biology for Chinese herbal medicine, taking Qianghuo Shengshi decoction in treating rheumatoid arthritis as an example.

Chinese herbal medicine (CHM) exhibits a broad spectrum of clinical applications and demonstrates favorable therapeutic efficacy. Nonetheless, elucidating the underlying mechanism of action (MOA) of CHM in disease treatment remains a formidable task due to its inherent characteristics of multi-level, multi-linked, and multi-dimensional non-linear synergistic actions. In recent years, the concept of a Quality marker (Q-marker) proposed by Liu et al. has significantly contributed to the monitoring and evaluation of CHM products, thereby fostering the advancement of CHM research. Within this study, a Q-marker screening strategy for CHM formulas has been introduced, particularly emphasising efficacy and biological activities, integrating absorption, distribution, metabolism, and excretion (ADME) studies, systems biology, and experimental verification. As an illustrative case, the Q-marker screening of Qianghuo Shengshi decoction (QHSSD) for treating rheumatoid arthritis (RA) has been conducted. Consequently, from a pool of 159 compounds within QHSSD, five Q-markers exhibiting significant in vitro anti-inflammatory effects have been identified. These Q-markers encompass notopterol, isoliquiritin, imperatorin, cimifugin, and glycyrrhizic acid. Furthermore, by employing an integrated analysis of network pharmacology and metabolomics, several instructive insights into pharmacological mechanisms have been gleaned. This includes the identification of key targets and pathways through which QHSSD exerts its crucial roles in the treatment of RA. Notably, the inhibitory effect of QHSSD on AKT1 and MAPK3 activation has been validated through western blot analysis, underscoring its potential to mitigate RA-related inflammatory responses. In summary, this research demonstrates the proposed strategy's feasibility and provides a practical reference model for the systematic investigation of CHM formulas.

Horizontal Data Augmentation Strategy for Industrial Quality Prediction.

In recent years, neural network-based soft sensor technology has been widely used in industrial production processes and has excellent optimization, monitoring, and quality prediction performance. This paper proposes a horizontal data augmentation strategy to provide highly available data for subsequent prediction models, called the combined autoencoder data augmentation (CADA) strategy. This paper has developed a CADA-based convolutional neural network (CADA-CNN) soft sensor model and applied it to the process of industrial debutanizer and industrial steam volume. In terms of method validation, this paper compares the output data of the proposed CADA by the Spearman correlation coefficient to verify the strategy’s feasibility. Then, the output data of the CADA strategy is fed into the artificial neural network (NN), support vector regression (SVR), and convolutional neural network (CNN) for comparison experiments. The final experimental results show that our proposed CADA-CNN model has lower prediction error and better prediction error distribution.

Comparative study of chemical discharge strategy to pretreat spent lithium-ion batteries for safe, efficient, and environmentally friendly recycling

The residual electricity contained in spent lithium-ion batteries probably triggers the thermal runaway and results in irreparable disaster during recycling. Chemical discharge is a common method to eliminate hazards by immersing batteries in an aqueous solution to release the remnant energy. However, a high-efficiency discharge solution usually causes severe corrosion of the battery shell, inducing safety accidents. In this work, theoretical and experimental studies are carried out in various solutions to investigate the efficiency and corrosion of chemical discharge strategy. The results suggest that zinc acetate solution is a potential discharge medium with excellent comprehensive properties compared with NaCl, MnSO 4 , FeSO 4 , and KAc solutions. Electrolytic reactions and external short circuit have been proved to be the essential causes of high discharge efficiency. Adsorption theory and Kolbe reaction are used to explain the low corrosion of battery shells in acetate solutions. Besides, the thermal stability of treated spent batteries also validates our discharge strategy's feasibility and ensures safety during transportation, storage, and recycling. The high-efficiency discharge mechanism and excellent corrosion inhibition of spent lithium-ion battery immersing in zinc acetate solution. • Electrolysis and external short circuit ensure the high discharge efficiency. • Manganese sulfate cannot effectively discharge spent batteries. • Zinc acetate solution is first proposed as a potential discharge medium. • Adsorption theory and Kolbe reaction explain the low corrosion in acetate solution.

Energy Management System for Hybrid Microgrids

Energy management in grid-connected Micro-grids (MG) has undergone rapid evolution in recent times due to several factors such as environmental issues, increasing energy demand and the opening of the electricity market. The Energy Management System (EMS) allows the optimal scheduling of energy resources and energy storage systems in MG in order to maintain the balance between supply and demand at low cost. The aim is to minimize peaks and fluctuations in the load and production profile on the one hand, and, on the other hand, to make the most of renewable energy sources and energy exchanges with the utility grid. In this paper, our attention has been focused on a Rule-based energy management system (RB EMS) applied to a residential multi-source grid-connected MG. A Microgrid model has been implemented that combines distributed energy sources (PV, WT, BESS), a number of EVs equipped with the Vehicle to Grid technology (V2G) and variable load. Different operational scenarios were developed to see the behaviour of the implemented management system during the day, including the random demand profile of EV users, the variation in load and production, grid electricity price variation. The simulation results presented in this paper demonstrate the efficacy of the suggested EMS and confirm the strategy's feasibility as well as its ability to properly share power among different sources, loads and vehicles by obeying constraints on each element.

Study of the reactive power control for a real UHVAC power transmission project

Summary The reactive power compensation mode used for the present commercial ultra-high-voltage alternating current power transmission system could not entirely meet the requirements of reactive power balance. The reactive power interchange (RPI) between the ultra-high-voltage (1000 kV) power grid and the 500 kV power grid would impact on the reactive power control strategy. In this paper, an in-depth analysis of the relationship between the RPI and the voltage modulus ratio (VMR) of the sending side to the receiving end is executed. It is proved that the RPI could be expressed as a function of the VMR. An optimal minimized RPI (MRPI) control strategy that minimizes the RPI while satisfying the steady state over-voltage restriction is proposed. To solve the optimal problem, the immune genetic algorithm is used in this work. By practical calculation examples, the MRPI control strategy's feasibility and validity under various operation modes in East China Grid is validated. Copyright © 2014 John Wiley & Sons, Ltd.