Optimal Structure Research Articles

Optimization of W-band interaction structure developed using three different micro-fabrication techniques.

In this paper, an experimental investigation has been performed for the three different micro-fabrication techniques for optimization in the development process of the W-band planar beam-wave interaction structure. The W-band planar beam-wave interaction structure has been developed using three different micro-fabrication methods, namely micro-EDM (electric discharge milling), wire-EDM, and micro-milling. The effect of each fabrication method on the developed structure is analyzed using scanning electron microscopy and ZETA 3D optical microscopy for their optimization. The experimental analysis has been performed for the developed W-band planar beam-wave interaction structure with an optimized process to achieve a dimensional deviation of less than 10μm and surface roughness of less than 50nm.

Creating CoRu Dual Active Sites Codecorated Stable Porous Ceria Support for Enhanced Li-CO2 Batteries Cathodes.

Lithium-carbon dioxide (Li-CO2) battery represents a high-energy density energy storage with excellent real-time CO2 enrichment and conversion, but its practical utilization is hampered by the development of an excellent catalytic cathode. Here, the synergistic catalytic strategy of designing CoRu bimetallic active sites achieves the electrocatalytic conversion of CO2 and the efficient decomposition of the discharge products, which in turn realizes the smooth operation of the Li-CO2 battery. Moreover, obtained support based on metal-organic frameworks precursors facilitates the convenient diffusion and adsorption of CO2, resulting in higher reaction concentration and lower mass transfer resistance. Meanwhile, the optimization of the interfacial electronic structure and the effective transfer of electrons are achieved by virtue of the strong interaction of CoRu at the support interface. As a result, the Li-CO2 cell assembled based on bimetallic CoRu active sites achieved a discharge capacity of 19,111mAhg-1 and a steady-state discharge voltage of 2.58V as well as a cycle life of >175 cycles at a rate of 100mAg-1. Further experiments combined with density-functional theory calculations achieve a deeply view of the connection between cathode and electrochemical performance and pave a way for the subsequent development of advanced Li-CO2 catalytic cathodes.

Research on the prediction and realization path of urban carbon peak along the Yellow River Basin

China and the international community attach great importance to sustainable development goals such as peaking carbon emissions. As an important energy base in China, the Yellow River Basin has significant capacity for reducing carbon emissions and increasing carbon sinks. To accelerate the achievement of carbon peak targets, social and natural development data as well as carbon emission data of 56 cities along the Yellow River Basin from 2000 to 2021 were analyzed. Based on the evaluation indicators of the model, the PSO-XGBoost-RF model was selected from the comparative models to predict the carbon peak of cities in the Yellow River Basin under different paths. Under the current economic and social development, cities along the Yellow River Basin in China are facing huge pressure to reduce emissions. It is expected to achieve a carbon peak in 2033, with a peak of 2,051,320,000 tons, so the target of reaching the peak of carbon emissions before 2030 will not be accomplished. But through comprehensive optimization of the industrial structure and reduction of energy consumption, cities along the Yellow River Basin as a whole may achieve carbon peak by 2026, with a peak of 1,917,132,000 tons.

Rate Splitting Multiple Access: Optimal Beamforming Structure and Efficient Optimization Algorithms

Rate Splitting Multiple Access: Optimal Beamforming Structure and Efficient Optimization Algorithms

Predicting the microstructure of composite plates using deep learning based on thermal expansion

In this paper, we introduce a novel method to predict composite plate microstructures based on thermal expansion using deep learning. Traditional analysis methods are cumbersome and resource intensive. We created a detailed dataset from finite element simulations, capturing edge displacements during thermal expansion. This dataset feeds into our multilayer perceptron model, generating precise microstructure matrices. Our approach achieves high accuracy and efficiency, markedly reducing computational overhead. By combining deep learning with finite element analysis, we streamline predictions and enhance precision. This integrated approach serves as a potent tool for engineers and materials scientists, facilitating composite structure design and optimization.

Kinetic investigation of the energy storage process in graphene fiber supercapacitors: Unraveling mechanisms, fabrications, property manipulation, and wearable applications

AbstractGraphene fiber supercapacitors (GFSCs) have garnered significant attention due to their exceptional features, including high power density, rapid charge/discharge rates, prolonged cycling durability, and versatile weaving capabilities. Nevertheless, inherent challenges in graphene fibers (GFs), particularly the restricted ion‐accessible specific surface area (SSA) and sluggish ion transport kinetics, hinder the achievement of optimal capacitance and rate performance. Despite existing reviews on GFSCs, a notable gap exists in thoroughly exploring the kinetics governing the energy storage process in GFSCs. This review aims to address this gap by thoroughly analyzing the energy storage mechanism, fabrication methodologies, property manipulation, and wearable applications of GFSCs. Through theoretical analysis of the energy storage process, specific parameters in advanced GF fabrication methodologies are carefully summarized, which can be used to modulate nano/micro‐structures, thereby enhancing energy storage kinetics. In particular, enhanced ion storage is realized by creating more ion‐accessible SSA and introducing extra‐capacitive components, while accelerated ion transport is achieved by shortening the transport channel length and improving the accessibility of electrolyte ions. Building on the established structure–property relationship, several critical strategies for constructing optimal surface and structure profiles of GF electrodes are summarized. Capitalizing on the exceptional flexibility and wearability of GFSCs, the review further underscores their potential as foundational elements for constructing multifunctional e‐textiles using conventional textile technologies. In conclusion, this review provides insights into current challenges and suggests potential research directions for GFSCs.

Advanced approach for active and durable proton exchange membrane fuel cells: Coupling synergistic effects of MNC nanocomposites

AbstractAtomically dispersed metal and nitrogen co‐doped carbon (MNC) is a promising oxygen reduction reaction (ORR) catalyst for electrochemical energy storage and conversion applications but typically suffers from low durability and activity under the acidic conditions of practical polymer electrolyte exchange membrane fuel cells (PEMFCs). Recently, the performance of MNC nanocomposites under acidic ORR conditions has been enhanced by exploiting the synergistic coupling effects of their constituents (single‐atom sites, nanoclusters, and nanoparticles). The unique geometric structures formed by the coupling of diverse sites in these nanocomposites provide optimal electronic structures and efficient reaction pathways, thus resulting in high activity and long‐term durability. This work provides an overview of MNC nanocomposites as ORR electrocatalysts under practical PEMFC conditions, focusing on activity and durability enhancement methods and highlighting the strategies used to prepare electrocatalytically efficient MNC nanocomposites containing no or low amounts of platinum group metals. Progress in the development of advanced MNC nanocomposites as acidic ORR catalysts is discussed, and the pivotal role of synergistic effects resulting from the coupling sites within the nanocomposites is explored together with the characterization methods used to elucidate these effects. Finally, the challenges and prospects of developing MNC nanocomposites as next‐generation electrocatalysts are presented.image

Federated Learning of Generalized Linear Causal Networks.

Causal discovery, the inference of causal relations among variables from data, is a fundamental problem of science. Nowadays, due to an increased awareness of data privacy concerns, there has been a shift towards distributed data collection, processing and storage. To meet the pressing need for distributed causal discovery, we propose a novel federated DAG learning method called distributed annealing on regularized likelihood score (DARLS) to learn a causal graph from data stored on multiple clients. DARLS simulates an annealing process to search over the space of topological sorts, where the optimal graphical structure compatible with a sort is found by distributed optimization. This distributed optimization relies on multiple rounds of communication between local clients and a central server to estimate the graphical structure. We establish its convergence to the solution obtained by an oracle with access to all the data. To the best of our knowledge, DARLS is the first distributed method for learning causal graphs with such finite-sample oracle guarantees. To establish the consistency of DARLS, we also derive new identifiability results for causal graphs parameterized by generalized linear models, which could be of independent interest. Through extensive simulation studies and a real-world application, we show that DARLS outperforms existing federated learning methods and is comparable to oracle methods on pooled data, demonstrating its great advantages in estimating causal networks from distributed data.

Unsupervised Structure-Adaptive Graph Contrastive Learning.

Graph contrastive learning, which to date has always been guided by node features and fixed-intrinsic structures, has become a prominent technique for unsupervised graph representation learning through contrasting positive-negative counterparts. However, the fixed-intrinsic structure cannot represent the potential relationships beneficial for models, leading to suboptimal results. To this end, we propose a structure-adaptive graph contrastive learning framework to capture potential discriminative relationships. More specifically, a structure learning layer is first proposed for generating the adaptive structure with contrastive loss. Next, a denoising supervision mechanism is designed to perform supervised learning on the structure to promote structure learning, which introduces the pseudostructure through the clustering results and denoises the pseudostructure to provide more reliable supervised information. In this way, under the dual constraints of denoising supervision and contrastive learning, the optimal adaptive structure can be obtained to promote graph representation learning. Extensive experiments on several graph datasets demonstrate that our proposed method outperforms state-of-the-art approaches on various tasks.

Neural architecture search for multi-sensor information fusion-based intelligent fault diagnosis

The multi-sensor information fusion-based intelligent fault diagnosis (MIF-IFD) method using deep learning aims to provide more accurate and reliable ways of identifying equipment health status than single-sensor methods. However, designing feature extraction modules in deep networks and determining information fusion execution layers still requires expertise and extensive experimentation, imposing high development costs. This limitation impedes the development and application of MIF-IFD. This paper proposes an architecture to mitigate the development challenges of MIF-IFD. Firstly, the basic module of the network consists of searchable feature extraction cells and a mask self-attention mechanism to achieve the extraction and fusion of multi-sensor information. Secondly, the optimal feature extraction cells and fusion starting layers are searched by constructing a continuous search space to compose the optimal fusion diagnostic structure, respectively. Finally, validation and analysis are conducted using two experimental datasets. The results demonstrate that the proposed method not only ensures diagnostic accuracy but also significantly improves development efficiency.

Facile synthesis of dual-MOF ultrathin nanosheets supported on layered double hydroxides heterostructure: Electron modulation strategy for enhanced electrocatalytic water splitting

In this study, we achieved electron environment modulation of MOF-based electrocatalyst by creating a dual-functional heterostructure using two-dimensional (2D) MOFs and layered double hydroxides (LDH), to enhance its electrocatalytic performance. Through a self-sacrificial template method, ultrathin hybrid dual-MOF (CoBDC and MIL-100(Fe)) nanosheets were grown on LDH, forming DM@CoFeRu-LDH, a 2D heterostructure benefiting from interference co-growth on the 2D scaffold. The thickness of dual-MOF nanosheets can be controlled by regulating the nucleation and reaction kinetics. Specifically, with a thickness ranging from 1–1.5 nm, it effectively exposes more active sites and accelerate electron and mass transfer. Additionally, electron transfer occurred between the dual-MOF and LDH heterostructures, leveraging synergistic effects from multi-metal centers to achieve an optimal electron structure. DM@CoFeRu-LDH exhibited the lowest energy barrier for the rate-determining step in the oxygen evolution reaction, along with a favorable hydrogen adsorption energy for the hydrogen evolution reaction. As a result, the DM@CoFeRu-LDH demonstrated efficient electrocatalytic activity, requiring only a voltage of 1.50 V to achieve a current density of 10 mA cm−2.

Data-driven Performance Indicators for SAGD Process of Oil Sands Using Support Vector Regression Machine with Parameter Optimization Algorithm

Abstract The rapid and accurate forecasting of performance in the Steam-Assisted Gravity Drainage (SAGD) process for oil sands is crucial for the reasonable design of the development plan. This study aims to address this need by presenting novel data-driven performance indicators based on support vector regression (SVR), a machine learning method that complements the traditional physics-driven approach. During the SAGD process, steam is injected into the reservoir to heat the bitumen, reducing its viscosity, and allowing it to flow towards a lower well where it can be collected. The performance of the SAGD process depends on various factors such as steam injection rate, reservoir heterogeneity, and operating conditions. Accurately forecasting the performance of the SAGD process can help optimize these parameters and improve the overall efficiency of oil sands recovery. The data-driven performance indicators proposed in this study utilize the SVR method to establish a relationship between input parameters and the desired performance outputs. In the constructing process, some parameter optimization algorithms, like grid search method, particle swarm optimization algorithm and genetic algorithm, are used to identify the optimal SVR model structure. The validation results show that the design meets the desired objectives. All in all, through proposed data-driven performance indicators, the performance of SAGD process in candidate oil sands projects could be rapidly and easily obtained.

Particle swarm optimization for performance enhancement of cadmium telluride thin film solar cells by embedded nano-grating structures with a plasmonic metal coating layer

As the demand for energy in world is increasing rapidly and there is pursuit for renewable energy sources which is cheap, easy to generate and requires low maintenance, solar energy is a top contender. The world is in dire need of photovoltaic solar cells that can aid in keeping up with the hiking energy demands. However, thin film solar cells (TFSCs) which are developed for cost effectivity, suffer in performance due to reduced thickness. Therefore, this computational study uses Finite-Difference Time-Domain (FDTD) and delves into the scope of using a 1-D nano-grating structure embedded into absorbing layer of cadmium telluride TFSC to enhance the optoelectronic performance. A unique nano-grating structure with SiO2 at its core and aluminium coating aims to enhance the performance of CdTe TFSC with cost effectivity and ease of fabricability as the main motifs. Additionally, an evolutionary computational technique, Particle Swarm Optimization (PSO), was used to determine the optimal parameters of nano-gratings on CdTe TFSCs, i.e., nano-grating height, angle, duty cycle, aluminium coating thickness and grating substrate thickness. The PSO algorithm resulted in a nano-grating structure that yielded an efficiency increase of 52.86% and increase in short-circuit current density of 25.45% with respect to bare CdTe TFSCs. Subsequently, the performance of the optimal nano-grating structure was also observed to be insensitive to variation of wide range of incidence angle of light, a key feature preferred for versatile application of TFSCs.

Optimization of the Regulation Needle Structure Based on the Flow Regulation Characteristic and Flow Loss Mechanism: Optimisation de la Structure de l'aiguille de régulation basée sur la caractéristique de régulation du débit et le mécanisme de perte de débit

The regulation needle is a key component of the variable ejector to guarantee the quality of flow control and improve ejector performance. However, the studies related to the variable ejector lack regulation needle structure optimization. In this paper, for both linear and arc-shaped nozzles, three different profiles of regulation needles: linear, elliptical, and parabolic types are optimized based on the flow regulation characteristic (FRC) and local flow loss mechanism. The calculation method of the variable nozzle FRC is established. The results show that for the arc-shaped nozzles when the bottom radius Rs of the regulation needle is 0.75 times throat radius Rt, a linear regulation needle with half-top angle αs being 32° is recommended, which makes the FRC ideal, minimizes the entropy production and maximizes the pressure recovery ratio (PRR). For the linear nozzles equipped with the elliptical regulation needle, as the ratio of the height to the bottom radius of the regulation needle head (RHR) increases, the FRCs get closer to the ideal parabolic type, however, the entropy production increases and the PRR decreases. Therefore, the RHR is advised to be 3 for linear nozzles. The findings can serve as important guidance for optimizing variable ejector design.

Numerical study on multiphase combustion characteristics of aluminum-based powder-fueled water ramjet engine

Powder-Fueled Water Ramjet Engine (PFWRE) is of great attraction for high-speed and long-voyage underwater propulsion, as well as air-water trans-media navigation applications due to its high energy density and thrust adjustability. However, the complex multiphase combustion process in the combustor significantly affects engine performance. In this study, a detailed model for aluminum particle combustion in water vapor is developed and validated via literature data as well as the ground direct-connected test we conducted. Thereafter, the numerical study on the multiphase combustion process inside the aluminum-based PFWRE combustor is carried out within the Euler–Lagrange framework using the developed model. Results show that a reverse rotating vortex pair before the primary water injection causes particles to flow back towards the combustor head and leads to product deposition. Aluminum particles external to the powder jet have shorter preheating time than internal particles and burn out in advance. The analysis of the particle combustion process indicates that the flame structure inside the combustor consists of the particle preheating zone, the surface combustion heat release zone, the gas-phase combustion heat release zone, and the post-flame zone. In the present configuration, as the particle size increases from 10 μm to 20 μm, the preheating zone length increases from 35 mm to 85 mm. Meanwhile, heat release from gas-phase combustion decreases, and the average temperature of the combustor head first increases and then decreases. This study not only provides insight into the multiphase combustion characteristics of the aluminum-based PFWRE combustor but also offers guidance for the design of the combustion organization schemes and engine structure optimization.

Structure optimization for the discharge arm of the self-propelled forage harvester based on CFD-DEM

To optimize the structure of the discharge arm of the self-propelled harvester and improve the harvesting efficiency to reduce the loss, we first established a mathematical model of the gas-solid two-phase of the forage in the unloading process. Next, we revealed the flow characteristics of forage and the influence of key structures of the discharge arm using the CFD-DEM. Then, we optimized the key structural parameters of the discharge arm using the orthogonal test and response surface method. Finally, we carried out the simulation and field experiment on the forage conveying performance of the discharge arm. The experimental results show that the maximum conveying distance of the new discharge arm is more than 27 m, significantly reducing the spraying loss and improving the harvesting efficiency. Our research findings provide a new reference for optimizing the agricultural material conveying machines and exploring the movement characteristics of agricultural materials in different pipelines.

Developing sustainable steel slag-based aerated concrete: Effects of accelerated carbonation on performance and carbon emissions

This research undertakes an in-depth examination of the properties of carbonated steel-slag aerated concrete (CSSAC), the mineral composition and microstructure were characterized using X-ray diffraction (XRD), scanning electron microscope (SEM), life cycle assessment (LCA) also employed for evaluating its environmental impacts. Results revealed that a strength of 6.6 MPa and a dry density of 958 kg/m3 for CSSAC was achieved through carbonation curing, utilizing a 30 vol% CO2 concentration at ambient pressure for a duration of 24 h. Nonetheless, elements such as high water-solid ratio, cement content, and foaming agent content were found to negatively affect the strength development. Furthermore, the overuse of lime as a cement substitute formed a dense pore structure on the surface layer, consequently lowering CO2 sequestration efficiency. It was found that compressive strength was enhanced and water absorption was reduced by 66 % with calcium stearate, through effective pore structure optimization. Significantly, the carbon footprint of CSSAC stood at a minimal 0.0673 kg∙CO2∙eq/kg, achieving a substantial 73.36 % reduction when compared with its original carbon footprint. This adoption of carbonation curing effectively mitigated the carbon footprint of the product, offering actionable insights for the manufacturing of zero-carbon and even negative-carbon building materials.

Decoupling the roles of grain boundary strength and grain size hidden in grain-level electro-chemo-mechanical failure of solid-state electrolyte

Garnet lithium lanthanum zirconium oxide (Li7La3Zr2O12, LLZO) is a benchmark solid-state electrolyte material receiving considerable attention owing to its high conductivity and chemical stability against Li metal. Although its electro-chemo-mechanical failure mechanisms have been much investigated, the equivocal roles of grain boundary strength and grain size of LLZO remain under-explored, hindering further performance improvements. Here we decoupled the effects of grain size and grain boundary strength of polycrystalline LLZO via the combination of electrochemical kinetics and the cohesive zone model. We discovered that the disintegration of LLZO is initiated by the accumulation of local displacements, which strongly relates to the changes in both grain size and grain boundary strength. However, variations in grain boundary strength affect the diffusion and propagation pathways of damage, while the failure of LLZO is determined by the grain size. Large LLZO grains facilitate transgranular damage under low grain boundary strength, which can alter local chemo-mechanics within the bulk of LLZO, leading to more extensive damage propagation. The results showcase the structure optimization pathways by preferentially controlling the growth of lithium dendrites at grain boundaries and their penetration in garnet-type SSE.

HSMAOA: An enhanced arithmetic optimization algorithm with an adaptive hierarchical structure for its solution analysis and application in optimization problems

The Arithmetic Optimization Algorithm (AOA) has recently gained significant attention as a novel meta-heuristic algorithm. However, it faces challenges such as premature convergence and entrapment in local optima when addressing complex optimization problems. To overcome these limitations, this paper proposes an enhanced AOA, termed the Self-Adaptive Hierarchical Arithmetic Optimization Algorithm (HSMAOA). The proposed method integrates three key strategies: Firstly, a spiral-guided random walk mechanism is introduced to improve global search ability. Secondly, a novel adaptive hierarchy leader and follower mechanism is proposed, which establishes a complete multi-branch tree hierarchy with decreasing branching degrees within the population, thereby increasing information exchange among population individuals to escape local optima. Finally, a differential mutation strategy based on ranked selection is introduced to enhance candidate solution quality. HSMAOA's performance was evaluated on the CEC2022 test suite against some state-of-the-art algorithms. Results, including optimization accuracy analysis, convergence curves, and various statistical tests, demonstrate HSMAOA's superior optimization capability and robustness. In addition, tests on eight engineering structure optimization problems, including the pressure vessel design problem, the multiple disk clutch brake design problem, and the step-cone pulley problem, and so forth, further validate its effectiveness. Thus, HSMAOA shows strong competitiveness in complex optimization tasks and potential for a wide range of applications, and is an advantageous and promising alternative solution for optimization problems.

Study on the influence of global tourism development on the upgrading of county industrial structure

Taking the global tourism as an important grasp, promoting the tourism to the industrial structure optimization and economic development of the driving force, is conducive to the high-quality development of China's county economy. Based on the panel data of 255 demonstration counties of global tourism in 30 provinces in China from 2012 to 2020, and on the basis of analyzing the correlation between the two, this paper empirically tests the relationship between the development of global tourism and the upgrading of county industrial structure by using static panel model and systematic GMM estimation method. It is found that the development of global tourism has a positive and significant impact on the upgrading of county industrial structure, and there are significant differences in this effect between the eastern, central and western regions of China. Therefore, this paper puts forward some countermeasures and suggestions to promote the upgrading of county industrial structure through the development of global tourism.