Optimization Perspective Research Articles

Review of Vibration Analysis and Structural Optimization Research for Rotating Blades

AbstractBlades are important parts of rotating machinery such as marine gas turbines and wind turbines, which are exposed to harsh environments during mechanical operations, including centrifugal loads, aerodynamic forces, or high temperatures. These demanding working conditions considerably influence the dynamic performance of blades. Therefore, because of the challenges posed by blades in complex working environments, in-depth research and optimization are necessary to ensure that blades can operate safely and efficiently, thus guaranteeing the reliability and performance of mechanical systems. Focusing on the vibration analysis of blades in rotating machinery, this paper conducts a comprehensive literature review on the research advancements in vibration modeling and structural optimization of blades under complex operational conditions. First, the paper outlines the development of several modeling theories for rotating blades, including one-dimensional beam theory, two-dimensional plate–shell theory, and three-dimensional solid theory. Second, the research progress in the vibrational analysis of blades under aerodynamic loads, thermal environments, and crack factors is separately discussed. Finally, the developments in rotating blade structural optimization are presented from material optimization and shape optimization perspectives. The methodology and theory of analyzing and optimizing blade vibration characteristics under multifactorial operating conditions are comprehensively outlined, aiming to assist future researchers in proposing more effective and practical approaches for the vibration analysis and optimization of blades.

Probabilistic modeling of renewable energy sources in smart grids: A stochastic optimization perspective

Machine learning (ML) techniques have emerged as powerful tools for optimizing renewable energy management in smart grids. This paper focuses on the application of ML algorithms to enhance the efficiency and reliability of renewable energy integration within smart grid systems. By leveraging predictive analytics, ML models can forecast energy production and consumption patterns, facilitating proactive decision-making for grid operators and energy stakeholders. The abstract explores various ML methodologies such as supervised learning, unsupervised learning, and reinforcement learning, tailored to address specific challenges in renewable energy management. Supervised learning algorithms enable accurate prediction of renewable energy generation, aiding in resource allocation and demand-response strategies. Unsupervised learning techniques facilitate anomaly detection and clustering of energy consumption patterns, contributing to grid stability and optimization. Reinforcement learning algorithms optimize control strategies, enabling autonomous decision-making in dynamic grid environments. The abstract highlights case studies and real-world implementations where ML techniques have demonstrated significant improvements in renewable energy integration, grid reliability, and cost-effectiveness. Through a synthesis of research findings and practical insights, this abstract elucidates the transformative potential of machine learning in shaping the future of smart grids and renewable energy management.

Strategies of regulating Zn2+ solvation structures toward advanced aqueous zinc-based batteries

Rechargeable aqueous zinc-based batteries not only pave the way for environmentally friendly and safe energy storage devices but also hold great promise for reducing the manufacturing costs of next-generation batteries, positioning them as the most promising energy storage system to replace lithium-ion batteries. However, the commercial application of aqueous zinc-based batteries (AZBs) is severely constrained by issues such as zinc dendrites, hydrogen evolution reaction (HER), and electrode corrosion. While significant efforts have been devoted to exploring electrode materials and their storage mechanisms in this system in recent years, research on the nature of Zn2+ solvation in electrolytes remains insufficient. Therefore, this review primarily provides a comprehensive and in-depth elucidation of the existing issues such as dendrite formation, hydrogen/oxygen evolution, electrode corrosion and passivation interactions. Additionally, from the perspective of electrolyte optimization, this review emphasizes existing strategies to address Zn2+ solvation, including adjusting electrolyte concentration, incorporating functional additives or co-solvents, utilizing ionic liquids, and developing hydrogel electrolytes, and summarizes approaches and mechanisms for improving their relevant performance. Finally, prospects for electrolyte modification and innovation directions of AZBs are proposed, providing guidance for future AZB studies.

An SDP relaxation in the complex domain for the efficient coordination of BESS and DGs in single-phase distribution grids while considering reactive power capabilities

This research proposes an efficient energy management system design for medium-voltage distribution networks from a perspective of convex optimization. The exact nonlinear programming problem that represents the operation of distributed energy resources (DERs), such as batteries and photovoltaic sources, is approximated as an equivalent semi-definite programming (SDP) problem. This SDP approximation is achieved in the complex domain using Hermitian matrices, which are the equivalent of positive semi-definite matrices in the real number domain. Two test feeders, one with 27 nodes and the other with 33 nodes, are modified to match the characteristics of Colombian rural and urban networks. The 27-bus grid is adapted to accommodate the average demand and solar generation conditions of the municipality of Capurganá, while those of the city of Medellín are used for the 33-bus grid. One of the main contributions of this research is that it considers a DER operation with variable power factors, which allows for significant improvements compared to the traditional use of a unitary power factor scheme. Numerical comparisons are conducted using metaheuristic optimizers, including parallel versions of the particle swarm optimizer, the vortex search algorithm, and the continuous version of the genetic algorithm. These comparisons demonstrate the effectiveness of the proposed SDP approximation. All simulations are carried out in the MATLAB software (version 2022b), using the disciplined convex optimizer (CVX) and the SDPT3 and SEDUMI solvers.

Integration of proton exchange membrane fuel cell with air gap membrane distillation for sustainable electricity and freshwater cogeneration: Performance, influential mechanism, multi-objective optimization and future perspective

Proton exchange membrane fuel cell (PEMFC) converts vast majority of hydrogen energy into waste heat, resulting in energy wasting, membrane drying and even lifetime shortening. Herein, air gap membrane distillation is proposed to immediately remove and harness the waste heat from PEMFC for additional freshwater production. By quantifying the irreversible thermodynamic and electrochemical losses, mathematical formulas for the water production, power generation and overall efficiency are derived to evaluate the system performance. After a rigorous model validation, the performance feature, feasibility and competitiveness of the proposed system are examined. The integration system achieves a peak power density 23.69 % higher than that of a single PEMFC at 353 K, with a corresponding increase in exergy efficiency by 3.61 %. In addition, the influential mechanisms of the PEMFC operating conditions, air gap thickness, coolant temperature, and properties of the proton exchange membrane and hydrophobic porous membrane are investigated to discern potential avenues for further performance improvement. The gradient-based local sensitivity analyses further determine the optimal parameter regulation strategies for different output targets. The economic study indicates that the levelized costs of water and electricity over the total lifecycle are 21.53 $ m−3 and 0.061 $ kWh−1, respectively. The multi-objective optimization, integrating genetic algorithm and the technique for order preference by similarity to an ideal solution, maximizes the exergy efficiency and output power density while minimizing the initial investment cost per unit area, with values of 42.84 %, 0.975 W cm−2, and 27.33 $ cm−2, respectively.

Discriminating Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Review.

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral-spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral-spatial representations for classification. In this article, the crucial factors to discriminate spectral-spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral-spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.

Flow and heat transfer characteristics of torsional tube cluster heater in a Stirling engine

The heater in a Stirling engine is a component connecting external heat source and internal working medium. Enhancing the heat transfer of the heater is key to improve the actual efficiency of the Stirling engine. Currently, tubular heaters are the most commonly used heat exchangers, and their tube clusters are primarily arranged in a U-shaped. In this study, the heat transfer performance of a novel torsional tube cluster heater was investigated using numerical simulations. The temperature, pressure, and velocity cloud patterns were analysed. The results indicated that the lateral scouring velocity component of the external flue gas was generated using the torsional tube cluster heater without altering the heating chamber volume, thereby prolonging the heating time and flow path of the working medium. The temperature of the outlet working medium was increased by 53 K by using a torsional tube cluster heater, thereby enhancing the work power of the working medium entering the expansion chamber. In addition, the torsional tube cluster heater displayed broad applicability to different working medium types for heat transfer enhancement. This study provides a feasible strategy to improve the practical operation efficiency of a Stirling engine from the perspective of tube cluster optimization of the heater.

An Optimization Perspective on the Interactions Between Models in the Internet of Drones

An Optimization Perspective on the Interactions Between Models in the Internet of Drones

Random Vibration Characteristics and Vibration Suppression of an Aero-engine Inlet Rake Fabricated by Laser Powder Bed Fusion

The inlet rake is generally installed in aero-engine during rig and flight test for flow field pressure and temperature measuring. There are usually complex flow channels and pipelines inside the rake to extract air flow to the sensor. The laser powder bed fusion(L-PBF) technique was applied in rake fabrication recently due to its progressive layer stacking molding procedure. However, the difference of thermoforming method between L-PBF and the traditional casting process leads to the mechanical property disparities of the material. In this work, the determinants of random vibration response of the inlet rake manufactured by L-PBF with GH4169 powder were studied individually. The stiffness and yield property of the material were investigated by the specimen tensile test, which indicates higher elasticity modulus and yield stress comparing to the ones by castling or forging process. The damping ratio of the inlet rake was obtained through stress attenuation characteristics experiment and analysis under stepwise excitation. The stress distribution and resonance margin of the inlet rake under the aero-engine random vibration spectrum were simulated by finite element modal and random vibration analysis. The results showed that the damping ratio of the L-PBF inlet rake is much smaller than engineering recommendations, and the resonance margin is insufficient. Thus, the stress level of the inlet rake was very high, especially in the blade root section, quite close to the yield limit of the material. To solve this problem, the vibration suppression methods of the inlet rake were studied from the perspective of structural design optimization and damping ratio increasing. Through the rational design of the resonance margin and addition of vibration damping structure, the vibration stress of inlet rake was significantly suppressed. As the result, the rake was installed in the inlet during the aero-engine rig test for flow field pressure and temperature measurement successfully.

Chance-constrained programs with convex underlying functions: a bilevel convex optimization perspective

Chance-constrained programs with convex underlying functions: a bilevel convex optimization perspective

Harnessing the Potential of Extracellular Polymeric Substances in Enhancing ANAMMOX Processes: Mechanisms, Strategies, and Perspectives

Anaerobic ammonium oxidation (ANAMMOX) has emerged as a promising sustainable nitrogen removal technology that offers significant advantages over conventional nitrification–denitrification processes, such as reduced energy consumption, a 60% reduction in oxygen demand, and a 90% reduction in sludge production. However, the practical application of ANAMMOX is hindered by several challenges, including the slow growth of ANAMMOX bacteria, long start-up periods, and high sensitivity to environmental disturbances. Recent studies have highlighted the crucial role of extracellular polymeric substances (EPSs) in the formation, activity, and stability of ANAMMOX biofilms and granules. An EPS is a complex mixture of high-molecular-weight polymers secreted by microorganisms, mainly composed of polysaccharides, proteins, nucleic acids, and lipids. The diverse physicochemical properties and functional groups of EPSs enable them to serve as a structural scaffold, protective barrier, sorption site, electron shuttle, and nutrient source for ANAMMOX bacteria. This review aims to provide an overview of the latest research progress on harnessing the potential of EPSs to enhance the ANAMMOX process. The characteristics, compositions, and extraction methods of ANAMMOX-derived EPSs are summarized. The mechanisms of how EPSs facilitate the enrichment, immobilization, aggregation, and adaptation of ANAMMOX bacteria are elucidated. The strategies and effects of EPS supplementation on improving the performance and robustness of ANAMMOX reactors under various stresses are critically reviewed. The challenges and future perspectives of the EPS-mediated optimization of the ANAMMOX process are also discussed. This review sheds new light on exploiting EPSs as a renewable bioresource to develop more efficient and stable ANAMMOX applications for sustainable wastewater treatment.

Ensemble learning for landslide displacement prediction: A perspective of Bayesian optimization and comparison of different time series analysis methods

Ensemble learning for landslide displacement prediction: A perspective of Bayesian optimization and comparison of different time series analysis methods

Ultrasound-assisted preparation of crumb rubber modified asphalt: Characterization and molecular dynamics simulation

The traditional method of crumb rubber modified asphalt (CRMA) preparation can improve the stability but is not conducive to cleaner production. From the perspective of process optimization, ultrasound is introduced into the crumb rubber modified asphalt preparation process, and the high temperature, high pressure and high energy accompanied by the cavitation effect are used to generate free radical triggering chemical effects at the two-phase interface between crumb rubber and asphalt. In this paper, the influence of ultrasound on the compatibility and stability of crumb rubber modified asphalt system was evaluated by combining experiment and molecular dynamics simulation. An experimental platform for ultrasound-assisted preparation of crumb rubber modified asphalt was established. The effects of ultrasonic on the physical properties of asphalt and crumb rubber modified asphalt were studied through Brookfield viscosity test, thin film oven aging test and Fourier infrared spectroscopy. The base, aged, and ultrasonically treated asphalt samples were simulated using molecular dynamics, and various parameters such as Cohesive Energy Density (CED), Mean Square Displacement (MSD), and Free Volume Fraction (FFV) of asphalt and its components were calculated and analyzed. The results showed that under the ultrasonic-assisted preparation, the agglomeration phenomenon of rubber particles is weakened, the particle size is reduced, the penetration degree is reduced, and the compatibility between rubber and asphalt is improved. Under the action of ultrasonic, the MSD and diffusion coefficient of crumb rubber modified asphalt are much higher than other kinds of asphalt, the dipole moment is increased, and the molecular motion range is wider. This indicates that the diffusion ability of crumb rubber modified asphalt is significantly enhanced under the external force of ultrasonic simulation. The molecular bonds of crumb rubber modified asphalt changes greatly, the energy decreases obviously, and the system stability increases. In conclusion, ultrasonic action on crumb rubber modified asphalt preparation is a green and effective way to improve system stability and compatibility.

Hierarchical optimal dispatching method of transmission system operators‐distribution system operators cooperated integrated energy system considering gas‐thermal equivalence model

AbstractThe integrated energy system (IES) is moving towards the large‐scale cooperation between transmission system operators (TSO) and distribution system operators (DSO) with the coupling of electricity, heating, and gas networks. The conventional optimization method is not time‐efficient for solving such a large‐scale problem when taking account of the cooperation of TSO‐DSO of the IES. To solve such a problem, this paper proposed a hierarchical optimization framework for TSO‐DSO cooperated IES. To further improve the computation efficiency, the equivalent models of heating and gas system that retain the heating inertia characteristic and gas pipe storage characteristic are presented. A computation method for the TSO‐DSO cooperated IES is also proposed from the hierarchical distributed optimization perspective. The case study confirms the efficacy of the proposed gas‐thermal equivalent model in significantly enhancing computational speed. Furthermore, the proven effectiveness of the hierarchical distributed optimization approach in leveraging the full potential of flexible resources in transmission and distribution systems while safeguarding internal information privacy enables the development of robust dispatching strategies for each system.

Numerical study on the coupling effect of river attributes and riverside building forms on the urban microclimate: A case study in Nanjing, China

Urban rivers play an important role in mitigating urban heat islands. However, a coupling analysis of river attributes and waterfront architectural forms is lacking. Most existing studies have used a single urban scale and research method and lack comprehensive application. Satellite remote sensing data were used in this study to examine the cooling law of rivers at the macroscale. At the microscale, a microclimate simulation was used to study the influences of river attributes, climatic conditions, and architectural spatial morphological characteristics on the cooling effect of rivers. The river cooling effect is positively correlated with river width, where a temperature drop caused by a 100 m river width can reach 0.75 °C. The cooling effect of rivers is easily influenced by the surrounding built-up areas; this effect is more significant for rivers greater than 40 m wide. In the study of the index of architectural space form, their influence on the thermal environment of the riverside is ranked from high to low: building density, building riverside enclosure degree, building height. The findings provide guidance for the planning and design of riverside buildings from the perspective of thermal environment optimization to construct sustainable cities.

Constructing a MOEA approach for product form Kansei design based on text mining and BPNN

Kansei Engineering (KE) is a product design method that aims to develop products to meet users’ emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using traditional methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user’s multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user’s multiple emotional images. Using the example of electric bicycle form design could show that this proposed method can effectively complete multi-objective product solutions innovation design.

Graph optimization perspective for low-depth Trotter-Suzuki decomposition

Graph optimization perspective for low-depth Trotter-Suzuki decomposition

The impacts of optimization approaches on BEB system configuration in transit

Battery-electric buses (BEBs) are considered suitable technology for transit to tackle climate change and promote environmentally friendly mobility solutions. However, the systemic configuration of BEBs in transit requires sophisticated planning efforts due to contradictory objectives and decisions. The optimal design of a BEB transit system is often approached from various perspectives, leading to different system configurations and distinct impacts on the electricity grid. Towards that end, this study develops three BEB system configuration optimization models, including minimizing capital costs, electricity costs, and greenhouse gas (GHG) emissions. All three models inform the optimal charging system configuration, BEBs battery capacity, and BEBs charging schedule for a general hub-and-spoke transit network. The proposed models are applied to a case study of the Belleville City, Ontario, Canada, bus transit network. The results demonstrate that BEB system configuration and GHG emissions vary significantly according to the optimization perspective. Moreover, the findings emphasize the importance of using the energy storage system to reduce electricity costs and GHG emissions.

CRITICAL ANALYSIS OF STOCK FROM AN OPTIMAL PORTFOLIO PERSPECTIVE

This study aims to conduct a comprehensive evaluation of the shares of Islamic banking companies listed on the Indonesia Stock Exchange (IDX) during the period 2020 to 2022, focusing on the optimal portfolio perspective. The method used in this research is a descriptive quantitative approach. The population studied was all Islamic banking companies listed on the IDX during the period mentioned. The sample was selected using purposive sampling technique, with certain criteria to select Islamic banking companies listed on the IDX during the period 2020 to 2022. As a result, three stocks were selected as samples of this study. This study uses a single index model to analyze the data. The results of the analysis showed that of the three stocks sampled, none met the criteria to be included in the optimal portfolio. These stocks are considered not meeting the standards set because the expected return value is smaller than the systematic risk value. Nonetheless, these findings provide valuable insights into the performance of stocks of Islamic banking companies on the IDX during the study period. In addition, it also highlights the importance of conducting periodic portfolio evaluations to identify potential changes in stock performance and optimize the allocation of financial resources. This study makes an important contribution to the understanding of optimal investment strategies in the context of Islamic banking companies in the Indonesian capital market.

Graph Neural Networks with Soft Association between Topology and Attribute

Graph Neural Networks (GNNs) have shown great performance in learning representations for graph-structured data. However, recent studies have found that the interference between topology and attribute can lead to distorted node representations. Most GNNs are designed based on homophily assumptions, thus they cannot be applied to graphs with heterophily. This research critically analyzes the propagation principles of various GNNs and the corresponding challenges from an optimization perspective. A novel GNN called Graph Neural Networks with Soft Association between Topology and Attribute (GNN-SATA) is proposed. Different embeddings are utilized to gain insights into attributes and structures while establishing their interconnections through soft association. Further as integral components of the soft association, a Graph Pruning Module (GPM) and Graph Augmentation Module (GAM) are developed. These modules dynamically remove or add edges to the adjacency relationships to make the model better fit with graphs with homophily or heterophily. Experimental results on homophilic and heterophilic graph datasets convincingly demonstrate that the proposed GNN-SATA effectively captures more accurate adjacency relationships and outperforms state-of-the-art approaches. Especially on the heterophilic graph dataset Squirrel, GNN-SATA achieves a 2.81% improvement in accuracy, utilizing merely 27.19% of the original number of adjacency relationships. Our code is released at https://github.com/wwwfadecom/GNN-SATA.