Optimal Configuration Research Articles

Day-Ahead Nonlinear Optimization Scheduling for Industrial Park Energy Systems with Hybrid Energy Storage

Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks, while lowering the industrial parks’ carbon emissions and accommodating diverse load demands from users. However, most optimization research on hybrid energy storage has adopted rule-based passive-control principles, failing to fully leverage the advantages of active energy storage. To address this gap in the literature, this study develops a detailed model for an industrial park energy system with hybrid energy storage (IPES-HES), taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks. An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day. An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm II (NSGA-II). A method using the improved NSGA-II is developed for day-ahead nonlinear scheduling, based on configuration optimization. The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD (5.5%) and 1600.3 kW (43.7%), respectively, compared with an operation strategy based on proportional electricity storage on a typical summer day. Overall, the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.

3D Tumor-Engineered Model Replicating the Osteosarcoma Stem Cell Niche and In Vivo Tumor Complexity.

Osteosarcoma, among all bone sarcomas, remains a challenge despite the unwavering efforts of medical professionals and scientists. To address this, the scientific community is actively pursuing the development of three-dimensional (3D) in vitro models to faithfully replicate the heterogeneity of osteosarcoma, thereby facilitating the reliable preclinical screening of potential therapies. In this study, we present the latest advancements in engineering an in vitro 3D osteosarcoma model comprising enriched Cancer Stem Cells (CSCs) and a hybrid hydroxyapatite-based scaffold (MgHA/CoII). The improvement of the model occurred through two primary steps: (1) serial passaging of sarcospheres as the CSCs enrichment system and (2) the optimization of the structural configuration of the niche in the scaffold. Two injection-mediated approaches of sarcosphere seeding were designed and extensively characterized in vitro and in vivo Chorioallantoic Membrane (CAM) models to explore their biological properties and tumorigenic potential. The combination of the selected enriched-CSCs and custom-made seeding into the scaffold resulted in the development of 3D osteosarcoma models exhibiting tumor-like features in vitro and tumorigenic properties in vivo. The outcomes of this study offer prospects for future endeavors involving more complex systems capable of replicating specific malignant tumor behaviors (metastatic process and drug resistance), pushing the discovery of new therapeutic strategies for clinical applications.

Machine learning driven building integrated photovoltaic (BIPV) envelope design optimization

By integrating renewable energy systems (RESs) into buildings, Building-Integrated Photovoltaics (BIPV) reshape the demand–supply relationship, offering substantial benefits such as reduced energy usage, lower greenhouse gas emissions, improved indoor comfort, and architectural enhancement. However, designing optimal BIPV systems involves balancing multiple parameters and conflicting objectives, making it a complex, computationally intensive process. This study proposes a data-driven optimization framework to enhance BIPV envelope design during the detailed design phase. The novelties of this research lie in 1) the use of Artificial Neural Networks (ANN) as a surrogate model for rapid prediction, and 2) a multi-objective optimization (MOO)framework tailored for BIPV design at the detailed design phase. The proposed framework integrates ANN-based predictive modelling with an NSGA-II optimization component to generate optimal BIPV configurations efficiently. Implemented in Python, this approach is validated through a benchmark case study in Melbourne, demonstrating its effectiveness in reducing computational demands while achieving balanced energy, economic, and indoor comfort performance. This research advances sustainable building design by addressing gaps in current optimization frameworks and providing practical tools for architects and designers, promoting the wider adoption of BIPV solutions with significant computational savings.

Research on click enhancement strategy of hand-eye dual-channel human-computer interaction system: Trade-off between sensing area and area cursor

This study aims to explore the application of click enhancement strategies in a “Sight Line Localization + Hand Triggered” eye-control human–computer interaction system, proposes a click enhancement strategy to solve two critical problems in eye-control human–computer interaction: Midas touch and low spatial accuracy. By conducting ergonomics experiments, we verify that the proposed click enhancement strategy can effectively improve the operational performance of hand-eye dual-channel HCI systems. The experimental results show that the accuracy of the operation can be significantly improved by using a cursor size equal to the diameter of the interaction control and a sensing area size of 1.8 times the diameter of the control. Based on the comprehensive consideration of operation efficiency and comfort, 0.75 times the control diameter of the cursor and 1.8 times the control diameter of the sensing area are the optimal parameter configurations. The results not only solve the problems of Midas touch and low spatial accuracy but also significantly reduce visual fatigue, thus improving the ease of use and robustness of the hand-eye dual-channel human–computer interaction system.

Net-zero carbon emission oriented Bi-level optimal capacity planning of integrated energy system considering carbon capture and hydrogen facilities

The integrated energy system (IES) is considered an efficient energy provision paradigm to improve the utilization efficiency of renewable generation (RG), reduce energy cost and improve energy supply reliability. However, the multi-dimensional uncertainties and the hydrogen utilization have posed enormous challenges to the optimal planning of the IES. This paper proposes a net-zero carbon emission oriented bi-level optimal planning model for the IES with full consideration of the multiple operational uncertainties in the IES (RG, multiple demands). The upper level is an optimal capacity configuration model, and the economic costs, energy selling profits, the penalty for power abandonment and energy supply shortage are settled as the objectives. The lower level is an optimal energy dispatch model with the objective function of minimizing the total operating cost of the IES on typical days. An integrated energy system framework consisting of multiple energy flows is formulated in the presence of carbon capture system (CCS) and hydrogen-to-power (H2P) facilities. To fully characterize the uncertainties of RG and multiple demands, a data-driven scenario generation method is adopted to expand the operational scenarios, in which probability distribution assumptions and prior knowledge are not necessary. Finally, the effectiveness of the proposed solution is extensively demonstrated through numerical simulation, and a sensitivity analysis is carried out to assess the impact of changes in electricity and gas prices on the IES planning results.

Research on the collaborative operation strategy of shared energy storage and virtual power plant based on double layer optimization

Large-scale access to distributed energy resources leads to new energy consumption problems and safe operation risks in the power system. Virtual power plants and shared energy storage are effective ways to promote the flexible consumption of distributed energy resources and improve the reliability and economy of power system operation. Based on the concept of sharing economy and considering the complementary characteristics of source and load resources between different virtual power plants, this paper focuses on the optimisation of shared energy storage and multi-virtual power plant operation. Firstly, distributed wind power, distributed photovoltaic and flexible load resources are aggregated into virtual power plants to analyze the cooperative operation mode of shared energy storage and multi-virtual power plant systems. Secondly, a two-layer decision model for shared energy storage configuration and multi-VPP system operation optimisation is constructed, with the upper model solving the optimal energy storage configuration scheme by maximising the revenue of the shared energy storage operator, and the lower model optimising the multi-VPP system operation strategy by minimising the total operation cost, the maximum amount of new energy consumed, and the minimum amount of carbon emission as the multi-objective optimisation strategy. Finally, a simulation analysis is carried out, and the results show that compared with the independent operation mode of each virtual power plant, the model proposed in this paper increases the annual profit of the shared energy storage operator by 7180¥, reduces the operating cost of the VPP system by 7.08 %, improves the rate of renewable energy consumption by 0.82 %, and reduces the annual carbon emission by 54.91 t, which realizes the mutual benefits of the virtual power plant and the shared energy storage.

An innovative guanidinium-based ionic covalent organic frameworks with abundant aromatic rings for high-efficiency naproxen removal: Adsorption behavior, mechanisms, and density functional theory calculations

In recent years, naproxen (NPX) has raised significant environmental concerns requiring urgent attention. This study presents an innovative guanidinium-based ionic covalent organic framework (TFPB-TgCl), containing abundant aromatic rings, which was successfully synthesized via Schiff base reaction for the efficient and selective removal of NPX. Remarkably, TFPB-TgCl manifested a maximal NPX adsorption capacity of 308 mg/g at pH = 5. The adsorption on the active site (AOAS) model highlighted the simultaneous occurrence of both physical and chemical adsorption during the NPX uptake. The coefficients of the physical adsorption rates (5.6479, 1.9224, and 1.9224 min−1 for NPX concentrations of 20, 50, and 100 mg/L, respectively) were significantly higher than those of the chemical adsorption rates (0.1759, 0.0681, and 0.0567 g·mg−1·min−1 for the same concentrations), indicating the predominance of physical adsorption. Furthermore, the multilayer adsorption (MLA) model suggested that an adsorption site can interact with more NPX molecules, benefiting from various electrostatic and π-π interactions. The negative values of enthalpy change (ΔH° = −11.956 kJ/mol) and Gibbs free energy (ΔG < 0) confirmed the exothermic and spontaneous nature of the adsorption process. Additionally, in the optimal adsorption configuration (−131.57 kcal/mol), the Independent Gradient Model based on Hirshfeld partition (IGMH) was utilized to identify interaction sites between the guanidinium group of TFPB-TgCl and the carboxyl group of NPX, with subsequent electrostatic potential (ESP) analysis emphasizing their crucial role in electrostatic adsorption. Overall, the adsorption mechanism of electrostatic and π-π interactions in TFPB-TgCl highlights the significant contribution of its guanidinium and aromatic rings in facilitating NPX adsorption.

Data-Driven Decision-Making for Flexible Natural Gas Allocation Under Uncertainties: An Agent-Based Modelling Approach

Despite the anticipated growth in the global demand for energy commodities, the frequently changing market dynamics imposed by environmental regulations and political sanctions create end-user demand uncertainties. This imposes the need for prompt quantitative decision-making approaches to understand how various market structures affect the planning of current natural gas projects. Agent-based modelling (ABM) emerges as a powerful approach to facilitate expedited and well-informed decisions amidst limited timeframes. This study deploys agent-based modelling to investigate natural gas allocation across various utilisation routes under diverse economic and environmental scenarios. Results from four main cases and two sub-scenarios imply that the allocation strategy is driven by utilisation routes considered in each case, followed by the allocation target (i.e., economic or environmental) and the operational bounds. The results reveal that cases prioritising natural gas monetisation for export outperform those meeting power requirements in average annual profitability. In case 4, considering a full network with power, the average annual profitability in the economic scenario reduces by approximately 47% compared to case 3, representing the optimal network configuration with $5.22 billion in average annual profitability. However, the economic scenario of case 3 demonstrates the second-highest rate of emissions (0.66 CO2-eq t/y), following the hydrogen-rich process routes in case 2. Overall, this study presents an innovative data-driven framework for enhancing strategic resource allocation in dynamic business environments. By integrating empirical evidence and technical data with an advanced technical tool (i.e., ABM), the framework provides decision-makers and policymakers with valuable insights for managing uncertainties and shifts in market structures, particularly in existing natural gas projects.

Structured pruning of neural networks for constraints learning

In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity in applications such as cancer treatment, algorithmic configuration, and chemical process optimization. This integration often uses Mixed Integer Programming (MIP) formulations to represent the chosen ML model, that is often an Artificial Neural Networks (ANNs) due to their widespread use. However, ANNs frequently contain a large number of parameters, resulting in MIP formulations impractical to solve. In this paper we showcase the effectiveness of a ANN pruning, when applied to models prior to their integration into MIPs. We discuss why pruning is more suitable in this context than other ML compression techniques, and we highlight the potential of appropriate pruning strategies via experiments on MIPs used to construct adversarial examples to ANNs. Our results demonstrate that pruning offers remarkable reductions in solution times without hindering the quality of the final decision, enabling the resolution of previously unsolvable instances.

Analysis of the effects of inclination and configuration of the electroosmotic field on the cooling performance of a microchannel

The study investigates the effect of using electroosmotic pumps on the cooling of electrical devices in micro scales. The mutual effects of the microchannel inclination (ranging from 0° to 75°) and configuration of the electric field on the heat transfer have not been investigated. To this end, a numerical code based on the finite volume method (FVM) and Semi-Implicit Method for Pressure Linked Equations (SIMPLE) was developed in Fortran to model the two-dimensional flow dynamics and heat transfer. Two different arrangements were considered for the discrete heat sources and electroosmotic fields to examine their effects on fluid dynamics and heat transfer rate at Re=10. In addition, the effects of electrical parameters, which directly affect the flow dynamics, were also considered. Results indicate that decreasing the heat transfer rate at higher angles is because of the velocity mitigation, whereas an increase in the Grashof number causes a reverse effect. Altering the layout of heaters and electric field from the condition in which heat sources are facing each other (Arrangement 1) to the condition in which heat sources are not facing each other (Arrangement 2), leads to the formation of swirling flow, increased flow rate, and decreased average Nusselt number. The optimum configuration for maximum cooling performance is found in Arrangement 1 with the Grashof number of 0 and inclination angle of 0°, in which the highest average Nusselt number of 5.815 is achieved. Despite the reduction in cooling efficiency at higher angles, Arrangement 1 outperforms Arrangement 2.

IMPLEMENTASI MACHINE LEARNING ALGORITMA NEURAL NETWORK DALAM MEMPREDIKSI HARGA SAHAM MENGGUNAKAN RAPIDMINER

The research is aimed at implementing the Neural Network algorithm in predicting stock prices using RapidMiner. Historical stock price data is analyzed to train neural network models in identifying patterns and trends that can be used to predict future stock prices. The research phases include data collection, data preprocessing, Neural Network model development, model training, and model performance evaluation. Data used include daily closing prices, trade volumes, and other technical indicators. The data is processed through several stages of preprocesing, including normalization and filling of missing values, before being used to train Neural network models. The model was built with several layers and the number of neurons adjusted based on early experiments to find the optimal configuration. Models are trained using datasets that are divided into training sets and testing sets. The predicted result is compared to the actual value to evaluate the performance of the model. The results showed that the Neural Network model was able to predict stock prices with a fairly high accuracy rate, with an RMSE of 0.989 indicating a low prediction error rate. The research concludes that the Neural Network algorithm implemented in RapidMiner is effective in predicting stock prices and has significant potential to be used in investment decision-making. The use of RapidMiner as a data processing tool has proven to be efficient and facilitating the development and evaluation of predictive models. Keywords: Neural Network, Stock Price Prediction, RapidMiner, Machine Learning, Investment

An MINLP-based decision-making tool to help microbreweries improve energy efficiency and reduce carbon footprint through retrofits

Microbreweries have greater production costs per litre of beer compared to large breweries, as well as higher carbon footprints. Due to the range of different retrofit technologies available and the different capacities and configurations of microbreweries, it is not always clear what retrofits will improve operations. Therefore, this work proposes a novel mixed-integer nonlinear programming decision-making tool to be used by any microbrewery, that determines the technoeconomic feasibility and sizing of energy efficiency-improving retrofits, including solar and wind power, battery storage, anaerobic digestion, boiler type selection, heat integration by heat storage, and carbon capture via dual-function materials. The model was demonstrated on a real UK microbrewery case study. The model gave an optimal configuration of a 10 m3 anaerobic digester, 30 solar panels outputting 380 W each, an 800 W wind turbine and a 2.3 m3 heat storage tank, reducing annual operating costs by 62.9 % and carbon dioxide emissions by 77.1 % with a payback period of 8 years. The tool is designed to be flexible for use by any microbrewery in any location with any brewing recipe and allow the owner(s) to develop more profitable and sustainable microbreweries.Tweetable abstractMicrobreweries can implement mathematically optimised renewable energy, heat integration and anaerobic digestion to reduce operating costs by 62.9 % and carbon emissions by 77.1 %.

A novel heat load prediction model of district heating system based on hybrid whale optimization algorithm (WOA) and CNN-LSTM with attention mechanism

Machine learning models, particularly long short-term memory (LSTM) networks, have been extensively employed for heat load prediction in district heating systems (DHS). Nevertheless, the over-reliance on default hyperparameter settings in most methods hinders further enhancement of prediction accuracy. A novel load prediction model is presented, which integrates the whale optimization algorithm (WOA) to refine the hyperparameters of an LSTM model bolstered by an attention mechanism (ATT) and convolutional neural network (CNN). Three hybrid models (WOA-CNN-ATT-LSTM, PSO-CNN-ATT-LSTM and GA-CNN-ATT-LSTM) are constructed by comparing WOA with particle swarm optimization (PSO) and genetic algorithm (GA). The proposed hybrid models are evaluated against traditional LSTM models using an 1100-hour dataset from a real DHS. The outcomes reveal that the WOA-CNN-ATT-LSTM model surpasses both the PSO-CNN-ATT-LSTM and GA-CNN-ATT-LSTM models in heat load prediction accuracy, achieving improvements of 1.9% and 3.2% respectively, and attaining the highest prediction accuracy (R2=0.9962, MSE=0.0001, MAE=0.0082). Moreover, the WOA-CNN-ATT-LSTM model demonstrates superior performance across various time scales (half-day, one-day, three-days, and one-week), highlighting its robustness in heat load prediction. This novel model adaptively adjusts its hyperparameters to identify the optimal configuration, thereby significantly augmenting the overall predictive capabilities of the model.

Fouling fault detection and diagnosis in district heating substations: Validation of a hybrid CNN-based PCA model with uncertainty quantification on virtual replica synthesis and real data

This research presents an advanced fouling fault detection (FFD) model tailored to district heating substations, integrating convolutional neural networks, principal component analysis, and uncertainty quantification (UQ). Through careful hyperparameter tuning, the model attained optimal configurations, showcasing accuracy metrics ranging from 96.58 % to 98.19 %. Key findings include the influence of input vectors, particularly the incorporation of overall heat transfer coefficient (UA), which contributed to heightened accuracy and precision. Visualizing predictive uncertainty emphasized the model's adaptability to dynamic conditions, crucial in scenarios involving progressive fault evolution. Generalizability analysis across diverse substations affirmed the model's adaptability, surpassing existing algorithms. Comparative evaluation against recent models underscored the proposed model's superiority, with an accuracy range exceeding 96.58 %. This research introduces UQ as a unique advantage, offering a state-of-the-art solution for FFD in dynamic systems. The model's validation on real data further solidifies its efficacy.

Proposing design strategies for contemporary courtyards based on thermal comfort in cold and semi-arid climate zones

The relationship between urban geometry and microclimate is crucial in urban planning and climatology, significantly affecting outdoor comfort and energy consumption. Compact, dense buildings provide shading in summer, while openness is preferred in winter to maximize sunlight access. Courtyards, as climate-responsive structures, can create localized microclimates tailored to specific climatic conditions. Various components, including geometry, openness, orientation, wall materials, and green cover, influence the microclimate within a courtyard. While previous studies have primarily focused on hot and arid climates, there is a notable research gap concerning the thermal behavior of courtyards in cold and semi-arid regions. This study seeks to fill this gap by evaluating the impact of different courtyard geometries on outdoor thermal comfort specifically in semi-dry and cold climates by exploring optimal configurations for orientation, form, height-to-width ratios, and plan aspect ratios, the research offers strategies that improve both summer and winter microclimates. The study examined the combined effects of geometry and orientation on shading and wind speed in courtyards, modeling their influence on microclimates during warm summers and cold winters using ENVI-met software. Thermal performance indices, specifically PET and UTCI, were employed to assess the courtyards in Tehran. The findings reveal that a height-to-width ratio of 3:1 significantly improves thermal comfort, resulting in a UTCI difference of 19 °C. Furthermore, the model with a 2:1 plan aspect ratio exhibited a UTCI that was 6.7 °C lower than that of the 3:1 ratio. These findings provide valuable insights for designing contemporary courtyards that optimize thermal comfort in similar climates.

Utilizing novel industrialized heat exchanger plate in air-based photovoltaic/thermal collectors to enhance thermal and electrical efficiency

This study aims to design a highly efficient and applicable air-based photovoltaic-thermal (PVT) collector that maximizes both electrical and thermal energy efficiencies. An innovative industrialization-ready configuration of the air-based PVT system is proposed, utilizing an industrialized heat exchanger (GRIPMetal or GM) as the absorber plate for the PVT air channel. The heat exchanger consists of spikes and cavities to enhance the heat transfer coefficient in the air channel. The proposed heat exchanger plate minimally affects the dimensions and weight of the PVT collector. A numerical model, validated against experimental results, is used to ensure the accuracy of the simulation. The study is followed by a parametric study that investigates the geometric effects of the heat exchanger and air channel, as well as the airflow rate, on the overall performance of the PVT system. It is observed that the utilization of GM plates significantly reduces the average PV panel temperature (with a maximum of 28 ℃ reduction) and enhances the convective heat transfer coefficient in the air channel, the electrical and thermal efficiencies by approximately 164%, 16.1%, and 50%, respectively, when compared to a flat plate PVT collector. The results demonstrate that the proposed PVT collector effectively compensates for the pressure drops and excess fan power consumption at low Reynolds numbers due to the GM heat exchanger, resulting in higher overall system efficiency. The optimal configuration for the proposed PVT system is achieved by employing a low airflow rate, a narrow air channel, and GM spikes of the largest size available.

Reliability-constrained configuration optimization for integrated power and natural gas energy systems: A stochastic approach

With the escalating dependence on electricity and natural gas infrastructure, ensuring both reliability and economic efficiency becomes paramount. It necessitates reliability centric measures to mitigate disruptions that could cascade between these interconnected systems. To address this challenges, this paper introduces a reliability-constrained two-stage stochastic model to optimize power-to-gas (P2 G) and gas-to-power (G2P) unit placement and sizing, aiming to enhance the reliability of both systems under stochastic scenarios. The proposed model, employing Sequential Monte Carlo (SMC) within its optimization framework, seeks to minimize investment, operation, and reliability costs. By addressing temporal uncertainties in component outages for both systems and considering uncertainties in power and gas system loads with a high temporal resolution and annual load growth, the model provides a comprehensive reliability perspective. Furthermore, sensitivity analysis is conducted to explore the impact of varying Values of Lost Load (VOLL) on the planning results. Numerical evaluation, using two integrated energy systems including IEEE 14-bus-10-gas node, and large-scale energy systems including IEEE 118-bus-85-gas node integrated power-gas system (IPGS), demonstrates a significant 12.53 % improvement in overall system reliability. Furthermore, a 2.81 % reduction in operation costs and a substantial 26.3 % reduction in reliability costs, validating the effectiveness of the proposed model.

Numerical modeling and optimization of fillet height of reinforced SAC305 solder joint in an ultra-fine capacitor assembly

PurposeThis study aims to investigate the design configuration for an optimum solder height of reinforced SAC305 solder joint in an ultra-fine capacitor assembly.Design/methodology/approachA multiphase finite volume model is developed for reflow soldering simulations to determine the fillet height of reinforced SAC305 solder joint in an ultra-fine capacitor assembly. Different solders, namely SAC305-x, SAC305-xNiO and SAC305-xTi, with varying percentage weight compositions of nanoparticles (x = 0 Wt.%, 0.01 Wt.%, 0.05 Wt.%, 0.10 Wt.%, 0.15 Wt.%) are investigated. A reflow soldering experiment is also conducted, and the cross-sections of the reflowed packages are examined using a High-Resolution Transmission Electron Microscope (HRTEM). The optimum design configurations (nanoparticle composition and material) for the solder fillet height are investigated using the Taguchi orthogonal array method.FindingsGood correlations were recorded between the HRTEM micrographs and the numerical predictions of the nanoparticles' distribution in the molten solder. The numerical prediction of the fillet height also agrees with the experiment, with a maximum disparity of 5.43%. It was found that Ti nanoparticles, having the smallest density compared to NiO and, exhibit the highest buoyancy effect in the molten solder. The Taguchi analysis revealed that the nanoparticles' material factor is more significant than the Wt.% factor for an optimum fillet height. An optimum design configuration for fillet height was established as SAC 305–0.15 Wt.% Ti, corresponding to a 41.13% improvement of the plain SAC 305 solder.Practical implicationsThe fillet height of solder joints greatly influences the solder joint reliability of miniaturized electronic packages. Solder joint reliability of ultra-fine capacitors can be improved using this study's findings on the optimum design configuration for the capacitor's solder fillet. The study’s findings can be practically implemented in industries such as electronics manufacturing, where enhanced solder joint reliability is critical.Originality/valueInvestigation of the optimum design configuration for reinforced SAC305 solder fillet is almost nonexistent in the literature. This study explored the optimization of fillet height of reinforced SAC305 solder joints in miniaturized capacitor assembly.

Stress-constrained concurrent multiscale topological design of porous composites based on discrete material optimisation

Porous composites have attracted increasing attention in recent decades. This study develops a concurrent multiscale topology optimisation (CMTO) method under a prescribed stress constraint for designing porous composites with multi-domain microstructures. First, to address the difficulty of predicting local stress due to varying of microstructural type throughout the optimisation process, a continuous and differentiable stress measure is proposed to effectively approximate the local stress. Second, an inverse homogenisation method based on isogeometric analysis (IGA) is developed to improve the accuracy of stress prediction, and then it is integrated into a CMTO which is developed based on the discrete material optimisation (DMO) interpolation scheme. Third, a stress constraint which is differentiable with respect to both macro and micro design variables is proposed to enable the stress-constrained concurrent optimisation of the macrostructural configuration, microstructural configuration and distribution. Fourth, a novel post-processing approach is established to achieve smooth while volume preserving contour of unit cells with layouts. Finally, two benchmark design examples, namely l-bracket and Crack problems, are implemented using the presented CMTO under a global stress constraint to demonstrate the effectiveness of the proposed method. The result indicates that the proposed method can effectively decrease the stress concentration via three design manners, i.e., the macrostructural configuration, microstructural configuration and distribution. Also, an “interface-enlarging” phenomenon was interestingly but reasonably found in those cases when subjected to stress-constraint considerations.

Optimal robust configuration in cloud environment based on heuristic optimization algorithm

To analyze performance in cloud computing, some unpredictable perturbations that may lead to performance degradation are essential factors that should not be neglected. To prevent performance degradation in cloud computing systems, it is reasonable to measure the impact of the perturbations and propose a robust configuration strategy to maintain the performance of the system at an acceptable level. In this article, unlike previous research focusing on profit maximization and waiting time minimization, our study starts with the bottom line of expected performance degradation due to perturbation. The bottom line is quantified as the minimum acceptable profit and the maximum acceptable waiting time, and then the corresponding feasible region is defined. By comparing between the system’s actual working performance and the bottom line, the concept of robustness is invoked as a guiding basis for configuring server size and speed in feasible regions, so that the performance of the cloud computing system can be maintained at an acceptable level when perturbed. Subsequently, to improve the robustness of the system as much as possible, discuss the robustness measurement method. A heuristic optimization algorithm is proposed and compared with other heuristic optimization algorithms to verify the performance of the algorithm. Experimental results show that the magnitude error of the solution of our algorithm compared with the most advanced benchmark scheme is on the order of 10−6, indicating the accuracy of our solution.