Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.     

Study of MW-scale biogas-fed SOFC-WGS-TSA-PEMFC hybrid power technology as distributed energy system: Thermodynamic,exergetic and thermo-economic evaluation

Advanced biogas power generation technology has been attracting attentions, which contributes to the waste disposal and the mitigation of greenhouse gas emissions. This work proposes and models a novel biogas-fed hybrid power generation system consisting of solid oxide fuel cell, water gas shift reaction, thermal swing adsorption and proton exchange membrane fuel cell (SOFC-WGS-TSA-PEMFC). The thermodynamic, exergetic, and thermo-economic analyses of this hybrid system for power generation were conducted to comprehensively evaluate its performance. It was found that the novel biogas-fed hybrid system has a gross energy conversion efficiency of 68.63% and exergy efficiency of 65.36%, indicating high efficiency for this kind of hybrid power technology. The market sensitivity analysis showed that the hybrid system also has a low sensitivity to market price fluctuation. Under the current subsidy level for the distributed biogas power plant, the levelized cost of energy can be lowered to 0.02942 $/kWh for a 1 MW scale system. Accordingly, the payback period and annual return on investment can reach 1.4 year and about 20%, respectively. These results reveal that the proposed hybrid system is promising and economically feasible as a distributed power plant, especially for the small power scale (no more than 2 MW).     

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.     

Towards optimization of the osmotic drying process of alumina-gelatin objects: Regression analysis and verification

In this study, further analysis of the osmotic drying process was conducted to identify the optimum combination of parameters for drying rectangular alumina-gelatin beams. This study was designed to determine the effect of three variables related to the osmotic drying process (osmotic pressure, molecular weight, and immersion time) on the interaction between the liquid desiccant and the submerged alumina-gelatin samples. The water loss from the alumina-gelatin samples was positively correlated with the molecular weight, osmotic pressure, and immersion time. Up to 40% by weight of the initial water content was removed during the osmotic drying process. The samples also experienced solids gain due to the counterflow of solute from the liquid desiccant. The least amount of solids gain resulted from drying for the shortest immersion time at low osmotic pressure and high molecular weight. Evidence of possible interactions between variables was noted for the sintered density metric. Statistical methods were used to form regression equations for the measured responses (water loss, solids gain, bulk density). A verification experiment was conducted to compare the experimental outcomes to the predicted outcomes. The responses were simultaneously optimized to identify the combination of variable settings required to meet specified goals. In order to maximize water loss, minimize solids gain, and maximize bulk density, the ceramic-gelatin object should be immersed for approximately 60?min in an aqueous solution of 100,000?g/mol poly(ethylene oxide) at an osmotic pressure of 2.50?MPa. These values are valid for the range of parameter settings tested and the sample fabrication and drying methods used.     

The roles of conceptual modelling in improving construction simulation studies: A comprehensive review

The conceptual modelling phase of simulation studies has proven to be effective in enhancing the impact of simulation modelling in different domains. However, this simulation phase did not receive much attention in the construction simulation domain. The objective of this paper is to identify the roles that conceptual modelling can play in advancing the engagement, accuracy, and adoption (among other things) of discrete-event simulation studies in construction. In this paper, a Systematic Literature Review (SLR) is conducted, which involves a comprehensive search of databases and researchers’ profiles to identify journal papers, conference articles, books, and theses that have reported the benefits of conceptual modelling for discrete-event simulation studies. The review resulted in 82 documents that were published from 2000 to 2020. Results indicate that the benefits of conceptual modelling include facilitating communications between stakeholders, capturing sufficient information for the simulation model, improving the quality of simulation models, guiding other simulation modelling activities, and facilitating verification and validation of simulation models. By linking these benefits to the current research agenda in construction simulation, this paper shows the significance and potential of the conceptual modelling phase to enhance the impact of discrete-event simulation studies in construction.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.