Effect of finite heat input on the power performance of micro heat engines

Micro heat engines have attracted considerable interest in recent years for their potential exploitation as micro power sources in microsystems and portable devices. Thermodynamic modeling can predict the theoretical performance that can be potentially achieved by micro heat engine designs. An appropriate model can not only provide key information at the design stage but also indicate the potential room for improvement in existing micro heat engines. However, there are few models reported to date which are suitable for evaluating the power performance of micro heat engines. This paper presents a new thermodynamic model for determining the theoretical limit of power performance of micro heat engines with consideration to finite heat input and heat leakage. By matching the model components to those of a representative heat engine layout, the theoretical power, power density, and thermal efficiency achievable for a micro heat engine can be obtained for a given set of design parameters. The effects of key design parameters such as length and thermal conductivity of the engine material on these theoretical outputs are also investigated. Possible trade-offs among these performance objectives are discussed. Performance results derived from the developed model are compared with those of a working micro heat engine (P3) as an example.     

A novel cellular automata based approach to storm sewer design

Optimal storm sewer design aims at minimizing capital investment on infrastructure whilst ensuring good system performance under specified design criteria. An innovative sewer design approach based on cellular automata (CA) principles is introduced in this paper. Cellular automata have been applied as computational simulation devices in various scientific fields. However, some recent research has indicated that CA can also be a viable and efficient optimization engine. This engine is heuristic and largely relies on the key properties of CA: locality, homogeneity, and parallelism. In the proposed approach, the CA-based optimizer is combined with a sewer hydraulic simulator, the EPA Storm Water Management Model (SWMM). At each optimization step, according to a set of transition rules, the optimizer updates all decision variables simultaneously based on the hydraulic situation within each neighbourhood. Two sewer networks (one small artificial network and one large real network) have been tested in this study. The CA optimizer demonstrated its ability to obtain near-optimal solutions in a remarkably small number of computational steps in a comparison of its performance with that of a genetic algorithm.     

Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm.

In order to successfully calibrate an urban drainage model, multiple calibration criteria should be considered. This raises the issue of adopting a method for comparing different solutions (parameter sets) according to a set of objectives. Amongst the global optimization techniques that have blossomed in recent years, Multi Objective Genetic Algorithms (MOGA) have proved effective in numerous engineering applications, including sewer network modelling. Most of the techniques rely on the condition of Pareto efficiency to compare different solutions. However, as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well. The pitfalls are twofold: the efficiency of the genetic algorithm search worsens and decision makers are presented with an overwhelming number of equally optimal solutions. This paper proposes a new MOGA, the Preference Ordering Genetic Algorithm, which alleviates the drawbacks of conventional Pareto-based methods. The efficacy of the algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model and the results are compared with those obtained by NSGA-II, a widely used MOGA.     

High-Performance Integrated Optics with Tellurite Glasses: Status and Prospects

Tellurite glasses have long been recognized as interesting materials for fiber and integrated optics, but it is only recently that the first steps toward high-performance planar devices have been taken. This study reviews the opportunities for planar tellurite devices, the current state of the art of planar waveguide devices, and considers the areas of research required to move the materials platform to the level of maturity needed to realize its promise.     

From single-objective to multiple-objective multiple-rainfall events automatic calibration of urban storm water runoff models using genetic algorithms.

The calibration of storm water runoff models is a complex task. Early attempts focused on the choice of a performance criterion function that could capture all the facets of the problem into a single-objective framework. Subsequently, the awareness that a good calibration must necessarily take into account conflicting objectives led to the adoption of more sophisticated multi-objective approaches. Only recently, the focus has shifted towards effective ways of exploiting the mounting information provided by the availability of many sets of concurrent rainfall and flow measurements. This paper revisits through a case study the transition just elucidated: the calibration of a SWMM model applied to a catchment in Singapore is tackled through a single-objective, a multi-objective and a multi-objective multiple-event (MOME) paradigm respectively. A new approach to support the latter is presented herein. It consists in formulating the problem of model calibration as a multi-objective problem with m x r objective functions, where m and r are the number of performance criteria and rainfall events respectively, that must be optimized simultaneously. Results suggest that the new MOME framework performs significantly better than the others tested on the case study presented.     

An integrated modelling approach for the assessment of land use change effects on wastewater infrastructures

The simulation of sewage systems and wastewater treatment plants is strategic for assessing the effect of new dwellings on the existing water facilities. This paper introduces an integrated framework made by a land use change model, a sewage system simulator, and a wastewater treatment plant simulator. This is a complex system since each element is characterized by different dynamics. The land use change model simulates the annual expansion of an urban area according to planners’ guidelines; the sewage system simulator investigates the response of the drainage system to the expansion. The wastewater treatment plant is simulated in order to assess the impact of the new outflows on the existing plant. The three models are integrated into a Simulink model. Two components of the developed framework are based on models well established in literature. The proposed framework is tested on a simple case study of a small town located in south west of Scotland.     

Impact of HIV Infection and Anti-Retroviral Therapy on the Immune Profile of and Microbial Translocation in HIV-Infected Children in Vietnam

CD4⁺ T-lymphocyte destruction, microbial translocation, and systemic immune activation are the main mechanisms of the pathogenesis of human immunodeficiency virus type 1 (HIV) infection. To investigate the impact of HIV infection and antiretroviral therapy (ART) on the immune profile of and microbial translocation in HIV-infected children, 60 HIV vertically infected children (31 without ART: HIV(+) and 29 with ART: ART(+)) and 20 HIV-uninfected children (HIV(−)) aged 2–12 years were recruited in Vietnam, and their blood samples were immunologically and bacteriologically analyzed. Among the HIV(+) children, the total CD4⁺-cell and their subset (type 1 helper T-cell (Th1)/Th2/Th17) counts were inversely correlated with age (all p < 0.05), whereas regulatory T-cell (Treg) counts and CD4/CD8 ratios had become lower, and the CD38⁺HLA (human leukocyte antigen)-DR⁺CD8⁺- (activated CD8⁺) cell percentage and plasma soluble CD14 (sCD14, a monocyte activation marker) levels had become higher than those of HIV(−) children by the age of 2 years; the CD4/CD8 ratio was inversely correlated with the plasma HIV RNA load and CD8⁺-cell activation status. Among the ART(+) children, the total CD4⁺-cell and Th2/Th17/Treg-subset counts and the CD4/CD8 ratio gradually increased, with estimated ART periods of normalization being 4.8–8.3 years, whereas Th1 counts and the CD8⁺-cell activation status normalized within 1 year of ART initiation. sCD14 levels remained high even after ART initiation. The detection frequency of bacterial 16S/23S ribosomal DNA/RNA in blood did not differ between HIV-infected and -uninfected children. Thus, in children, HIV infection caused a rapid decrease in Treg counts and the early activation of CD8⁺ cells and monocytes, and ART induced rapid Th1 recovery and early CD8⁺-cell activation normalization but had little effect on monocyte activation. The CD4/CD8 ratio could therefore be an additional marker for ART monitoring.     

给水管网中氯衰减速率系数可变模型的研究

综述了国内外氯衰减模型的研究现状。针对目前研究模型参数随加氯量和二次加氯变化的缺点,提出一种新的氯衰减模型——速率系数可变模型(Variable Rate Coefficient,VRC)。该模型可适应较大的加氯量变化范围和二次加氯的情况。     

Efficient multi-objective optimal design of water distribution networks on a budget of simulations using hybrid algorithms

The design of water distribution networks is a large-scale combinatorial, non-linear optimisation problem, involving many complex implicit constraint sets, such as nodal mass balance and energy conservation, which are commonly satisfied through the use of hydraulic network solvers. These problem properties have motivated several prior studies to use stochastic search optimisation, because these derivative-free global search algorithms have been shown to obtain higher quality solutions for large network design problems. Global stochastic search methods, however, require many iterations to be performed in order to achieve a satisfactory solution, and each iteration may involve running computationally expensive simulations. Recently, this problem has been compounded by the evident need to embrace more than a single measure of performance into the design process, since by nature multi-objective optimisation methods require even more iterations. The use of metamodels as surrogates for the expensive simulation functions has been investigated as a possible remedy to this problem. However, the identification of reliable surrogates is not always a viable alternative. Under these circumstances, methods that are capable of achieving a satisfactory level of performance with a limited number of function evaluations represent a valuable alternative. This paper represents a first step towards filling this gap. Two recently introduced multi-objective, hybrid algorithms, ParEGO and LEMMO, are tested on the design problem of a real medium-size network in Southern Italy, and a real large-size network in the UK under a scenario of a severely restricted number of function evaluations. The results obtained suggest that the use of both algorithms, in particular LEMMO, could be successfully extended to the efficient design of large-scale water distribution networks.     

An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization

It may be generalized that all Evolutionary Algorithms (EA) draw their strength from two sources: exploration and exploitation. Surprisingly, within the context of multiobjective (MO) optimization, the impact of fitness assignment on the exploration-exploitation balance has drawn little attention. The vast majority of multiobjective evolutionary algorithms (MOEAs) presented to date resort to Pareto dominance classification as a fitness assignment methodology. However, the proportion of Pareto optimal elements in a set P grows with the dimensionality of P. Therefore, when the number of objectives of a multiobjective problem (MOP) is large, Pareto dominance-based ranking procedures become ineffective in sorting out the quality of solutions. This paper investigates the potential of using preference order-based approach as an optimality criterion in the ranking stage of MOEAs. A ranking procedure that exploits the definition of preference ordering (PO) is proposed, along with two strategies that make different use of the conditions of efficiency provided, and it is compared with a more traditional Pareto dominance-based ranking scheme within the framework of NSGA-II. A series of extensive experiments is performed on seven widely applied test functions, namely, DTLZ1, DTLZ2, DTLZ3, DTLZ4, DTLZ5, DTLZ6, and DTLZ7, for up to eight objectives. The results are analyzed through a suite of five performance metrics and indicate that the ranking procedure based on PO enables NSGA-II to achieve better scalability properties compared with the standard ranking scheme and suggest that the proposed methodology could be successfully extended to other MOEAs