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Appl. Sci. 2018, 8, x FOR PEER REVIEW 3 of 17 Appl. Sci. 2018, 8, 2038 3 of 16 The preliminary design process of radial inflow turbines often requires a certain level of previous empirical knowledge and it usually makes the beginners feel a bit confused. Although it is an automated and integrated solution that can fully consider all the problems is seldom seen in the well known that the aerodynamic performance of radial inflow turbines is dominated by loading open literature. This is a motivation of the present study. coefficient and flow coefficient [26], the selection of these two parameters and rotational speed still The preliminary design process of radial inflow turbines often requires a certain level of previous needs comprehensive consideration. In this paper, as Part I, an automated Genetic Algorithm (GA) empirical knowledge and it usually makes the beginners feel a bit confused. Although it is well known driventhparteltihmeianearroydydneasmigicnperrofocremduanrceeforf radiialliinflfolowwtuturbribnienseisidsodmevinealtoepdebdy,alonaditnhgeceofeffeficctievnetnessof and flow coefficient [26], the selection of these two parameters and rotational speed still needs the design approach is validated by the experimental data of the radial inflow turbine in Sundstrand comprehensive consideration. In this paper, as Part I, an automated Genetic Algorithm (GA) driven Power Systems T-100 Multipurpose Small Power Unit [27]. This procedure not only alleviates preliminary design procedure for radial inflow turbines is developed, and the effectiveness of the dependency on the designer’s technical expertise but also guarantees the optimal design at the design approach is validated by the experimental data of the radial inflow turbine in Sundstrand Power preliminary design stage. Systems T-100 Multipurpose Small Power Unit [27]. This procedure not only alleviates dependency on the designer’s technical expertise but also guarantees the optimal design at the preliminary 2. Automated Preliminary Design Methodology design stage. Radial inflow turbines comprise of mainly a volute, nozzle, and rotor. Figure 1 illustrates the 2. Automated Preliminary Design Methodology principal components and stations. Because almost all energy loss produces in nozzle and rotor parts, Radial inflow turbines comprise of mainly a volute, nozzle, and rotor. Figure 1 illustrates the the design and optimization of radial inflow turbines will focus on those two components in this principal components and stations. Because almost all energy loss produces in nozzle and rotor parts, paper.the design and optimization of radial inflow turbines will focus on those two components in this paper. Figure 1. Components of a radial inflow turbine. Figure 1. Components of a radial inflow turbine. According to References [26,28], loading coefficient ψ and flow coefficient φ are defined as AEccqouradtiiongs (t1o) aRnedfe(2r)e,nrecsepse[c2ti6v,e2l8y.],Inloaaddintigonc,otheefyficaiennbteψselecatnedffrloomwFcigouerfefi2c:ient φ Equations (1) and (2), respectively. In addition, they can be selected from Figure 2: ψ = Δ H4 u U2 (1) C4, φ= m6. (2) designers have to use values significantly different from those optima, and this is mainly because the Itacpapnlicbaetisoenernesftraoimns tFhiegduerseig2n.thFoartetxhaemmplae,xtiumrbuomchaerfgfeicrsiehnacvye tocucsuerhsigwhehreflnowthceoleoffiacdieintgtocoloewffeircient is ψ = ∆Hu , U2 are defined as (1) U4 Cm6 It can be seen from Figure 2 that the maximum efficiency occurs when the loading coefficient is φ= (2) in the range of 0.9–1.0, and the flow coefficient is Ubetween about 0.2–0.3. However, in many cases, 4. outlet blade height to result in a more compact design. Therefore, it becomes difficult to determine the in the range of 0.9–1.0, and the flow coefficient is between about 0.2–0.3. However, in many cases, maximum efficiency that the design can achieve under specified constraints in a short time. designers have to use values significantly different from those optima, and this is mainly because the application restrains the design. For example, turbochargers have to use higher flow coefficient to lower outlet blade height to result in a more compact design. Therefore, it becomes difficult to determine the maximum efficiency that the design can achieve under specified constraints in a short time.PDF Image | Design and Optimization Approach for Radial Inflow Turbines
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