Performance prediction of wind turbines utilizing passive smart blades: approaches and evaluation

The induced deformation, because of the presence of elastic coupling in the structure of passive smart blades, is the key parameter that affects the wind turbine aerodynamic performance, namely rotor mechanical power and blade loading. Therefore, in order to determine the aerodynamic performance of these turbines, a structural analyser is also required to bring the effect of the induced deformation into account. When predicting the rotor mechanical power, additional complexity arises when the blades are bend-twist-coupled. In this case, an iterative coupled-aero-structure analysis must be carried out at each given wind speed. Further difficulties in simulation of these turbines are posed by the fact that the current analytical models for analysis of structures made of anisotropic composite materials are not accurate enough. This differentiates the numerical simulation of wind turbines utilizing passive smart blades from the simulation of wind turbines with conventional blades. Different strategies have been proposed and followed by investigators in simulation of wind turbines utilizing passive smart blades. These methods can be categorized by the approach adopted in treating the torsional-induced deformation. In these studies, the induced twist has been predicted, planned or a combination of both. The present paper describes, evaluates and compares these approaches

Identification and mapping of quantitative resistance to late blight (Phytophthora infestans) in Solanum habrochaites LA1777

Late blight (Phytophthora infestans) can have devastating effects on tomato production over the whole world. Most of the commercial cultivars of tomato, Solanum lycopersicum, are susceptible. Qualitative and quantitative resistance has been described in wild relatives of tomato. In general qualitative resistance can more easily be overcome by newly evolved isolates. Screening of three S. habrochaites accessions (LA1033, LA2099 and LA1777) through a whole plant assay showed that accession LA1777 had a good level of resistance to several isolates of P. infestans. To explore the potential in this wild species, an introgression line (IL) population of S. habrochaites LA1777 was used to screen individual chromosome regions of the wild species by a detached leaf assay. Two major isolates (T1,2 and T1,2,4) were used and two parameters were measured: lesion size (LS), and disease incidence (DI). Substantial variation was observed between the individual lines. QTLs were identified for LS but not for DI. The presence of five QTLs derived from LA1777 (Rlbq4a, Rlbq4b, Rlbq7, Rlbq8 and Rlbq12) results in unambiguous higher levels of resistance. All QTLs co-localized with previously described QTLs from S. habrochaites LA2099 except QTL Rlbq4b, which is therefore a novel QT

ShenZhen transportation system (SZTS): a novel big data benchmark suite

Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets