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CONVENTIONAL aircraft thermometers are of the platinum‐resistance type, comprising one arm of a Wheatstone bridge network. The meter connected to the network is graduated on a temperature scale and different values are due to changes in the resistance of the platinum element. These arise from variations in the temperature of the air immediately in contact with the element or its protective casing. Any indicated temperature is a measure of the temperature of the air in contact with the thermometer. It is also the temperature of those parts of the aircraft over which the air is moving at the same speed as it is flowing past the thermometer. Since the air does not flow at the same speed over all parts of the aircraft it is clear that by mounting thermometers in different positions on an aircraft, different temperatures will be obtained. Due mainly to kinetic heating none of these will be the true temperature of the air in the free stream—i.e. away from the influence of the aircraft—and to obtain the free stream air temperature it is necessary to apply corrections. For many purposes, particularly meteorological research, it is important to be able to obtain true air temperatures from indicated air temperatures quickly and accurately and it is with this purpose in mind that the diagram to be described was devised and constructed.

The purpose of this paper is to develop a new tool for aircraft performance analysis and optimization.

In this paper, the methodology of converting nomogram curves into mathematical functions is presented. Aircraft performance nomograms represent graphical interpretation of influence of several variables on performance such as environmental conditions, runway conditions and aircraft mass. The aircraft performance nomograms are converted in mathematical functions that describe several independent variables’ influence on aircraft performance parameters. To achieve greater accuracy in calculation of aircraft performance parameters, it is necessary to determine mathematical functions presented by dependent variable variations with several independent variables. The method of determining mathematical functions is illustrated on Fokker 100 landing gear extended net climb gradient determination graph.

To evaluate model, it was necessary to determine net climb gradient both graphically and analytically using model and compare the results. After solving both analytically and graphically, it was concluded that results are a match. During model evaluation, it was observed that model has a lot of advantages such as it has great precision of calculation, requires less time to calculate results and has minimum error possibility.

Final result of digitalization of aircraft performance nomograms is software production. The usage of this software can reduce flight planning and aircraft exploitation costs in several different manners. Airliners can produce such a software for those types of aircraft where there is no software provided from aircraft manufacturer.

Digitalization of aircraft performance nomogram has never been analyzed before, although there is a possibility of this particular methodology implementation in a practical manner in aviation industry.

This chapter estimates heterogeneous productivity growth and spatial spillovers through industrial linkages in the United States and China from 1981 to 2010. The authors employ a spatial Durbin stochastic frontier model and estimates with a spatial weight matrix based on inter-country input–output linkages to describe the spatial interdependencies in technology. The authors estimate productivity growth and spillovers at the industry level using the World KLEMS database. The spillovers of factor inputs and productivity growth are decomposed into domestic and international effects. Most of the spillover effects are found to be significant and the spillovers of productivity growth offered and received provide detailed information reflecting interdependence of the industries in the global value chain (GVC). The authors use this model to evaluate the impact of a US–Sino decoupling of trade links based on simulations of four scenarios of the reductions in bilateral intermediate trade. Their estimation results and their simulations are as mentioned based on date that ends in 2010, as this is the only KLEMS data available for these countries at this level of industrial disaggregation. As the GVC linkages between the United States and China have expanded since the end of their sample period their results can be viewed as informative in their own right for this period as well as possible lower bounds on the extent of the spillovers generated by an expanding GVC.

This study evaluates low Reynolds number models of turbulence for numerical computations on the heat transfer and fluid flow behavior in a rectangular channel with streamwise‐periodic ribs mounted on one of the principal walls. The models include k − ε models of Launder and Sharma (1974), Chien (1982), k − ε model of Lin and Hwang (1998), Wilcox’s k−ω model (Wilcox, 1994) and Durbin’s model k − ε −v² (Durbin, 1995). The numerical results show that all these models can predict the flowfield reasonably well, and the inclusion of the Yap term (Yap, 1987) in the ε – equation (or ε – equation) can further improve the prediction in these k − ε models, k − ε model and k − ε − v² model. However, these models behave differently in heat transfer computations. The k − ω model leads to too low a level of heat transfer and turbulence. Among these k − ε models and the k − ε model, Lin’s model with the Yap term predicts the heat transfer level best. Durbin’s model with extra v², f equations and the Yap term exhibits further improvement.

This paper considers a class of parametric models with nonparametric autoregressive errors. A new test is established and studied to deal with the parametric specification of the nonparametric autoregressive errors with either stationarity or nonstationarity. Such a test procedure can initially avoid misspecification through the need to parametrically specify the form of the errors. In other words, we estimate the form of the errors and test for stationarity or nonstationarity simultaneously. We establish asymptotic distributions of the proposed test. Both the setting and the results differ from earlier work on testing for unit roots in parametric time series regression. We provide both simulated and real-data examples to show that the proposed nonparametric unit root test works in practice.