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The purpose of this study is to analyse numerically and experimentally the effects of lip thickness (LT) and bypass ratio on co-flowing nozzle under subsonic and correctly expanded sonic jet decay at different Mach numbers.

Co-flowing jets from co-flowing nozzles of different LTs, 0.2, 1 and 1.5 Dp (where Dp is the primary nozzle exit diameter = 10 mm), with an annular gap of 10 mm at main jet exit Mach numbers 0.6 have been studied experimentally and the other cases have been performed numerically. The co-flowing jet with 2 mm LT was used for comparison.

Co-flowing jet axial pitot pressure decay, axial static pressure decay, axial velocity decay, radial velocity decay and streamline velocity contours were analyzed. The results illustrate that the mixing of the co-flowing jet with profound LT is prevalent to the co-flowing jet with 2 mm LT, at all Mach numbers of the current study. Also, the LT of the co-flowing jet has a strong impact on jet mixing. Co-flowing jets with 10 mm and 15 mm LT with a constant co-flow width of 10 mm experience a considerably advanced mixing than co-flowing jets with 2 mm LT and a co-flow width of 10 mm.

The application of bypassed co-flow jet is in turbofan engine operates efficiently in modern civil aircraft.

All subsonic jets are considered correctly expanded with negligible variation in axial static pressure. However, in the present study, static pressure along the centerline varies sinusoidally up to 9% and 12% above and below atmospheric pressure, respectively, for primary jet exit Mach number 1.0. The sinusoidal variation is less for primary jet exit Mach numbers 0.6 and 0.8 in static pressure decay.

THE following notes, principally for the use of ground engineers, refer to the super‐charging of the four‐stroke cycle petrol engine; two‐stroke cycle engines and compression ignition engines are not here dealt with.

An investigation has been carried out by Quo‐Tec Limited, on behalf of the Department of Trade and Industry's Advanced Sensors Technology Transfer Programme, to determine the opportunities which exist in the UK transportation industries for advanced sensors. The study was concerned particularly with the identification of new business opportunities for UK Small and Medium‐sized Enterprises (SMEs). The study's boundaries were defined as the automotive, aerospace, rail and marine transportation sectors and the advanced sensor technologies of optical fibres and solid state. Piezoelectric, capacitive, inductive magnetoresistive, thin film, thick film and micromachined silicon devices were all included in the term solid state. These were highlighted because of the proven strength of UK research in many of these areas and yet, in many cases, a current lack of significant UK commercial exploitation. Through literature reviews, extensive telephone interviews and face‐to‐face discussions with key individuals in over 90 transportation companies, sensor companies and research institutions, a similar number of sensor requirements were identified. From this number, those requirements best addressed by optical or solid state sensor technology were selected. A criterion applied in the selection was that the need could be addressed by a UK SME (either alone or in collaboration) with a reasonable expectation that a sensor could be commercially available within five years. Preferably, proven technology should be available — the job of a sensor company is to develop the technology into a commercial product, not to do the fundamental research work to prove the technology itself. This article comprises some “prime” opportunities, thus identified, applicable to the automotive industry.

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.

This emergency generating system was first fitted on the 01 and 02 pre‐production Concordes and will be installed on the production aircraft. Initial design work commenced in 1967 and a fully developed and type‐tested unit went into production in 1971.

Purpose of the study is to further expand insights into how weather impacts attendance at sporting events. With the NFL having only eight home games a year per team, it is more of an event than other North American sports. We explore this in terms of how sensitive fans are to weather, by not only looking at traditional factors, but also other weather variables available through Accuweather. In addition, the authors explore team success, outcome uncertainty and other factors as determinants of demand.

The method includes Tobit model of attendance in terms of percent of capacity in the National Football League. Model includes factors such as outcome uncertainty, team success, etc. but mainly focuses on weather. Weather factors studied include traditional variables such as temperature and precipitation, and also includes cloud cover, barometric pressure, wind speed and humidity. Different model specifications are included to explore results. Key findings allow for differences between games played outdoors versus indoors.

In terms of control variables, team success, new stadiums and stadium age play a significant role in attendance in terms of percentage of capacity. Outcome uncertainty does not appear to be important, and fans desire the opposite when the home team is an underdog. The main results concern the weather. When only traditional weather variables are included, precipitation plays a key role. With further expansion of the weather variables, it appears that cloud cover offers some additional information beyond precipitation. In addition, barometric pressure plays a minor, but statistically significant role as it relates to attendance in terms of capacity.

Including deeper and richer weather data helps to further explain attendance at sporting events. With the NFL, this may be limited by it being such as event due to the scarcity of games in a season. In addition, the weather variables are not truly independent, although they are not as correlated as may be anticipated on the surface. Use of different types of weather variables in models of attendance may help to deepen our understanding of factors influencing consumer decisions. These factors may play larger roles in sports with wider variance in attendance during the season.

The practical implications are that other weather-related variables besides temperature and precipitation may offer insight into consumer decisions related to attendance at sporting events. Cloud cover gives insights into anticipated poor weather in addition to it directly leading to less of a sunny day to be outdoors at an event. Barometric pressure has been shown to influence headaches and joint pain and may also influence consumer decisions to venture out to sporting events.

As data becomes more widely available in general, it's possible to add additional insights into factors influencing various forms of decision-making. In this study, we show that more information on weather can shed insights into consumer decisions as it relates to attending events such as sports. These decisions likely differ based upon whether the event is held outdoors or indoors. With more entertainment choices as substitutes, it is important to identify key factors which influence consumer decisions to help better structure events in the future.

Weather variables beyond temperature and precipitation are included in a Tobit model for NFL attendance using percentage of capacity as the dependent variable. These weather variables are cloud cover, wind speed, humidity, and barometric pressure. Cloud cover and barometric pressure were found to have some significant effects on percentage of capacity. When included, precipitation itself is no longer found to be significant, but precipitation interacted with games played in domes retains statistical significance as there are key differences between games held outdoors versus indoors.

The annual and seasonal variation of meteorological parameters has been studied for twenty stations in Saudi Arabia. The contour maps showed temperature and rainfall increasing and wind speed decreasing trends with decreasing latitudes. The mean temperature varied between a minimum of 8.4°C and a maximum of 34.61°C while overall mean was 23.95°C. The Mean, minimum and maximum values of pressure were 932.12, 923.3 and 944.6mb, respectively. The corresponding relative humidity values were 42.7, 78.5 and 21.5%, respectively. The empirical models developed for the prediction of monthly mean values of individual parameters were able to estimate the values with acceptable accuracy