High bandwidth stagnation temperature measurements in a Mach 6 gun tunnel flow

Temperature is an important parameter in most high speed flow experiments, but it is sometimes a difficult parameter to measure, particularly in short-duration facilities. Stagnation temperature measurements have been obtained using transient thin film heat flux probes in a Mach 6 carbon dioxide flow produced by the Oxford University Gun Tunnel. The probes were operated over a range of surface temperatures so that the flow stagnation temperature could be identified independently of the convective heat transfer coefficient of the probes. The time-averaged measurements indicate a significant drop in stagnation temperature with time and this implies that significant cooling of the test gas occurred within the barrel during the compression process and/or during the flow discharge process. During the last 12 ms of flow, the time-averaged stagnation temperature indicated by the probe was 610+/-10 K for the present operating conditions. During the same 12 ms flow period, the probe measurements also indicate stagnation temperature fluctuations of about 2.3 K (rms) for frequencies between 1 and 25 kHz. Based on pitot pressure fluctuation measurements at essentially the same location within the nozzle, it is concluded that the measured temperature fluctuations are primarily due to fluctuations in entropy. Entropy fluctuations within the Mach 6 flow probably arise because of the turbulent heat transfer to the barrel

CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network

This study investigates the use of artificial neural network (ANN) modelling to predict brake power, torque, break specific fuel consumption (BSFC), and exhaust emissions of a diesel engine modified to operate with a combination of both compressed natural gas CNG and diesel fuels. A single cylinder, four-stroke diesel engine was modified for the present work and was operated at different engine loads and speeds. The experimental results reveal that the mixtures of CNG and diesel fuel provided better engine performance and improved the emission characteristics compared with the pure diesel fuel. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception network was used for non-linear mapping between the input and output parameters. It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.9884, 0.9838, 0.95707, and 0.9934 for the engine torque, BSFC, NOx and exhaust temperature, respectively.CNG fuel ANN Engine performance Engine emission

Fundamental Study on Cold Energy Release by Direct Contact Heat Exchange between Ice Water Slurry and Hot Air

This paper has dealt with direct contact heat exchange characteristics between ice water slurry (average ice particle diameter : 3.10 mm) and hot air bubbles. The hot air bubbles ascending in the layer fluidized the ice water slurry layer, and the bubbles were cooled down directly by the ice water slurry. The following results were obtained from the experiment. In case of ice water slurry layer, the hot air bubbles fluidized the layer in earlier stage and heat exchange performance was higher than using only ice particles layer. The maximum temperature efficiency increased as Reynolds number Re increased because fluid in the layer became active and kept at the fixed value in the region of Re >__=900. Dehumidity efficiency increased as modified Stefan number and Re increased since the heat capacity of inlet air and heat transfer coefficient increased. Some empirical correlations for temperature efficiency, dehumidity efficiency and the completion time of latent cold heat release were derived in terms of various nondimensional parameters.近年、電力需要の急増傾向に対応すべく、夜間電力を利用した氷蓄熱システムの開発が盛んに行われ、ビル用空調を初めとして一般業務用空調にも製品として展開されてきている。その中で、蓄熱槽内にて氷と熱交換した冷水を利用側空調へ搬送させて冷房運転を行う水搬送方式に替わって、氷水スラリーを直接、利用側空調機へ搬送する氷水搬送技術の研究開発が行われている。このシステムは氷の潜熱を直接、利用側へ供給することにより、冷水搬送方法と比較して単位流量当たりの冷熱輸送能力が格段に増大するため、配管口径の縮小や輸送動力の低減が図れるものである。さらに、氷水スラリー搬送技術に加えて、搬送された氷水スラリーを冷房熱源として二重管式熱交換器の内管に流動させ、その外管に温水を流して採冷熱する方法や、角氷を充填層へ搬送して空気と直接接触熱交換させる採冷熱方法が提案されている。また、各種潜熱蓄冷熱材を被熟媒体とし、空気泡を熱媒体として両者を直接接触させて熱交換する直接接触熱交換に関する研究も行われている。上記直接接触熱交換では、噴射空気泡により潜熱蓄冷熱材が流動するため、潜熱蓄冷熱材の温度場が均一となり、潜熱蓄冷熱材と空気泡との熱交換が比較的均一に進行するという利点がある。しかしながら、氷水と空気との直接接触型潜熱放冷特性に関する研究が少ないのが現状である。氷水と空気との直接接触型放冷方式では、空気は氷水により冷却されるだけでなく、空気に含まれる水蒸気が氷水により凝縮されるため、冷却と除湿を同時に行える効果が期待できる。本研究は均一な氷水スラリー層を対象として、氷水スラリー層下部に多数の円形ノズルを均一に配列した分散板を設置し、そこから温空気を層内に噴射し、発生した空気泡と氷水スラリーとを直接接触熱交換させた場合の氷の融解挙動および通過空気の熱交換特性について実験的に検討するものである。その際の現象は粒状氷と水との混合割合や通過空気流速等を変化させることによりさまざまに変化させることが可能である。本研究では実験因子である流入空気温度、湿度、空気流量および氷質量充填率が、熱交換後の空気温度、湿度および潜熱放冷完了時間に及ぼす影響について実験的に検討し、これらの相関関係を実験式の形でまとめることを狙いとする

Pressure Fluctuations in a Hypersonic Ludwieg Tube with Free Piston Compression Heating

The free stream acoustic disturbance environment in hypersonic wind tunnel testing has a significant impact on boundary layer stability and transition to turbulence, and can influence the results of fluid-structure interaction studies. The pressure disturbance level and spectral content of the University of Southern Queensland’s Mach 6 Ludwieg tube with free piston compression facility is identified via pitot pressure measurements that were analyzed using a high-pass filter method and a power spectral density technique. Using the power spectral density method, the acoustic environment was found to change properties during the run, and it was therefore appropriate to define two root-mean-square percentage pressure fluctuation levels across frequencies from 300 Hz to 25 kHz: 2.53 % in the run period from 5 to 85 ms, and 2.98 % from 85 to 200 ms. The sensitivity of the root-mean-square results of the power spectral density analysis to the windowing technique was minimized through the assessment and selection of appropriate windowing parameters. Results obtained using this optimized windowing method compare favorably with the more traditional high pass filter analysis method

Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network

The purpose of this study is to experimentally analyse the performance and the pollutant emissions of a four-stroke SI engine operating on ethanol-gasoline blends of 0%, 5%, 10%, 15% and 20% with the aid of artificial neural network (ANN). The properties of bioethanol were measured based on American Society for Testing and Materials (ASTM) standards. The experimental results revealed that using ethanol-gasoline blended fuels increased the power and torque output of the engine marginally. For ethanol blends it was found that the brake specific fuel consumption (bsfc) was decreased while the brake thermal efficiency ([eta]b.th.) and the volumetric efficiency ([eta]v) were increased. The concentration of CO and HC emissions in the exhaust pipe were measured and found to be decreased when ethanol blends were introduced. This was due to the high oxygen percentage in the ethanol. In contrast, the concentration of CO2 and NOx was found to be increased when ethanol is introduced. An ANN model was developed to predict a correlation between brake power, torque, brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and emission components using different gasoline-ethanol blends and speeds as inputs data. About 70% of the total experimental data were used for training purposes, while the 30% were used for testing. A standard Back-Propagation algorithm for the engine was used in this model. A multi layer perception network (MLP) was used for nonlinear mapping between the input and the output parameters. It was observed that the ANN model can predict engine performance and exhaust emissions with correlation coefficient (R) in the range of 0.97-1. Mean relative errors (MRE) values were in the range of 0.46-5.57%, while root mean square errors (RMSE) were found to be very low. This study demonstrates that ANN approach can be used to accurately predict the SI engine performance and emissions.Artificial neural network SI engine Engine performance Exhaust emissions Ethanol-gasoline blends