4,341 research outputs found

    Very Low Thrust Gaseous Oxygen-hydrogen Rocket Engine Ignition Technology

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    An experimental program was performed to determine the minimum energy per spark for reliable and repeatable ignition of gaseous oxygen (GO2) and gaseous hydrogen (GH2) in very low thrust 0.44 to 2.22-N (0.10 to 0.50-lb sub f) rocket engines or spacecraft and satellite attitude control systems (ACS) application. Initially, the testing was conducted at ambient conditions, with the results subsequently verified under vacuum conditions. An experimental breadboard electrical exciter that delivered 0.2 to 0.3 mj per spark was developed and demonstrated by repeated ignitions of a 2.22-N (0.50-lb sub f) thruster in a vacuum chamber with test durations up to 30 min

    Global Sentry: NASA/USRA high altitude reconnaissance aircraft design, volume 2

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    The Global Sentry is a high altitude reconnaissance aircraft design for the NASA/USRA design project. The Global Sentry uses proven technologies, light-weight composites, and meets the R.F.P. requirements. The mission requirements for the Global Sentry are described. The configuration option is discussed and a description of the final design is given. Preliminary sizing analyses and the mass properties of the design are presented. The aerodynamic features of the Global Sentry are described along with the stability and control characteristics designed into the flight control system. The performance characteristics are discussed as is the propulsion installation and system layout. The Global Sentry structural design is examined, including a wing structural analysis. The cockpit, controls and display layouts are covered. Manufacturing is covered and the life cost estimation. Reliability is discussed. Conclusions about the current Global Sentry design are presented, along with suggested areas for future engineering work

    Performance, emissions, and physical characteristics of a rotating combustion aircraft engine

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    The RC2-75, a liquid cooled two chamber rotary combustion engine (Wankel type), designed for aircraft use, was tested and representative baseline (212 KW, 285 BHP) performance and emissions characteristics established. The testing included running fuel/air mixture control curves and varied ignition timing to permit selection of desirable and practical settings for running wide open throttle curves, propeller load curves, variable manifold pressure curves covering cruise conditions, and EPA cycle operating points. Performance and emissions data were recorded for all of the points run. In addition to the test data, information required to characterize the engine and evaluate its performance in aircraft use is provided over a range from one half to twice its present power. The exhaust emissions results are compared to the 1980 EPA requirements. Standard day take-off brake specific fuel consumption is 356 g/KW-HR (.585 lb/BHP-HR) for the configuration tested

    Liquid Oxygen/Liquid Methane Propulsion and Cryogenic Advanced Development

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    Exploration Systems Architecture Study conducted by NASA in 2005 identified the liquid oxygen (LOx)/liquid methane (LCH4) propellant combination as a prime candidate for the Crew Exploration Vehicle Service Module propulsion and for later use for ascent stage propulsion of the lunar lander. Both the Crew Exploration Vehicle and Lunar Lander were part the Constellation architecture, which had the objective to provide global sustained lunar human exploration capability. From late 2005 through the end of 2010, NASA and industry matured advanced development designs for many components that could be employed in relatively high thrust, high delta velocity, pressure fed propulsion systems for these two applications. The major investments were in main engines, reaction control engines, and the devices needed for cryogenic fluid management such as screens, propellant management devices, thermodynamic vents, and mass gauges. Engine and thruster developments also included advanced high reliability low mass igniters. Extensive tests were successfully conducted for all of these elements. For the thrusters and engines, testing included sea level and altitude conditions. This advanced development provides a mature technology base for future liquid oxygen/liquid methane pressure fed space propulsion systems. This paper documents the design and test efforts along with resulting hardware and test results

    Combustion, Ionization And Sporadic Pre-Ignition In A Turbocharged Gasoline Direct Injection Engine.

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    This research is focused on the use of ionization of combustion products in hydrocarbons-air flames to gain a better understanding of the combustion process in turbocharged gasoline direct injection engines. A GM 2.0 L Ecotec GDI-T engine is used in this investigation. The ion current is measured simultaneously by two in-cylinder combustion sensors: the spark plug and the fuel injector. The characteristics of the ion current signals produced by the two sensors are analyzed and correlated with the characteristics of the rate of heat release computed from the cylinder gas pressure. Since this is the first time for the fuel injector to be used as an ion current sensor, it was possible to determine many features of combustion in the engine which could not be determined from the spark plug signal. For example, the phase shift between the two ion current signals was used to determine the burning velocity. The results are compared with the burning velocity measured in optically accessible port injected engine in which high speed imaging techniques were used. In addition, it was possible to investigate the impact of the burning velocity on the indicated thermal efficiency and indicated mean effective pressure at different speeds and loads. Also, this research included the use of the two ion current signals for the feedback closed loop control of the engine. The engine was able to consistently operate on MBT 208 (Maximum Brake Torque) point by adjusting the ignition and injection timings. In addition, engine knock was detected and controlled by retarding ignition timing using the ion current signals. The findings from the experimental investigations are supported by a 3D gasoline cycle simulation of combustion and the ion current produced at the locations of the spark plug and the injector. The research in progress that will be documented in the dissertation will include a detailed analysis of the factors that contribute to combustion instability and cycle-to-cycle variations. Finally, combustion ionization will be used to investigate the low speed sporadic pre-ignition phenomenon (LSPI) which is currently limiting the progress toward higher power density and more efficient turbocharged gasoline engines

    WCD-20 spark diagnostic

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    Spark plugs are used to ignite the fuel in the WĂ€rtsilÀ’s SG engine ignition process and over-time they can suffer from various conditions such as wearing and fouling. More diagnostic information about the health condition of the spark plugs is needed and machine learning can be used to train a model with data from spark plugs to find the underlying relationship between the created model’s inputs and outputs. This thesis evaluates if machine learning can be used to provide such diagnostic information from the WCD-20 engine module data, and as a result a concept machine learning model is implemented and tested. The machine learning model is first designed, and the chosen learning technique and algorithm are supervised learning and neural network, respectively. The designed machine learning model classifies spark plugs into three different classes based on the input features, and these classes present the health conditions of the spark plugs. The data for the model’s training and validation processes is gathered by testing spark plugs in different conditions with a spark plug test rig machine. During this testing, the spark plugs are labeled into the three different classes according to their conditions. The machine learning model is implemented with Python programming language using Tensorflow library, and after implementing and training, the model is saved and downloaded into an engine module. The engine module’s source code is programmed to be able to run the machine learning model. The machine learning model’s accuracy is tested, and it achieves an overall accuracy of 82% when testing it with unseen data. The model has a high recall value for the output class that presents the spark plugs in good condition, but the model does not classify the spark plugs that are in bad condition as well. The model increases the overall CPU usage of the used engine module by 4,3%, which is relatively high, and this is due to the many matrix multiplication that are performed in the model’s dense layers for each spark plug separately. Based on these results it is evident that spark plug health condition can be generally diagnosed by using machine learning, but some misclassifications can still occur.Sytytystulppia kĂ€ytettÀÀn WĂ€rtsilĂ€n SG moottoreissa sytyttĂ€mÀÀn polttoaine, ja ajan kuluessa ne voivat kĂ€rsiĂ€ monenlaisista kuntoa heikentĂ€vistĂ€ asioista, kuten likaantumisesta ja kulumisesta. Sytytystulppien kunnosta tarvitaan lisÀÀ diagnostiikka informaatioita ja koneoppimisen avulla voidaan kouluttaa malli mikĂ€ kĂ€yttÀÀ sytytystulpista saatavaa dataa löytÀÀkseen suhteen luodun mallin sisÀÀn- ja ulostulojen vĂ€lillĂ€. TĂ€mĂ€ opinnĂ€ytetyö arvio koneoppimisen soveltuvuutta tuottamaan tarvittavaa diagnostiikka informaatioita sytytystulpista WCD-20 moottorimoduulista saatavalla datalla, ja lopputuloksena konsepti koneoppimismalli toteutetaan ja testataan. Koneoppimismalli suunnitellaan ensimmĂ€isenĂ€ ja valittu koneoppimisen oppimistekniikka ja algoritmi ovat valvottu oppiminen ja hermoverkko. Suunniteltu koneoppimismalli luokittelee sytytystulppia kolmeen eri luokkaan valittujen sisÀÀnmeno piirteiden perusteella, ja nĂ€mĂ€ luokat edustavat sytytystulppien kunnon tiloja. Koneoppimismallin opetuksessa kĂ€ytetty on kerĂ€tty testaamalla erikuntoisia sytytystulppia kĂ€yttĂ€en sytytystulppien testaus laitteistoa. NĂ€iden testien aikana sytytystulpat luokitellaan kolmeen eri luokkaan niiden kunnon perusteella. Koneoppimismalli toteutetaan ja koulutetaan kĂ€yttĂ€en Python ohjelmointikieltĂ€ ja Tensorflow kirjastoa, mikĂ€ jĂ€lkeen malli tallennetaan ja ladataan moottorimoduulille. Moottorimoduulin lĂ€hdekoodia ohjelmoidaan siten ettĂ€ se pystyy kĂ€yttĂ€mÀÀn koneoppimismallia. Koneoppimismallin tarkkuus testataan ja se saavuttaa 82 %:n kokonaistarkkuuden testattaessa sitĂ€ ennennĂ€kemĂ€ttömĂ€llĂ€ datalla. Mallilla on korkea herkkyysarvo ulostuloluokalle mikĂ€ edustaa sytytystulppia hyvĂ€ssĂ€ kunnossa, mutta malli ei luokittele huonokuntoisia sytytystulppia yhtĂ€ hyvin. Malli kasvattaa prosessorin kĂ€yttöastetta 4,3 %, mikĂ€ on melko korkea lisĂ€ys. TĂ€mĂ€ lisĂ€ys johtuu monista matriisien kertolaskuista mitkĂ€ suoritetaan mallin tiheissĂ€ kerroksissa jokaiselle sytytystulpalle erikseen. NĂ€iden tuloksien perusteella koneoppimista voidaan yleisesti kĂ€yttÀÀ sytytystulppien kunnon luokittelemiseen, mutta vÀÀriĂ€ luokittelutuloksia voi silti tapahtua

    Impact of Discharge Duration on Lean Combustion in Spark Ignition Engines

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    Fuel-lean combustion in spark ignition engines is a promising strategy to improve engine efficiency. However, a fuel lean cylinder charge tends to lower the burning velocity because of the lowered chemical reactivity of the mixture, unless the flame propagation is accelerated by introducing an intensified flow field in the combustion chamber. Nevertheless, the literature reveals that the lean burn strategy with intensified flow fields can impose severe challenges on the ignition and flame development processes both in present and upcoming production engines. To address these issues and to better secure the flame kernel at the initial stage of combustion, various ignition strategies have been proposed with the aim of developing higher discharge current and longer discharge duration in the ignition processes, compared to those encountered with conventional spark ignition techniques. Moreover, while both current amplitude and duration of the plasma channel are fundamental to the flame kernel formation and development, their roles have not been fully clarified, let alone adequately quantified, in respect to the extensive variations in pressure, temperature, flow status, and mixture strength. Consequently, in this study, the impacts of discharge current amplitude and duration on the flame kernel initiation were investigated empirically using a constant volume combustion chamber and a single-cylinder research engine platform. The constant volume combustion chamber system was constructed so that a gas mixture with independently controlled pressure, composition, and flow intensity could be supplied. High-speed imaging was used to enable spatial and temporal characterizations of the flame kernel initiation process. Turbulence was generated inside the combustion chamber by a jet flow setup. A field programmable gate array (FPGA) controller was used to synchronize the controls of the sparking events, jet flow, and high-speed imaging. To achieve independent control of the discharge current amplitude and duration, the discharge current profile was modulated to form a quasi-rectangular shape by using a variety of hardware configurations and event controls. Ignition studies with various discharge current amplitudes and durations were conducted under both quiescent and flow conditions. Combustion test results showed that both discharge current amplitude and discharge duration had minimal impact on the ignition process under quiescent condition. However, under flow conditions, a longer discharge duration contributed to tailing flame kernels near the spark gap, and a higher discharge current amplitude contributed to larger flame kernels. Based on the experimental results and analysis, a correlation between the discharge current profiles and the flame kernel development was established with ultra-lean mixtures under intensified flow conditions. Additionally, the operational principles of the single-coil repetitive discharge and dual-coil offset discharge strategies were explored and explained. The necessary control algorithms for the repetitive and offset discharge strategies were established by analyzing the empirically acquired electrical waveforms of the discharge events. Finally, a preliminary investigation of the impact of discharge duration on the ignition stability was conducted using a single-cylinder research engine fitted with precise coolant conditioning, flexible air and fuel management, and comprehensive measurement and data acquisition. The experimental results indicated that a longer discharge duration contributed to improved combustion stability. However, ignition delay and combustion duration were unaffected by the prolonged discharge duration

    Design and development of auxiliary components for a new two-stroke, stratified-charge, lean-burn gasoline engine

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    A unique stepped-piston engine was developed by a group of research engineers at Universiti Teknologi Malaysia (UTM), from 2003 to 2005. The development work undertaken by them engulfs design, prototyping and evaluation over a predetermined period of time which was iterative and challenging in nature. The main objective of the program is to demonstrate local R&D capabilities on small engine work that is able to produce mobile powerhouse of comparable output, having low-fuel consumption and acceptable emission than its crankcase counterpart of similar displacement. A two-stroke engine work was selected as it posses a number of technological challenges, increase in its thermal efficiency, which upon successful undertakings will be useful in assisting the group in future powertrain undertakings in UTM. In its carbureted version, the single-cylinder aircooled engine incorporates a three-port transfer system and a dedicated crankcase breather. These features will enable the prototype to have high induction efficiency and to behave very much a two-stroke engine but equipped with a four-stroke crankcase lubrication system. After a series of analytical work the engine was subjected to a series of laboratory trials. It was also tested on a small watercraft platform with promising indication of its flexibility of use as a prime mover in mobile platform. In an effort to further enhance its technology features, the researchers have also embarked on the development of an add-on auxiliary system. The system comprises of an engine control unit (ECU), a directinjector unit, a dedicated lubricant dispenser unit and an embedded common rail fuel unit. This support system was incorporated onto the engine to demonstrate the finer points of environmental-friendly and fuel economy features. The outcome of this complete package is described in the report, covering the methodology and the final characteristics of the mobile power plant

    Studies on SI engine simulation and air/fuel ratio control systems design

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    This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.More stringent Euro 6 and LEV III emission standards will immediately begin execution on 2014 and 2015 respectively. Accurate air/fuel ratio control can effectively reduce vehicle emission. The simulation of engine dynamic system is a very powerful method for developing and analysing engine and engine controller. Currently, most engine air/fuel ratio control used look-up table combined with proportional and integral (PI) control and this is not robust to system uncertainty and time varying effects. This thesis first develops a simulation package for a port injection spark-ignition engine and this package include engine dynamics, vehicle dynamics as well as driving cycle selection module. The simulations results are very close to the data obtained from laboratory experiments. New controllers have been proposed to control air/fuel ratio in spark ignition engines to maximize the fuel economy while minimizing exhaust emissions. The PID control and fuzzy control methods have been combined into a fuzzy PID control and the effectiveness of this new controller has been demonstrated by simulation tests. A new neural network based predictive control is then designed for further performance improvements. It is based on the combination of inverse control and predictive control methods. The network is trained offline in which the control output is modified to compensate control errors. The simulation evaluations have shown that the new neural controller can greatly improve control air/fuel ratio performance. The test also revealed that the improved AFR control performance can effectively restrict engine harmful emissions into atmosphere, these reduce emissions are important to satisfy more stringent emission standards
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