The Experts below are selected from a list of 46770 Experts worldwide ranked by ideXlab platform

Anna G Stefanopoulou - One of the best experts on this subject based on the ideXlab platform.

  • Stability analysis in homogeneous charge compression ignition (HCCI) engines with high dilution
    IEEE Transactions on Control Systems Technology, 2007
    Co-Authors: Chia Jui Chiang, Anna G Stefanopoulou
    Abstract:

    This paper analyzes the stability of the autoignition process of homogeneous charge compression ignition (HCCI) engines with exhaust dilution. We find conditions under which steady-state multiplicity exists with stable and unstable equilibria. This analysis is conducted taking into account the Internal Feedback structure of the thermal dynamics. Specifically, HCCI combustion timing determines the combustion heat produced and is determined by the heat provided through high Internal exhaust gas recirculation from the previous combustion cycle. It is shown that the thermal equilibria are characterized by a simple returning map consisting of two curves, namely the breathing temperature curve and the combustion temperature curve. The influence of heat transfer and the cooling system in the system stability is also analyzed. The returning map and the stability of the multiple steady-state equilibria are confirmed with a high-order dynamic nonlinear model. The high-order dynamic model includes manifold filling and composition dynamics and has been validated both at steady state and during transient. It is shown that a static feedforward controller can cause instability during switching from a high to a low load. A dynamic feedforward controller, on the other hand, is able to stabilize the transition by reducing temperature excursions and, hence, keeping the temperature trajectories within stable regions

  • a mean value model for control of homogeneous charge compression ignition hcci engines
    Journal of Dynamic Systems Measurement and Control-transactions of The Asme, 2005
    Co-Authors: D.j. Rausen, J.a. Eng, Anna G Stefanopoulou, Junmo Kang, T.w. Kuo
    Abstract:

    A Mean Value Model (MVM) for a Homogeneous Charge Compression Ignition (HCCI) engine is presented. Using a phenomenological zero-dimensional approach with five continuous and three discrete states we first model the effects of the Exhaust Gas Recirculation (EGR) valve, the exhaust Rebreathing Lift (RBL), and the fueling rate on the state of charge in the cylinder at intake valve closing. An Arrhenius integral is then used to model the start of combustion, θ soc . A series of simple algebraic relations that captures the combustion duration and heat release is finally used to model the state of charge after the HCCI combustion and the Location of Peak Pressure (LPP). The model is parametrized and validated using steady-state test data from an experimental gasoline engine at the General Motors Corporation. The simple model captures the temperature, pressure, air-to-fuel ratio, and inert gas fraction of the exhausted mass flow. This characterization is important for the overall HCCI dynamics because the thermodynamic state (pressure, temperature) and concentration (oxygen and inert gas) of the exhausted mass flow affect the next combustion event. The high dilution level in HCCI engines increases the significance of this Internal Feedback that generally exists to a smaller extent in conventional spark-ignition and compression-ignition Internal combustion engines.

  • A mean-value model for control of homogeneous charge compression ignition (HCCI) engines
    Proceedings of the 2004 American Control Conference, 2004
    Co-Authors: D.j. Rausen, J.a. Eng, J M Kang, Anna G Stefanopoulou, T.w. Kuo
    Abstract:

    A Mean Value Model (MVM) for a Homogeneous Charge Compression Ignition (HCCI) engine is presented. Using a phenomenological zero-dimensional approach with five continuous and three discrete states we first model the effects of the Exhaust Gas Recirculation (EGR) valve, the exhaust Rebreathing Lift (RBL), and the fueling rate on the state of charge in the cylinder at intake valve closing. An Arrhenius integral is then used to model the start of combustion, theta(soc). A series of simple algebraic relations that captures the combustion duration and heat release is finally used to model the state of charge after the HCCI combustion and the Location of Peak Pressure (LPP). The model is parametrized and validated using steady-state test data from an experimental gasoline engine at the General Motors Corporation. The simple model captures the temperature, pressure, air-to-fuel ratio, and inert gas fraction of the exhausted mass flow. This characterization is important for the overall HCCI dynamics because the thermodynamic state (pressure, temperature) and concentration (oxygen and inert gas) of the exhausted mass flow affect the next combustion event. The high dilution level in HCCI engines increases the significance of this Internal Feedback that generally exists to a smaller extent in conventional spark-ignition and compression-ignition Internal combustion engines.

  • Steady-state multiplicity and stability of thermal equilibria in homogeneous charge compression ignition (HCCI) engines
    2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), 2004
    Co-Authors: Chia Jui Chiang, Anna G Stefanopoulou
    Abstract:

    The stability of the autothermic process governing the autoignition of HCCI engines is analyzed in this paper. We find conditions under which steady-state multiplicity exists with stable, unstable, and limit-cycle equilibria. This analysis is conducted taking into account the Internal Feedback structure of the thermal autoignition dynamics. Specifically, HCCI autoignition tuning determines the combustion heat produced and is determined by the heat provided through high Internal exhaust gas recirculation from previous combustion cycle. It is shown that the thermal equilibria are characterized by a simple returning map consisting of two curves, namely the breathing temperature curve and the combustion temperature curve. The returning map and the stability analysis of the multiple steady state equilibria is confirmed with a high order dynamic nonlinear model that includes manifold filling and composition dynamics.

T.w. Kuo - One of the best experts on this subject based on the ideXlab platform.

  • a mean value model for control of homogeneous charge compression ignition hcci engines
    Journal of Dynamic Systems Measurement and Control-transactions of The Asme, 2005
    Co-Authors: D.j. Rausen, J.a. Eng, Anna G Stefanopoulou, Junmo Kang, T.w. Kuo
    Abstract:

    A Mean Value Model (MVM) for a Homogeneous Charge Compression Ignition (HCCI) engine is presented. Using a phenomenological zero-dimensional approach with five continuous and three discrete states we first model the effects of the Exhaust Gas Recirculation (EGR) valve, the exhaust Rebreathing Lift (RBL), and the fueling rate on the state of charge in the cylinder at intake valve closing. An Arrhenius integral is then used to model the start of combustion, θ soc . A series of simple algebraic relations that captures the combustion duration and heat release is finally used to model the state of charge after the HCCI combustion and the Location of Peak Pressure (LPP). The model is parametrized and validated using steady-state test data from an experimental gasoline engine at the General Motors Corporation. The simple model captures the temperature, pressure, air-to-fuel ratio, and inert gas fraction of the exhausted mass flow. This characterization is important for the overall HCCI dynamics because the thermodynamic state (pressure, temperature) and concentration (oxygen and inert gas) of the exhausted mass flow affect the next combustion event. The high dilution level in HCCI engines increases the significance of this Internal Feedback that generally exists to a smaller extent in conventional spark-ignition and compression-ignition Internal combustion engines.

  • A mean-value model for control of homogeneous charge compression ignition (HCCI) engines
    Proceedings of the 2004 American Control Conference, 2004
    Co-Authors: D.j. Rausen, J.a. Eng, J M Kang, Anna G Stefanopoulou, T.w. Kuo
    Abstract:

    A Mean Value Model (MVM) for a Homogeneous Charge Compression Ignition (HCCI) engine is presented. Using a phenomenological zero-dimensional approach with five continuous and three discrete states we first model the effects of the Exhaust Gas Recirculation (EGR) valve, the exhaust Rebreathing Lift (RBL), and the fueling rate on the state of charge in the cylinder at intake valve closing. An Arrhenius integral is then used to model the start of combustion, theta(soc). A series of simple algebraic relations that captures the combustion duration and heat release is finally used to model the state of charge after the HCCI combustion and the Location of Peak Pressure (LPP). The model is parametrized and validated using steady-state test data from an experimental gasoline engine at the General Motors Corporation. The simple model captures the temperature, pressure, air-to-fuel ratio, and inert gas fraction of the exhausted mass flow. This characterization is important for the overall HCCI dynamics because the thermodynamic state (pressure, temperature) and concentration (oxygen and inert gas) of the exhausted mass flow affect the next combustion event. The high dilution level in HCCI engines increases the significance of this Internal Feedback that generally exists to a smaller extent in conventional spark-ignition and compression-ignition Internal combustion engines.

Akitsugu Inaba - One of the best experts on this subject based on the ideXlab platform.

  • Differential expression and Internal Feedback regulation of 1-aminocyclopropane-1-carboxylate synthase, 1-aminocyclopropane-1-carboxylate oxidase, and ethylene receptor genes in tomato fruit during development and ripening.
    Plant physiology, 1998
    Co-Authors: Akira Nakatsuka, Hironori Okunishi, Shinjiro Shiomi, S Murachi, Ryohei Nakano, Yasutaka Kubo, Akitsugu Inaba
    Abstract:

    We investigated the Feedback regulation of ethylene biosynthesis in tomato (Lycopersicon esculentum) fruit with respect to the transition from system 1 to system 2 ethylene production. The abundance of LE-ACS2, LE-ACS4, and NR mRNAs increased in the ripening fruit concomitant with a burst in ethylene production. These increases in mRNAs with ripening were prevented to a large extent by treatment with 1-methylcyclopropene (MCP), an ethylene action inhibitor. Transcripts for the LE-ACS6 gene, which accumulated in preclimacteric fruit but not in untreated ripening fruit, did accumulate in ripening fruit treated with MCP. Treatment of young fruit with propylene prevented the accumulation of transcripts for this gene. LE-ACS1A, LE-ACS3, and TAE1 genes were expressed constitutively in the fruit throughout development and ripening irrespective of whether the fruit was treated with MCP or propylene. The transcripts for LE-ACO1 and LE-ACO4 genes already existed in preclimacteric fruit and increased greatly when ripening commenced. These increases in LE-ACO mRNA with ripening were also prevented by treatment with MCP. The results suggest that in tomato fruit the preclimacteric system 1 ethylene is possibly mediated via constitutively expressed LE-ACS1A and LE-ACS3 and negatively Feedback-regulated LE-ACS6 genes with preexisting LE-ACO1 and LE-ACO4 mRNAs. At the onset of the climacteric stage, it shifts to system 2 ethylene, with a large accumulation of LE-ACS2, LE-ACS4, LE-ACO1, and LE-ACO4 mRNAs as a result of a positive Feedback regulation. This transition from system 1 to system 2 ethylene production might be related to the accumulated level of NR mRNA.

  • Expression and Internal Feedback regulation of ACC synthase and ACC oxidase genes in ripening tomato fruit
    Plant and Cell Physiology, 1997
    Co-Authors: Akira Nakatsuka, Shinjiro Shiomi, Yasutaka Kubo, Akitsugu Inaba
    Abstract:

    We have examined whether or not a positive Feedback regulation of gene expression for 1-aminocyclopropane-1-carboxylate (ACC) synthase and ACC oxidase also operates in ripening tomato (Lycopersicon esculentum) fruit during the burst of ethylene production. Two cDNA fragments for ACC synthase and one for ACC oxidase were cloned with high homology to already known genes involved in ethylene biosynthesis in ripening tomato fruit. Accumulation of mRNAs which hybridize to these cDNA probes were induced in mature green fruit within two days by treatment with propylene. In the fruit ripened from the turning stage, red color development, ethylene production, ACC content, and activities of ACC synthase and ACC oxidase increased as maturity progressed. The abundance of two ACC synthase and one ACC oxidase mRNAs in the fruit increased from the turning to pink stage and were followed by a slight decline towards the red stage. These increases in mRNAs abundance with ripening were prevented to a large extent by treatment with the ethylene action inhibitor, 1-methylcyclopropene (MCP). This was most pronounced in the fruit treated with MCP at the turning stage, in which the accumulation of ACC synthase and ACC oxidase transcripts was almost completely eliminated in the first two d, precisely the same stage at which the control fruit had the greatest level of each mRNA accumulation. The inhibition of transcript accumulation recovered to the control level within two to four d. MCP also decreased ethylene biosynthetic activity, although this decrease did not reflect the reduction in the mRNAs accumulation. These results suggest that a strong positive Feedback regulation is involved in ethylene biosynthesis at the gene transcriptional level in tomato fruit, even at the stage with a burst of ethylene production.

J B Theocharis - One of the best experts on this subject based on the ideXlab platform.

  • a locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation
    Neurocomputing, 2007
    Co-Authors: T G Barbounis, J B Theocharis
    Abstract:

    In this paper, a locally Feedback dynamic fuzzy neural network (LF-DFNN) for modeling of temporal processes is suggested. The model is composed of dynamic TSK-type fuzzy rules where the consequent sub-models are implemented by recurrent neural networks with Internal Feedback paths and dynamic neuron synapses. The LF-DFNN exhibits some interesting features, such as enhanced representation power, local modeling characteristics, model parsimony, and stable learning. Training of the LF-DFNN models is achieved using an optimal on-line learning scheme, the decoupled recursive prediction error algorithm (DRPE). The method has reduced computational demands and is derived through decomposition of the weight vector to several mutually exclusive weight groups. The partial derivatives required for the implementation of the training algorithm are calculated using the adjoint model approach, adapted to the fuzzy network's architecture exercised here. The paper deals with the wind speed prediction in wind farms, using spatial information from remote measurement stations. The LF-DFNN networks are used as advanced forecast models, providing multi-step ahead wind speed estimates from 15min to 3h ahead. Extensive simulation results demonstrate that our models exhibit superior performance compared to other network types suggested in the literature. Furthermore, it is shown that DRPE outperforms three gradient descent algorithms, in training of the recurrent forecast models.

  • long term wind speed and power forecasting using local recurrent neural network models
    IEEE Transactions on Energy Conversion, 2006
    Co-Authors: T G Barbounis, J B Theocharis, Minas C Alexiadis, P S Dokopoulos
    Abstract:

    This paper deals with the problem of long-term wind speed and power forecasting based on meteorological information. Hourly forecasts up to 72-h ahead are produced for a wind park on the Greek island of Crete. As inputs our models use the numerical forecasts of wind speed and direction provided by atmospheric modeling system SKIRON for four nearby positions up to 30 km away from the wind turbine cluster. Three types of local recurrent neural networks are employed as forecasting models, namely, the infinite impulse response multilayer perceptron (IIR-MLP), the local activation Feedback multilayer network (LAF-MLN), and the diagonal recurrent neural network (RNN). These networks contain Internal Feedback paths, with the neuron connections implemented by means of IIR synaptic filters. Two novel and optimal on-line learning schemes are suggested for the update of the recurrent network's weights based on the recursive prediction error algorithm. The methods assure continuous stability of the network during the learning phase and exhibit improved performance compared to the conventional dynamic back propagation. Extensive experimentation is carried out where the three recurrent networks are additionally compared to two static models, a finite-impulse response NN (FIR-NN) and a conventional static-MLP network. Simulation results demonstrate that the recurrent models, trained by the suggested methods, outperform the static ones while they exhibit significant improvement over the persistent method.

  • a recurrent fuzzy neural model for dynamic system identification
    Systems Man and Cybernetics, 2002
    Co-Authors: Paris A Mastorocostas, J B Theocharis
    Abstract:

    This paper presents a fuzzy modeling approach for identification of dynamic systems. In particular, a new fuzzy model, the Dynamic Fuzzy Neural Network (DFNN), consisting of recurrent TSK rules, is developed. The premise and defuzzification parts are static while the consequent parts of the fuzzy rules are recurrent neural networks with Internal Feedback and time delay synapses. The network is trained by means of a novel learning algorithm, named Dynamic-Fuzzy Neural Constrained Optimization Method (D-FUNCOM), based on the concept of constrained optimization. The proposed algorithm is general since it can be applied to locally as well as fully recurrent networks, regardless of their structures. An adaptation mechanism of the maximum parameter change is presented as well. The proposed dynamic model, equipped with the learning algorithm, is applied to several temporal problems, including modeling of a NARMA process and the noise cancellation problem. Performance comparisons are conducted with a series of static and dynamic systems and some existing recurrent fuzzy models. Simulation results show that DFNN compares favorably with its competing rivals and thus it can be considered for efficient system identification.

D.j. Rausen - One of the best experts on this subject based on the ideXlab platform.

  • a mean value model for control of homogeneous charge compression ignition hcci engines
    Journal of Dynamic Systems Measurement and Control-transactions of The Asme, 2005
    Co-Authors: D.j. Rausen, J.a. Eng, Anna G Stefanopoulou, Junmo Kang, T.w. Kuo
    Abstract:

    A Mean Value Model (MVM) for a Homogeneous Charge Compression Ignition (HCCI) engine is presented. Using a phenomenological zero-dimensional approach with five continuous and three discrete states we first model the effects of the Exhaust Gas Recirculation (EGR) valve, the exhaust Rebreathing Lift (RBL), and the fueling rate on the state of charge in the cylinder at intake valve closing. An Arrhenius integral is then used to model the start of combustion, θ soc . A series of simple algebraic relations that captures the combustion duration and heat release is finally used to model the state of charge after the HCCI combustion and the Location of Peak Pressure (LPP). The model is parametrized and validated using steady-state test data from an experimental gasoline engine at the General Motors Corporation. The simple model captures the temperature, pressure, air-to-fuel ratio, and inert gas fraction of the exhausted mass flow. This characterization is important for the overall HCCI dynamics because the thermodynamic state (pressure, temperature) and concentration (oxygen and inert gas) of the exhausted mass flow affect the next combustion event. The high dilution level in HCCI engines increases the significance of this Internal Feedback that generally exists to a smaller extent in conventional spark-ignition and compression-ignition Internal combustion engines.

  • A mean-value model for control of homogeneous charge compression ignition (HCCI) engines
    Proceedings of the 2004 American Control Conference, 2004
    Co-Authors: D.j. Rausen, J.a. Eng, J M Kang, Anna G Stefanopoulou, T.w. Kuo
    Abstract:

    A Mean Value Model (MVM) for a Homogeneous Charge Compression Ignition (HCCI) engine is presented. Using a phenomenological zero-dimensional approach with five continuous and three discrete states we first model the effects of the Exhaust Gas Recirculation (EGR) valve, the exhaust Rebreathing Lift (RBL), and the fueling rate on the state of charge in the cylinder at intake valve closing. An Arrhenius integral is then used to model the start of combustion, theta(soc). A series of simple algebraic relations that captures the combustion duration and heat release is finally used to model the state of charge after the HCCI combustion and the Location of Peak Pressure (LPP). The model is parametrized and validated using steady-state test data from an experimental gasoline engine at the General Motors Corporation. The simple model captures the temperature, pressure, air-to-fuel ratio, and inert gas fraction of the exhausted mass flow. This characterization is important for the overall HCCI dynamics because the thermodynamic state (pressure, temperature) and concentration (oxygen and inert gas) of the exhausted mass flow affect the next combustion event. The high dilution level in HCCI engines increases the significance of this Internal Feedback that generally exists to a smaller extent in conventional spark-ignition and compression-ignition Internal combustion engines.