The Experts below are selected from a list of 814569 Experts worldwide ranked by ideXlab platform
Chun Cheng Yang - One of the best experts on this subject based on the ideXlab platform.
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Effect of pressure on melting temperature of carbon dioxide
The Journal of Chemical Thermodynamics, 2005Co-Authors: Chun Cheng Yang, Qing JiangAbstract:Abstract The melting temperature–pressure phase diagram for carbon dioxide with cubic Pa3 structure (CO2-I) is predicted through the Clapeyron equation where the pressure-dependent volume difference is Modeled by introducing the effect of surface stress induced pressure. The Model Prediction is found to be consistent with the present experimental results.
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temperature pressure phase diagram of germanium determined by clapeyron equation
Scripta Materialia, 2004Co-Authors: Chun Cheng Yang, Q.c. JiangAbstract:The temperature–pressure phase diagram (T–P) for germanium (Ge) is predicted through the Clapeyron equation with several considerations on the pressure-dependent volume difference, solid transition enthalpy and surface stress induced pressure. The Model Prediction is found to be consistent with other experimental and theoretical results.
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Temperature¿pressure phase diagram of germanium determined by Clapeyron equation
Scripta Materialia, 2004Co-Authors: Chun Cheng Yang, Q.c. JiangAbstract:The temperature–pressure phase diagram (T–P) for germanium (Ge) is predicted through the Clapeyron equation with several considerations on the pressure-dependent volume difference, solid transition enthalpy and surface stress induced pressure. The Model Prediction is found to be consistent with other experimental and theoretical results.
Q.c. Jiang - One of the best experts on this subject based on the ideXlab platform.
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temperature pressure phase diagram of germanium determined by clapeyron equation
Scripta Materialia, 2004Co-Authors: Chun Cheng Yang, Q.c. JiangAbstract:The temperature–pressure phase diagram (T–P) for germanium (Ge) is predicted through the Clapeyron equation with several considerations on the pressure-dependent volume difference, solid transition enthalpy and surface stress induced pressure. The Model Prediction is found to be consistent with other experimental and theoretical results.
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Temperature¿pressure phase diagram of germanium determined by Clapeyron equation
Scripta Materialia, 2004Co-Authors: Chun Cheng Yang, Q.c. JiangAbstract:The temperature–pressure phase diagram (T–P) for germanium (Ge) is predicted through the Clapeyron equation with several considerations on the pressure-dependent volume difference, solid transition enthalpy and surface stress induced pressure. The Model Prediction is found to be consistent with other experimental and theoretical results.
Michael Frenklach - One of the best experts on this subject based on the ideXlab platform.
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Sensitivity analysis of uncertainty in Model Prediction.
The journal of physical chemistry. A, 2008Co-Authors: Trent Russi, Andrew Packard, Ryan Feeley, Michael FrenklachAbstract:Data Collaboration is a framework designed to make inferences from experimental observations in the context of an underlying Model. In the prior studies, the methodology was applied to Prediction on chemical kinetics Models, consistency of a reaction system, and discrimination among competing reaction Models. The present work advances Data Collaboration by developing sensitivity analysis of uncertainty in Model Prediction with respect to uncertainty in experimental observations and Model parameters. Evaluation of sensitivity coefficients is performed alongside the solution of the general optimization ansatz of Data Collaboration. The obtained sensitivity coefficients allow one to determine which experiment/parameter uncertainty contributes the most to the uncertainty in Model Prediction, rank such effects, consider new or even hypothetical experiments to perform, and combine the uncertainty analysis with the cost of uncertainty reduction, thereby providing guidance in selecting an experimental/theoretical strategy for community action.
Trent Russi - One of the best experts on this subject based on the ideXlab platform.
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Sensitivity analysis of uncertainty in Model Prediction.
The journal of physical chemistry. A, 2008Co-Authors: Trent Russi, Andrew Packard, Ryan Feeley, Michael FrenklachAbstract:Data Collaboration is a framework designed to make inferences from experimental observations in the context of an underlying Model. In the prior studies, the methodology was applied to Prediction on chemical kinetics Models, consistency of a reaction system, and discrimination among competing reaction Models. The present work advances Data Collaboration by developing sensitivity analysis of uncertainty in Model Prediction with respect to uncertainty in experimental observations and Model parameters. Evaluation of sensitivity coefficients is performed alongside the solution of the general optimization ansatz of Data Collaboration. The obtained sensitivity coefficients allow one to determine which experiment/parameter uncertainty contributes the most to the uncertainty in Model Prediction, rank such effects, consider new or even hypothetical experiments to perform, and combine the uncertainty analysis with the cost of uncertainty reduction, thereby providing guidance in selecting an experimental/theoretical strategy for community action.
Jan Van Impe - One of the best experts on this subject based on the ideXlab platform.
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a tutorial on uncertainty propagation techniques for predictive microbiology Models a critical analysis of state of the art techniques
International Journal of Food Microbiology, 2018Co-Authors: Simen Akkermans, Philippe Nimmegeers, Jan Van ImpeAbstract:Abstract Building mathematical Models in predictive microbiology is a data driven science. As such, the experimental data (and its uncertainty) has an influence on the final Predictions and even on the calculation of the Model Prediction uncertainty. Therefore, the current research studies the influence of both the parameter estimation and uncertainty propagation method on the calculation of the Model Prediction uncertainty. The study is intended as well as a tutorial to uncertainty propagation techniques for researchers in (predictive) microbiology. To this end, an in silico case study was applied in which the effect of temperature on the microbial growth rate was Modelled and used to make simulations for a temperature profile that is characterised by variability. The comparison of the parameter estimation methods demonstrated that the one-step method yields more accurate and precise calculations of the Model Prediction uncertainty than the two-step method. Four uncertainty propagation methods were assessed. The current work assesses the applicability of these techniques by considering the effect of experimental uncertainty and Model input uncertainty. The linear approximation was demonstrated not always to provide reliable results. The Monte Carlo method was computationally very intensive, compared to its competitors. Polynomial chaos expansion was computationally efficient and accurate but is relatively complex to implement. Finally, the sigma point method was preferred as it is (i) computationally efficient, (ii) robust with respect to experimental uncertainty and (iii) easily implemented.
