Experimental Data Extrapolation by Using V Order Logarithmic Polynomials

2002 ◽  
Author(s):  
R. Lanzafame ◽  
M. Messina
Keyword(s):  
Experimental Data ◽  
Computational Models ◽  
Functional Form ◽  
Least Squares Method ◽  
Correlation Factor ◽  
Mathematical Function ◽  
Mathematical Functions ◽  
Wide Range ◽  
Data Extrapolation ◽  
Fifth Order

In this work, the authors consider the possibility to extrapolate experimental data on fuels specific heat at constant pressure, beyond the range of temperature investigated in the experimental measurements. With a proper extrapolation it is possible to avoid the necessary but empirical linear extrapolation, often used by CFD programs. Mathematical functions obtained from fitting experimental data are very useful when computational models on ICE are implemented. To obtain reliable results from these models, a great precision is required to the mathematical functions. In this work a new polynomial, used in order to fit experimental data on gases properties at low pressure, is presented. The new mathematical function presented has the functional form of a fifth order Logarithmic Polynomial, and it is evaluated through the least squares method, on the basis of experimental thermodynamic data found in literature. This new function presents three great advantage in respect to traditional polynomials used in literature: 1) it offers a great fitting precision (correlation factor R2 greater than 0.99); 2) it is able to cover wide range of temperature with a single polynomial; 3) it gives the possibility to extrapolate data beyond experimental temperature range.

Confidence-Based Uncertainty Quantification and Model Validation for Simulations of High-Speed Impact Problems

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Min-Yeong Moon ◽  
Oishik Sen ◽  
Nirmal Kumar Rai ◽  
Nicholas J. Gaul ◽  
Kyung K. Choi ◽  
...  
Keyword(s):  
Experimental Data ◽  
High Speed ◽  
Computational Models ◽  
Classical Problem ◽  
Simulation Models ◽  
Confidence Levels ◽  
Model Confidence ◽  
Wide Range ◽  
Final Length ◽  
The Impact

Abstract Validation exercises for computational models of materials under impact must contend with sparse experimental data as well as with uncertainties due to microstructural stochasticity and variabilities in thermomechanical properties of the material. This paper develops statistical methods for determining confidence levels for verification and validation of computational models subject to aleatoric and epistemic uncertainties and sparse stochastic experimental datasets. To demonstrate the method, the classical problem of Taylor impact of a copper bar is simulated. Ensembles of simulations are performed to cover the range of variabilities in the material properties of copper, specifically the nominal yield strength A, the hardening constant B, and the hardening exponent n in a Johnson–Cook material model. To quantify uncertainties in the simulation models, we construct probability density functions (PDFs) of the ratios of the quantities of interest, viz., the final bar diameter Df to the original diameter D0 and the final length Lf to the original length L0. The uncertainties in the experimental data are quantified by constructing target output distributions for these QoIs (Df/D0 and Lf/L0) from the sparse experimental results reported in literature. The simulation output and the experimental output distributions are compared to compute two metrics, viz., the median of the model prediction error and the model confidence at user-specified error level. It is shown that the median is lower and the model confidence is higher for Lf/L0 compared to Df/D0, implying that the simulation models predict the final length of the bar more accurately than the diameter. The calculated confidence levels are shown to be consistent with expectations from the physics of the impact problem and the assumptions in the computational model. Thus, this paper develops and demonstrates physically meaningful metrics for validating simulation models using limited stochastic experimental datasets. The tools and techniques developed in this work can be used for validating a wide range of computational models operating under input uncertainties and sparse experimental datasets.

LIQUIDUS THERMO-BAROMETER FOR THE MODELING OF MAGNETITE — MELT EQUILIBRIUM

2018 ◽  
pp. 71-80
Author(s):  
N. S. Aryaeva ◽  
E. V. Koptev-Dvornikov ◽  
D. A. Bychkov
Keyword(s):  
Experimental Data ◽  
Liquidus Temperature ◽  
Vertical Structure ◽  
Maximum Error ◽  
Silicate Melt ◽  
System Of Equations ◽  
Wide Range ◽  
Basaltic Melts ◽  
Multidimensional Statistics

A system of equations of thermobarometer for magnetite-silicate melt equilibrium was obtained by method of multidimensional statistics of 93 experimental data of a magnetite solubility in basaltic melts. Equations reproduce experimental data in a wide range of basalt compositions, temperatures and pressures with small errors. Verification of thermobarometers showed the maximum error in liquidus temperature reproducing does not exceed ±7 °C. The level of cumulative magnetite appearance in the vertical structure of Tsypringa, Kivakka, Burakovsky intrusions predicted with errors from ±10 to ±50 m.

scholarly journals Adjustment models for multivariate geodetic time series with vector-autoregressive errors

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Boris Kargoll ◽  
Alexander Dorndorf ◽  
Mohammad Omidalizarandi ◽  
Jens-André Paffenholz ◽  
Hamza Alkhatib
Keyword(s):  
Least Squares Method ◽  
Functional Model ◽  
Expectation Maximization Algorithm ◽  
Two Dimensions ◽  
Vector Autoregressive ◽  
Wide Range ◽  
Engineering Geodesy ◽  
Cross Correlations ◽  
T Distribution ◽  
Multivariate T

Abstract In this contribution, a vector-autoregressive (VAR) process with multivariate t-distributed random deviations is incorporated into the Gauss-Helmert model (GHM), resulting in an innovative adjustment model. This model is versatile since it allows for a wide range of functional models, unknown forms of auto- and cross-correlations, and outlier patterns. Subsequently, a computationally convenient iteratively reweighted least squares method based on an expectation maximization algorithm is derived in order to estimate the parameters of the functional model, the unknown coefficients of the VAR process, the cofactor matrix, and the degree of freedom of the t-distribution. The proposed method is validated in terms of its estimation bias and convergence behavior by means of a Monte Carlo simulation based on a GHM of a circle in two dimensions. The methodology is applied in two different fields of application within engineering geodesy: In the first scenario, the offset and linear drift of a noisy accelerometer are estimated based on a Gauss-Markov model with VAR and multivariate t-distributed errors, as a special case of the proposed GHM. In the second scenario real laser tracker measurements with outliers are adjusted to estimate the parameters of a sphere employing the proposed GHM with VAR and multivariate t-distributed errors. For both scenarios the estimated parameters of the fitted VAR model and multivariate t-distribution are analyzed for evidence of auto- or cross-correlations and deviation from a normal distribution regarding the measurement noise.

scholarly journals Experimental analysis and ANN prediction on performances of finned oval-tube heat exchanger under different air inlet angles with limited experimental data

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 968-980
Author(s):  
Xueping Du ◽  
Zhijie Chen ◽  
Qi Meng ◽  
Yang Song
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Heat Exchanger ◽  
Correlation Equation ◽  
Tube Heat Exchanger ◽  
Flow Friction ◽  
Air Inlet ◽  
Comparison Results ◽  
Wide Range ◽  
Oval Tube

Abstract A high accuracy of experimental correlations on the heat transfer and flow friction is always expected to calculate the unknown cases according to the limited experimental data from a heat exchanger experiment. However, certain errors will occur during the data processing by the traditional methods to obtain the experimental correlations for the heat transfer and friction. A dimensionless experimental correlation equation including angles is proposed to make the correlation have a wide range of applicability. Then, the artificial neural networks (ANNs) are used to predict the heat transfer and flow friction performances of a finned oval-tube heat exchanger under four different air inlet angles with limited experimental data. The comparison results of ANN prediction with experimental correlations show that the errors from the ANN prediction are smaller than those from the classical correlations. The data of the four air inlet angles fitted separately have higher precisions than those fitted together. It is demonstrated that the ANN approach is more useful than experimental correlations to predict the heat transfer and flow resistance characteristics for unknown cases of heat exchangers. The results can provide theoretical support for the application of the ANN used in the finned oval-tube heat exchanger performance prediction.

scholarly journals Modelling the Inflation and Elastic Instabilities of Rubber-Like Spherical and Cylindrical Shells Using a New Generalised Neo-Hookean Strain Energy Function

2021 ◽  
Author(s):  
Afshin Anssari-Benam ◽  
Andrea Bucchi ◽  
Giuseppe Saccomandi
Keyword(s):  
Experimental Data ◽  
Energy Function ◽  
Limit Point ◽  
Strain Energy ◽  
Cylindrical Shells ◽  
Strain Energy Function ◽  
Model Parameters ◽  
Wide Range ◽  
Cylindrical Tubes ◽  
Gent Model

AbstractThe application of a newly proposed generalised neo-Hookean strain energy function to the inflation of incompressible rubber-like spherical and cylindrical shells is demonstrated in this paper. The pressure ($P$ P ) – inflation ($\lambda $ λ or $v$ v ) relationships are derived and presented for four shells: thin- and thick-walled spherical balloons, and thin- and thick-walled cylindrical tubes. Characteristics of the inflation curves predicted by the model for the four considered shells are analysed and the critical values of the model parameters for exhibiting the limit-point instability are established. The application of the model to extant experimental datasets procured from studies across 19th to 21st century will be demonstrated, showing favourable agreement between the model and the experimental data. The capability of the model to capture the two characteristic instability phenomena in the inflation of rubber-like materials, namely the limit-point and inflation-jump instabilities, will be made evident from both the theoretical analysis and curve-fitting approaches presented in this study. A comparison with the predictions of the Gent model for the considered data is also demonstrated and is shown that our presented model provides improved fits. Given the simplicity of the model, its ability to fit a wide range of experimental data and capture both limit-point and inflation-jump instabilities, we propose the application of our model to the inflation of rubber-like materials.

scholarly journals Relative Entropy Method Applied for the Fatigue Life Distribution of Carbon Fiber/Epoxy Composites

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 224
Author(s):  
Changsheng Yuan ◽  
Yingjie Liang
Keyword(s):  
Experimental Data ◽  
Carbon Fiber ◽  
Relative Entropy ◽  
Least Squares Method ◽  
Entropy Method ◽  
Epoxy Composites ◽  
Order Moment ◽  
Kolmogorov Smirnov ◽  
Relative Entropy Method ◽  
Carbon Fiber Epoxy

This paper verifies the feasibility of the relative entropy method in selecting the most suitable statistical distribution for the experimental data, which do not follow an exponential distribution. The efficiency of the relative entropy method is tested through the fractional order moment and the logarithmic moment in terms of the experimental data of carbon fiber/epoxy composites with different stress amplitudes. For better usage of the relative entropy method, the efficient range of its application is also studied. The application results show that the relative entropy method is not very fit for choosing the proper distribution for non-exponential random data when the heavy tail trait of the experimental data is emphasized. It is not consistent with the Kolmogorov–Smirnov test but is consistent with the residual sum of squares in the least squares method whenever it is calculated by the fractional moment or the logarithmic moment. Under different stress amplitudes, the relative entropy method has different performances.

The Petrogenetic Model, an Extension of Bowen's Petrogenetic Grid

1962 ◽  
Vol 99 (6) ◽  
pp. 558-569 ◽  
Author(s):  
Peter J. Wyllie
Keyword(s):  
Experimental Data ◽  
Constant Pressure ◽  
Three Dimensional ◽  
Pore Fluid ◽  
Fluid Composition ◽  
Solid Reaction ◽  
Petrogenetic Model ◽  
Wide Range ◽  
Opposite Face ◽  
Reaction Surfaces

AbstractBowen's petrogenetic grid is a PT projection containing univariant curves for decarbonation, dehydration, and solid-solid reactions, with vapour pressure (Pf) equal to total pressure (Ps). Analysis of experimental data in the system MgO–CO2–H2O leads to an expansion of this grid. Three of the important variables in metamorphism when Pf = Ps are P, T, and variation of the pore fluid composition between H2O and CO2. These can be illustrated in a three-dimensional petrogenetic model; one face is a PT plane for reactions occurring with pure H2O, and the opposite face is a similar plane for reactions with pure CO2; these are separated by an axis for pore fluid composition varying between H2O and CO2. Superposition of the PT faces of the model provides the petrogenetic grid. The reactions within the model are represented by divariant surfaces, which may meet along univariant lines. For dissociation reactions, the surfaces curve towards lower temperatures as the proportion of non-reacting volatile increases, and solid-solid reaction surfaces are parallel to the vapour composition axis and perpendicular to the PT axes. The relative temperatures of reactions and the lines of intersections of the surfaces can be illustrated in isobaric sections. Isobaric sections are used to illustrate reactions proceeding at constant pressure with (1) pore fluid composition remaining constant during the reaction, with temperature increasing (2) pore fluid composition changing during the reaction, with temperature increasing, and (3) pore fluid changing composition at constant temperature. The petrogenetic model provides a convenient framework for a wide range of experimental data.

Effects of Multiple Impacts and Size Distribution of Particles on Erosion Predictions Using CFD

2007 ◽  
Author(s):  
Yongli Zhang ◽  
Brenton S. McLaury ◽  
Siamack A. Shirzai
Keyword(s):  
Experimental Data ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Target Material ◽  
Average Particle ◽  
Multiple Impacts ◽  
Cfd Simulations ◽  
Erosion Damage ◽  
Wide Range

Erosion equations are usually obtained from experiments by impacting solid particles entrained in a gas or liquid on a target material. The erosion equations are utilized in CFD (Computational Fluid Dynamics) models to predict erosion damage caused by solid particle impingements. Many erosion equations are provided in terms of an erosion ratio. By definition, the erosion ratio is the mass loss of target material divided by the mass of impacting particles. The mass of impacting particles is the summation of (particle mass × number of impacts) of each particle. In erosion experiments conducted to determine erosion equations, some particles may impact the target wall many times and some other particles may not impact the target at all. Therefore, the experimental data may not reflect the actual erosion ratio because the mass of the sand that is used to run the experiments is assumed to be the mass of the impacting particles. CFD and particle trajectory simulations are applied in the present work to study effects of multiple impacts on developing erosion ratio equations. The erosion equation as well as the CFD-based erosion modeling procedure is validated against a variety of experimental data. The results show that the effect of multiple impacts is negligible in air cases. In water cases, however, this effect needs to be accounted for especially for small particles. This makes it impractical to develop erosion ratio equations from experimental data obtained for tests with sand in water or dense gases. Many factors affecting erosion damage are accounted for in various erosion equations. In addition to some well-studied parameters such as particle impacting speed and impacting angle, particle size also plays a significant role in the erosion process. An average particle size is usually used in analyzing experimental data or estimating erosion damage cases of practical interest. In petroleum production applications, however, the size of sand particles that are entrained in produced fluids can vary over a fairly broad range. CFD simulations are also performed to study the effect of particle size distribution. In CFD simulations, particle sizes are normally distributed with the mean equaling the average size of interest and the standard deviation varying over a wide range. Based on CFD simulations, an equation is developed and can be applied to account for the effect of the particle size distribution on erosion prediction for gases and liquids.

Fuel Droplet Evaporation in a Supercritical Environment

2002 ◽  
Vol 124 (4) ◽  
pp. 762-770 ◽  
Author(s):  
G. S. Zhu ◽  
S. K. Aggarwal
Keyword(s):  
Experimental Data ◽  
Phase Equilibrium ◽  
Ambient Pressure ◽  
Droplet Surface ◽  
Fuel Vapor ◽  
Phase Solubility ◽  
Liquid Density ◽  
Ambient Temperatures ◽  
Droplet Vaporization ◽  
Wide Range

This paper reports a numerical investigation of the transcritical droplet vaporization phenomena. The simulation is based on the time-dependent conservation equations for liquid and gas phases, pressure-dependent variable thermophysical properties, and a detailed treatment of liquid-vapor phase equilibrium at the droplet surface. The numerical solution of the two-phase equations employs an arbitrary Eulerian-Lagrangian, explicit-implicit method with a dynamically adaptive mesh. Three different equations of state (EOS), namely the Redlich-Kwong (RK), the Peng-Robinson (PR), and Soave-Redlich-Kwong (SRK) EOS, are employed to represent phase equilibrium at the droplet surface. In addition, two different methods are used to determine the liquid density. Results indicate that the predictions of RK-EOS are significantly different from those obtained by using the RK-EOS and SRK-EOS. For the phase-equilibrium of n-heptane-nitrogen system, the RK-EOS predicts higher liquid-phase solubility of nitrogen, higher fuel vapor concentration, lower critical-mixing-state temperature, and lower enthalpy of vaporization. As a consequence, it significantly overpredicts droplet vaporization rates, and underpredicts droplet lifetimes compared to those predicted by PR and SRK-EOS. In contrast, predictions using the PR-EOS and SRK-EOS show excellent agreement with each other and with experimental data over a wide range of conditions. A detailed investigation of the transcritical droplet vaporization phenomena indicates that at low to moderate ambient temperatures, the droplet lifetime first increases and then decreases as the ambient pressure is increased. At high ambient temperatures, however, the droplet lifetime decreases monotonically with pressure. This behavior is in accord with the reported experimental data.

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