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Modelling of casings
Published in Marek Pawelczyk, Stanislaw Wrona, Noise-Controlling Casings, 2023
Marek Pawelczyk, Stanislaw Wrona, C. W. Isaac, J. Klamka, J. Wyrwal
Model is referred to as a physical, if it is based on a priori information about system it describes. In other words, physical model is based on mathematical description of phenomenon or process that is to be represented by the model. Physical models usually involve physically interpretable parameters, that is, parameters which represent real properties of the system what is one of their most important advantages. As an essential prerequisite, physical models need a priori knowledge of the system, which is its characteristic feature. If nothing is known about the system, then empirical models have to be applied. An empirical model relies on observation rather than theory. Empirical models are focused on describing the data with the specification of very few assumptions about the data being analysed. In case of an empirical model the structure of the model is determined by the observed relationships among experimental data. An important advantage of empirical models is that they can be used in black box situations and that they typically require much less time and resources compared to physical models. Empirical models are universally applicable, easy to set up, but limited in scope. Physical models, in contrast, allow usually deeper insights into system performance and better predictions, but they require a priori knowledge about the system and often need more time and resources to be synthesized.
Exposure–Dose Relationships
Published in S.M. Rappaport, Thomas J. Smith, Exposure Assessment for Epidemiology and Hazard Control, 2020
Where the empirical model requires few assumptions to be developed, it has important problems of generalization. First, the model estimates the concentration profile present in the measured fluid, such as blood, which may not be the same as in the target tissue and, therefore, must be assumed to be correlated with it. Second, the model only applies for the conditions (e.g., activity level) and range of exposures observed. Finally, it is not clear how individual physiologic differences and activity patterns will affect the model’s parameters. Some extrapolation outside the model’s conditions of derivation is probably reasonable, but the limits are not known and it may be difficult to estimate the error introduced. The range of application of the empirical model can only be extended by fitting additional data.
Review of Vadose Zone Flow and Transport Models
Published in L.G. Wilson, Lorne G. Everett, Stephen J. Cullen, Handbook of Vadose Zone Characterization & Monitoring, 2018
John H. Kramer, Stephen J. Cullen
Other approaches including nontheory based empirical models (e.g., transfer-function models) have been employed with some success on a site-specific basis (Jury, 1982). An empirical model is based on observations through time or space which are fitted, usually involving a regression, to a simplified mathematical expression. These can be scaled to extend past the domain of the original empirical observations. Empirical models have no theoretical basis and if transferred from one place to another may become invalid. These models can also not be used to interpret the importance of physically measurable properties. Projecting such models into the future is uncertain, particularly in transient flow regimes where input and through-flow may not bear any relation to the conditions for which the model was derived.
Water erosion assessment methods: a review
Published in ISH Journal of Hydraulic Engineering, 2021
Empirical models are developed based on relating main erosion controlling factors to soil loss through detailed field observation and measurement (Beach 1987). Hudson also articulates it in a similar way. An empirical model is one based on observation or experiment, and not derived from theory. It fits observed facts and allows us to predict what will happen in certain circumstances because we know what has happened before in those situations (Hudson 1995). Empirical models are not considered the intrinsic processes involved and how the system functions. These models can only be operated in the designed direction where inputs go into one side of the equation and the output on the other side. Scientist developed the parameters under particular environmental circumstances, and for that reason, the parameters are appropriate to only those situation. Although empirical models are speedy in predicting soil erosion, they are applicable only within the boundaries and needs a plausibly extensive (long-term) data (Beach 1987). The Universal Soil Loss Equation (USLE) model and its derivatives (modified USLE [MUSLE] and revised USLE [RUSLE]) are the most commonly used empirical water erosion models and are described in brief below.
An analysis of used lubricant recycling unit in Turkey
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019
Serap Ulusam Seçkiner, Al Mothana Al Shareef
One of the main goals of this study was to spotlight the performance of various empirical models generated for the purposes of controlling the chemical processes. Prediction ability and accuracy is the key character of any empirical model. In this study, four different empirical models with different techniques described in Table 2 were generated in order to forecast chemical process future values, i.e. production and quality. Performance measurement of MLR, MNLR, ANN, and RR were evaluated with MAD, MSE, RMSE, and MAPE. ANN accuracy performance with all performance measures was superior to the accuracy performances of MLRn, MNR, and RR. It seems to be that the ANN technique is the best choice for evaluating oil-recycling production planning.
Concrete cracking prediction under combined prestress and strand corrosion
Published in Structure and Infrastructure Engineering, 2019
Lei Wang, Lizhao Dai, Hanbing Bian, Yafei Ma, Jianren Zhang
The establishment of the prediction model is another important issue for investigating corrosion-induced cracking. Many studies have been undertaken to predict corrosion-induced cracking in reinforced concrete (RC) structures, which can be summarised as: empirical, numerical and analytical models. The empirical model is developed based on experimental data. The primary disadvantages of empirical model is that different experimental methods can lead to a different model parameter (Al-Harthy, Stewart, & Mullard, 2011; Cabrera, 1996; Gonzalez, Andrade, Alonso, & Feliu, 1995; Jin, Zhao, Zhao, & Yang, 2016). For numerical model, the finite-element modeling is mostly used to numerically simulate the corrosion-induced cracking (Biondini & Vergani, 2015; Chernin, Val, & Volokh, 2010). However, the validation of numerical simulation is still a complex issue. For analytical models, corrosion-induced cracking is usually modeled by the thick-walled cylinder theory (Bažant, 1979; Bhargava, Ghosh, Mori, & Ramanujam, 2005; Li, Melchers, & Zhang, 2006; Li & Yang, 2011; Zhong, Gardoni, & Rosowsky, 2010). A detailed introduction of the prediction models can be found elsewhere (Al-Harthy et al., 2011; Jamali et al., 2013). The investigation of existing studies indicates that the corrosion-induced cracking process can be divided into two stages: crack initiation and propagation (Bertolini, 2008; El Maaddawy & Soudki, 2007; Val, Chernin, & Stewart, 2009). Crack initiation represents the stage from corrosion initiation to cover cracking, and crack propagation contains the stage from cover cracking to the critical crack width.