Modeling and uncertainty

The foregoing example illustrates the importance of combining independent chemical and isotopic data for maximum resolving power and reliability. Several cautions are evident, however. As indicated in figure 4 (matrix equation) all significant carbonaceous sources must be represented in the model and uncertainties for both the sample (y.) and the source matrix (A.j) must be... 

Mathematical model and uncertainty propagation analysis of a hypothetical measurement method in the context of the three-step model. 

Reckhow KH. 1994. Water quality simulation modeling and uncertainty analysis for risk assessment and decision making. Ecol Model 72 1-20. 

Formulating the assessment problem well is an essential foundation for risk assessment. The workshop considered how the use of probabilistic models and uncertainty analysis affects problem formulation and its main components the integration of available information, definition of the assessment endpoint, specification of the conceptual model, and planning of the analysis phase. 

B show the model and pilot plant predictions respectively. Figure 12.6 clearly shows that there are large process-model mismatches in the composition profiles although for a given batch time of tdiS = 220 min the amount of distillate achieved by the experiment was the same as that obtained by the simulation. These process-model mismatches can be attributed to factors such as use of constant Vmodei instead of a dynamic one constant relative volatility parameter used in the model and uncertainties associated with it actual efficiency of the plates. 

Carry out a full experimental investigation of reagent kinetics to generate a model and uncertainty description and apply worst-case optimization. 

This section discusses the development of the models used in the case studies presented in Section V, providing an illustration of the challenges involved in developing models and uncertainty descriptions to support integrated design. 

After the individual discipline modelling and uncertainty assessment, a phase of overall... 

Coupled THMC modelling has to incorporate uncertainties. These uncertainties mainly concern uncertainties in the conceptual model and uncertainty in data, where the latter is related to fact that geologic media usually show strong spatial variability. 

Uncertainty in modeling lies in the fact that a three-dimensional model can either support a structural hypothesis or can be the result of modeling artifacts. Computer modeling is subject to imprecision in the low resolution data, subjectivity in the generation of three-dimensional models, and uncertainty in the formation of structural hypotheses. The theory of possibility, based on fuzzy logic, is used to classify structural hypotheses according to their likelihood to contain multiple-sequence data consistent conformations based upon the sequence-structure relation, R. 

The concept of a dataset lays down the foundation for the Data Collaboration methodology. We associate with experiment E a dataset unit, which consists of the measured value, de, reported uncertainty in the measurements, /g and Ue, and a mathematical model. Me. The model Me is defined as the functional relation between the model active variables, x, and the prediction for dg, yielding leties together data, model, and uncertainty. A dataset, D, is a collection of such dataset units Ue = de, Ug, le, M ). In the present Data Collaboration methodology, the models are the statistical surrogates developed in computer experiments, namely Mg = e(x), and so Ug = dg, Ug, le, i e) and D = C/g. ... 

Gerigk, M. 2012. Assessment of safety of ships after the collision and during the ship salvage using the matrix type risk model and uncertainties. In (IMAM 2012). London Balkema, pp. 715 719. 

IrKorrect physical basis fin model and uncertainties in pl rsical data Source terms selected incorrectly. 

However, all the referenced procedures are based on simulations which are performed at the level of a MDOF model. Recently, an approximate procedure utilizing a deterministic MDOF model and uncertainty analysis at the level of a SDOF model was proposed (Kosic et al. 2014). It can be classified as being somewhere in between the aforementioned procedures for the determination of fragility parameters on the basis of pushover methods, both in terms of computational time and in terms of accuracy. Such an approach is computationally less demanding, since all the simulations are performed at the level of the so-called probabilistic SDOF model. It can therefore be attractive for the fragility analysis of more extensive building stock. 

Geostatistics is applied extensively in these two areas and is increasingly applied to problems of spatial modeling and uncertainty in environmental studies, hydrogeology, and agriculture. 

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