A model study of the 30-50 day oscillation In the tropical atmosphere

Using a global spectral model with wave-CISK formulation we have generated an eastward de which. Resembles the observed 30-50 day mode. This has a scale of global wave number one and two years structure in the vertical. It has the structure of a composite of Kelvin and Rossby waves. This composite system moves eastwards. We have also studied a linear two-level analytical model to understand the nonlinear spectral model response. In the linear as well as in the nonlinear spectral model, as we Increase the moisture availability factor the speeds of the waves decrease. In the linear model this speed is found to be independent of drag for all types of waves. In the nonlinear spectral model for a given drag there is a critical value of the moisture availability factor for which the wave becomes stationary and beyond which even shows westward propagation. Thus both moisture availability and nonlinearity appear to contribute to the slow eastward speed of the equatorial 30-50 day mode.

A Comparison of Surrogate Modeling Techniques for Global Sensitivity Analysis in Hybrid Simulation

Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation is extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation are conceived as being deterministic. However, associated uncertainties are present, whilst simulation deviation, due to their presence, could be significant. In this regard, global sensitivity analysis based on Sobol’ indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol’ sensitivity indices requires an unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques using machine learning data-driven regression are utilized to alleviate this burden. This study extends the current global sensitivity analysis practices in hybrid simulation by employing various different surrogate modeling methodologies as well as providing comparative results. In particular, polynomial chaos expansion, Kriging and polynomial chaos Kriging are used. A case study encompassing a virtual hybrid model is employed, and hybrid model response quantities of interest are selected. Their respective surrogates are developed, using all three aforementioned techniques. The Sobol’ indices obtained utilizing each examined surrogate are compared with each other, and the results highlight potential deviations when different surrogates are used.

Full-Field History-Matching of Commingling Stacked Reservoirs: A Case Study of an Oman Southern Asset

This paper presents an innovative and practical workflow framework implemented in an Oman southern asset. The asset consists of three isolated accumulations or fields or structures that differ in rock and fluid properties. Each structure has multiple stacked members of Gharif and Alkhlata formations. Oil production started in 1986, with more than 60 commingling wells. The accumulations are not only structurally and stratigraphically complicated but also dynamically complex with numerous input uncertainties. It was impossible to assist the history matching process using a modern optimization-based technique due to the structural complexities of the reservoirs and magnitudes of the uncertain parameters. A structured history-matching approach, Stratigraphic Method (SM), was adopted and guided by suitable subsurface physics by adjusting multi-uncertain parameters simultaneously within the uncertainty envelope to mimic the model response. An essential step in this method is the preliminary analysis, which involved integrating various geological and engineering data to understand the reservoir behavior and the physics controlling the reservoir dynamics. The first step in history-matching these models was to adjust the critical water saturation to correct the numerical water production by honoring the capillary-gravity equilibrium and reservoir fluid flow dynamics. The significance of adjusting the critical water saturation before modifying other parameters and the causes of this numerical water production is discussed. Subsequently, the other major uncertain parameters were identified and modified, while a localized adjustment was avoided except in two wells. This local change was guided by a streamlined technique to ensure minimal model modification and retain geological realism. Overall, acceptable model calibration results were achieved. The history-matching framework's novelty is how the numerical water production was controlled above the transition zone and how the reservoir dynamics were understood from the limited data.

Online Estimation of Terminal Airspace Sector Capacity from ATC Workload

Neural Partial Differentiation (NPD) approach is applied to estimate terminal airspace sector capacity in real-time from the ATC (Air Traffic Controller) dynamical neural model with permissible safe separation and affordable workload. A neural model of a multi-input-single-output (MISO) ATC dynamical system is primarily established and used to estimate parameters from the experimental data using NPD. Since the relative standard deviations of these estimated parameters are lesser, the predicted neural model response is well matched with the intervention of ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters that are unknown in practice.

Probabilistic screening and behavior of solar cells under Gaussian parametric uncertainty using polynomial chaos representation model

The paper presents a hierarchical polynomial chaos expansion-based probabilistic approach to analyze the single diode solar cell model under Gaussian parametric uncertainty. It is important to analyze single diode solar cell model response under random events or factors due to uncertainty propagation. The optimal values of five electrical parameters associated with the single diode model are estimated using six deterministic optimization techniques through the root-mean-square minimization approach. Values corresponding to the best objective function response are further utilized to describe the probabilistic design space of each random electrical parameter under uncertainty. Adequate samples of each parameter corresponding Gaussian uncertain distribution are generated using Latin hypercube sampling. Furthermore, a multistage probabilistic approach is adopted to evaluate the model response using low-cost polynomial chaos series expansion and perform global sensitivity analysis under specified Gaussian distribution. Coefficients of polynomial basis functions are calculated using least square and least angle regression techniques. Unlike the highly non-linear and complex single diode representation of solar cells, the polynomial chaos expansion model provides a low computational burden and reduced complexity. To ensure reproducibility, probabilistic output response computed using proposed polynomial chaos expansion model is compared with the true model response. Finally, a multidimensional sensitivity analysis is performed through Sobol decomposition of polynomial chaos series representation to quantify the contribution of each parameter to the variance of the probabilistic response. The validation and assessment result shows that the output probabilistic response of the solar cell under Gaussian parametric uncertainty correlates to a Rayleigh probability distribution function. Output response is characterized by a mean value of 0.0060 and 0.0760 for RTC France and Solarex MSX83 solar cells, respectively. The standard deviation of $$ \pm $$ ± 0.0034 and $$ \pm $$ ± 0.0052 was observed in the probabilistic response for RTC France and Solarex MSX83 solar cells, respectively.

A survey of surrogate modeling techniques for global sensitivity analysis in hybrid simulation

Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual loading-rate-sensitive substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation has been extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation is conceived as deterministic. However, associated uncertainties are present whilst simulation deviation due to their presence could be significant. To this regard, global sensitivity analysis based on Sobol' indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol' sensitivity indices requires unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques are used to alleviate this burden. In this paper, three different surrogate modeling methods are examined, namely polynomial chaos expansion, Kriging and polynomial chaos Kriging. A case study encompassing a virtual hybrid model is employed and hybrid model response quantities of interest are selected. Their respective surrogates are developed using all three aforementioned techniques. The Sobol' indices obtained utilizing each examined surrogate are compared with each other and results highlight potential deviations.

DIGITAL MODEL OF BOILER HEAT BALANCE

The normative method (NM) of boilers thermal calculation, repeatedly confirmed and refined, contains the structure of ideas and methods that were retained and adapted during the transition to digital technologies. As applied to the analysis of the heat balance of a boiler with flare furnaces, this required the transformation of a large array of initial and reference data, which cannot be applied unchanged when using a computer. This applies to graphical and tabular data, which form up to 80 % of the volume of NM. To obtain the correlation dependences, the authors use a simple and reliable method of unknown coefficients with the inclusion of a verification algorithm, in the case of equidistant arguments these are the Gregory-Newton coefficients. As shown by a preliminary analysis, for almost all dependencies a polynomial of the second degreesometimes replaced by two polynomials is sufficient. By varying the determining factors in the range of nominal values ±20 %, the model response was obtained in the form of a change in fuel consumption. Quantitatively, all material corre-sponds to the normative data, is presented in digital format and methodically corresponds to the Mathcad-15 package. In contrast to the well-known works in this area, all factors affecting the heat balance are represented by approximations taking into account the variability of temperature and pressure.

Probabilistic Mesoscale Analysis of Concrete Beams Subjected to Flexure

In this paper, the probabilistic behavior of plain concrete beams subjected to flexure is studied using a continuous mesoscale model. The model is two-dimensional where aggregate and mortar are treated as separate constituents having their own characteristic properties. The aggregate is represented as ellipses and generated under prescribed grading curves. Ellipses are randomly placed so it requires probabilistic analysis for model using the Monte Carlo simulation with 20 realizations to represent geometry uncertainty. The nonlinear behavior is simulated with an isotropic damage model for the mortar, while the aggregate is assumed to be elastic. The isotropic damage model softening behavior is defined in terms of fracture mechanics parameters. This damage model is compared with the fixed crack model in macroscale study before using it in the mesoscale model. Then, it is used in the mesoscale model to simulate flexure test and compared to experimental data and shows a good agreement. The probabilistic behavior of the model response is presented through the standard deviation, moment parameters and cumulative probability density functions in different loading stages. It shows variation of the probabilistic characteristics between pre-peak and post-peak behaviour of load-CMOD curves.

Qualitative Investigation of Damage Initiation at Meso-Scale in Spheroidized C45EC Steels by Using Crystal Plasticity-Based Numerical Simulations

This research uses EBSD data of two thermo-mechanically processed medium carbon (C45EC) steel samples to simulate micromechanical deformation and damage behavior. Two samples with 83% and 97% spheroidization degrees are subjected to virtual monotonic quasi-static tensile loading. The ferrite phase is assigned already reported elastic and plastic parameters, while the cementite particles are assigned elastic properties. A phenomenological constitutive material model with critical plastic strain-based ductile damage criterion is implemented in the DAMASK framework for the ferrite matrix. At the global level, the calibrated material model response matches well with experimental results, with up to ~97% accuracy. The simulation results provide essential insight into damage initiation and propagation based on the stress and strain localization due to cementite particle size, distribution, and ferrite grain orientations. In general, it is observed that the ferrite–cementite interface is prone to damage initiation at earlier stages triggered by the cementite particle clustering. Furthermore, it is observed that the crystallographic orientation strongly affects the stress and stress localization and consequently nucleating initial damage.