Convergence on Population Dynamics and High-Dimensional Haddock Conjecture

One fundamental step towards grasping the global dynamic structure of a population system involves characterizing the convergence behavior (specifically, how to characterize the convergence behavior). This paper focuses on the neutral functional differential equations arising from population dynamics. With the help of monotonicity techniques and functional methods, we analyze the subtle relations of both the ω-limited set and special point. Meanwhile, we prove that every bounded solution converges to a constant vector, as t tends to positive infinity. Our results correlate with the findings from earlier publications, and our proof yields an improved Haddock conjecture.

A Survey of Econometric Approaches to Convergence Tests of Emissions and Measures of Environmental Quality

The analysis of convergence behavior with respect to emissions and measures of environmental quality can be categorized into four types of tests: absolute and conditional β-convergence, σ-convergence, club convergence, and stochastic convergence. In the context of emissions, absolute β-convergence occurs when countries with high initial levels of emissions have a lower emission growth rate than countries with low initial levels of emissions. Conditional β-convergence allows for possible differences among countries through the inclusion of exogenous variables to capture country-specific effects. Given that absolute and conditional β-convergence do not account for the dynamics of the growth process, which can potentially lead to dynamic panel data bias, σ-convergence evaluates the dynamics and intradistributional aspects of emissions to determine whether the cross-section variance of emissions decreases over time. The more recent club convergence approach tests the decline in the cross-sectional variation in emissions among countries over time and whether heterogeneous time-varying idiosyncratic components converge over time after controlling for a common growth component in emissions among countries. In essence, the club convergence approach evaluates both conditional σ- and β-convergence within a panel framework. Finally, stochastic convergence examines the time series behavior of a country’s emissions relative to another country or group of countries. Using univariate or panel unit root/stationarity tests, stochastic convergence is present if relative emissions, defined as the log of emissions for a particular country relative to another country or group of countries, is trend-stationary. The majority of the empirical literature analyzes carbon dioxide emissions and varies in terms of both the convergence tests deployed and the results. While the results supportive of emissions convergence for large global country coverage are limited, empirical studies that focus on country groupings defined by income classification, geographic region, or institutional structure (i.e., EU, OECD, etc.) are more likely to provide support for emissions convergence. The vast majority of studies have relied on tests of stochastic convergence with tests of σ-convergence and the distributional dynamics of emissions less so. With respect to tests of stochastic convergence, an alternative testing procedure accounts for structural breaks and cross-correlations simultaneously is presented. Using data for OECD countries, the results based on the inclusion of both structural breaks and cross-correlations through a factor structure provides less support for stochastic convergence when compared to unit root tests with the inclusion of just structural breaks. Future studies should increase focus on other air pollutants to include greenhouse gas emissions and their components, not to mention expanding the range of geographical regions analyzed and more robust analysis of the various types of convergence tests to render a more comprehensive view of convergence behavior. The examination of convergence through the use of eco-efficiency indicators that capture both the environmental and economic effects of production may be more fruitful in contributing to the debate on mitigation strategies and allocation mechanisms.

Constrained neural network training and its application to hyperelastic material modeling

Neural networks (NN) have been studied and used widely in the field of computational mechanics, especially to approximate material behavior. One of their disadvantages is the large amount of data needed for the training process. In this paper, a new approach to enhance NN training with physical knowledge using constraint optimization techniques is presented. Specific constraints for hyperelastic materials are introduced, which include energy conservation, normalization and material symmetries. We show, that the introduced enhancements lead to better learning behavior with respect to well known issues like a small number of training samples or noisy data. The NN is used as a material law within a finite element analysis and its convergence behavior is discussed with regard to the newly introduced training enhancements. The feasibility of NNs trained with physical constraints is shown for data based on real world experiments. We show, that the enhanced training outperforms state-of-the-art techniques with respect to stability and convergence behavior within FE simulations.

Computationally efficient source grid selection and source interpolation in computational aeroacoustics applied to an axial fan.

The noise generation of an axial fan is mainly caused by flow-induced noise and can therefore be extracted from its aeroacoustics. To do so, a hybrid approach separating flow and acoustics is well suited due to its low Mach number. Such a computationally efficient hybrid workflow requires a robust conservative mesh-to-mesh transformation of the acoustic sources as well as a suitable mesh refinement to guarantee good convergence behavior. This contribution focuses on the mesh-to-mesh transformation, comparing two interpolation algorithms of different complexity towards the applicability to the aeroacoustic computation of an axial fan. The basic cell-centroid approach is generally suited for fine computational acoustic (CA) meshes and low phase shift, while the more complex cut-volume method generally yields better results for coarse acoustic meshes. While the cell-centroid interpolation scheme produces source artifacts inside the propagation domain, a grid study using the grid convergence index shows monotonic convergence behavior for both interpolation methods. By selection of a proper size for the source grid and source interpolation algorithm, the computational effort of the experimentally validated simulation model could be reduced by a factor 4.06.

Application of advanced RANS turbulence models for the prediction of turbomachinery flows

The current trend in turbomachinery towards broader operating characteristics requires that operating points in the off-design region can be captured accordingly from the simulation models. Complex processes like separation and vortex formation/dissipation occur under these conditions. Linear two equation models are often not able to represent these effects correctly since their derivation is based on over-simplifications, such as the Boussinesq hypothesis, which makes it impossible to capture anisotropic turbulence. Advanced RANS models are usually not considered in the design process of turbomachines because (1) they are usually more delicate with regards to stability and convergence behavior and (2) require additional computational effort. To make the usage of advanced RANS models more applicable for complex turbomachinery simulations a selected group of models were implemented into a robust framework of a pressure-based fully coupled solver. To further enhance stability, coupling terms between the turbulent transport equations were derived for several models. Anisotropic turbulence is introduced by computing an algebraic expression or by solving the transport equations for the Reynolds stress components. The evaluation of the models is performed on the RWTH Aachen “Radiver” centrifugal compressor case with vaned diffuser. For design conditions and operation points near the stability limit, all investigated turbulence models predict the compressor characteristic. Operation points in the choking region on the other hand are only predicted well by anisotropic models. The good results and improved convergence behavior of the advanced RANS models clearly indicates their applicability in the design process of turbomachines.

Convergence behavior of single-step GBLUP and SNPBLUP for different termination criteria

Background The preconditioned conjugate gradient (PCG) method is the current method of choice for iterative solving of genetic evaluations. The relative difference between two successive iterates and the relative residual of the system of equations are usually chosen as a termination criterion for the PCG method in animal breeding. However, our initial analyses showed that these two commonly used termination criteria may report that a PCG method applied to a single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) is not converged yet, whereas the solutions are accurate enough for practical use. Therefore, the aim of this study was to propose two termination criteria that have been (partly) developed in other fields, but are new in animal breeding, and to compare their behavior to that of the two termination criteria widely used in animal breeding for the PCG method applied to ssSNPBLUP. The convergence patterns of ssSNPBLUP were also compared to the convergence patterns of single-step genomic BLUP (ssGBLUP). Results Building upon previous work, we propose two termination criteria that take the properties of the system of equations into account. These two termination criteria are directly related to the relative error of the iterates with respect to the true solutions. Based on pig and dairy cattle datasets, we show that the preconditioned coefficient matrices of ssSNPBLUP and ssGBLUP have similar properties when using a second-level preconditioner for ssSNPBLUP. Therefore, the PCG method applied to ssSNPBLUP and ssGBLUP converged similarly based on the relative error of the iterates with respect to the true solutions. This similar convergence behavior between ssSNPBLUP and ssGBLUP was observed for both proposed termination criteria. This was, however, not the case for the termination criterion defined as the relative residual when applied to the dairy cattle evaluations. Conclusion Our results showed that the PCG method can converge similarly when applied to ssSNPBLUP and to ssGBLUP. The two proposed termination criteria always depicted these similar convergence behaviors, and we recommend them for comparing convergence properties of different models and for routine evaluations.

A numerical comparative study of generalized Bernstein-Kantorovich operators

<p style='text-indent:20px;'>In this paper, a new generalization of the Bernstein-Kantorovich type operators involving multiple shape parameters is introduced. Certain Voronovskaja and Grüss-Voronovskaya type approximation results, statistical convergence and statistical rate of convergence of proposed operators are obtained by means of a regular summability matrix. Some illustrative graphics that demonstrate the convergence behavior, accuracy and consistency of the operators are given via Maple algorithms. The proposed operators are comprehensively compared with classical Bernstein, Bernstein-Kantorovich and other new modifications of Bernstein operators such as <inline-formula><tex-math id="M1">\begin{document}$ \lambda $\end{document}</tex-math></inline-formula>-Bernstein, <inline-formula><tex-math id="M2">\begin{document}$ \lambda $\end{document}</tex-math></inline-formula>-Bernstein-Kantorovich, <inline-formula><tex-math id="M3">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula>-Bernstein and <inline-formula><tex-math id="M4">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula>-Bernstein-Kantorovich operators.</p>