Rotor support stiffness estimation by sensitivity analysis

In the present work, a method for rotor support stiffness estimation via a model updating process using the sensitivity analysis is presented. This method consists in using the eigenvalues sensitivity analysis, relating to the rotor support stiffnesses variation to perform the adjustment of the model based on the minimization of the difference between eigenvalues of reference and eigenvalues obtained via mathematical model from previously adopted support bearing stiffness values. The mathematical model is developed by the finite element method and the method of adjustment should converge employing an iterative process. The performance and robustness of the method have been analyzed through a numerical example.

Sensitivity analysis for overload-relief in transmission lines using an implicitly coupled method (CRIC)

The restructuring of energy markets to provide free access to the networks and the consequent increase of the number of power transactions has been causing congestions in transmission systems. As consequence, the networks suffer overloads in a more frequent way. One parameter that has strong influence on transfer capability is the reactive power flow. A sensitivity analysis can be used to find the best solution to minimize the reactive power flows and relief, the overload in one transmission line. The proposed methodology consists on the computation of two sensitivities based on the use of the Lc matrix from CRIC (Constant Reactive Implicitly Coupled) power flow method, that provide a set of actions to reduce the reactive power flow and alleviate overloads in the lines: (a) sensitivity between reactive power flow in lines and reactive power injections in the buses, (b) sensitivity between reactive power flow in lines and transformer's taps. © 2006 IEEE.

Structural reforms and economic growth in Latin America: a sensitivity analysis

Includes bibliography

Chaos control and sensitivity analysis of a double pendulum arm excited by an RLC circuit based nonlinear shaker

In this paper the dynamical interactions of a double pendulum arm and an electromechanical shaker is investigated. The double pendulum is a three degree of freedom system coupled to an RLC circuit based nonlinear shaker through a magnetic field, and the capacitor voltage is a nonlinear function of the instantaneous electric charge. Numerical simulations show the existence of chaotic behavior for some regions in the parameter space and this behaviour is characterized by power spectral density and Lyapunov exponents. The bifurcation diagram is constructed to explore the qualitative behaviour of the system. This kind of electromechanical system is frequently found in robotic systems, and in order to suppress the chaotic motion, the State-Dependent Riccati Equation (SDRE) control and the Nonlinear Saturation control (NSC) techniques are analyzed. The robustness of these two controllers is tested by a sensitivity analysis to parametric uncertainties.

Multiobjective engineering design optimization problems: a sensitivity analysis approach

This paper proposes two new approaches for the sensitivity analysis of multiobjective design optimization problems whose performance functions are highly susceptible to small variations in the design variables and/or design environment parameters. In both methods, the less sensitive design alternatives are preferred over others during the multiobjective optimization process. While taking the first approach, the designer chooses the design variable and/or parameter that causes uncertainties. The designer then associates a robustness index with each design alternative and adds each index as an objective function in the optimization problem. For the second approach, the designer must know, a priori, the interval of variation in the design variables or in the design environment parameters, because the designer will be accepting the interval of variation in the objective functions. The second method does not require any law of probability distribution of uncontrollable variations. Finally, the authors give two illustrative examples to highlight the contributions of the paper.

Contribution to assessing the stiffness reduction of structural elements in the global stability analysis of precast concrete multi-storey buildings

This study deals with the reduction of the stiffness in precast concrete structural elements of multi-storey buildings to analyze global stability. Having reviewed the technical literature, this paper present indications of stiffness reduction in different codes, standards, and recommendations and compare these to the values found in the present study. The structural model analyzed in this study was constructed with finite elements using ANSYS® software. Physical Non-Linearity (PNL) was considered in relation to the diagrams M x N x 1/r, and Geometric Non-Linearity (GNL) was calculated following the Newton-Raphson method. Using a typical precast concrete structure with multiple floors and a semi-rigid beam-to-column connection, expressions for a stiffness reduction coefficient are presented. The main conclusions of the study are as follows: the reduction coefficients obtained from the diagram M x N x 1/r differ from standards that use a simplified consideration of PNL; the stiffness reduction coefficient for columns in the arrangements analyzed were approximately 0.5 to 0.6; and the variation of values found for stiffness reduction coefficient in concrete beams, which were subjected to the effects of creep with linear coefficients from 0 to 3, ranged from 0.45 to 0.2 for positive bending moments and 0.3 to 0.2 for negative bending moments.

Parametric Sensitivity Analysis of the Most Recent Computational Models of Rabbit Cardiac Pacemaking

The cellular basis of cardiac pacemaking activity, and specifically the quantitative contributions of particular mechanisms, is still debated. Reliable computational models of sinoatrial nodal (SAN) cells may provide mechanistic insights, but competing models are built from different data sets and with different underlying assumptions. To understand quantitative differences between alternative models, we performed thorough parameter sensitivity analyses of the SAN models of Maltsev & Lakatta (2009) and Severi et al (2012). Model parameters were randomized to generate a population of cell models with different properties, simulations performed with each set of random parameters generated 14 quantitative outputs that characterized cellular activity, and regression methods were used to analyze the population behavior. Clear differences between the two models were observed at every step of the analysis. Specifically: (1) SR Ca2+ pump activity had a greater effect on SAN cell cycle length (CL) in the Maltsev model; (2) conversely, parameters describing the funny current (If) had a greater effect on CL in the Severi model; (3) changes in rapid delayed rectifier conductance (GKr) had opposite effects on action potential amplitude in the two models; (4) within the population, a greater percentage of model cells failed to exhibit action potentials in the Maltsev model (27%) compared with the Severi model (7%), implying greater robustness in the latter; (5) confirming this initial impression, bifurcation analyses indicated that smaller relative changes in GKr or Na+-K+ pump activity led to failed action potentials in the Maltsev model. Overall, the results suggest experimental tests that can distinguish between models and alternative hypotheses, and the analysis offers strategies for developing anti-arrhythmic pharmaceuticals by predicting their effect on the pacemaking activity.

Sensitivity analysis of a parabolic-elliptic problem

We consider the heat flux through a domain with subregions in which the thermal capacity approaches zero. In these subregions the parabolic heat equation degenerates to an elliptic one. We show the well-posedness of such parabolic-elliptic differential equations for general non-negative L-infinity-capacities and study the continuity of the solutions with respect to the capacity, thus giving a rigorous justification for modeling a small thermal capacity by setting it to zero. We also characterize weak directional derivatives of the temperature with respect to capacity as solutions of related parabolic-elliptic problems.

High sensitivity analysis of BRAF mutations in neoplastic and non-neoplastic thyroid lesions

The clonal distribution of BRAFV600E in papillary thyroid carcinoma (PTC) has been recently debated. No information is currently available about precursor lesions of PTCs. My first aim was to establish whether the BRAFV600E mutation occurs as a subclonal event in PTCs. My second aim was to screen BRAF mutations in histologically benign tissue of cases with BRAFV600E or BRAFwt PTCs in order to identify putative precursor lesions of PTCs. Highly sensitive semi-quantitative methods were used: Allele Specific LNA quantitative PCR (ASLNAqPCR) and 454 Next-Generation Sequencing (NGS). For the first aim 155 consecutive formalin-fixed and paraffin-embedded (FFPE) specimens of PTCs were analyzed. The percentage of mutated cells obtained was normalized to the estimated number of neoplastic cells. Three groups of tumors were identified: a first had a percentage of BRAF mutated neoplastic cells > 80%; a second group showed a number of BRAF mutated neoplastic cells < 30%; a third group had a distribution of BRAFV600E between 30-80%. The large presence of BRAFV600E mutated neoplastic cell sub-populations suggests that BRAFV600E may be acquired early during tumorigenesis: therefore, BRAFV600E can be heterogeneously distributed in PTC. For the second aim, two groups were studied: one consisted of 20 cases with BRAFV600E mutated PTC, the other of 9 BRAFwt PTCs. Seventy-five and 23 histologically benign FFPE thyroid specimens were analyzed from the BRAFV600E mutated and BRAFwt PTC groups, respectively. The screening of BRAF mutations identified BRAFV600E in “atypical” cell foci from both groups of patients. “Unusual” BRAF substitutions were observed in histologically benign thyroid associated with BRAFV600E PTCs. These mutations were very uncommon in the group with BRAFwt PTCs and in BRAFV600E PTCs. Therefore, lesions carrying BRAF mutations may represent “abortive” attempts at cancer development: only BRAFV600E boosts neoplastic transformation to PTC. BRAFV600E mutated “atypical foci” may represent precursor lesions of BRAFV600E mutated PTCs.

Dynamics of coastal aquifers: data-driven forecasting and risk analysis

This work is focused on the study of saltwater intrusion in coastal aquifers, and in particular on the realization of conceptual schemes to evaluate the risk associated with it. Saltwater intrusion depends on different natural and anthropic factors, both presenting a strong aleatory behaviour, that should be considered for an optimal management of the territory and water resources. Given the uncertainty of problem parameters, the risk associated with salinization needs to be cast in a probabilistic framework. On the basis of a widely adopted sharp interface formulation, key hydrogeological problem parameters are modeled as random variables, and global sensitivity analysis is used to determine their influence on the position of saltwater interface. The analyses presented in this work rely on an efficient model reduction technique, based on Polynomial Chaos Expansion, able to combine the best description of the model without great computational burden. When the assumptions of classical analytical models are not respected, and this occurs several times in the applications to real cases of study, as in the area analyzed in the present work, one can adopt data-driven techniques, based on the analysis of the data characterizing the system under study. It follows that a model can be defined on the basis of connections between the system state variables, with only a limited number of assumptions about the "physical" behaviour of the system.

Studio di Flow-Assurance e sensitivity analysis in sistemi di distribuzione di petrolio e gas naturale

Argomento del lavoro è stato lo studio di problemi legati alla Flow-Asurance. In particolare, si focalizza su due aspetti: i) una valutazione comparativa delle diverse equazioni di stato implementate nel simulatore multifase OLGA, per valutare quella che porta a risultati più conservativi; ii) l’analisi della formazione di idrati, all’interno di sistemi caratterizzati dalla presenza di gas ed acqua. Il primo argomento di studio nasce dal fatto che per garantire continuità del flusso è necessario conoscere il comportamento volumetrico del fluido all’interno delle pipelines. Per effettuare tali studi, la Flow-Assurance si basa sulle Equazioni di Stato cubiche. In particolare, sono state confrontate: -L’equazione di Soave-Redlich-Kwong; -L’equazione di Peng-Robinson; -L’equazione di Peng-Robinson modificata da Peneloux. Sono stati analizzati 4 fluidi idrocarburici (2 multifase, un olio e un gas) con diverse composizioni e diverse condizioni di fase. Le variabili considerate sono state pressione, temperatura, densità e viscosità; sono state poi valutate le perdite di carico, parametro fondamentale nello studio del trasporto di un fluido, valutando che l'equazione di Peng-Robinson è quella più adatta per caratterizzare termodinamicamente il fluido durante una fase di design, in quanto fornisce l'andamento più conservativo. Dopo aver accertato la presenza di idrati nei fluidi multifase, l’obiettivo del lavoro è stato analizzare come il sistema rispondesse all’aggiunta di inibitori chimici per uscire dalla regione termodinamica di stabilità dell’idrato. Gli inibitori utilizzati sono stati metanolo e mono-etilen-glicole in soluzione acquosa. L’analisi è stata effettuata confrontando due metodi: -Metodo analitico di Hammerschmidt; -Metodo iterativo con PVTSim. I risultati ottenuti hanno dimostrato che entrambi gli inibitori utilizzati risolvono il problema della formazione di idrato spostando la curva di stabilità al di fuori delle pressioni e temperature che si incontrano nella pipeline. Valutando le quantità da iniettare, il metodo di Hammerschmidt risulta quello più conservativo, indicando portate maggiori rispetto al PVTsim, soprattutto aggiungendo metanolo.

Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials

Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.

Semiparametric Theory for Causal Mediation Analysis: efficiency bounds, multiple robustness, and sensitivity analysis

Whilst estimation of the marginal (total) causal effect of a point exposure on an outcome is arguably the most common objective of experimental and observational studies in the health and social sciences, in recent years, investigators have also become increasingly interested in mediation analysis. Specifically, upon establishing a non-null total effect of the exposure, investigators routinely wish to make inferences about the direct (indirect) pathway of the effect of the exposure not through (through) a mediator variable that occurs subsequently to the exposure and prior to the outcome. Although powerful semiparametric methodologies have been developed to analyze observational studies, that produce double robust and highly efficient estimates of the marginal total causal effect, similar methods for mediation analysis are currently lacking. Thus, this paper develops a general semiparametric framework for obtaining inferences about so-called marginal natural direct and indirect causal effects, while appropriately accounting for a large number of pre-exposure confounding factors for the exposure and the mediator variables. Our analytic framework is particularly appealing, because it gives new insights on issues of efficiency and robustness in the context of mediation analysis. In particular, we propose new multiply robust locally efficient estimators of the marginal natural indirect and direct causal effects, and develop a novel double robust sensitivity analysis framework for the assumption of ignorability of the mediator variable.