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.

Multiphase flow in porous media: meta-modeling techniques for sensitivity analysis and risk assessment

One of the biggest challenges that contaminant hydrogeology is facing, is how to adequately address the uncertainty associated with model predictions. Uncertainty arise from multiple sources, such as: interpretative error, calibration accuracy, parameter sensitivity and variability. This critical issue needs to be properly addressed in order to support environmental decision-making processes. In this study, we perform Global Sensitivity Analysis (GSA) on a contaminant transport model for the assessment of hydrocarbon concentration in groundwater. We provide a quantification of the environmental impact and, given the incomplete knowledge of hydrogeological parameters, we evaluate which are the most influential, requiring greater accuracy in the calibration process. Parameters are treated as random variables and a variance-based GSA is performed in a optimized numerical Monte Carlo framework. The Sobol indices are adopted as sensitivity measures and they are computed by employing meta-models to characterize the migration process, while reducing the computational cost of the analysis. The proposed methodology allows us to: extend the number of Monte Carlo iterations, identify the influence of uncertain parameters and lead to considerable saving computational time obtaining an acceptable accuracy.

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.

UWB radar sensor networks: Detection algorithms design and experimental analysis

In the last years radar sensor networks for localization and tracking in indoor environment have generated more and more interest, especially for anti-intrusion security systems. These networks often use Ultra Wide Band (UWB) technology, which consists in sending very short (few nanoseconds) impulse signals. This approach guarantees high resolution and accuracy and also other advantages such as low price, low power consumption and narrow-band interference (jamming) robustness. In this thesis the overall data processing (done in MATLAB environment) is discussed, starting from experimental measures from sensor devices, ending with the 2D visualization of targets movements over time and focusing mainly on detection and localization algorithms. Moreover, two different scenarios and both single and multiple target tracking are analyzed.