Derivative based global sensitivity measures

The method of derivative based global sensitivity measures (DGSM) has recently become popular among practitioners. It has a strong link with the Morris screening method and Sobol' sensitivity indices and has several advantages over them. DGSM are very easy to implement and evaluate numerically. The computational time required for numerical evaluation of DGSM is generally much lower than that for estimation of Sobol' sensitivity indices. This paper presents a survey of recent advances in DGSM concerning lower and upper bounds on the values of Sobol' total sensitivity indices $S\_{i}^{tot}$. Using these bounds it is possible in most cases to get a good practical estimation of the values of $S\_{i}^{tot}$. Several examples are used to illustrate an application of DGSM

Open TURNS: An industrial software for uncertainty quantification in simulation

The needs to assess robust performances for complex systems and to answer tighter regulatory processes (security, safety, environmental control, and health impacts, etc.) have led to the emergence of a new industrial simulation challenge: to take uncertainties into account when dealing with complex numerical simulation frameworks. Therefore, a generic methodology has emerged from the joint effort of several industrial companies and academic institutions. EDF R&D, Airbus Group and Phimeca Engineering started a collaboration at the beginning of 2005, joined by IMACS in 2014, for the development of an Open Source software platform dedicated to uncertainty propagation by probabilistic methods, named OpenTURNS for Open source Treatment of Uncertainty, Risk 'N Statistics. OpenTURNS addresses the specific industrial challenges attached to uncertainties, which are transparency, genericity, modularity and multi-accessibility. This paper focuses on OpenTURNS and presents its main features: openTURNS is an open source software under the LGPL license, that presents itself as a C++ library and a Python TUI, and which works under Linux and Windows environment. All the methodological tools are described in the different sections of this paper: uncertainty quantification, uncertainty propagation, sensitivity analysis and metamodeling. A section also explains the generic wrappers way to link openTURNS to any external code. The paper illustrates as much as possible the methodological tools on an educational example that simulates the height of a river and compares it to the height of a dyke that protects industrial facilities. At last, it gives an overview of the main developments planned for the next few years

Sensitivity analysis methods for uncertainty budgeting in system design

Quantification and management of uncertainty are critical in the design of engineering systems, especially in the early stages of conceptual design. This paper presents an approach to defining budgets on the acceptable levels of uncertainty in design quantities of interest, such as the allowable risk in not meeting a critical design constraint and the allowable deviation in a system performance metric. A sensitivity-based method analyzes the effects of design decisions on satisfying those budgets, and a multi-objective optimization formulation permits the designer to explore the tradespace of uncertainty reduction activities while also accounting for a cost budget. For models that are computationally costly to evaluate, a surrogate modeling approach based on high dimensional model representation (HDMR) achieves efficient computation of the sensitivities. An example problem in aircraft conceptual design illustrates the approach.United States. National Aeronautics and Space Administration. Leading Edge Aeronautics Research Program (Grant NNX14AC73A)United States. Department of Energy. Applied Mathematics Program (Award DE-FG02-08ER2585)United States. Department of Energy. Applied Mathematics Program (Award DE-SC0009297

Local Sensitivity Analysis of Kinetic Models for Cellulose Pyrolysis

Abstract: The first and nth order kinetic models are usually used to describe cellulose pyrolysis. In this work, the local sensitivities of the conversion and derivative conversion with respect to the frequency factor, the logarithm of the frequency factor, the activation energy and the reaction order for the first and nth order kinetic models are calculated by using the finite difference method. The results show that the sensitivities of the first and nth order kinetic models with respect to the activation energy and the logarithm of the frequency factor are significant, while the frequency factor and the reaction order affect the nth order kinetic model slightly. Compared with the frequency factor, the natural logarithm of the frequency factor is a better choice in the parameter estimation of the first and nth order kinetic models. Graphical Abstract: [Figure not available: see fulltext.

The e-Business Readiness Composite Indicator for 2003. A Pilot Study

Abstract not availableJRC.G-Institute for the Protection and the Security of the Citizen (Ispra

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. Machine learning (ML) approaches - one of the typologies of algorithms underpinning artificial intelligence - are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision making are outlined: a) Risk assessment in the criminal justice system, and b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined

Improved understanding of dynamic water and mass budgets of high‐alpine karst systems obtained from studying a well‐defined catchment area

Large areas of Europe, especially in the Alps, are covered by carbonate rocks and in many alpine regions, karst springs are important sources for drinking water supply. Because of their high variability and heterogeneity, the understanding of the hydrogeological functioning of karst aquifers is of particular importance for their protection and utilisation. Climate change and heavy rainfall events are major challenges in managing alpine karst aquifers which possess an enormous potential for future drinking water supply. In this study, we present research from a high‐alpine karst system in the UNESCO Biosphere Reserve Großes Walsertal in Austria, which has a clearly defined catchment and is drained by only one spring system. Results show that (a) the investigated system is a highly dynamic karst aquifer with distinct reactions to rainfall events in discharge and electrical conductivity; (b) the estimated transient atmospheric CO2 sink is about 270 t/a; (c) the calculated carbonate rock denudation rate is between 23 and 47 mm/1000a and (d) the rainfall‐discharge behaviour and the internal flow dynamics can be successfully simulated using the modelling package KarstMod. The modelling results indicate the relevance of matrix storage in determining the discharge behaviour of the spring, particularly during low‐flow periods. This research and the consequent results can contribute and initiate a better understanding and management of alpine karst aquifers considering climate change with more heavy rainfall events and also longer dry periods.The investigated karst system contributes to the transient atmospheric CO2 sink with about 270 t/a. Carbonate denudation rates vary between 23 and 47 mm/1000a. Rainfall‐discharge modelling results indicate the importance of matrix storage particularly during low‐flow periods. imageBundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347FP7 People: Marie‐Curie Actions http://dx.doi.org/10.13039/10001126

Comparison of Two Mathematical Models for Greenhouse Gas Emission from Membrane Bioreactors

In this study two mathematical models (Model I and Model II), able to predict the nitrous oxide (N2O) and carbon dioxide (CO2) emission from an University Cape Town (UCT) \u2013 membrane bioreactor (MBR) plant, have been compared. Model I considers the N2O production only during the denitrification. Model II takes into account the two ammonia-oxidizing bacteria (AOB) formation pathways for N2O. Both models were calibrated adopting real data. Results highlight that Model II had a better capability of reproducing the measured data especially in terms of N2O model outputs. Indeed, the average efficiency related to the N2O model outputs was equal to 0.3 and 0.38 for Model I and Model II respectively