Loss of off-site power transient analysis for a sodium-cooled fast reactor equipped with a gas power conversion system and preliminary optimisation of its operation

International audienc

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

Coupling models of cattle and farms with models of badgers for predicting the dynamics of bovine tuberculosis (TB)

Bovine TB is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5%, while the accuracy of the test employed to detect infected cattle explained less than 3% of the variance in the number of infected cattle

Design of Experiments for Screening

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared

Global sensitivity analysis of stochastic computer models with joint metamodels

The global sensitivity analysis method used to quantify the influence of uncertain input variables on the variability in numerical model responses has already been applied to deterministic computer codes; deterministic means here that the same set of input variables gives always the same output value. This paper proposes a global sensitivity analysis methodology for stochastic computer codes, for which the result of each code run is itself random. The framework of the joint modeling of the mean and dispersion of heteroscedastic data is used. To deal with the complexity of computer experiment outputs, nonparametric joint models are discussed and a new Gaussian process-based joint model is proposed. The relevance of these models is analyzed based upon two case studies. Results show that the joint modeling approach yields accurate sensitivity index estimatiors even when heteroscedasticity is strong

Critical care staffing ratio and outcome of COVID-19 patients requiring intensive care unit admission during the first pandemic wave: a retrospective analysis across Switzerland from the RISC-19-ICU observational cohort.

STUDY AIM The surge of admissions due to severe COVID-19 increased the patients-to-critical care staffing ratio within the ICUs. We investigated whether the daily level of staffing was associated with an increased risk of ICU mortality (primary endpoint), length of stay (LOS), mechanical ventilation and the evolution of disease (secondary endpoints). METHODS We employed a retrospective multicentre analysis of the international Risk Stratification in COVID-19 patients in the ICU (RISC-19-ICU) registry, limited to the period between March 1 and May 31, 2020, and to Switzerland. Hierarchical regression models were used to investigate crude and adjusted effects of the critical care staffing ratio on study endpoints. We adjusted for disease severity and weekly caseload. RESULTS Among the 38 participating Swiss ICUs, 17 recorded staffing information. The study population included 437 patients and 2,342 daily assessments of patient-to-critical care staffing ratio. Median of daily patient-to-nurse ratio started at 1.0 [IQR 0.5-1.5; calendar week 9] and peaked at 2.4 (IQR 0.4-2.0; calendar week 16), while the median of daily patient-to-physician ratio started at 4.0 (IQR 2.1-5.0; calendar week 9) and peaked at 6.8 (IQR 6.3-7.3; calendar week 19). Neither the patient-to-nurse (adjusted OR 1.28, 95% CI 0.85-1.93; doubling of ratio) nor the patient-to-physician ratio (adjusted OR 1.07, 95% CI 0.87-1.32; doubling of ratio) were associated with ICU mortality. We found no association of daily critical care staffing on the secondary endpoints in adjusted models. CONCLUSION We found no association of reduced availability of critical care staffing resources in Swiss ICUs with overall ICU length of stay nor mortality. Whether long-term outcome of critically ill patients with COVID-19 have been affected remains to be studied

Critical care staffing ratio and outcome of COVID-19 patients requiring intensive care unit admission during the first pandemic wave: a retrospective analysis across Switzerland from the RISC-19-ICU observational cohort

STUDY AIM: The surge of admissions due to severe COVID-19 increased the patients-to-critical care staffing ratio within the ICUs. We investigated whether the daily level of staffing was associated with an increased risk of ICU mortality (primary endpoint), length of stay (LOS), mechanical ventilation and the evolution of disease (secondary endpoints). METHODS: We employed a retrospective multicentre analysis of the international Risk Stratification in COVID-19 patients in the ICU (RISC-19-ICU) registry, limited to the period between March 1 and May 31, 2020, and to Switzerland. Hierarchical regression models were used to investigate crude and adjusted effects of the critical care staffing ratio on study endpoints. We adjusted for disease severity and weekly caseload. RESULTS: Among the 38 participating Swiss ICUs, 17 recorded staffing information. The study population included 437 patients and 2,342 daily assessments of patient-to-critical care staffing ratio. Median of daily patient-to-nurse ratio started at 1.0 [IQR 0.5–1.5; calendar week 9] and peaked at 2.4 (IQR 0.4–2.0; calendar week 16), while the median of daily patient-to-physician ratio started at 4.0 (IQR 2.1–5.0; calendar week 9) and peaked at 6.8 (IQR 6.3–7.3; calendar week 19). Neither the patient-to-nurse (adjusted OR 1.28, 95% CI 0.85–1.93; doubling of ratio) nor the patient-to-physician ratio (adjusted OR 1.07, 95% CI 0.87–1.32; doubling of ratio) were associated with ICU mortality. We found no association of daily critical care staffing on the secondary endpoints in adjusted models. CONCLUSION: We found no association of reduced availability of critical care staffing resources in Swiss ICUs with overall ICU length of stay nor mortality. Whether long-term outcome of critically ill patients with COVID-19 have been affected remains to be studied

Implications of early respiratory support strategies on disease progression in critical COVID-19: a matched subanalysis of the prospective RISC-19-ICU cohort.

Uncertainty about the optimal respiratory support strategies in critically ill COVID-19 patients is widespread. While the risks and benefits of noninvasive techniques versus early invasive mechanical ventilation (IMV) are intensely debated, actual evidence is lacking. We sought to assess the risks and benefits of different respiratory support strategies, employed in intensive care units during the first months of the COVID-19 pandemic on intubation and intensive care unit (ICU) mortality rates. Subanalysis of a prospective, multinational registry of critically ill COVID-19 patients. Patients were subclassified into standard oxygen therapy ≥10 L/min (SOT), high-flow oxygen therapy (HFNC), noninvasive positive-pressure ventilation (NIV), and early IMV, according to the respiratory support strategy employed at the day of admission to ICU. Propensity score matching was performed to ensure comparability between groups. Initially, 1421 patients were assessed for possible study inclusion. Of these, 351 patients (85 SOT, 87 HFNC, 87 NIV, and 92 IMV) remained eligible for full analysis after propensity score matching. 55% of patients initially receiving noninvasive respiratory support required IMV. The intubation rate was lower in patients initially ventilated with HFNC and NIV compared to those who received SOT (SOT: 64%, HFNC: 52%, NIV: 49%, p = 0.025). Compared to the other respiratory support strategies, NIV was associated with a higher overall ICU mortality (SOT: 18%, HFNC: 20%, NIV: 37%, IMV: 25%, p = 0.016). In this cohort of critically ill patients with COVID-19, a trial of HFNC appeared to be the most balanced initial respiratory support strategy, given the reduced intubation rate and comparable ICU mortality rate. Nonetheless, considering the uncertainty and stress associated with the COVID-19 pandemic, SOT and early IMV represented safe initial respiratory support strategies. The presented findings, in agreement with classic ARDS literature, suggest that NIV should be avoided whenever possible due to the elevated ICU mortality risk