Workflows for Construction of Spatio-Temporal Probabilistic Maps for Volcanic Hazard Assessment

Probabilistic hazard assessments for studying overland pyroclastic flows or atmospheric ash clouds under short timelines of an evolving crisis, require using the best science available unhampered by complicated and slow manual workflows. Although deterministic mathematical models are available, in most cases, parameters and initial conditions for the equations are usually only known within a prescribed range of uncertainty. For the construction of probabilistic hazard assessments, accurate outputs and propagation of the inherent input uncertainty to quantities of interest are needed to estimate necessary probabilities based on numerous runs of the underlying deterministic model. Characterizing the uncertainty in system states due to parametric and input uncertainty, simultaneously, requires using ensemble based methods to explore the full parameter and input spaces. Complex tasks, such as running thousands of instances of a deterministic model with parameter and input uncertainty require a High Performance Computing infrastructure and skilled personnel that may not be readily available to the policy makers responsible for making informed risk mitigation decisions. For efficiency, programming tasks required for executing ensemble simulations need to run in parallel, leading to twin computational challenges of managing large amounts of data and performing CPU intensive processing. The resulting flow of work requires complex sequences of tasks, interactions, and exchanges of data, hence the automatic management of these workflows are essential. Here we discuss a computer infrastructure, methodology and tools which enable scientists and other members of the volcanology research community to develop workflows for construction of probabilistic hazard maps using remotely accessed computing through a web portal.

Uncertainty and sensitivity analyses of operational errors in air handling units and unexpected user behavior for energy efficiency and thermal comfort

Resource scarcity and anthropogenic climate change require the reduction of performance gaps in existing buildings. In addition to unexpected user behavior, performance gaps are primarily caused by the technical gap due to operational errors in building technology. The main objective of this paper is to quantify model input uncertainty incorporating uncertain boundary conditions in terms of operational errors using thermo-dynamic building performance simulations and to identify the most relevant input parameters for the performance gaps in air conditioning systems by means of sensitivity analyses. Model input uncertainty is stochastically determined using Monte-Carlo Simulations to calculate the target values “primary energy demand” as well as “over- and under-temperature degree hours” for an office building. Selected parameters are simulated in a specific uncertainty and sensitivity analyses using the Sobol’ and Jansen estimators, which distinguish between a direct influence on the target variables and interactions between the parameters. The methodology requires a selection process, which is carried out as part of relative uncertainty and relative sensitivity analyses. Furthermore, the operational errors are compared with construction factors as well as building physics inputs and design parameters for building technology systems to show their reciprocal effects as part of a comprehensive investigation. The main findings of this paper are that operational errors in air conditioning systems play an essential role in decreasing energy efficiency and thermal comfort, but do not warrant the significance of certain construction factors as well as setpoints in building technology. Moreover, the impact of operational errors on thermal overheating of the building investigated is minor compared to other targets that cause greater model input uncertainty.

Stator Blades Manufacturing Geometrical Variability in Axial Compressors and Impact on the Aeroelastic Excitation Forces

The manufacturing geometrical variability is an unavoidable source of uncertainty in the realization of machinery components. Deviations of a part geometry from its nominal design are inevitably present due to the manufacturing process. In the aeroelastic forced response problem within axial compressors, these uncertainties may affect the vibration characteristics. Therefore, the impact of geometrical uncertainties due to the manufacturing process onto the modal forcing of axial compressor blades is investigated. The research focuses on the vibrational behavior of an axial compressor rotor blisk. In particular, the amplitude of the forces acting as source of excitation on the vibrating blades is studied. The geometrical variability of the upstream stator is investigated as input uncertainty. The variability is modeled starting from a series of optical surface scans. A stochastic model is created to represent the measured manufacturing geometrical deviations from the nominal model. A data reduction methodology is proposed to represent the uncertainty with a minimal set of variables. The manufacturing geometrical variability model allows to represent the input uncertainty and probabilistically evaluate its impact on the aeroelastic problem. An uncertainty quantification is performed in order to evaluate the resulting variability on the modal forcing acting on the vibrating rotor blades. Of particular interest is the possible rise of low engine orders due to the mistuned flow field along the annulus. A reconstruction algorithm allows the representation of the variability during one rotor revolution. The uncertainty on low harmonics of the modal rotor forcing can be therefore identified and quantified.