scholarly journals How biased are our models? – a case study of the alpine region

2021 ◽  
Vol 14 (11) ◽  
pp. 7133-7153
Author(s):  
Denise Degen ◽  
Cameron Spooner ◽  
Magdalena Scheck-Wenderoth ◽  
Mauro Cacace
Keyword(s):  
Geothermal Energy ◽  
Model Calibration ◽  
Clean Energy ◽  
Physical Models ◽  
Reduced Basis ◽  
Process Simulations ◽  
Sensitivity Studies ◽  
High Bias ◽  
Basis Method

Abstract. Geophysical process simulations play a crucial role in the understanding of the subsurface. This understanding is required to provide, for instance, clean energy sources such as geothermal energy. However, the calibration and validation of the physical models heavily rely on state measurements such as temperature. In this work, we demonstrate that focusing analyses purely on measurements introduces a high bias. This is illustrated through global sensitivity studies. The extensive exploration of the parameter space becomes feasible through the construction of suitable surrogate models via the reduced basis method, where the bias is found to result from very unequal data distribution. We propose schemes to compensate for parts of this bias. However, the bias cannot be entirely compensated. Therefore, we demonstrate the consequences of this bias with the example of a model calibration.

scholarly journals How biased are our models? – A Case Study of the Alpine Region

2021 ◽  
Author(s):  
Denise Degen ◽  
Cameron Spooner ◽  
Magdalena Scheck-Wenderoth ◽  
Mauro Cacace
Keyword(s):  
Geothermal Energy ◽  
Model Calibration ◽  
Clean Energy ◽  
Physical Models ◽  
Reduced Basis ◽  
Process Simulations ◽  
Sensitivity Studies ◽  
High Bias ◽  
Basis Method

Abstract. Geophysical process simulations play a crucial role in the understanding of the subsurface. This understanding is required to provide, for instance, clean energy sources such as geothermal energy. However, the calibration and validation of the physical models heavily rely on state measurements such as temperature. In this work, we demonstrate that focusing analyses purely on measurements introduces a high bias. This is illustrated through global sensitivity studies. The extensive exploration of the parameter space becomes feasible through the construction of suitable surrogate models via the reduced basis method, where the bias is found to result from very unequal data distribution. We propose schemes to compensate for parts of this bias. However, the bias cannot be entirely compensated. Therefore, we demonstrate the consequences of this bias with the example of a model calibration.

How well do we know our models?

2020 ◽  
Author(s):  
Denise Degen ◽  
Karen Veroy ◽  
Mauro Cacace ◽  
Magdalena Scheck-Wenderoth ◽  
Florian Wellmann
Keyword(s):  
Finite Element ◽  
Surrogate Model ◽  
Sensitivity Study ◽  
Reduced Basis Method ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Reduced Basis ◽  
Global Sensitivity ◽  
Sensitivity Studies ◽  
Basis Method

<p>In Geosciences, we face the challenge of characterizing uncertainties to provide reliable predictions of the earth surface to allow, for instance, a sustainable and renewable energy management. In order, to address the uncertainties we need a good understanding of our geological models and their associated subsurface processes.</p><p>Therefore, the essential pre-step for uncertainty analyses are sensitivity studies. Sensitivity studies aim at determining the most influencing model parameters. Hence, we require them to significantly reduce the parameter space to avoid unfeasibly large compute times.</p><p>We distinguish two types of sensitivity analyses: local and global studies. In contrast, to the local sensitivity study, the global one accounts for parameter correlations. That is the reason, why we employ in this work a global sensitivity study. Unfortunately, global sensitivity studies have the disadvantage that they are computationally extremely demanding. Hence, they are prohibitive even for state-of-the-art finite element simulations.</p><p>For this reason, we construct a surrogate model by employing the reduced basis method. The reduced basis method is a model order reduction technique that aims at significantly reducing the spatial and temporal degrees of freedom of, for instance, finite element solves. In contrast to other surrogate models, we obtain a surrogate model that preserves the physics and is not restricted to the observation space. As we will show, the reduced basis method leads to a speed-up of five to six orders of magnitude with respect to our original problem while retaining an accuracy higher than the measurement accuracy.</p><p>In this work, we elaborate on the advantages of global sensitivity studies in comparison to local ones. We use several case studies, from large-scale European sedimentary basins to demonstrate how the global sensitivity studies are used to learn about the influence of transient, such as paleoclimate effects, and stationary effects. We also demonstrate how the results can be used in further analyses, such as deterministic and stochastic model calibrations. Furthermore, we show how we can use the analyses to learn about the subsurface processes and to identify model short comes.</p>

Reduced basis method for the electromagnetic scattering problem: a case study for FinFETs

2016 ◽  
Vol 48 (4) ◽  
Author(s):  
Martin Hammerschmidt ◽  
Sven Herrmann ◽  
Sven Burger ◽  
Jan Pomplun ◽  
Frank Schmidt
Keyword(s):  
Electromagnetic Scattering ◽  
Scattering Problem ◽  
Reduced Basis Method ◽  
Reduced Basis ◽  
Basis Method

Benefits of Global Sensitivity Analysis and Reduced Order Modeling for Basin-Scale Process Simulations

2021 ◽  
Author(s):  
Denise Degen ◽  
Mauro Cacace ◽  
Cameron Spooner ◽  
Magdalena Scheck-Wenderoth ◽  
Florian Wellmann
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Sensitivity Analyses ◽  
Rock Properties ◽  
Reduced Basis ◽  
Global Sensitivity ◽  
Temporal Domain ◽  
Basin Scale ◽  
Process Simulations ◽  
Basis Method

<p>Geophysical process simulations pose several challenges including the determination of i) the rock properties, ii) the underlying physical process, and iii) the spatial and temporal domain that needs to be considered.</p><p>Often it is not feasible or impossible to include the entire complexity of the given application. Hence, we need to evaluate the consequences of neglecting certain processes, properties, etc. by using, for instance, sensitivity analyses. However, this evaluation is for basin-scale application non-trivial due to the high computational costs associated with them. These high costs arise from the high-dimensional character of basin-scale applications in the parameter, spatial, and temporal domain.</p><p>Therefore, this evaluation is often not performed or via computationally fast algorithms as, for example, the local sensitivity analysis. The problem with local sensitivity analyses is that they cannot account for parameter correlations. Thus, a global sensitivity analysis is preferential. Unfortunately, global sensitivity analyses are computationally demanding.</p><p>To allow the usage of global sensitivity analysis for a better evaluation of the changes in the influencing parameters, we construct in this work a surrogate model via the reduced basis method.</p><p>The reduced basis method is a model order reduction technique that is physics-preserving.  Hence, we are able to retrieve the entire state variable (i.e. temperature) instead of being restricted to the observation space.</p><p>To showcase the benefits of this methodology, we demonstrate with the Central European Basin System how the influences of the thermal rock properties change when moving from a steady-state to a transient system.</p><p>Furthermore, we use the case study of the Alpine Region to highlight the influences of the spatial distribution of measurements on the model response. This latter aspect is especially important since measurements are often used to calibrate and validate a given geological model. Thus, it is crucial to determine which amount of bias is introduced through our commonly unequal data distribution.</p>

Evaluating Community Engagement and Benefit-Sharing Practices in Australian Wind Farm Development

2017 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Nina Lansbury Hall ◽  
Jarra Hicks ◽  
Taryn Lane ◽  
Emily Wood
Keyword(s):  
Community Engagement ◽  
Community Involvement ◽  
Wind Farm ◽  
Social Acceptance ◽  
Wind Farms ◽  
Clean Energy ◽  
Benefit Sharing ◽  
Delphi Panel ◽  
Government Organizations

The wind industry is positioned to contribute significantly to a clean energy future, yet the level of community opposition has at times led to unviable projects. Social acceptance is crucial and can be improved in part through better practice community engagement and benefit-sharing. This case study provides a “snapshot” of current community engagement and benefit-sharing practices for Australian wind farms, with a particular emphasis on practices found to be enhancing positive social outcomes in communities. Five methods were used to gather views on effective engagement and benefit-sharing: a literature review, interviews and a survey of the wind industry, a Delphi panel, and a review of community engagement plans. The overarching finding was that each community engagement and benefit-sharing initiative should be tailored to a community’s context, needs and expectations as informed by community involvement. This requires moving away from a “one size fits all” approach. This case study is relevant to wind developers, energy regulators, local communities and renewable energy-focused non-government organizations. It is applicable beyond Australia to all contexts where wind farm development has encountered conflicted societal acceptance responses.

Sugar factory audits, debottlenecking and efficiency improvement

2020 ◽  
pp. 607-612
Author(s):  
Bernard Coûteaux
Keyword(s):  
Evaluation Method ◽  
Predictive Modelling ◽  
Cane Sugar ◽  
Sugar Factory ◽  
Low Energy Consumption ◽  
Process Simulations ◽  
Capital Efficiency ◽  
Potential Improvement ◽  
Primary Focus

This paper elaborates on the key solutions offered by De Smet Engineers & Contractors (DSEC) to optimize the efficiency of cane sugar producing and processing facilities. In order to meet customer needs, DSEC offers proprietary predictive models built using the latest versions of specialized software. These models allow factory managers to envision the whole picture of increased operational and capital efficiency before it becomes reality. An integrated energy model and the CAPEX/OPEX evaluation method are discussed as ways to estimate and optimize costs, both for new greenfield projects and revamping of existing factories. The models demonstrate that factory capacities can be successfully increased using equipment that is already available. Special attention is paid to crystallization and centrifugation process simulations and the potential improvement of the global energy balance. One case study shows the transformation of a beet sugar factory into a refinery to process raw cane sugar after beet crop season and the second case shows the integration of a refinery into a cane sugar factory. The primary focus of the article is optimization of the technological process through predictive modelling. DSEC’s suggested solutions, which lead to great improvements in a plant’s efficiency and its ability to obtain very low energy consumption, are discussed.

An application of Austrian legal requirements for CSO emissions

2011 ◽  
Vol 64 (5) ◽  
pp. 1081-1088 ◽  
Author(s):  
Manfred Kleidorfer ◽  
Wolfgang Rauch
Keyword(s):  
Model Calibration ◽  
Model Building ◽  
Treatment Plant ◽  
Cost Effective ◽  
Combined Sewer Overflows ◽  
Sewer System ◽  
Legal Requirements ◽  
Innovative Methods ◽  
Combined Sewer

The Austrian standard for designing combined sewer overflow (CSO) detention basins introduces the efficiency of the combined sewer overflows as an indicator for CSO pollution. Additionally criteria for the ambient water quality are defined, which comprehend six kinds of impacts. In this paper, the Austrian legal requirements are described and discussed by means of hydrological modelling. This is exemplified with the case study Innsbruck (Austria) including a description for model building and model calibration. Furthermore an example is shown in order to demonstrate how – in this case – the overall system performance could be improved by implementing a cost-effective rearrangement of the storage tanks already available at the inflow of the wastewater treatment plant. However, this guideline also allows more innovative methods for reducing CSO emissions as measures for better usage of storage volume or de-centralised treatment of stormwater runoff because it is based on a sewer system simulation.

Sign in / Sign up

Export Citation Format

Share Document