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  1. Article ; Online: Uncertainty quantification for basin-scale geothermal conduction models.

    Degen, Denise / Veroy, Karen / Wellmann, Florian

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 4246

    Abstract: Geothermal energy plays an important role in the energy transition by providing a renewable energy source with a low ... ...

    Abstract Geothermal energy plays an important role in the energy transition by providing a renewable energy source with a low CO
    Language English
    Publishing date 2022-03-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-08017-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: 3D multi-physics uncertainty quantification using physics-based machine learning.

    Degen, Denise / Cacace, Mauro / Wellmann, Florian

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 17491

    Abstract: Quantitative predictions of the physical state of the Earth's subsurface are routinely based on numerical solutions of complex coupled partial differential equations together with estimates of the uncertainties in the material parameters. The resulting ... ...

    Abstract Quantitative predictions of the physical state of the Earth's subsurface are routinely based on numerical solutions of complex coupled partial differential equations together with estimates of the uncertainties in the material parameters. The resulting high-dimensional problems are computationally prohibitive even for state-of-the-art solver solutions. In this study, we introduce a hybrid physics-based machine learning technique, the non-intrusive reduced basis method, to construct reliable, scalable, and interpretable surrogate models. Our approach, to combine physical process models with data-driven machine learning techniques, allows us to overcome limitations specific to each individual component, and it enables us to carry out probabilistic analyses, such as global sensitivity studies and uncertainty quantification for real-case non-linearly coupled physical problems. It additionally provides orders of magnitude computational gain, while maintaining an accuracy higher than measurement errors. Although in this study we use a thermo-hydro-mechanical reservoir application to illustrate these features, all the theory described is equally valid and applicable to a wider range of geoscientific applications.
    MeSH term(s) Uncertainty ; Machine Learning ; Physics
    Language English
    Publishing date 2022-10-19
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-21739-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Crustal-scale thermal models

    Degen, D. / Veroy, K. / Scheck-Wenderoth, M. / Wellmann, F.

    Environmental Earth Sciences

    revisiting the influence of deep boundary conditions

    2022  

    Abstract: The societal importance of geothermal energy is significantly increasing because of its low carbon-dioxide footprint. However, geothermal exploration is also subject to high risks. For a better assessment of these risks, extensive parameter studies are ... ...

    Abstract The societal importance of geothermal energy is significantly increasing because of its low carbon-dioxide footprint. However, geothermal exploration is also subject to high risks. For a better assessment of these risks, extensive parameter studies are required that improve the understanding of the subsurface. This yields computationally demanding analyses. Often, this is compensated by constructing models with a small vertical extent. This paper demonstrates that this leads to entirely boundary-dominated and hence uninformative models. It demonstrates the indispensable requirement to construct models with a large vertical extent to obtain informative models with respect to the model parameters. For this quantitative investigation, global sensitivity studies are essential since they also consider parameter correlations. To compensate for the computationally demanding nature of the analyses, a physics-based machine learning approach is employed, namely the reduced basis method, instead of reducing the physical dimensionality of the model. The reduced basis method yields a significant cost reduction while preserving the physics and a high accuracy, thus providing a more efficient alternative to considering, for instance, a small vertical extent. The reduction of the mathematical instead of physical space leads to less restrictive models and, hence, maintains the model prediction capabilities. The combination of methods is used for a detailed investigation of the influence of model boundary settings in typical regional-scale geothermal simulations and highlights potential problems.
    Subject code 550
    Language English
    Publishing country de
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Global sensitivity analysis to optimize basin-scale conductive model calibration – A case study from the Upper Rhine Graben

    Degen, D. / Veroy, K. / Freymark, J. / Scheck-Wenderoth, M. / Poulet, T. / Wellmann, F.

    Geothermics

    2021  

    Abstract: Calibrating geothermal simulations is a critical step, both in scientific and industrial contexts, with suitable model parameterizations being optimized to reduce discrepancies between simulated and measured temperatures. Here we present a methodology to ...

    Abstract Calibrating geothermal simulations is a critical step, both in scientific and industrial contexts, with suitable model parameterizations being optimized to reduce discrepancies between simulated and measured temperatures. Here we present a methodology to identify model errors in the calibration and compensate for measurement sparsity. With an application to the Upper Rhine Graben, we demonstrate the essential need for global sensitivity studies to robustly calibrate geothermal models, showing that local studies overestimate the influence of some parameters. We ensure the feasibility of the study through a physics-based machine learning approach (reduced basis method), reducing computation time by several orders of magnitude.
    Publishing country de
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Conference proceedings ; Online: Uncertainty Quantification for Basin Scale Heat Flow Models with a Physics-Based Machine Learning Approach

    Degen, D. / Veroy, K. / Wellmann, F. / Scheck-Wenderoth, M.

    Geophysical Research Abstracts, Vol. 21, EGU2019-15910

    2019  

    Abstract: In order to determine suitable locations for geothermal exploration, reliable predictions of the earth’s subsurface temperature field are essential. For these predictions, it is necessary to consider the uncertainties of the involved parameters. However, ...

    Abstract In order to determine suitable locations for geothermal exploration, reliable predictions of the earth’s subsurface temperature field are essential. For these predictions, it is necessary to consider the uncertainties of the involved parameters. However, with the current state-of-the-art simulations standard uncertainty quantification methods,such as Markov Chain Monte Carlo are computationally intractable for basin-scale models at high resolution. We thus require numerical methods that considerably accelerate the forward simulation to enable the use of uncertainty quantification approaches that can easily require up to a million forward simulations.For this purpose, we introduce the reduced basis method, a physics-based machine learning approach. Our previous studies show that we obtain speed-ups of four to six orders of magnitude in comparison to standard finite element simulations. One main advantage of the reduced basis method in contrast to other surrogate models is that we obtain temperature values at every point in the model and not only at the observation points. Consequently, we can generate uncertainty maps of the temperatures at the target depth of the geothermal wells for the entire extent of the basin. We use the Brandenburg (Germany) model to illustrate the application and benefits of the reduced basis method for large-scale geological models. The numerical simulations are realized within the DwarfElephant package, an open-source high-performance application based on the Multiphysics Object Oriented Simulation Environment(MOOSE) developed by the Idaho National Laboratory. The DwarfElephant package offers a physics-independent and user-friendly access to the reduced basis method within a high-performance finite element library, allowing computations of spatially high dimensional models. In addition, we present how the method can be used for other inverse processes, such as automated model calibrations. Inverse problems are becoming rapidly extremely ex-pensive computationally even without including all major sources of uncertainty. In that regard, the reduced basis method is very promising because it allows a significant reduction in computation time without introducing additional physical uncertainties.
    Subject code 550
    Language English
    Publishing country de
    Document type Conference proceedings ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Low noise 400 W coherently combined single frequency laser beam for next generation gravitational wave detectors.

    Wellmann, Felix / Bode, Nina / Wessels, Peter / Overmeyer, Ludger / Neumann, Jörg / Willke, Benno / Kracht, Dietmar

    Optics express

    2021  Volume 29, Issue 7, Page(s) 10140–10149

    Abstract: Design studies for the next generation of interferometric gravitational wave detectors propose the use of low-noise single-frequency high power laser sources at 1064 nm. Fiber amplifiers are a promising design option because of their high output power ... ...

    Abstract Design studies for the next generation of interferometric gravitational wave detectors propose the use of low-noise single-frequency high power laser sources at 1064 nm. Fiber amplifiers are a promising design option because of their high output power and excellent optical beam properties. We performed filled-aperture coherent beam combining with independently amplified beams from two low-noise high-power single-frequency fiber amplifiers to further scale the available optical power. An optical power of approximately 400 W with a combining efficiency of more than 93% was achieved. The combined beam contained 370 W of linearly polarized TEM
    Language English
    Publishing date 2021-04-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1491859-6
    ISSN 1094-4087 ; 1094-4087
    ISSN (online) 1094-4087
    ISSN 1094-4087
    DOI 10.1364/OE.420350
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Conference proceedings ; Online: How well do we know our models?

    Degen, D. / Veroy, K. / Cacace, M. / Scheck-Wenderoth, M. / Wellmann, F.

    Abstracts

    2020  

    Abstract: 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 ... ...

    Abstract 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. 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. 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. 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. 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.
    Subject code 910
    Publishing country de
    Document type Conference proceedings ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Water Table Uncertainties due to Uncertainties in Structure and Properties of an Unconfined Aquifer.

    Hauser, Juerg / Wellmann, Florian / Trefry, Mike

    Ground water

    2018  Volume 56, Issue 2, Page(s) 251–265

    Abstract: We consider two sources of geology-related uncertainty in making predictions of the steady-state water table elevation for an unconfined aquifer. That is the uncertainty in the depth to base of the aquifer and in the hydraulic conductivity distribution ... ...

    Abstract We consider two sources of geology-related uncertainty in making predictions of the steady-state water table elevation for an unconfined aquifer. That is the uncertainty in the depth to base of the aquifer and in the hydraulic conductivity distribution within the aquifer. Stochastic approaches to hydrological modeling commonly use geostatistical techniques to account for hydraulic conductivity uncertainty within the aquifer. In the absence of well data allowing derivation of a relationship between geophysical and hydrological parameters, the use of geophysical data is often limited to constraining the structural boundaries. If we recover the base of an unconfined aquifer from an analysis of geophysical data, then the associated uncertainties are a consequence of the geophysical inversion process. In this study, we illustrate this by quantifying water table uncertainties for the unconfined aquifer formed by the paleochannel network around the Kintyre Uranium deposit in Western Australia. The focus of the Bayesian parametric bootstrap approach employed for the inversion of the available airborne electromagnetic data is the recovery of the base of the paleochannel network and the associated uncertainties. This allows us to then quantify the associated influences on the water table in a conceptualized groundwater usage scenario and compare the resulting uncertainties with uncertainties due to an uncertain hydraulic conductivity distribution within the aquifer. Our modeling shows that neither uncertainties in the depth to the base of the aquifer nor hydraulic conductivity uncertainties alone can capture the patterns of uncertainty in the water table that emerge when the two are combined.
    MeSH term(s) Bayes Theorem ; Geology ; Groundwater ; Water Movements ; Western Australia
    Language English
    Publishing date 2018
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 246212-6
    ISSN 1745-6584 ; 0017-467X
    ISSN (online) 1745-6584
    ISSN 0017-467X
    DOI 10.1111/gwat.12577
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    In stock of ZB MED Bonn / Germany

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    Order via subito

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  9. Article ; Online: CO

    Brockmüller, Eike / Wellmann, Felix / Lutscher, Daniel / Kimmelma, Ossi / Lowder, Tyson / Novotny, Steffen / Lachmayer, Roland / Neumann, Jörg / Kracht, Dietmar

    Optics express

    2022  Volume 30, Issue 15, Page(s) 25946–25957

    Abstract: We report on the development of a side-fused signal-pump combiner with an integrated feed-through 34/250-µm chirally coupled core fiber. The manufacturing process involves a novel rotationally symmetrical cladding restructuring using a ... ...

    Abstract We report on the development of a side-fused signal-pump combiner with an integrated feed-through 34/250-µm chirally coupled core fiber. The manufacturing process involves a novel rotationally symmetrical cladding restructuring using a CO
    Language English
    Publishing date 2022-10-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1491859-6
    ISSN 1094-4087 ; 1094-4087
    ISSN (online) 1094-4087
    ISSN 1094-4087
    DOI 10.1364/OE.455606
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Conference proceedings ; Online: The Reduced Basis Method in Geosciences

    Degen, D. / Freymark, J. / Veroy, K. / Wellmann, F. / Scheck-Wenderoth, M.

    Abstracts

    Application to the Upper Rhine Graben Model

    2018  

    Abstract: Because of the highly heterogeneous character of the earth's subsurface, the complex coupling of thermal, hydrological, mechanical, and chemical processes, and the limited accessibility geoscientific applications have a high-dimensional character. Hence, ...

    Abstract Because of the highly heterogeneous character of the earth's subsurface, the complex coupling of thermal, hydrological, mechanical, and chemical processes, and the limited accessibility geoscientific applications have a high-dimensional character. Hence, the usage of automated calibration algorithms with a reasonable number of iterations is often prohibitively expansive using the standard finite element (FE) method. Therefore, we using the reduced basis (RB) method, being a model order reduction (MOR) technique, that constructs low-order approximations to, for instance, the FE space. We use the RB method to address this computationally challenging simulations because this method significantly reduces the degrees of freedom. The RB method is based on a decomposable implementation of an offline and online stage. This allows performing all the expensive pre-computations beforehand to get real-time results during the online stage, which can be, for instance, directly used during field measurements. Generally, the RB approach is most beneficial in the many-query and real-time context. We will illustrate the advantages of the RB method by applying it to the Upper Rhine Graben. For the forward simulation of the Upper Rhine Graben model we are considering a geothermal conduction problem demonstrating the implementation of the RB method, within the DwarfElephant package, for a steady-state case. We will not only compare the runtimes for both the FE and the RB simulations but also evaluate the quality of the RB approximation. We will emphasize the advantages of this method for repetitive simulations by performing a parameter study for the Upper Rhine Graben model. Here, we are going to emphasize especially the speed-up that is gained by using the RB instead of the FE method. Furthermore, we will demonstrate how the used implementation is usable in high-performance computing (HPC) infrastructures.
    Subject code 000
    Publishing country de
    Document type Conference proceedings ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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