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Article: Pyrometric-Based Melt Pool Monitoring Study of CuCr1Zr Processed Using L-PBF.

Artzt, Katia / Siggel, Martin / Kleinert, Jan / Riccius, Joerg / Requena, Guillermo / Haubrich, Jan

2020 Volume 13, Issue 20

Abstract: The potential of in situ melt pool monitoring (MPM) for parameter development and furthering the process understanding in Laser Powder Bed Fusion (LPBF) of CuCr1Zr was investigated. Commercial MPM systems are currently being developed as a quality ... ...

Abstract	The potential of in situ melt pool monitoring (MPM) for parameter development and furthering the process understanding in Laser Powder Bed Fusion (LPBF) of CuCr1Zr was investigated. Commercial MPM systems are currently being developed as a quality monitoring tool with the aim of detecting faulty parts already in the build process and, thus, reducing costs in LPBF. A detailed analysis of coupon specimens allowed two processing windows to be established for a suitably dense material at layer thicknesses of 30 µm and 50 µm, which were subsequently evaluated with two complex thermomechanical-fatigue (TMF) panels. Variations due to the location on the build platform were taken into account for the parameter development. Importantly, integrally averaged MPM intensities showed no direct correlation with total porosities, while the robustness of the melting process, impacted strongly by balling, affected the scattering of the MPM response and can thus be assessed. However, the MPM results, similar to material properties such as porosity, cannot be directly transferred from coupon specimens to components due to the influence of the local part geometry and heat transport on the build platform. Different MPM intensity ranges are obtained on cuboids and TMF panels despite similar LPBF parameters. Nonetheless, besides identifying LPBF parameter windows with a stable process, MPM allowed the successful detection of individual defects on the surface and in the bulk of the large demonstrators and appears to be a suitable tool for quality monitoring during fabrication and non-destructive evaluation of the LPBF process.
Language	English
Publishing date	2020-10-16
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2487261-1
ISSN	1996-1944
ISSN	1996-1944
DOI	10.3390/ma13204626
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany.

Kühn, Martin J / Abele, Daniel / Binder, Sebastian / Rack, Kathrin / Klitz, Margrit / Kleinert, Jan / Gilg, Jonas / Spataro, Luca / Koslow, Wadim / Siggel, Martin / Meyer-Hermann, Michael / Basermann, Achim

BMC infectious diseases

2022 Volume 22, Issue 1, Page(s) 333

Abstract: Background: Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies ... ...

Abstract	Background: Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. Methods: We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. Results: We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics. Conclusions: Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.
MeSH term(s)	COVID-19/epidemiology ; COVID-19/prevention & control ; Communicable Disease Control ; Germany/epidemiology ; Humans ; SARS-CoV-2/genetics
Language	English
Publishing date	2022-04-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2041550-3
ISSN	1471-2334 ; 1471-2334
ISSN (online)	1471-2334
ISSN	1471-2334
DOI	10.1186/s12879-022-07302-9
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Book ; Online: TiGL - An Open Source Computational Geometry Library for Parametric Aircraft Design

Siggel, Martin / Kleinert, Jan / Stollenwerk, Tobias / Maierl, Reinhold

2018

Abstract: This paper introduces the software TiGL: TiGL is an open source high-fidelity geometry modeler that is used in the conceptual and preliminary aircraft and helicopter design phase. It creates full three-dimensional models of aircraft from their parametric ...

Abstract	This paper introduces the software TiGL: TiGL is an open source high-fidelity geometry modeler that is used in the conceptual and preliminary aircraft and helicopter design phase. It creates full three-dimensional models of aircraft from their parametric CPACS description. Due to its parametric nature, it is typically used for aircraft design analysis and optimization. First, we present the use-case and architecture of TiGL. Then, we discuss it's geometry module, which is used to generate the B-spline based surfaces of the aircraft. The backbone of TiGL is its surface generator for curve network interpolation, based on Gordon surfaces. One major part of this paper explains the mathematical foundation of Gordon surfaces on B-splines and how we achieve the required curve network compatibility. Finally, TiGL's aircraft component module is introduced, which is used to create the external and internal parts of aircraft, such as wings, flaps, fuselages, engines or structural elements.
Keywords	Computer Science - Computational Geometry ; 65D17 ; 65D05 ; 65D10
Subject code	670
Publishing date	2018-10-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)

Coquelin, Daniel / Debus, Charlotte / Götz, Markus / von der Lehr, Fabrice / Kahn, James / Siggel, Martin / Streit, Achim

2021

Abstract: With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) to utilize large-scale ... ...

Abstract	With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) to utilize large-scale distributed resources on computer clusters. Current DPNN approaches implement the network parameter updates by synchronizing and averaging gradients across all processes with blocking communication operations. This synchronization is the central algorithmic bottleneck. To combat this, we introduce the Distributed Asynchronous and Selective Optimization (DASO) method which leverages multi-GPU compute node architectures to accelerate network training. DASO uses a hierarchical and asynchronous communication scheme comprised of node-local and global networks while adjusting the global synchronization rate during the learning process. We show that DASO yields a reduction in training time of up to 34% on classical and state-of-the-art networks, as compared to other existing data parallel training methods.
Keywords	Computer Science - Machine Learning ; Computer Science - Distributed ; Parallel ; and Cluster Computing
Subject code	006
Publishing date	2021-04-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants

Kuehn, Martin J / Abele, Daniel / Binder, Sebastian / Rack, Kathrin / Klitz, Margrit / Kleinert, Jan / Gilg, Jonas / Spataro, Luca / Koslow, Wadim / Siggel, Martin / Meyer-Hermann, Michael / Basermann, Achim

medRxiv

Abstract: Background: Despite the accelerating vaccination process, a large majority of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening ... ...

Abstract	Background: Despite the accelerating vaccination process, a large majority of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. Methods: We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the counties are initialized randomly with incidences between 75-150 weekly new cases per 100,000 inhabitants (red zones) or below 10 (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20 % of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. Results: We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, less strict lockdowns with the same commuter testing rate quickly and substantially lead to overall higher infection dynamics. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable.
Keywords	covid19
Language	English
Publishing date	2021-04-26
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2021.04.23.21255995
Database	COVID19

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Article ; Online: Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution.

Mathematical biosciences

2021 Volume 339, Page(s) 108648

Abstract: Non-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions ... ...

Abstract	Non-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions that were in place during the course of the Coronavirus disease 2019 (Covid-19) pandemic in Germany. Our results are based on hybrid models, combining SIR-type models on local scales with spatial resolution. In order to account for the age-dependence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we include realistic prepandemic and recently recorded contact patterns between age groups. The implementation of non-pharmaceutical interventions will occur on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. In order to account for spatial heterogeneity, we use a graph approach and we include high-quality information on commuting activities combined with traveling information from social networks. The remaining uncertainty will be accounted for by a large number of randomized simulation runs. Based on the derived factors for the effectiveness of different non-pharmaceutical interventions over the past months, we provide different forecast scenarios for the upcoming time.
MeSH term(s)	Age Factors ; COVID-19/prevention & control ; COVID-19/transmission ; Communicable Disease Control/methods ; Communicable Disease Control/standards ; Communicable Disease Control/statistics & numerical data ; Germany ; Humans ; Models, Statistical ; Social Network Analysis ; Spatial Analysis
Language	English
Publishing date	2021-06-30
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1126-5
ISSN	1879-3134 ; 0025-5564
ISSN (online)	1879-3134
ISSN	0025-5564
DOI	10.1016/j.mbs.2021.108648
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online ; Thesis: Concepts for the efficient Monte Carlo-based treatment plan optimization in radiotherapy

Siggel, Martin [Verfasser] / Oelfke, Uwe [Akademischer Betreuer]

2012

Author's details	Martin Siggel ; Betreuer: Uwe Oelfke
Keywords	Medizin, Gesundheit ; Medicine, Health
Subject code	sg610
Language	English
Publisher	Universitätsbibliothek Heidelberg
Publishing place	Heidelberg
Document type	Book ; Online ; Thesis
Database	Digital theses on the web

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Book ; Online: HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics

Götz, Markus / Coquelin, Daniel / Debus, Charlotte / Krajsek, Kai / Comito, Claudia / Knechtges, Philipp / Hagemeier, Björn / Tarnawa, Michael / Hanselmann, Simon / Siggel, Martin / Basermann, Achim / Streit, Achim

2020

Abstract: To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are ... ...

Abstract	To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly lowering the barrier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude. Comment: 10 pages, 8 figures, 5 listings, 1 table
Keywords	Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Machine Learning ; Computer Science - Mathematical Software ; C.1.2 ; C.2.4 ; D.1.3 ; G.1.3 ; G.4 ; I.2.0 ; I.2.5 ; I.5.5
Subject code	006 ; 004
Publishing date	2020-07-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution.

339 ; 108648 ; Mathematical biosciences ; United States

2021

Abstract: on-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions that ...

Abstract	on-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions that were in place during the course of the Coronavirus disease 2019 (Covid-19) pandemic in Germany. Our results are based on hybrid models, combining SIR-type models on local scales with spatial resolution. In order to account for the age-dependence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we include realistic prepandemic and recently recorded contact patterns between age groups. The implementation of non-pharmaceutical interventions will occur on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. In order to account for spatial heterogeneity, we use a graph approach and we include high-quality information on commuting activities combined with traveling information from social networks. The remaining uncertainty will be accounted for by a large number of randomized simulation runs. Based on the derived factors for the effectiveness of different non-pharmaceutical interventions over the past months, we provide different forecast scenarios for the upcoming time.
Keywords	Coronavirus disease ; Covid-19 ; Forecast ; Mitigation ; Non-pharmaceutical interventions ; SARS-CoV-2
Language	English
Publishing date	2021-06-30
Publisher	Elsevier
Publishing country	de
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution

medRxiv

Abstract	Non-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-phar\-ma\-ceu\-ti\-cal interventions that were in place during the course of the Coronavirus disease 2019 (Covid-19) pandemic in Germany. Our results are based on hybrid models, combining SIR-type models on local scales with spatial resolution. In order to account for the age-dependence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we include realistic pre-pandemic and recently recorded contact patterns between age groups. The implementation of non-pharmaceutical interventions will occur on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. In order to account for spatial heterogeneity, we use a graph approach and we include high-quality information on commuting activities combined with traveling information from social networks. The remaining uncertainty will be accounted for by a large number of randomized simulation runs. Based on the derived factors for the effectiveness of different non-pharmaceutical interventions over the past months, we provide different forecast scenarios for the upcoming time.
Keywords	covid19
Language	English
Publishing date	2020-12-22
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.12.18.20248509
Database	COVID19

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