Speeding up Energy System Models - a Best Practice Guide

Background Energy system models (ESM) are widely used in research and industry to analyze todays and future energy systems and potential pathways for the European energy transition. Current studies address future policy design, analysis of technology pathways and of future energy systems. To address these questions and support the transformation of today’s energy systems, ESM have to increase in complexity to provide valuable quantitative insights for policy makers and industry. Especially when dealing with uncertainty and in integrating large shares of renewable energies, ESM require a detailed implementation of the underlying electricity system. The increased complexity of the models makes the application of ESM more and more difficult, as the models are limited by the available computational power of today’s decentralized workstations. Severe simplifications of the models are common strategies to solve problems in a reasonable amount of time – naturally significantly influencing the validity of results and reliability of the models in general. Solutions for Energy-System Modelling Within BEAM-ME a consortium of researchers from different research fields (system analysis, mathematics, operations research and informatics) develop new strategies to increase the computational performance of energy system models and to transform energy system models for usage on high performance computing clusters. Within the project, an ESM will be applied on two of Germany’s fastest supercomputers. To further demonstrate the general application of named techniques on ESM, a model experiment is implemented as part of the project. Within this experiment up to six energy system models will jointly develop, implement and benchmark speed-up methods. Finally, continually collecting all experiences from the project and the experiment, identified efficient strategies will be documented and general standards for increasing computational performance and for applying ESM to high performance computing will be documented in a best-practice guide

Horizons of modern molecular dynamics simulation in digitalized solid freeform fabrication with advanced materials

Our ability to shape and finish a component by combined methods of fabrication including (but not limited to) subtractive, additive, and/or no theoretical mass-loss/addition during the fabrication is now popularly known as solid freeform fabrication (SFF). Fabrication of a telescope mirror is a typical example where grinding and polishing processes are first applied to shape the mirror, and thereafter, an optical coating is usually applied to enhance its optical performance. The area of nanomanufacturing cannot grow without a deep knowledge of the fundamentals of materials and consequently, the use of computer simulations is now becoming ubiquitous. This article is intended to highlight the most recent advances in the computation benefit specific to the area of precision SFF as these systems are traversing through the journey of digitalization and Industry-4.0. Specifically, this article demonstrates that the application of the latest materials modelling approaches, based on techniques such as molecular dynamics, are enabling breakthroughs in applied precision manufacturing techniques

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Convective-scale data assimilation of thermodynamic lidar data into the weather research and forecasting model

This thesis studies the impact of assimilating temperature and humidity profiles from ground-based lidar systems and demonstrates its value for future short-range forecast. Thermodynamic profile obtained from the temperature Raman lidar and the water-vapour differential absorption lidar of the University of Hohenheim during the High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) project Observation Prototype Experiment (HOPE) are assimilated into the Weather Research and Forecasting model Data Assimilation (WRFDA) system by means of a new forward operator. The impact study assimilating the high-resolution thermodynamic lidar data was conducted using variational and ensemble-based data assimilation methods. The first part of the thesis describes the development of the thermodynamic lidar operator and its implementation through a deterministic DA impact study. The operator facilitates the direct assimilation of water vapour mixing ratio (WVMR), a prognostic variable in the WRF model, without conversion to relative humidity. Undesirable cross sensitivities to temperature are avoided here so that the complete information content of the observation with respect to the water vapour is provided. The assimilation experiments were performed with the three-dimensional variational (3DVAR) DA system with a rapid update cycle (RUC) with hourly frequency over ten hours. The DA experiments with the new operator outperformed the previously used relative humidity operator, and the overall humidity and temperature analyses improved. The simultaneous assimilation of temperature and WVMR resulted in a degradation of the temperature analysis compared to the improvement observed in the sole temperature assimilation experiment. The static background error covariance matrix (B) in the 3DVAR was identified as the reason behind this behaviour. The correlation between the temperature and WVMR variables in the background error covariance matrix of the 3DVAR, which is static and not flow-dependent, limited the improvement in temperature. The second part of the thesis provides a solution for overcoming the static B matrix issue. A hybrid, ensemble-based approach was applied using the Ensemble Transform Kalman Filter (ETKF) and the 3DVAR to add flow dependency to the B matrix. The hybrid experiment resulted in a 50% lower temperature and water vapour root mean square error (RMSE) than the 3DVAR experiment. Comparisons against independent radiosonde observations showed a reduction of RMSE by 26% for water vapour and 38% for temperature. The planetary boundary layer (PBL) height of the analyses also showed an improvement compared to the available ceilometer. The impact of assimilating a single lidar vertical profile spreads over a 100 km radius, which is promising for future assimilation of water vapour and temperature data from operational lidar networks for short-range weather forecasting. A forecast improvement was observed for 7 hours lead time compared with the ceilometer derived planetary boundary layer height observations and 4 hours with Global Navigation Satellite System (GNSS) derived integrated water vapour observations. With the help of sophisticated DA systems and a robust network of lidar systems, the thesis throws light on the future of short-range operational forecasting.Die Einfluss der Integration von Temperatur- und Feuchtigkeitsprofilen aus bodengestützten aktiven Lidar-Systemen wird untersucht und ihr Nutzen für künftige Kurzstreckenvorhersagen wird demonstriert. Thermodynamische Profile, die mit dem Temperatur-Raman-Lidar und dem Wasserdampf-DIAL der Universität Hohenheim während des Observation Prototype Experiment (HOPE), das Teil des Projekts High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) war, gewonnen wurden, werden mit Hilfe eines neuen Vorwärtsoperators in das Datenasimilations-System (DA) des Wetterforschungs- und -vorhersagemodells (WRF) Modells assimiliert. Die Untersuchungen zum Einfluss der Assimilation der hochauflösenden thermodynamischen Lidar-Daten wurden dabei mit Variations- und Ensemble-basierten Datenassimilationsmethoden durchgeführt. Der erste Teil des Arbeit beschreibt die Entwicklung des Lidar-Operators und seine Implementierung mit Hilfe einer deterministischen Datenassimilationsstudie. Der Operator ermöglicht die direkte Assimilation des Wasserdampf-Mischungsverhältnisses (WVMR), einer prognostischen Variable im WRF-Modell, ohne Umrechnung in relative Feuchte. Unerwünschte Querempfindlichkeiten zur Temperatur werden hierbei vermieden, und der vollständige Informationsgehalt der Beobachtung in Bezug auf den Wasserdampf wird genutzt. Das Assimilations-Experiment wurde mit einer 3-dimensionalen Variationsdatenassimilation (3DVAR) durchgeführt, wobei über einen Zeitraum von zehn Stunden jede Stunde eine 3DVAR durchgeführt wurde. Die DA-Experimente mit dem neuen Operator verbesserten die Ergebnisse gegenüber dem zuvor verwendeten Operator für die relative Luftfeuchtigkeit, und die Wasserdampf- und Temperaturanalysen wurden insgesamt optimiert. Die gleichzeitige Assimilation von Temperatur und WVMR führte dabei zu einer geringfügigen Verschlechterung des Temperaturfeldes in der Analyse, während eine Verbesserung des Temperaturfeldes beobachtet wurde, wenn die Temperatur allein assimiliert wurde. Die statische Hintergrundfehler-Kovarianzmatrix (B) in der 3DVAR wurde als Grund für dieses Verhalten identifiziert. Die Korrelation zwischen den Temperatur- und den WVMR-Variablen in der statischen und nicht-strömungsbedingten Hintergrundfehler-Kovarianzmatrix der 3DVAR begrenzte die Verbesserung im Hinblick auf die Temperatur. Der zweite Teil der Arbeit zeigt eine Lösung zur Überwindung des Problems der statischen B-Matrix auf. Es wurde ein hybrider Ansatz angewandt, der den Ensemble Transform Kalman Filter (ETKF) zusammen mit der 3DVAR verwendet, um der Hintergrundfehler-Kovarianzmatrix eine Strömungsabhängigkeit hinzuzufügen. Das Hybridexperiment führte, im Vergleich zum 3DVAR, zu einem 50% niedrigeren mittleren quadratischen Fehler (RMSE) für die Temperatur undWasserdampf. Vergleiche mit unabhängigen Radiosondenbeobachtungen zeigten eine Verringerung des RMSE um 26% für Wasserdampf und 38% für die Temperatur. Vergleiche mit Ceilometern, die während HOPE zur Verfügung standen, zeigten, dass die prognostizierte Höhe der planetarischen Grenzschicht (PBL) deutlich näher an den Beobachtungen war. Der Einflussbereich der Assimilation eines einzelnen Lidar-Vertikalprofils erstreckte sich über einen Radius von 100 km, was für die Assimilation vonWasserdampfund Temperaturdaten aus operationellen Lidar-Netzwerken für die kurzfristige Vorhersage vielversprechend ist. Eine Verbesserung der Vorhersage bezüglich der Entwicklung der planetarischen Grenzschicht konnte in den ersten 7 Stunden nach der letzten 3DVAR erreicht werden. Ein Vergleich mit vom Globalen Navigationssatellitensystem (GNSS) abgeleiteten Beobachtungen des integrierten Wasserdampfs ergab eine Verbesserung der Vorhersage während der ersten 4 Stunden nach dem letzten 3DVAR. Mit Hilfe von hochentwickelten DA-Systemen und einem robusten Netzwerk von Lidar-Systemen wirft die Arbeit ein Licht auf die Verbesserung der Zukunft der operativen Vorhersage im Nahbereich

The Shield 1990

Murray State University Yearbook, 1990https://digitalcommons.murraystate.edu/yearbooks/1064/thumbnail.jp