Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

A high-fidelity multiphysics system for neutronic, thermalhydraulic and fuel-performance analysis of Light Water Reactors

Das Verhalten des Kerns in einem Leichtwasserreaktor (LWR) wird von neutronenphysikalischen, thermohydraulischen und thermomechanischen Phänomenen dominiert. Komplexe Rückkopplungsmechanismen verbinden diese physikalischen Bereiche. Einer der aktuellen Tendenzen in der Reaktorphysik ist daher die Implementierung von Multiphysik-Methoden, die diese Wechselwirkungen erfassen, um eine konsistente Beschreibung des Kerns zu liefern. Ein weiterer wichtiger Arbeitsbereich ist die Entwicklung von High-Fidelity-Rechenprogrammen, die die Modellierungsauflösung erhöhen und starke Vereinfachungen eliminieren, die in räumlich homogenisierten Simulationen verwendet werden. Multiphysik- und High-Fidelity-Methoden sind auf die Verfügbarkeit von Hochleistungsrechnern angewiesen, die die Machbarkeit und den Umfang dieser Art von Simulationen begrenzen. Das Ziel dieser Arbeit ist die Entwicklung eines Multiphysik-Simulationssystems, das in der Lage ist, gekoppelte neutronenphysikalische, thermohydraulische und thermomechanische Analysen von LWR-Kernen mit einer High-Fidelity-Methodik durchzuführen. Um dies zu erreichen, wird die Monte-Carlo-Teilchentransportmethode verwendet, um das Verhalten der neutronenphysikalischen Effekte zu simulieren, ohne auf größere physikalische Näherungen zurückzugreifen. Für die Abbrandrechnungen bezüglich des gesamten Kerns, wird eine gebietsbezogene Datenaufteilung der Partikelverfolgung vorgeschlagen und implementiert. Die Kombination der Monte-Carlo-Methode mit der Thermohydraulik auf Unterkanalebene und eine vollständige Analyse des Brennstoffverhaltens aller Brennstäbe beschreibt eine extrem detaillierte Darstellung des Kerns. Die erforderliche Rechenleistung erreicht die Grenzen aktueller Hochleistungsrechner. Auf der Softwareseite wird ein innovativer objektorientierter Kopplungsansatz verwendet, um die Modularität, Flexibilität und Wartbarkeit des Programms zu erhöhen. Die Genauigkeit dieses gekoppelten Systems von drei Programmen wird mit experimentellen Daten von zwei in Betrieb befindlichen Kraftwerken, einem Pre-Konvoi DWR und dem Temelín II WWER-1000 Reaktor, bewertet. Für diese beiden Fälle werden die Ergebnisse der Abbrandrechnung des gesamten Kerns anhand von Messungen der kritischen Borkonzentration und des Brennstabneutronenflusses validiert. Diese Simulationen dienen der Darstellung der hochmodernen Modellierungsfähigkeiten des entwickelten Werkzeugs und zeigen die Durchführbarkeit dieser Methodik für industrielle Anwendungen

A nested hybridizable discontinuous Galerkin method for computing second-harmonic generation in three-dimensional metallic nanostructures

In this paper, we develop a nested hybridizable discontinuous Galerkin (HDG) method to numerically solve the Maxwell's equations coupled with the hydrodynamic model for the conduction-band electrons in metals. By means of a static condensation to eliminate the degrees of freedom of the approximate solution defined in the elements, the HDG method yields a linear system in terms of the degrees of freedom of the approximate trace defined on the element boundaries. Furthermore, we propose to reorder these degrees of freedom so that the linear system accommodates a second static condensation to eliminate a large portion of the degrees of freedom of the approximate trace, thereby yielding a much smaller linear system. For the particular metallic structures considered in this paper, the resulting linear system obtained by means of nested static condensations is a block tridiagonal system, which can be solved efficiently. We apply the nested HDG method to compute the second harmonic generation (SHG) on a triangular coaxial periodic nanogap structure. This nonlinear optics phenomenon features rapid field variations and extreme boundary-layer structures that span multiple length scales. Numerical results show that the ability to identify structures which exhibit resonances at $\omega$ and $2\omega$ is paramount to excite the second harmonic response.Comment: 31 pages, 7 figure

Integrated Heart - Coupling multiscale and multiphysics models for the simulation of the cardiac function

Mathematical modelling of the human heart and its function can expand our understanding of various cardiac diseases, which remain the most common cause of death in the developed world. Like other physiological systems, the heart can be understood as a complex multiscale system involving interacting phenomena at the molecular, cellular, tissue, and organ levels. This article addresses the numerical modelling of many aspects of heart function, including the interaction of the cardiac electrophysiology system with contractile muscle tissue, the sub-cellular activation-contraction mechanisms, as well as the hemodynamics inside the heart chambers. Resolution of each of these sub-systems requires separate mathematical analysis and specially developed numerical algorithms, which we review in detail. By using specific sub-systems as examples, we also look at systemic stability, and explain for example how physiological concepts such as microscopic force generation in cardiac muscle cells, translate to coupled systems of differential equations, and how their stability properties influence the choice of numerical coupling algorithms. Several numerical examples illustrate three fundamental challenges of developing multiphysics and multiscale numerical models for simulating heart function, namely: (i) the correct upscaling from single-cell models to the entire cardiac muscle, (ii) the proper coupling of electrophysiology and tissue mechanics to simulate electromechanical feedback, and (iii) the stable simulation of ventricular hemodynamics during rapid valve opening and closure

Reengineering Aircraft Structural Life Prediction Using a Digital Twin

Reengineering of the aircraft structural life prediction process to fully exploit advances in very high performance digital computing is proposed. The proposed process utilizes an ultrahigh fidelity model of individual aircraft by tail number, a Digital Twin, to integrate computation of structural deflections and temperatures in response to flight conditions, with resulting local damage and material state evolution. A conceptual model of how the Digital Twin can be used for predicting the life of aircraft structure and assuring its structural integrity is presented. The technical challenges to developing and deploying a Digital Twin are discussed in detail

Research and Education in Computational Science and Engineering

This report presents challenges, opportunities, and directions for computational science and engineering (CSE) research and education for the next decade. Over the past two decades the field of CSE has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers with algorithmic inventions and software systems that transcend disciplines and scales. CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society, and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution and increased attention to data-driven discovery, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. With these many current and expanding opportunities for the CSE field, there is a growing demand for CSE graduates and a need to expand CSE educational offerings. This need includes CSE programs at both the undergraduate and graduate levels, as well as continuing education and professional development programs, exploiting the synergy between computational science and data science. Yet, as institutions consider new and evolving educational programs, it is essential to consider the broader research challenges and opportunities that provide the context for CSE education and workforce development