Frederic G. Gibou, Ph.D. - US grants

In this work, the investigator devises innovative computational methods for free surface microfluidics in order to understand and control absorption and evaporation processes in the sub-micron scale open channels, as well as to understand how to efficiently control the effect of surface tension and temperature profiles along the channels. In particular, efficient numerical schemes on adaptive meshes are constructed in order to address the multiscale nature of the problem and the limitations of computer resources. The investigator introduces a new paradigm that allows the discretization of PDEs on highly adaptive meshes as if the mesh was uniform, while attaining second-order accuracy in the maximum norm. The numerical simulations are used to supplement physical experiments in order to optimize the design of free surface fluidics devices. The simulations take into account the temperature field in the liquid and the surrounding channel walls, the temperature-induced variations in surface tension, the subsequent surface tension driven flows in the transversal direction of the main flow (Marangoni effect) and their effects on the transport of airborne particles from the surface of the liquid to its bulk. Furthermore, the simulations take the configuration of the whole air-liquid system into account and determine the shape of the free surface in order to find a design that maximizes contact between the air and the liquid surface for efficient capture of airborne species.

In the past 15 years, significant advances have been made in using micro/nanofluidic-based platforms for detecting chemical and biological agents. However, all the `lab-on-a-chip' platforms reported in the literature can process samples only after the samples are injected into a microchannel. This longstanding technological and scientific barrier limits the viability of these platforms for monitoring airborne species at time when the great importance of this technology to national security issues has become clear. Free-surface fluidics is an innovative technology pioneered by the investigator's collaborators at UCSB which removes this barrier by enabling previously impossible detection thresholds for certain trace airborne chemical agents and pathogens. Airborne molecules can be directly absorbed through the free surface, where they can interact with gold or silver colloidal particles and be detected using Surface-Enhanced Raman Spectroscopy. This platform could be used, for example, for public safety in a variety of venues to detect explosives and toxic chemicals, and for continuous monitoring of biological or chemical warfare agents in air ventilation systems. In this work, the investigator devises numerical methods in order to optimize the design of free-surface fluidic devices and to better understand the physical phenomena involved. These methods have an important impact on other fields as well, as they have key applications in the fields of semi-conductor processing and in the energy industry, in bio-nanotechnology and tissue engineering, in combustion as well as in the modeling of tumor growth to name a few. In addition, the investigator develops a freely available interactive web site on computational science and engineering which guides the users through a computational science journey, exploring intriguing topics such as microfluidics, crystal growth, single and multiphase flows. The users have the possibility of further interactive exploration by altering parameters to observe the effect on simulations, hence providing a tutorial on computational science.

This research project creates a multidisciplinary intellectual partnership with the goal of designing transformative computation-based methods for the simulation of quantum dots (QDs) growth by molecular beam epitaxy. This research enables a better understanding of the kinetics and thermodynamics during the growth of quantum dots allowing for an improved control of the positioning, growth and size distribution in QD structures. The project combines expertise in materials science with state-of-the-art algorithm design for partial differential equations and moving boundary problems.
This research impacts scientific and industrial communities that make use of QDs as single photon emitters or make use of the carrier spins to develop and manipulate q-bits. Outcomes help guide experiments, and the design and manufacture of new optical and electronic devices, such as QDs photonic crystal lasers, nonvolatile storage, laser scalpel and optical coherence tomography for use in medicine, and QD-based product enhancements in the energy sector. The impact on computational science is important for the simulation and design of diffusion dominated processes, moving boundary problems, and multiscale modeling where macroscopic behaviors are simulated while incorporating microscopic rates.

The research program involves the training of two students, including one student with Latino background. The research program also fosters the development of an interactive web-based computational lab for students to experiment with computational tools. In addition, advances made through this research program are disseminated through the Materials Research Laboratory at University of California Santa Barbara (UCSB), the Institute for Pure and Applied Mathematics at University of California Los Angeles (UCLA) and the California Nano Science Institute at both UCLA and UCSB.

This award is part of the Cyber-Enabled Discovery and Innovation program, and the recipients are Frederic Gibou of UCSB and Christian Ratsch of UCLA.

The objective of this award is the development of novel methodologies for treating cardiac arrhythmias associated with abnormal electrical activity in the heart. This will include the computation of energy-optimal stimuli which drive the states of myocardial cells to a region of phase space for which they become nearly synchronized, thereby terminating spiral wave activity and preventing spiral wave reentry. The algorithm used to accomplish this will involve new computational approaches for finding appropriate target regions of phase space and solving the Hamilton-Jacobi-Bellman equation in higher dimensions. Another methodology will use the action potential duration restitution curve, which is an experimentally measurable function that describes the duration of an action potential as a function of the time since the end of the preceding action potential. Given this curve, the methods of dynamic programming will be applied to determine an optimal pulse train within a family of possibilities for synchronization of myocardial activity.

Cardiac arrhythmias can disrupt the coordination that is essential for the effective pumping of blood, and can lead to cardiac arrest. Survivors of cardiac arrest often receive an implantable cardioverter defibrillator (ICD), but defibrillating shocks from ICDs can lead to fibrosis in the myocardium, making it necessary to use stronger defibrillating shocks. The shocks can also cause adverse short-term side effects such as intense chest pain and post-shock dysfunction. If successful, this research will represent an early step toward the use of energy-optimal stimuli for ICDs, which would reduce the need for surgical replacement of their batteries,and might mitigate some of the deleterious side effects of defibrillation. This work will also contribute to the development of computational tools which can be used to find optimal control strategies in a wide range of applications.

Ratsch/Margetis/Gibou
1412392/1412769/1412695

Epitaxial growth is a process in which one material is deposited on top of another. It is of fundamental technological importance, as many modern opto-electronic devices are fabricated by this process. Important examples include transistors in microelectronics, quantum dots for photonic crystal lasers, quantum dot-based product enhancements in the energy sector, and nonvolatile storage media, which are sought to replace hard drives, flash memories, and RAM memories. Other applications include, for example, catalysts (speeding up chemical reactions), which often rely on metal epitaxy. Catalysis is used in the energy sector, in food processing, in environmental science, and elsewhere; a well-known example is the catalytic converter for vehicle emissions control. The investigators of this collaborative project develop models that lead to a better and more fundamental understanding of epitaxial growth. Results of the work are of interest not only to mathematicians but also to physicists, material scientists and engineers, and medical scientists to whose problems the mathematical results apply. Three graduate students are included in this interdisciplinary project.

The goal of the project is to develop analytical and computational tools to seamlessly and efficiently connect several length and time scales in epitaxy. These tools encompass asymptotic methods for linking atomistic models to mesoscale phenomena as well as efficient numerical methods on adaptive quadtree/octree grids in a parallel computing environment to enable the simulation of large material systems. The investigators design and implement a hierarchy of growth models that combine atomistic master equations in the context of kinetic solid-on-solid models, density-functional theory (DFT), a partial differential equation-based island dynamics growth model, and an efficient scheme to solve the elastic equations to include elastic strain. This collaborative effort provides the means to address a long-standing controversy about the role played by kinetic and thermodynamics effects in the formation of crystal structures such as mounds and quantum dots (QDs). Three graduate students are included in this interdisciplinary project.

NON-TECHNICAL:

Unprecedented advances in computational capabilities, advanced characterization techniques and the ability to generate and harness large-scale data enable new pathways for the design and synthesis of a broad array of advanced materials systems. However, critical gaps exist in the infrastructure for multiphase, multicomponent metallic materials, where the design space is extraordinarily large and synthesis processes are complex and expensive. A multidisciplinary UCSB team will develop an integrated framework for design of multicomponent, multiphase single crystal alloys. Novel complementary computational and experimental tools developed will be integrated with existing tools to address fundamental barriers that challenge the design and synthesis of a new class of L12-strengthened cobalt-base alloys. The emerging class of alloys promises to positively impact the temperature capability and efficiency of a broad array of high temperature propulsion and energy systems. The program will develop new capabilities that substantially enhance the iterative feedback process between design, characterization and synthesis, rapidly expanding the knowledge base for this new class of materials.


TECHNICAL:

New coordinated experimental and computational tools will be developed and deployed for discovery of new Co-base single crystal compositions. The technical developments that will enable this approach include: 1) A self-consistent thermodynamic framework for alloy design that rigorously couples first principles calculations, multicomponent thermodynamics, internal stresses and diffusion in these solid systems. 2) New parallelized, sharp interface computational methods that can predict the behavior of multicomponent alloys in a single crystal growth environment. 3) New approaches for rapid 3D characterization of the material structure and parallel computational tools that predict structure evolution in 3D. 4) Tools for prediction of basic substrate mechanical properties and rapid characterization of mechanical properties. The experimental and computational tools developed in this program will be broadly applicable to the development of multicomponent metallic alloys in other domains. Additionally, computational tools, thermodynamic, kinetic data and 3-D data will be transferred to industry and broadly shared through a variety of data and software hubs.

This project combines modeling with numerical methods for the investigation of the self-assembly of block copolymers. In particular, this project will result in the development of efficient and predictive computational tools that predict the self-assembly of block copolymers that present a free boundary. In addition, this formalism will be used to develop a methodology for solving the inverse problem of finding the geometry of a mask component that will direct the self-assembly of copolymer towards a target design. Copolymers are ubiquitous in science and engineering, they provide unique characteristics that modern industrial processes depend on to keep up with Moore's law and constitute an excellent model system for scientific studies of self-assembly. Therefore, the research has a broader impact in the multiscale modeling and computation of more general self-assembly processes that arise in other physical and biological sciences. The specific objectives of the project are: 1) to develop an innovative, effective computational framework for predicting the self-assembly of block copolymer in both two and three spatial dimensions in the case of free surfaces; 2) to use this framework to understand the coupling between thermodynamic, kinetic and surface tension forces on the self-assembly and on the geometry of the free surface; 3) to apply this framework to the prediction of a template's geometry that will direct the self-assembly towards a target design.

Diblock copolymers are melts made of molecular chains with two chemically different species along their backbone that self-assemble into ordered structures used in high density hard drives, drug delivery systems, magnetic dots, nano-pores, nano-wires, membranes with tailored nano-scales porosity, in battery fuel cells and silicon capacitors. Since the surface of the melt plays a crucial role in the self-assembly, this research will develop a computational paradigm that enables the simulation of the self-assembly of free boundary diblock copolymers. This paradigm combines the level-set methodology for dynamic interfaces with the self-consistent field theory describing the self-assembly of diblock copolymers at equilibrium. These studies will be carried out by the PI who works in an interdisciplinary environment with close collaborations with experts in the field of diblock copolymers that bring forth a synergy of modeling and computational ideas. The broader impacts of the work also include 1) the training of computational students in an interdisciplinary, international team, 2) the integration of undergraduate from underrepresented groups, and 3) potential industrial relevance.

This project, acquiring a computer cluster (mini-supercomputer) to replace the old one, aims to serve the computational needs facilitating scientific research and education in multiple areas. The machine includes 120 node Infiniband interconnected cluster for efficient message passing interface (MPI) parallel processing, four shared memory "fat nodes" with 1 Terabyte (TB) of memory/node, four graphic processing unit (GPU) nodes built around NVIDIA Tesla P100 1 Gigabyte (GB) GPUs, and four Intel Knight's Landing nodes. This blended system will serve the computational needs of the vast majority of campus researchers. This system will also service users needing large-scale resources by allowing development, prototyping, and benchmarking calculations locally, prior to production runs at supercomputer centers.

The research enabled spans multiple departments and supports research in many campus centers including AlloSphere Research Facility, the California Nanosystems Institute, the Center of Polymers and Organic Solids, the Earth Research Institute, the Kavli Institute of Theoretical Physics, the Marine Science Institute, the Materials Research Laboratory, and the National Center for Ecological Analysis and Synthesis. This interdisciplinary and mainly collaborative research will be facilitated by the presence of an available local facility, without the administrative hurdles and delays associated with application to supercomputing centers.

Broader Impacts:
The cluster is expected to play a prominent role in educating the next generation of scientists, engineers, and mathematicians. The NSF Integrated Graduate Education and Research Traineeship (IGERT) program in Network Science and Big Data will also employ the cluster that will additionally be utilized by undergraduates, high school, and community college students and K-12 teachers via existing sponsored programs. It will also contribute to attract others students and researchers. Furthermore the facility will service graduate participants in the University of California Leadership Excellence through Advanced Degrees (UCLEADS) and the Bridges to Doctorate programs. These programs help increase the number of under-represented students involved.