Computational and Multi-Scale Methods for Nonlinear Electromagnetic Models in Plasmas and Nanocomposites

该项目是一个跨学科合作项目，涉及数学建模、计算模拟和实验数据，以加速先进电磁纳米复合材料和替代发电机的设计。纳米复合材料由铁磁性纳米颗粒在介电非磁性基质中制成，为电磁材料的创新提供了无与伦比的机会。通过基于物理模型的计算模拟，预测电磁材料的性质作为基质中包裹体的大小、形状和浓度的函数的能力，将对纳米复合材料的数字化制造至关重要。这些设计上的进步将使微波频率天线和梯度折射率透镜、印刷电子电路和系统等应用成为可能。这一目标与材料基因组计划的使命有关，即通过计算加速材料创新。第二个目标涉及磁流体动力（MHD）发电，这可能是美国安全能源组合的重要组成部分。MHD发电机缺少活动部件，提高了发电厂的整体效率，并有可能显著减少碳排放。基于物理模型的计算模拟将有助于这些热效率能源系统的优化设计。我们考虑的模型对于正确模拟太阳耀斑也是必不可少的，太阳耀斑可以引发地磁风暴，破坏电力和通信，造成数百万美元的损失。因此，我们的技术将推动天体物理学、空间天气预报和清洁能源系统等领域的应用。本项目的主要目标是发展新的数值离散化和计算多尺度电磁模型，包括非线性材料性质的不确定性。所得到的方法将使上述应用中的最佳设计策略成为可能。我们将使用合作者提供的实验数据验证我们方法的有效性，这些数据也将用于校准不确定性的统计描述。其中一个研究领域涉及模型驱动的磁性纳米复合材料的稳健设计方法，该材料具有先进设备所需的所需性能。第二个领域涉及等离子体磁场的非线性模型。每一位pi都展示了将研究与教育相结合的记录，并且是致力于招募少数民族和创造友好环境的专业教育者。pi将在这个跨学科项目的理论、计算和实验方面培养三名博士生。与政府实验室和工业界的合作将为研究生提供实习机会。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project is an inter-disciplinary collaboration involving mathematical modeling, computational simulation and experimental data for accelerating the design of advanced electromagnetic nanocomposite materials as well as alternative power generators. Nanocomposites, made of ferromagnetic nanoparticles in a dielectric, non-magnetic matrix, offer unparalleled opportunities for innovation in electromagnetic materials. The ability to predict electromagnetic material properties as a function of size, shape and concentration of inclusions in the host matrix, from computational simulations of physics-based models, will crucially aid in the digital fabrication of nanocomposites. These advances in design will enable applications including microwave frequency antennas and gradient refractive index lenses, printed electronic circuits and systems, to name a few. This objective is related to the Materials Genome Initiative's mission to accelerate materials innovation via computation. A second objective involves Magnetohydrodynamic (MHD) power generation, which is potentially a significant component of a secure U.S. energy portfolio. The lack of moving parts in an MHD power generator increases the overall efficiency of the power plant and potentially decreases carbon emissions significantly. Computational simulations of physics-based models will aid in the optimal design of these thermally efficient energy systems. The models we consider are also essential to correctly modeling solar flares which can trigger geomagnetic storms disrupting power and communications costing millions of dollars in losses. Thus our techniques will advance applications in astrophysics, space weather prediction and clean energy systems, among others. The major goal of this project is to develop novel numerical discretizations and computational multiscale electromagnetic models incorporating uncertainties in nonlinear material properties. The resulting methods will enable optimal design strategies in the applications discussed above. We will validate the effectiveness of our methods using experimental data provided by our collaborators, which will also be used to calibrate statistical descriptions of uncertainties. One area of research involves a model-driven robust design methodology for a magnetic nanocomposite material with the desired properties critically needed for advanced devices. A second area, involves nonlinear models for magnetic fields in plasma. The PIs each have demonstrated records of integration of research into education and are dedicated educators committed to recruiting minorities and creating welcoming environments. The PIs will train three doctoral students on theoretical, computational and experimental aspects of this interdisciplinary project. The partnerships with government labs and industry will provide internship opportunities for the graduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.