美國阿貢國家實(shí)驗(yàn)室2023年招聘博士后職位（多相流模型）

美國阿貢國家實(shí)驗(yàn)室（Argonne National Laboratory，簡稱ANL）是美國政府最早建立的國家實(shí)驗(yàn)室，也是美國最大的科學(xué)與工程研究實(shí)驗(yàn)室之一——在美國中西部為最大。阿貢前身是芝加哥大學(xué)的冶金實(shí)驗(yàn)室 (Metallurgical Lab)，現(xiàn)在隸屬于美國能源部和芝加哥大學(xué)。諾貝爾物理學(xué)獎得主費(fèi)米于1942年在此領(lǐng)導(dǎo)小組建立了人類第一臺可控核反應(yīng)堆（芝加哥一號堆，Chicago Pile-1)，完成了曼哈頓計(jì)劃的重要一環(huán)，并且使人類從此邁入原子能時代。

Postdoctoral Appointee – Multi-Phase Flow Modeling

Argonne National Laboratory

Job Description

The Multiphysics Computation Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee. The successful candidate’s research will involve synergistic collaborations with a multidisciplinary team involving engine modelers, CFD and AI/ML experts, and computational scientists to enhance the predictive capability and scalability of multi-scale and multi-physics simulation codes.

The candidate will perform multi-scale computational fluid dynamics (CFD) simulations involving two-phase flows applied to heavy-duty and aerospace engines, taking advantage of both commercial and in-house codes, and leveraging high-performance computing (HPC).

· Develop accurate and computationally efficient CFD models to simulate the fuel injection, atomization dynamics and fuel-air mixing for high-pressure nozzles (e.g., Eulerian-Lagrangian Spray Atomization – ELSA – model).

· Define a high-fidelity framework to capture jet-in-crossflow dynamics applied to novel sustainable aviation fuels (SAFs).

· Develop robust libraries to accurately model non-ideal thermophysical properties of real fuels.

· Perform high-fidelity nozzle-flow simulations of realistic atomizers (e.g., pre-filming and swirling atomizers) to capture liquid jet breakup characteristics.

· Work as a part of a multidisciplinary team involving experimentalists, CFD experts, and computational scientists to enable cutting-edge CFD modeling & simulations on the next generation supercomputing architectures.

Position Requirements

· Ph.D. in mechanical/aerospace engineering, applied mathematics, chemical engineering, or a related discipline.

· Experience in modeling and simulation of three-dimensional two-phase and/or turbulent reacting flow applications using CFD codes (e.g., CONVERGE, Ansys Fluent, OpenFOAM, etc.).

· The candidate must show good collaborative skills, including the ability to work well with other divisions, laboratories, and universities.

· Skilled in communication skills at all levels of the organization.

· Ability to present and publish results in peer reviewed society technical reports and journal articles.

· A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Preferred Qualifications:

· Knowledge of engine combustion theory and modeling, extensive knowledge of liquid and gaseous fuels for engine applications, good understanding of turbulence, spray, chemical kinetics, reacting flow physics, and turbulent combustion modeling.

· Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and turbulent combustion applications, and parallel scientific computing.

· Experience in geometry manipulation with computer-aided design software.

· Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and analysis of large datasets, and parallel scientific computing.

· Experience in interdisciplinary collaborative research.

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