Postdoctoral Appointee –CFD Modeling
- Argonne National Laboratory
- Location: Lemont, IL
- Job Number: 7103907 (Ref #: 413630)
- Posting Date: Jun 23, 2022
- Application Deadline: Open Until Filled
Leverage high-performance computing (HPC) to perform multi-physics and multi-scale computational fluid dynamics (CFD) simulations to improve understanding of multiphase mixing, combustion, and heat transfer in turbulent flows prevalent in industrial applications and develop novel numerical techniques and workflows for rapid simulation-driven design optimization.
Develop accurate and computationally-efficient physics-based models for near-wall turbulence, multiphase mixing, gas-phase and catalytic combustion, and conjugate heat transfer (CHT) for high-fidelity CFD of industrial applications using the open-source code OpenFOAM.
Create high-quality meshes for industrial mixers and combustors with complex geometries for use in OpenFOAM.
Perform high-fidelity CFD simulations and reduced order modeling of both reacting and non-reacting turbulent flows.
Perform mechanism reduction for complex fuels, enabling efficient incorporation of detailed chemistry in reacting flow CFD.
Develop and demonstrate integrated CFD-Machine Learning (ML) frameworks for accelerating model development and design optimization on HPC platforms.
Work as a part of a multidisciplinary team involving experimentalists, CFD experts, and computational scientists to use next-generation supercomputing architectures for scalable high-fidelity simulations.
Disseminate research outcomes in the form of technical reports, peer-reviewed journal articles, and conference papers and presentations.
Ph.D. in Mechanical engineering, or a related discipline.
0 to 3 years since Ph.D.
Knowledge of turbulent flows and turbulent combustion is required. Good understanding of multiphase flow physics and turbulent combustion modeling is desired.
Experience in development and application with OpenFOAM is required. Experience in turbulent reacting flow simulations, CHT and wall modeling in OpenFOAM is a plus. Experience with other CFD codes (e.g., Ansys Fluent, CONVERGE, etc.) is a plus.
Proficiency in CAD preparation and mesh generation for complex geometries is required. Automation and scripting skills for pre-processing and post-processing routines are desired.
Knowledge of optimization techniques is a required. Experience in the use of ML libraries (Scikit-learn, TensorFlow, PyTorch, Julia, etc.) for reduced-order modeling is a plus.
Collaborative skills, including the ability to work well with other divisions, laboratories, universities, and industry.
Skilled verbal and written communication skills at all levels of the organization.
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.
All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.
Please note that all Argonne employees are required to be vaccinated against COVID-19. All successful applicants will be required to provide their COVID-19 vaccination verification as a condition of employment, subject to limited legally recognized exemptions to COVID-19 vaccination.
Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.