Postdoctoral Appointee – Fuel Spray, Emissions, and Aftertreatment Modeling
Job posting number: #7184116 (Ref:416609)
Posted: October 3, 2023
Application Deadline: Open Until Filled
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 comprised of fellow postdoctoral appointees and staff scientists with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with the goal to enhance predictive capability and scalability of multi-scale and multi-physics simulation codes.
The prospective postdoctoral appointee will perform multi-physics and multi-scale CFD simulations of complex systems involving modeling of multi-phase flows, turbulent combustion, heat transfer, combustion, and emissions of low-carbon propulsion systems by further developing commercial/in-house codes and high-performance computing (HPC).
Develop accurate and computationally efficient CFD models to simulate the chain of physics and chemistry involved with fuel injection, fuel-air mixing, turbulent combustion, and emissions of propulsion systems.
Perform high-fidelity simulations of ICEs that involve both conventional and low-carbon fuels.
Improve the computational efficiency and accuracy of physics-based and data-driven models for liquid fuel injection and integrate them in simulations of direct injection engines.
Perform simulations of turbulent combustion in combustion engines involving fuel sprays, fuel-air mixing, combustion, and emission modeling.
Perform modeling and simulations of aftertreatment systems used in the combustion engines simulated above.
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
Ph.D. in mechanical/aerospace engineering, applied mathematics, chemical engineering, or a related discipline earned no more than three years ago.
Experience in modeling and simulation of three-dimensional two-phase and/or multiphase turbulent reacting flow applications using CFD codes (e.g., CONVERGE, Ansys Fluent, OpenFOAM, etc.).
The candidate must have the ability to demonstrate collaborative skills, including the ability to work well with other divisions, laboratories, and universities.
Skilled in communication at all levels of the organization.
The successful candidate is expected 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.
Knowledge of combustion engine theory and modeling, extensive knowledge of fuel properties and their behavior in internal combustion engine applications, good understanding of turbulence, spray, spray-wall interaction, chemical kinetics, reacting flow physics, and turbulent combustion modeling are all highly desirable.
Knowledge of and experience with modeling of aftertreatment devices for propulsion systems is highly desirable.
Experience in geometry manipulation with computer-aided design software is highly desirable.
Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and turbulent combustion applications, and parallel scientific computing is desirable.
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 is desirable.
Experience in carrying out research tasks with industry partners is desirable.
Experience in interdisciplinary collaborative research is desired.
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.
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.