The U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE) High Performance Computing for Manufacturing (HPC4Mfg) Internship Program offers 10-week, hands-on, practical internships at DOE national laboratories.
For half a century, America has led the world in high performance computing (HPC) thanks to sustained federal government investments in research and development and regular deployment of new systems. The strong synergy between hardware development and software and application development has been a defining strength of the U.S. approach. HPC4Mfg unites world-class computing resources and the expertise of national laboratories to deliver solutions that could revolutionize manufacturing.
The HPC4Mfg program is part of the High Performance Computing for Energy Innovation (HPC4EI) initiative which partners public and private sectors to facilitate the use of advanced computational techniques in the private sector with the aim of reducing national energy consumption. In the HPC4Mfg Internship Program, student projects typically involve performing advanced simulation and modeling in topic areas such as materials, computational fluid dynamics, combustion and machine learning applied to scientific computational results. More information about the HPC4Mfg program can be found at hpc4energyinnovation.org. This program is sponsored by the Advanced Materials and Manufacturing Technologies Office (AMMTO) within the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE).
As a participant in the EERE HPC4Mfg Internship Program, you will perform research-level computational activities under the guidance of a mentor who is a technical staff scientist or engineer at a federal national laboratory. You will gain a competitive edge as you apply your education, talent, and skills to research and development projects focused on HPC. You will also be able to establish connections with DOE scientists and subject matter experts that promote long-term relationships between yourself, researchers, and DOE.
EERE HPC4Mfg Internship Details
Application Cycle
AMO Summer Internships are open for applications during the Fall/Winter of each year.
2023 Application Year
Online Applications Open
December 2022
Applicants will have the opportunity to review a project catalog of projects provided by hosting facilities to find a suitable match for their interests and educational discipline. Project catalogs can be found on the respective program page.
Application Deadline
February 2023
Applicants should not contact research facilities/mentors after the application deadline
Applicants may contact mentors to ask questions about projects during the application period.
Application Review
February 2023
Internship Notification
March 2023
Candidates are notified of selections and receive offer letter to accept or decline internship
Internship Period
May - September 2023
Candidates accept their internship offer and begin their ORISE internship.
Interns must complete 10-weeks of internship
In most cases, interns will have the opportunity to collaborate with their hosting laboratory to ensure internship dates work best for the intern and mentor. However, some hosting laboratories have separate requirements for summer internship periods.
Application Review and Selection
In the application process, you will review available projects for your ORISE summer internship and provide your preference for which project and mentor you want to intern with for the summer. Mentors will review complete applications and project preferences to determine their ORISE intern selections and best match for their projects. Mentors may contact you directly or schedule interviews with you as part of the review process, and we encourage you to engage with them to determine the best fit for you and your potential mentor.
However, mentor selection is only one part of the review process. Mentor selection of your application does not guarantee you will be selected to participate in an internship program.
Once selections are finalized by EERE AMMTO, ORISE will notify you and your mentor if you are selected for an internship program.
Appointment Details
Appointments will be for 10 consecutive weeks during the months of May-September. Factors such as class schedules, housing availability, and laboratory schedules may be taken into consideration when determining appointment start and end dates.
An appointment involves a full-time commitment at the host laboratory with the intern in residence on-site at the specified location.
Interns are required to have health insurance coverage during the appointment period and to provide proof of this coverage prior to the start of the appointment.
Eligiblity
In order to be considered, applicants must meet each of the following criteria:
Be a U.S. citizen.
Be at least 18 years old by May 1 of the internship
Meet one of the following conditions:
Recent graduate: Have earned an undergraduate or graduate degree in the past two years in a discipline related to high performance computing.
Undergraduate Student: Be enrolled as a full-time student as a junior or senior at a U.S. accredited college or university during the winter/spring semester and be pursuing a degree in a discipline related to high performance computing.
Graduate Student: Be enrolled as a full-time graduate student at a U.S. accredited college or university duringthe winter/spring semester and be pursuing a degree in a discipline related to high performance computing.
students, graduate students, and postgraduates, earning a degree in the past two years, are eligible to apply.
For detailed information about eligibility, review the current Zintellect Opportunity posting.
Stipend and Other Benefits
Stipend: Based on academic level at the start of your internship appointment.
Travel: Travel reimbursement for inbound and outbound expenses if you live more than fifty miles, one-way, from your assigned hosting laboratory.
Housing Allowance: A housing stipend will be provided. Additional housing stipend may be provided to offset high cost of living in certain locations.
Training/Research Allowance: A training allowance may be provided to offset relevant costs, such as fees for submitting research for publication, access to relevant training, etc.
Project Catalog for the High Performance Computing Summer Internships
Applicants submitting an application to the EERE High Performance Computing Summer Internship Program are required to select one to three projects. Review the list below to determine which projects you are most interested in for your internship. Submit your project preferences in the relevant section in your Zintellect application. While your preferences will be taken into consideration during the final selection process, it is not guaranteed that you will be offered one of the projects listed.
This project catalog will be updated throughout the application period. If you do not see any projects of interest to you, check back often for updates throughout the application period. All available projects will be finalized 2 weeks prior to the application deadline.
For technical assistance with navigating Zintellect, contact Zintellect Support at Zintellect@orau.org.
Project Title
Citizenship Required
Reference Code
Posted Date
Posted Datetime
Hosting Site
Internship Location
Description
Yes
FP-H-21.2-28037
12/20/2022
1671512400000
Argonne National Laboratory
Lemont, IL or virtual
U.S. Citizenship is a requirement for this internship
Project Description:
In this HPC4EI project, we propose large eddy simulation (LES) based computational fluid dynamics (CFD) modeling of Solar Turbines’ industrial gas turbines equipped with a novel carbon capture system. This new technology utilizes exhaust gas recirculation (EGR) in a semi-closed cycle to capture CO2, which allows reducing majority CO2 emission without significantly modifying engine hardware. Our goal is to use LES to simulate gas turbine combustion with EGR to ensure stable combustion for natural gas (NG) and/or NG-H2 blends. The student intern, with guidance from the mentor, will use Argonne National Laboratory's high performance computing clusters and leadership-class supercomputers to perform high-fidelity LES for investigating the complex reacting flow physics in industrial gas turbines. The student will also have the opportunity to access the state-of-the-art AI/ML software and hardware at Argonne to accelerate the research activities.
Hosting Site:
Argonne National Laboratory
Internship location: Lemont, IL or virtual
Mentor:
Chao Xu
chaoxu@anl.gov
(630) 252-6435
Internship Coordinator:
Vicki Gardner
vgardner@anl.gov
(630) 252-4071
Yes
FP-G-21.2-26463
12/20/2022
1671512400000
Pacific Northwest National Laboratory
Richland, WA or virtual
U.S. Citizenship is a requirement for this internship
Project Description:
Fairmount Technologies (FT) seeks to optimize the thermomechanical processing of AA 7075. The target yield strength is 20% higher than commercially available 7075-T6 temper sheet which will enable structural light weighting by a similar magnitude and reduce the carbon footprint of the transportation sector. To reduce processing cost and make this commercially viable, it is important to understand physical mechanisms driving microstructural changes. A mesoscale phase-field model seeded with experimentally measured microstructures and assessed thermodynamic and kinetic properties will be developed and exercised using HPC at PNNL. It will account for the effect of deformation, temperature, and chemistry on precipitation kinetics in nanocrystalline microstructures. This will help (i) design the processing schedule to minimize cost, (ii) optimize precipitation heat treatment to maximize yield strength and ductility, and (iii) increase the stability of the final microstructure.
In coarse grain AA 7075, aging parameters such as stress, dislocation density (plastic strain), heating rate, and temperature dramatically affect the nucleation and growth of GP zone and η', hence, the microstructure (phases, precipitate density, size and stability) and mechanical properties. Most plastic deformation recovers during grain refinement when the grain size is less than 100 nm. Our hypothesis is that 1) inhomogeneous stress in nanograin structures affect the nucleation and growth of both GP zone and η', and 2) the competition between GP zone and η' formation can be tailored by stress-aging parameters. The project is to help students learning and understanding how “stress-aging” with theoretical and mesoscale models affect GP zone and η' formation in nanocrystalline 7075 Al as well as mechanical properties.
Hosting Site:
Pacific Northwest National Laboratory
Internship location: Richland, WA or virtual
Mentor:
Shenyang Hu
shenyang.hu@pnnl.gov
Internship Coordinator:
Nancy Roe
nancy.roe@pnnl.gov
(509) 375-4530
Yes
FP-G-21.2-26450
12/20/2022
1671512400000
Oak Ridge National Laboratory
Oak Ridge, TN or virtual
U.S. Citizenship is a requirement for this internship
Project Description:
In this project, Computational Fluid Dynamics (CFD) models will be developed to simulate and optimize an Induced Flow Generator (IFG) technology. The IFG uses ambient air as the working fluid and a convergent-divergent nozzle to convert heat into kinetic energy. The IFG system employs a wind tunnel-like structure, called the Back-Pressure Control Channel (BPCC), to create a negative pressure in its narrow throat region. A vacuum ejector-like system attached to the throat efficiently entrains air from a waste heat reservoir into this negative pressure region. The entrained flow is channeled through a convergent-divergent nozzle, called the Energy Extractor (ET), to enable compressibility-driven conversion of heat to kinetic energy, before running through a turbine to generate power. The IFG technology represents a new device class for tapping low-temperature waste heat economically. This technology can improve energy efficiency, reduce emissions, and enhance the competitiveness of the U.S. manufacturing industry.
Hosting Site:
Oak Ridge National Laboratory
Internship location: Oak Ridge, TN or virtual
Mentor:
Prashant Jain
jainpk@ornl.gov
(865) 574-6272
Internship Coordinator:
Ja'Wanda Grant
grantjs@ornl.gov
(865) 341-1644
Yes
FP-I-22.2-28692
12/20/2022
1671512400000
Sandia National Laboratories
Albuquerque, NM or virtual
U.S. Citizenship is a requirement for this internship
Project Description:
Electric vehicles (EVs) are a rapidly expanding market due in part to the push to reduce carbon emissions and achieve carbon neutrality. Manufacturing the batteries to be used in EVs is one of the most energy-intensive steps in the production of EVs and significantly affects the overall performance of the battery. This project aims to understand and improve the electrode drying process to enhance the overall electrochemical performance of the batteries to be used in EVs. We will seek to use the computational resources at Sandia to construct high-fidelity simulations of the electrode drying process. Using these simulations, we plan to study how drying conditions affect binder deposition, pore structure, and particle realignment within the electrode in an effort to improve the overall performance of the batteries and reduce energy costs during manufacturing.
Through this project, the applicant will gain valuable experience at running simulations in a high performance computing environment. Additionally, the core of this project relies on transport phenomena modeling, from both an electrochemistry and fluid mechanical perspective. The applicant will enhance their core understanding for these critical engineering concepts and learn how to connect the microscopic physics of such effects with the macroscopic influence through a mesoscale modeling approach. More advanced topics in mesoscale modeling that the applicant may pursue throughout the project include but are not limited to fluid structure interactions, phase change physics, and moving interface modeling. In addition to the technical learning objectives for the applicant, the applicant will also gain professional experience in interfacing with industry and lab partners and improve their communication abilities through various technical discussions and presentations with project collaborators. Note that SNL’s ability to work with interns on this project is contingent upon completion of a Cooperative Research and Development Agreement (CRADA) prior to the beginning of the student’s internship.
Students interested in this project are advised to contact the PI directly before listing this project as one of their top choices.
Hosting Site:
Sandia National Laboratories
Internship location: Albuquerque, NM or virtual
Mentor:
Jeffrey Horner
jshorne@sandia.gov
(609) 784-5448
Internship Coordinator:
Ron Manginell
rpmangi@sandia.gov
Yes
FP-I-22.2-28691
12/20/2022
1671512400000
National Renewable Energy Laboratory
Golden, CO or virtual
U.S. Citizenship is a requirement for this internship
Project Description:
Iron and steelmaking account for seven percent of global carbon emissions. Decarbonizing this manufacturing sector requires reengineering high temperature processes. NREL will partner with industry to simulate the steelmaking processes to allow iron produced by low-carbon processes to be integrated into steelmaking. These simulations must include phase change, chemical reactions, and multi-phase flow physics to accurately predict how process physics will change. NREL plans to build around the OpenFOAM open-source computational fluid dynamics (CFD) software to simulate these processes, and enable low-carbon steelmaking.
The intern will have the opportunity to be involved in building necessary simulation tools and in obtaining initial results. Interns will have the opportunity to perform simulations using Eagle, NREL’s petascale high-performance computing system, and Kestrel, its planned successor. They will interact with NREL staff in high performance computing, advanced manufacturing, and energy efficiency. A background in mechanical, aerospace, or chemical engineering, mathematics, or physics is preferred, but other degrees will be considered. The intern will have the opportunity build skills in advanced simulation, high performance computing, and scientific visualization. The intern is expected to present within NREL and at local conferences, and to contribute to peer-reviewed publications.
Note that NREL’s ability to work with interns on this project is contingent upon completion of a Cooperative Research and Development Agreement (CRADA) prior to the beginning of the student’s internship. Students interested in this project are advised to contact the PI directly before listing this project as one of their top choices.
Hosting Site:
National Renewable Energy Laboratory
Internship location: Golden, CO or virtual
Mentor:
Michael Martin
michael.martin@nrel.gov
(202) 731-1207
U.S. Citizenship is a requirement for this internship
Project Description:
This project aims to optimize a pre-chamber spark-ignition system for an engine operating on the Argon Power Cycle. During the internship program, the candidate will perform optimization of pre-chamber geometry design by using Computational Fluid Dynamics (CFD) simulations and High-Performance Computing (HPC) resources, analyze the effect of investigated parameters on the engine performance and combustion characteristics, and make a final report that summarizes the optimization procedure and outcomes.
The candidate’s research will involve synergistic collaboration with mentors, and the candidate will have opportunities to learn how to run CFD simulations and leverage HPC resources for design optimization problems. The successful candidate’s research will be beneficial for developing efficient and stable engine operating strategies on the Argon Power Cycle, delivering clean, dispatchable, and efficient power to the grid and hence accelerating the decarbonization of the grid.
Education and Experience Requirements: • Major in mechanical/aerospace engineering, or a related discipline. • Knowledge of thermodynamics, internal combustion engine. • Knowledge of combustion, CFD simulation, optimization algorithm (supplemental). • Experience in commercial CFD codes, design optimization with computer-aided design software.
Hosting Site:
Argonne National Laboratory
Internship location: Lemont, IL or virtual
Mentor:
Joohan Kim
joohan.kim@anl.gov
6302524248
Internship Coordinator:
David Martin
dem@alcf.anl.gov
(630) 252-0929
Yes
FP-I-22.1-28721
12/20/2022
1671512400000
Oak Ridge National Laboratory
Oak Ridge, TN
U.S. Citizenship is a requirement for this internship
Project Description:
Modification of fossil-fueled industrial gas turbines to accept no/low carbon fuels (Hydrogen, H2/natural gas blends) is a significant undertaking towards achieving carbon neutrality. Successful deployment of this technology sits at the intersection of three design criteria (1) new functional fuel injectors that can burn these fuels, (2) manufacturability to meet cost and time-to-market targets, and (3) durability in the harsh environment of an operating turbine. Additive manufacturing (AM) technology enables rapid fabrication of highly complex and integrated structures and is deployed by gas turbine manufacturers to accelerate the hydrogen-based gas turbines design and manufacturing. However, machining of AM part surface is often infeasible or cost excessive. The as-printed AM part often exhibits surfaces with different roughness and microstructures (texture, grain morphology, etc.) depending on the local print conditions, which impact the parts’ long-term fatigue durability. The fatigue crack often initiates from the rough surface and its propagation can be either promoted or hindered by the near-surface microstructure. These factors result in uncertainty in the long-term fatigue durability of AM fabricated parts, and they must be addressed before widescale industrial adoption. Currently, there is limited understanding of the surface roughness-microstructure-fatigue resistance relationship, which requires turbine manufacturers to adopt overly conservative reliability models.
In this project, Oak Ridge National Laboratory (ORNL) and a manufacturing partner will utilize an accelerated crystal plasticity finite element fatigue model to quantify the factors that drive AM surface fatigue behavior. The computation will be performed on ORNL’s high-performance supercomputers to explore the surface roughness-microstructure-fatigue life relationship from AM technology. The obtained computation results, along with targeted experimental data, will be applied in the form of a surrogate model into manufacturer's part durability assessment tool. The output will provide the ability to quantify the fatigue life uncertainty and allow the rapid rollout of retrofittable hydrogen combustion system designs, which will have a significant and long-lasting impact in transforming the existing energy infrastructure to hydrogen-capable systems towards net-zero carbon emissions.
During this project, the intern student will have the chance to learn and develop modeling and simulation skillsets for use in tackling various energy and manufacturing-related research problems, especially with respect to the following topics: - learn about the finite element (FE) method to simulate and predict material’s elastic and plastic deformation, fatigue and crack formation behavior - gain experience in high-performance supercomputing with ORNL's HPC system - learn about metallic material’s microstructure and microstructure-mechanical properties relationship - learn about integrating numerical models with experimental characterization and test data - learn about crystal plasticity theory - gain experience in surrogate model construction with machine-learning tool - gain experience in advanced numerical topics such as multi-temporal-scale analysis for high cycle fatigue simulation. Note that ORNL’s ability to work with interns on this project is contingent upon completion of a Cooperative Research and Development Agreement (CRADA) prior to the beginning of the student’s internship.
Students interested in this project are advised to contact the PI directly before listing this project as one of their top choices.
Hosting Site:
Oak Ridge National Laboratory
Internship location: Oak Ridge, TN
Mentor:
Jiahao Cheng
chengj@ornl.gov
(865) 574-4872
Internship Coordinator:
Ja'Wanda Grant
grantjs@ornl.gov
(865) 341-1644
Yes
FP-D-20.1-23754
12/20/2022
1671512400000
Lawrence Livermore National Laboratory
Livermore, CA or virtual
U.S. Citizenship is a requirement for this internship
Project Description:
The project goal is to combine recent advances in topology optimization-based design, high-performance computing (HPC), and additive manufacturing (AM) technology to develop high pressure and temperature heat exchangers (HEX) concepts with greater than 85% effectiveness and a 50% reduction in volume to overcome the current design and economic limitations of conventional manufacturing methods. This technology could provide significant energy savings for power generation, aviation, and space industries if realized.
The applicant will collaborate with the project team to validate some of the models for heat sinks and exchangers. He/she will develop parametric CAD geometries and meshes for simulations. The prepared geometries will be used as inputs for running CFD simulations on the HPC machines. The obtained results will be analyzed and reported with the help of the supervisor. Depending on the applicant's background and interests, the work can include extending the current software capabilities based on MFEM, running large-scale simulations and optimizations of heat sinks/exchangers, and combining machine learning techniques to reduce computational cost.
Hosting Site:
Lawrence Livermore National Laboratory
Internship location: Livermore, CA or virtual
Mentor:
Boyan Lazarov
lazarov2@llnl.gov
(925) 498-3394
Internship Coordinator:
Michelle Herawi
herawi1@llnl.gov
(925) 423-4964
Yes
FP-H-21.2-28041
12/20/2022
1671512400000
Oak Ridge National Laboratory
Oak Ridge, TN or virtual
U.S. Citizenship is a requirement for this internship
Project Description:
Secondary lead is an important industry in the United States, with over 1 million tonnes produced in 2020 (Rainford, 2020). There is an excellent opportunity to improve the efficiency of pyrometallurgical furnaces used within the industry, which would provide environmental and energy-saving benefits. Gopher Resource (“Gopher”) is proposing a project on the use of high-performance computing (HPC) and numerical modeling to perform process optimization of secondary lead furnaces. This work would be a continuation of modeling efforts performed at Oak Ridge National Laboratory (ORNL) on multiphysics modeling of thermal transport with species chemistry, phase change, and turbulent flows. Improvements in furnace thermal efficiency can potentially result in energy savings of up to 750 billion BTUs per year and at least half a million tonnes of carbon dioxide emissions leading to a total cost savings of $30 million per year for the lead industry.
Hosting Site:
Oak Ridge National Laboratory
Internship location: Oak Ridge, TN or virtual
Mentor:
Vivek Rao
raovm@ornl.gov
(573) 953-1743
Internship Coordinator:
Ramanan Sankaran
sankaranr@ornl.gov
Yes
FP-G-21.2-26470
12/20/2022
1671512400000
Lawrence Livermore National Laboratory
Livermore, CA or virtual
U.S. Citizenship is a requirement for this internship
Project Description:
The student will be working on implementing and developing learning-based algorithms for physical simulation and computational geometry (e.g. mesh generation). Before and throughout the code development process, students will need to work with data, which includes data generation, processing, and curation. The student will have the opportunity to publish work in top-tier conferences.
The student is expected to have strong analytical and problem-solving skills. Specifically, the student should have solid background in physical sciences, computational geometry, and machine learning. Strong programming skill in Python and experiences in using deep learning tools (e.g. pytorch/tensorflow) are necessary. Experience in using physical simulation engines and C++ is a plus.
We are currently generating our own data on the Lab’s HPC system, and the student will be responsible to construct designated physical simulation setups and generate more data when necessary
Hosting Site:
Lawrence Livermore National Laboratory
Internship location: Livermore, CA or virtual
Mentor:
Victor Castillo
castillo3@llnl.gov
(925) 424-5049
Internship Coordinator:
(925) 424-5049
(925) 424-5049
(925) 423-4964
The name and contact information of the hosting site internship coordinator is provided for further assistance with questions regarding the hosting site; local housing availability, cost, or roommates; local transportation; security clearance requirements; internship start and end dates; and other administrative issues specific to that research facility. If you contact the internship coordinator, identify yourself as an applicant to the NSF Mathematical Sciences Graduate Internship (MSGI) Program.
Interns will not enter into an employee/employer relationship with the Hosting Site, ORAU/ORISE, EERE or DOE. No commitment with regard to later employment is implied or should be inferred.
