Skip to main content

High Performance Computing Summer Internships

High Performance Computing Summer Internships

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 a partnership between the 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 https://hpc4mfg.llnl.gov/. This program is sponsored by the Advanced Manufacturing Office (AMO) 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.

    2022 Application Year

    Online Applications Open  November-December 2021 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 January-March 2022
    • 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/March 2022
    Internship Notification March/April 2022 Candidates are notified of selections and receive offer letter to accept or decline internship
    Internship Period May - September 2022

    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.

    After mentors have submitted their selections, ORISE, who manages the summer internship programs for EERE AMO, will review selected applications for eligibility and completeness, and provide detailed information to EERE AMO representatives. EERE AMO will make final selections based on reviewer results.

    Once selections are finalized by EERE AMO, ORISE will notify you and your mentor if you are selected for an internship program. Formal offers will be sent through Zintellect, the ORISE application and participant tracking system.

  • 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. [Add link]

  • Stipend and Other Benefits

    • Stipend: Based on academic level at the start of your internship appointment.
      • Undergraduate students, and post-bachelors receive $700 per week
      • Masters students or post- masters receive $900 per week
      • Doctoral students and postdoctoral receive $1000 per week
    • Travel: Travel reimbursement for inbound and outbound expenses up to a combined maximum of $2,000 if you live more than fifty miles, one-way, from your assigned hosting laboratory.
    • Housing Allowance: A housing stipend starting at $150 per week. Additional housing stipend may be provided to offset high cost of living in certain locations.
    • Training/Research Allowance: Up to $250 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.

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.

The Summer 2022 Project Catalog will be updated every two weeks until the end of December 2021. Please check back regularly for updates on new projects!

 

Project Title Citizenship Required Reference Code Posted Date Posted Datetime Hosting Site Internship Location Description

Yes FP-D-20.1-23733 11/11/2021 1636606800000 Pacific Northwest National Laboratory Richland, WA

U.S. Citizenship is a requirement for this internship

Project Description:

The goal of this project is to develop ROMs using data from physics-based commercial codes for manufacturing simulation and micromechanical analysis to support the U.S. manufacturing of novel lightweight composites. ESI’s PAM-FORM (a module of PAM-COMPOSITES suite) allows thermoforming simulation of thermoplastic composites such as organo-sheets to support process and mold design, and part manufacturing simulation. The software is a process oriented finite element solution dedicated to the simulation of various forming processes for continuous fiber reinforced sheet materials such as dry or prepreg carbon/glass fabrics or UD. The simulation predicts defects, thickness, fiber orientation and fiber content in order to optimize the process (tool kinematics, temperature cycles, clamping condition, blank pattern, etc.). Eventually, the resulting fiber orientations that can be transferred to ESI’s VPS (a general-purpose finite element program for crash and other performance simulations) to perform "as-built" performance prediction. In this proposed project, the QoI will be crashworthiness.

Activities to participate:
Run finite element simulations on the high-performance cluster to simulate the thermoforming process of an electric vehicle’s battery enclosure made with carbon fiber composite. Use this formed geometry to run crash simulations again using FEM software to predict the damage in carbon fiber battery enclosure under different loading conditions. All simulations are to be run using ESI PAM COMPOSITE and ESI VPS software suite. Short python scripts are to be used to manage and automate the process to run a large number of simulations on the cluster.

Learning experience:
-Understanding of finite element methods
-Use of programming language python
-Experience of using high performance cluster
-Better understanding of carbon fiber composite materials

Hosting Site:

Pacific Northwest National Laboratory

Internship location: Richland, WA

Mentor:

  • Shank Kulkarni
    shank.kulkarni@pnnl.gov
    509-375-6722

Internship Coordinator:

  • Nancy Roe
    nancy.roe@pnnl.gov
    509-375-4530

Yes FP-F-20.2-25712 11/11/2021 1636606800000 Oak Ridge National Laboratory Oak Ridge, TN

U.S. Citizenship is a requirement for this internship

Project Description:

The proposed work is focused on establishing computational framework, foundational knowledge, and additive manufacturing (AM) capabilities for accelerating the use of refractory metals for gas turbine generators, which is considered a significant enabling technology for increasing operating temperatures and improving efficiency of these systems. The work will develop and apply high-fidelity process and material models for simulation of potential defects, deposition geometry, and resultant microstructure of refractory alloys produced using directed energy deposition (DED) AM. Upon validation of the developed model, virtual and physical experiments will be designed and conducted to create process and material maps. The maps will assist in establishing quantitative relationships to define the influence of primary processing parameters on attributes used to delineate process consistency and product quality for meeting the stringent requirements for this industry. The developed models will be used to conduct significant virtual experimentation at the supercomputing facilities within Oak Ridge National Laboratory.

Hosting Site:

Oak Ridge National Laboratory

Internship location: Oak Ridge, TN

Mentor:

  • Yousub Lee
    leey@ornl.gov
    865-241-2647

Internship Coordinator:

  • Ja'Wanda Grant
    grantjs@ornl.gov
    865-576-2311

Yes FP-F-20.2-25459 11/11/2021 1636606800000 Oak Ridge National Laboratory Oak Ridge, TN

U.S. Citizenship is a requirement for this internship

Project Description:

The use of electrochromic (EC) dyes in Glass Dyenamic’s devices has shown to significantly reduce assembly cost for smart glass building windows with improved energy efficiency.  Low manufacturing cost and aesthetic consideration has the potential of significantly increasing the adaptation of this technology in commercial and residential glass markets.  However, the experimental design of suitable EC dyes with desired photophysical properties is highly resource intensive.  We therefore propose a combined high-performance computing (HPC)- and machine learning (ML)-driven inverse structural design of anodic EC dyes based on high-level electronic structure theory to predict their photophysical properties in neutral and oxidized states.  Highly accurate ab initio multireference wavefunction methods will be employed on OLCF’s Summit supercomputer to compute UV/Vis absorption spectra for a large training set of dye molecules.  This data will drive a novel ML approach to predict novel EC dyes with superior properties.

Hosting Site:

Oak Ridge National Laboratory

Internship location: Oak Ridge, TN

Mentor:

  • Stephan Irle
    irles@ornl.gov
    865-574-7192

Internship Coordinator:

  • Ja'Wanda Grant
    grantjs@ornl.gov
    865-341-0416

Yes FP-D-20.1-23660 11/11/2021 1636606800000 Oak Ridge National Laboratory Oak Ridge, TN

U.S. Citizenship is a requirement for this internship

Project Description:

RTRC (Participant) in collaboration with ORNL (Contractor) proposes use of model-based tools to design alloys for additive manufacturing (AM) in order to obtain as-desired microstructure for performance improvement in aerospace and automotive applications. The performance and cost of AM products still controls the business value of deploying AM to replace conventional manufacturing processes. The digital benefit of digitally designing a component and rapidly manufacturing it through AM is often lost due to extensive experimental iterations to remedy poor performance of fabricated components. The lack of performance is often attributed to intrinsic defects formation and undesirable microstructural features since the alloy composition and microstructure are not designed optimally for the given application. Contractor and Participant will use high performance computing (HPC) based phase-field simulations along with experimental validation, respectively, to design novel Ti alloy compositions based on forming fine equiaxed grains during AM to potentially replace currently used wrought Ti alloys.
 
A survey of wrought titanium alloys used in the aerospace industry indicates that they all have typically a narrow equilibrium solidification range. A direct consequence of this to the additive manufacturability of these alloys is that the as-solidified microstructures based on typical thermal conditions in AM are expected to be of cellular / columnar-dendritic type, which invariably leads to anisotropic mechanical properties. Columnar dendrites have also been shown to be prone to solidification cracking. In this proposal we will focus on identifying eutectoid forming beta Ti alloys that can undergo such columnar to equiaxed transition (CET) in grain morphology during the AM process. We propose to utilize the large-scale, HPC based phase field (PF) simulation capabilities of solidification currently existing at ORNL to optimize Ti-Cu-X alloy compositions to obtain CET under AM conditions. Our challenge is to identify eutectoid forming alloys that can demonstrate CET over a wide range of thermal conditions given the complex geometry of the aerospace components.  The simulations will be used to optimize potential Ti-Cu-X compositions that Participant will use in proof-of-principle experiments to test the validity of such a modeling-based approach to design AM titanium alloys. Participant will also pursue using low-cost AM powder produced from machining scraps/chips to lower the production cost. 

The proposed concept can advance the material-by-design technology investigated to solve a specific problem using “bottoms-up” approach and expedite design of new alloys for AM. This is currently done using only computational thermodynamics without considering the microstructural aspect. The proposed phase field simulations can better predict CET under AM conditions, given the non-steady-state solidification process and potential deviation from thermodynamics at fast-moving s-l interfaces.
Coupling the proposed alloy and AM-process specific predictive phase field simulations with Direct Energy Deposition (DED) based AM will enable: 1) microstructural design and tailoring based on CET to achieve the required target properties, 2) Moving away from the high-cost, time consuming, iterative approach to develop certified custom alloys 3) Moving towards Objective Physics Based criteria rather than Rules Based Design of alloys, 4) novel AM alloy design in many other alloy systems for potential use in other energy-intensive industrial applications.

Hosting Site:

Oak Ridge National Laboratory

Internship location: Oak Ridge, TN

Mentor:

  • Bala Radhakrishnan
    radhakrishnb@ornl.gov
    865-405-1318

Internship Coordinator:

  • Ja'Wanda Grant
    grantjs@ornl.gov
    865-341-0416

Yes FP-F-20.2-25552 11/11/2021 1636606800000 National Renewable Energy Laboratory Golden, CO

U.S. Citizenship is a requirement for this internship

Project Description:

NREL is currently partnering with Element 16, a California-based start-up company, to optimize molten-sulfur thermal energy storage (TES).   These systems are expected to allow large decreases in carbon emissions and large increases in the energy efficiency of manufacturing processes by allowing heat generated at one stage of a manufacturing process to be used when needed in other parts of the manufacturing processes, or for other purposes.
 
The intern will have the opportunity to be involved with, and develop skills in, in one or more of the following phases of the project:
 
1. Computational Fluid Dynamics (CFD) simulations of TES that incorporates the complex physics of molten sulfur.

2. Data science and machine learning (ML) approaches based on simulation data to allow rapid optimization of these systems.

3. Techno-economic analysis of how these systems may lead to reduced energy use and emissions as they are deployed.
 
Interns will have the opportunity to perform simulations using Eagle, NREL’s petascale high-performance computing system, and interact with NREL staff in high performance computing, advanced energy systems, 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 to present within NREL and at local conferences, and to contribute to peer-reviewed publications.

Hosting Site:

National Renewable Energy Laboratory

Internship location: Golden, CO

Mentor:

  • Michael Martin
    michael.martin@nrel.gov
    202-731-1207

Internship Coordinator:

  • Michael Martin
    michael.martin@nrel.gov
    202-731-1207

Yes FP-C-19.2-21838 11/11/2021 1636606800000 Lawrence Livermore National Laboratory Livermore, CA

U.S. Citizenship is a requirement for this internship

Project Description:

Flat glass “melting” is an energy intensive continuous process involving several processes that occur in two chambers prior to forming of a glass ribbon. CFD simulations of production glass furnaces currently take several days to process.

Fast-running statistical emulators can achieve comparable accuracy to the CFD simulation in seconds on a standard computer in a production facility.

In doing so, a reduction of 5% or more in U.S. float glass energy consumption can be possible. This would result in savings of roughly 2.5 million GJ per year from natural gas. The associated cost savings are crucial for maintaining global competitiveness of U.S. glass manufacturing while also reducing the environmental impact of evolved CO2.

Hosting Site:

Lawrence Livermore National Laboratory

Internship location: Livermore, CA

Mentor:

  • Victor Castillo
    castillo3@llnl.gov
    925-424-5049

Internship Coordinator:

  • Michelle Herawi
    herawi1@llnl.gov
    925-423-4964

Yes FP-E-20.2-23839 11/11/2021 1636606800000 Lawrence Livermore National Laboratory Livermore, CA

U.S. Citizenship is a requirement for this internship

Project Description:

This HPC effort is aimed at developing a lean, reduced-order model based on process simulation and sensor data to enable performance-informed thermo-mechanical processing of sheet metal parts. Broad adoption of such strategy across the industry would reduce the process energy of sheet metal parts, lead to development of novel products, while improving manufacturing yields.

The total onsite energy use for the Fabricated Metals (NAICS 332) sector in the U.S. is about 344 TBTU (with 11 MMT CO2-equiv of emissions) [1]. It is possible to reduce this energy and emissions footprint by ensuring that energy is only used when and where dictated by product performance needs.

Machina Labs is working on the factories of the future where software-defined, off-the-shelf robotic solutions are used to carry out different manufacturing operations as opposed to single purpose, heavy machinery. The company has developed a large-envelope, robotic cell for manufacturing of sheet metal parts using incremental forming technology. The cell is integrated with a localized heating system allowing for local heat treating of metal parts. Through DOE sponsored work it is estimated that incremental sheet metal forming can save up to ~ 8.4 TBTU of energy and $12.3B per year in the U.S. This HPC effort will build upon that effort and develop the models that would allow final part performance (e.g., strength and thickness profile) to be used as an input into the forming process. Similarly, use the starting state of the sheet/blank as a variable (this state can be varied in practice using the localized heating system) that can use an additional lever for tuning in the target part performance.

Hosting Site:

Lawrence Livermore National Laboratory

Internship location: Livermore, CA

Mentor:

  • Victor Castillo
    castillo3@llnl.gov
    925-424-5049

Internship Coordinator:

  • Michelle Herawi
    herawi1@llnl.gov
    925-423-4964

Yes FP-E-20.2-23837 11/11/2021 1636606800000 Oak Ridge National Laboratory Oak Ridge, TN

U.S. Citizenship is a requirement for this internship

Project Description:

This project addresses the use of microwaves to intensify the manufacturing process of ceramic matrix composites (CMCs) that enable light-weighting and improvements in energy efficiency of gas turbines when deployed in the hot section. High performance computing (HPC) will be used to develop modeling capabilities of an advanced Chemical Vapor Infiltration (CVI) process that addresses the technical challenge of uniform heating and temperature control, as required for manufacturing high-quality CMCs in a fraction of the current manufacturing time. The CVI process involves infiltration of a complex porous preform with reactive gases that undergo chemical transformation to deposit a ceramic phase. The competing effects of transport and kinetics makes it challenging to achieve uniform densification while keeping the manufacturing times and costs low. The work will be performed in collaboration with Raytheon Technologies Research Center (RTRC), a leading aerospace industry through the High Performance Computing for Manufacturing (HPC4Mfg) program.

Two specific tasks in the project relate to:

1. Modeling the pore scale densification behavior resulting from MW heating and
2. Application of the model in a reactor scale analysis to incorporate large scale effects.

The internship will focus on the first task while providing opportunities to collaborate with the other task as well. Specific aim of the internship will be to apply an in-house pore-resolved direct numerical simulations code to analyze the densification characteristics of the CVI process. A range of preform geometries and processing conditions will be explored to understand the effects of MW heating on temperature control and quality of the processed part. The DNS data will be used to develop porous media models that can be incorporated in reactor-scale analysis to enable design and process optimizations.

The internship will provide opportunities to enhance the fundamental understanding of porous materials and numerical modeling of coupled physical phenomena. A major focus of the internship will be on development and application of portable large scale simulations code, specifically targeting multiphase/embedded interface applications.

Hosting Site:

Oak Ridge National Laboratory

Internship location: Oak Ridge, TN

Mentor:

  • Vimal Ramanuj
    ramanujva@ornl.gov
    865-341-0363

Internship Coordinator:

  • Ja'Wanda Grant
    grantjs@ornl.gov
    865-341-0416

Yes FP-D-20.1-23754 11/11/2021 1636606800000 Lawrence Livermore National Laboratory Livermore, CA

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

Mentor:

  • Boyan Lazarov
    lazarov2@llnl.gov
    925-498-3394

Internship Coordinator:

  • Michelle Herawi
    herawi1@llnl.gov
    925-423-4964

Yes FP-E-20.2-23891 11/11/2021 1636606800000 Lawrence Livermore National Laboratory Livermore, CA

U.S. Citizenship is a requirement for this internship

Project Description:

Electrochemical energy storage technologies that are durable, efficient, energy dense, cheap, safe, and industrially scalable are highly demanded by a wide range of applications. Solid-state battery technologies are promising in this regard, but they remain challenged by difficulties in simultaneously achieving energy-efficient processability, mechanical durability, and efficient performance of manufactured electrolyte components. Toyota Research Institute of North America has developed a new class of Li-ion solid-state electrolytes that promise highly efficient performance and easier processability and therefore are expected to enable practical production of solid-state batteries. However, optimizing processing requires understanding the critical connection between mechanical robustness, ionic transport, and thermodynamic properties, which is very challenging utilizing available experimental tools due to the high levels of structural complexity. This proposal integrates experiments with a multiscale modeling approach that can offer the necessary insights to advance this area and accelerate the deployment of practical and easily processible solid-state batteries. The student would help to assemble multiscale battery models, simulate mechanical and transport behavior, and analyze results.

Hosting Site:

Lawrence Livermore National Laboratory

Internship location: Livermore, CA

Mentor:

  • Brandon Wood
    wood37@llnl.gov
    925-422-8391

Internship Coordinator:

  • Michelle Herawi
    herawi1@llnl.gov
    925-423-4964

Yes FP-E-20.2-23793 11/11/2021 1636606800000 National Renewable Energy Laboratory Golden, CO

U.S. Citizenship is a requirement for this internship

Project Description:

Fossil plastics in single-use packaging is one of the top existential problems in the world, and post-consumer collection of discarded materials continues to be elusive. Compostable packaging offers substantial energy savings relative to plastic packaging that requires recycling or upcycling in circular economy scenarios, and brands across the globe are seeking compostable options for flexible packaging. Cellulose, particularly dissolvable pulp that can be converted into high barrier packaging films, is currently in very high demand. This project leverages high-performance computing to accelerate evolution of art and science related to cellulose-derived films to meet societal demands and displace environmentally detrimental incumbent products. Specifically, molecular variations of cellulose dissolving pulp will be designed in-silico and their performance metrics, including mechanical, thermal, and barrier properties, will be predicted by large-scale simulation of polymer assemblies. The results will be used to identify production targets for next generation cellulose-based packaging materials to meet industry needs.

This internship project will apply results obtained from molecular modeling to develop finite element simulations for cellophane production and performance. The student will learn to analyze and interpret the results from molecular dynamic simulations to obtain parameters necessary for simulation at larger length scales. This information will be incorporated into simulations that combine chemical reaction and transport phenomena to model the formation of cellophane film in order to predict formulations and conditions that provide optimal performance.

Hosting Site:

National Renewable Energy Laboratory

Internship location: Golden, CO

Mentor:

  • Peter Ciesielski
    peter.ciesielski@nrel.gov
    303-384-7691

Internship Coordinator:

  • Michael Martin
    michael.martin@nrel.gov
    303-275-4280

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.