Postdoctoral Appointee - Hydrogen Modeling and Analysis
- Argonne National Laboratory
- Location: Lemont, USA
- Job Number: 7208060 (Ref #: 417093)
- Posting Date: Jan 17, 2024
Job Description
The Systems Assessment Center (SAC) of the Energy Systems and Infrastructure Analysis Division at Argonne National Laboratory (ANL) has been conducting hydrogen production, delivery, storage, and infrastructure analysis for 20 years. With support from DOE, SAC has developed the HDSAM (Hydrogen Delivery Scenario Analysis Model) for hydrogen delivery analysis, the HRSAM (Hydrogen Refueling Station Analysis Model) for hydrogen refueling analysis, the HDRSAM (Hydrogen Refueling Station Analysis Model) for heavy-duty fuel cell vehicle refueling analysis, the H2SCOPE (Hydrogen Station Cost Optimization and Performance Evaluation) model for thermodynamic evaluation of various onboard storage pressure and dispensing options/methods, and the GREET® (Greenhouse gas, Regulated Emissions, and Energy use in Technologies) model for life-cycle analysis of hydrogen and other advanced fuels for transportation applications.
SAC has been interacting with industries, government agencies, and research institutions in evaluating the cost of delivering hydrogen from the production facility to the vehicles’ tanks. The current research includes examining various components in the gaseous and liquid hydrogen delivery pathways, identifying major cost drivers in various delivery options, and suggesting options to reduce the total delivery cost of hydrogen. SAC is also examining use of hydrogen across various industries and developing demand scenarios for any required locations, in addition to exploring the potential of new innovative technologies and pathways for reducing the hydrogen cost for the consumer.
The post-doctoral appointee would conduct in-depth techno-economic analysis of various liquid and gaseous hydrogen delivery pathways suitable for use of hydrogen as fuel for non-road applications including rail, ports, and marine sectors. The candidate will use existing models to evaluate various technology options for modeling the hydrogen delivery and refueling infrastructure for these applications and do a techno-economic analysis to estimate the cost of hydrogen. The candidate will collect data from technical literature and through interactions with other national laboratories, academia, industry, and other organizations. The candidate will work with the hydrogen analysis team in expanding the existing Argonne models to evaluate the potential of current and new technologies in achieving the DOE performance and cost targets of dispensed hydrogen.
Position Requirements
Qualified individuals should have obtained a Ph.D. in chemical engineering, mechanical engineering, or other related areas.
Knowledge of hydrogen production and delivery pathways and technologies.
Knowledge about hydrogen storage and refueling technologies.
Knowledge of hydrogen compression and liquefaction technologies.
Experience in modeling fluid flow processes using Python.
Skilled in oral and written communications and presentations.
Quantitative skills to analyze a large quantity of data to convey trends in results.
Skills using computer software such as Excel and other computer languages like python.
Ability to work in a team environment.
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Preferred Qualifications:
Knowledge of Rail operations.
Job Family
Postdoctoral FamilyJob Profile
Postdoctoral AppointeeWorker Type
Long-Term (Fixed Term)Time Type
Full timeAs 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.
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