Horizontal Dry Cask Simulator Computational Fluid Dynamics Model Validation, Verification and Uncertainty Quantification

expired opportunity(Expired)
From: Federal Government(Federal)
31310019R0050

Basic Details

started - 14 Aug, 2019 (about 4 years ago)

Start Date

14 Aug, 2019 (about 4 years ago)
due - 21 Aug, 2019 (about 4 years ago)

Due Date

21 Aug, 2019 (about 4 years ago)
Bid Notification

Type

Bid Notification
31310019R0050

Identifier

31310019R0050
Nuclear Regulatory Commission

Customer / Agency

Nuclear Regulatory Commission
unlockUnlock the best of InstantMarkets.

Please Sign In to see more out of InstantMarkets such as history, intelligent business alerts and many more.

Don't have an account yet? Create a free account now.

The U.S. Nuclear Regulatory Commission (NRC) intends to award a non-competitive, fixed-price contract to Alden Research Laboratory, Inc. for the project entitled, "Horizontal Dry Cask Simulator Computational Fluid Dynamics Model Validation, Verification, and Uncertainty Quantification." The acquisition is conducted under the authority of Federal Acquisition Regulation (FAR) 6.302-1 which provides for limiting competition in those situations where the supplies or services are available from only one responsible source and no other supplies or services will satisfy the agency requirements. The designated North American Industry Classification System (NAICS) Code is 541690 - Other Scientific and Technical Consulting Services. The performance period is estimated from date of award through September 30, 2020.The contractor shall create the geometry, mesh and computational fluid dynamics (CFD) models for the U.S. Department of Energy (DOE) horizontal dry cask simulator containing one
Boiling Water Reactor (BWR) assembly. The model must use the same validation methods Alden Research Laboratory, Inc. applied to the model developed for the TN-32 dray cask storage systems held at North Anna power plant. The CFD model will be benchmarked against observed thermal data obtained from DOE's Sandia National Laboratory. Additionally, uncertainty quantification (UQ) of the target variables such as, peak cladding temperature (PCT), cask walls heat transfer, structural temperature profiles, maximum axial temperature profile and air cooling mass flow rate shall be performed using the same methods Alden Research Laboratory, Inc. used for the TN-32 model.The results of this work will assist NRC in reviewing designs of dry cask storage systems that are expected to increase in technical complexity and heat load and determining the adequacy of the applied analytical method.This Notice of Intent is NOT a request for proposal NOR a solicitation of offers; however, if any interested party believes they can meet the above requirement, it may submit a statement of capabilities. The statement of capabilities must be submitted in writing and must contain material in sufficient detail to allow the government to determine if the party can perform the requirement. Additionally, in order to perform work for the NRC potential sources must be free of organizational conflict of interest (OCOI). For information on the NRC COI regulations, visit NRC Acquisition Regulation Subpart 2009.5 (https://www.nrc.gov/about-nrc/contracting/48cfr-ch20.html).A determination not to compete this proposed procurement based on responses to the notice is solely within the discretion of the Government. All interested parties must express their interest and capabilities in writing via email to Jennifer Dudek, Contract Specialist via e-mail at Jennifer.Dudek@nrc.gov no later than August 21, 2019 by 4:00 P.M. Eastern Time (i.e. 7 calendar days from the date this announcement was posted).Contact Information: Jennifer A. Dudek, Sr. Contract Specialist, Phone 3014152257, Email Jennifer.Dudek@nrc.gov Office Address :11555 Rockville Pike Rockville MD 20852-2738 Location: Acquisition Management Division Set Aside: N/A

Acquisition Management DivisionLocation

Address: Acquisition Management Division

Country : United States

You may also like

INTERPRETABLE MACHINE LEARNING FOR HIGH-SPEED, HIGH-FIDELITY GEOS-CHEM MODEL SIMULATIONS WITH UNCERTAINTY QUANTIFICATION

Due: 11 Oct, 2024 (in 5 months)Agency: NATIONAL AERONAUTICS AND SPACE ADMINISTRATION

DEVELOPING A VERIFICATION AND VALIDATION OPTIMIZATION METHODOLOGY WITH UNCERTAINTY QUANTIFICATION

Due: 31 Jul, 2025 (in 15 months)Agency: NATIONAL AERONAUTICS AND SPACE ADMINISTRATION

Classification

NAISC: 541690 GSA CLASS CODE: R