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Experimental and Mathematical Biomechanics Injury Research IDIQ

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693JJ927R000001Federal

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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The National Highway Traffic Safety Administration (NHTSA), under the Department of Transportation, is establishing multiple Indefinite Delivery, Indefinite Quantity (IDIQ) contracts to support the Experimental and Mathematical Biomechanics Injury Research (EMBIR) program. This initiative aims to advance the understanding of human injury biomechanics and improve vehicle safety through specialized research, analysis, and technical support. The scope of work is divided into four primary research domains: experimental biomechanics to identify injury thresholds, the development and assessment of Anthropomorphic Test Devices (ATDs), advanced computational modeling and simulation of crash scenarios, and the analysis of real-world crash data using data science tools such as AI and machine learning to develop injury criteria and performance metrics. Solicitation number 693JJ927R000001 is open for full and open competition under NAICS code 541715. The total period of performance is not to exceed 60 months, structured as a two-year base period, three one-year option periods, and one final six-month extension option. The anticipated effective date for these contracts is December 30, 2026. Eligible offerors must be registered in the System for Award Management (SAM) to be considered for award.

General Info

NHTSA IDIQ contracts for biomechanics research to improve vehicle safety and injury analysis.

Agency

Department Of Transportation → 693JJ9 Nhtsa Office Of Acquistion

NAICS

541715 - Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)

Place of Performance

DC, USA

Set-Aside

NONE

Documents

1

Synopsis for Solicitation 693JJ927R000001 - Experimental and Mathematical Biomechanics Injury Research (EMBIR)

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Organization & Contact Information

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AgencyDepartment Of Transportation → 693JJ9 Nhtsa Office Of Acquistion
Contacts1 person available
OfficeWASHINGTON, DC, 20590, USA
Organization / AgencyDepartment Of Transportation → 693JJ9 Nhtsa Office Of Acquistion
Office AddressWASHINGTON, DC, 20590, USA
Contacts
Danna Brooks
contact@example.gov

Full Description

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The National Highway Traffic Safety Administration (NHTSA) is committed to saving lives, preventing injuries, and
reducing traffic-related health care and economic costs. As part of that mission, the Office of Vehicle Safety
Research (OVSR) supports a robust scientific research program to provide the technical foundation for NHTSA’s
vehicle and traffic safety initiatives.



This procurement seeks to establish an Indefinite Delivery, Indefinite Quantity (IDIQ) contract to support NHTSA’s
Experimental and Mathematical Biomechanics Injury Research (EMBIR) program. The IDIQ will enable the issuance
of Task Orders (TOs) that require a broad range of specialized research, analysis, and technical support services
related to human injury mechanisms and prevention in motor vehicle crashes.



The contractor shall provide services across the following four research domains:
Experimental Biomechanics: Conduct physical testing to evaluate human injury tolerance under crashrelevant
loading conditions and identify the mechanisms and thresholds for injury.
1.Anthropomorphic Test Device (ATD) Support: Provide technical expertise to develop and assess ATDs,
ensuring biofidelity, durability, and measurement accuracy.
2.Computational Modeling: Develop and utilize advanced computational models (e.g., finite element models of
the human body and ATDs) to simulate crash scenarios, validate injury mechanisms, and support virtual
testing environments.
3.Crash and Injury Data Analysis: Analyze real-world crash data, ATD testing, and experimental research to
support injury criteria development, identify emerging crash trends, and enhance vehicle safety metrics.
Data science tools such as AI/machine learning may be applied to enhance insights from large-scale datasets.

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