FY 2026 Competitive Academic Agreement Program (CAAP)
Status: Forecasted
Posted date: May 18, 2026
Archive date: July 19, 2026
Close date: June 19, 2026
Opportunity ID: 362444
Opportunity number: 693JK326NF0003
Opportunity category: Discretionary
Agency name: Pipeline and Hazardous Materials Safety Admin
Agency code: DOT-PHMSA
Award floor: $250,000
Award ceiling: $1,000,000
Cost sharing required: Yes
Funding Instrument Types
- Cooperative Agreement
Category of Funding Activity
- Science and Technology and other Research and Development
Eligible Applicants
- Private institutions of higher education
- Public and State controlled institutions of higher education
Categories (use these for quoted searches)
- agency_code:dot_phmsa
- category_of_funding_activity:science_and_technology_and_other_research_and_development
- cost_sharing_or_matching_requirement:true
- eligible_applicants:private_institutions_of_higher_education
- eligible_applicants:public_and_state_controlled_institutions_of_higher_education
- funding_instrument_type:cooperative_agreement
- opportunity_category:discretionary
- status:forecasted
The Pipeline and Hazardous Materials Safety Administration¿s (PHMSA) FY 2026 CAAP NOFO solicits research proposals from nonprofit institutions of higher education for the Pipeline Safety Research and Development (R&D) Program. This program is authorized by section 12 of the Pipeline Safety Improvement Act of 2002 (Pub. L. 107-355) and 49¿U.S.C. § 60117(l). The CAAP competitive research announcement will focus on the following objectives: - Early corrosion prediction for pipelines and implementing appropriate mitigation measures. - Economical and novel agents to mitigate internal or external pipeline corrosion due to microbial-induced corrosion.- Artificial intelligence (AI)-assisted software package to assist with composite repair design.¿¿- AI-assisted software tool for interpretation of standards/regulations and properly applying the requirements.¿- Non-destructive inspection technology for cast iron pipeline inspection.- AI-enhanced liquid pipeline leak detection methodologies. ¿