Part of the DoW SBIR/STTR FY27 Release 2 topic list. Browse all 83 topics by industry or service.
Topic DAF27TZ02-DV004 from the DoW STTR 2027 BAA, Release 2. Pre-release now; proposals due January 6, 2027, 12:00 PM ET.
Quick answer
The Department of the Air Force wants an offline Electronic Flight Bag (EFB) application that digitizes B-52 Technical Order performance charts and uses deterministic algorithms to compute takeoff and landing distances, brake energy limits, and other performance data with auditable outputs. This is a Direct-to-Phase II STTR topic with no Phase I awards. Proposals are due January 6, 2027, 12:00 PM ET.
Solicitation at a glance
- Topic number: DAF27TZ02-DV004
- Official title: B-52 Multi-Factor AI Performance Charts
- Agency: Department of the Air Force
- Program: STTR (DoW STTR 2027 BAA, Release 2)
- Award type: Direct to Phase II (no Phase I awards)
- Award amount: Up to $2,000,000. Source: Department of the Air Force Release 2 instructions in DSIP.
- CMMC level: Level 2 (Self)
- ITAR: No
- Technology areas: Air Platform
- Pre-release (direct TPOC contact allowed): November 4 to December 2, 2026, 8:00 AM ET
- Opens in DSIP: December 2, 2026, 8:00 AM ET
- Proposals due: January 6, 2027, 12:00 PM ET
- Official source: DSIP Topics app (search DAF27TZ02-DV004)
BW&CO's take
What the Department of the Air Force wants
B-52 crews still work out takeoff and landing distances, brake energy limits, crosswind restrictions, and runway condition impacts from paper Technical Order charts and worksheets. The Air Force wants AI-assisted chart mining to digitize that data, then deterministic algorithms that calculate performance from approved sources and refuse to produce values when inputs are unclear or outside approved limits. The application must run closed loop on existing government hardware with no reliance on internet, cloud, or AI inference. Phase II delivers a prototype, validated datasets, verified calculation methods, user workflows, test reports, and transition documents for accreditation and airworthiness review, moving from TRL 3-4 to TRL 6.
Who is a good fit
- Prior digitization of aircraft performance charts or technical orders, with validated outputs.
- Deterministic performance calculation engines, such as EFB performance planning tools, with demonstrated offline operation.
- Model optimization and reduction for resource-constrained EFB platforms, plus secure environments for handling CUI data.
- Ties to a U.S. National Laboratory or FFRDC already investing in related AI security, model assurance, or secure deployment work.
Watch-outs
- Direct-to-Phase II: feasibility documentation must include validated deterministic algorithm outputs and prototype results proving accuracy and offline functionality.
- This is an STTR topic. The topic text does not describe partnering requirements, so confirm research institution partner rules in the solicitation instructions. The topic separately calls for coordination with National Laboratories or FFRDCs.
- The flight application must not generate unvalidated, extrapolated, or inferred performance values, and it cannot rely on AI inference in flight. The topic describes AI for chart mining and digitization, with deterministic algorithms for the calculations.
- The topic lists CMMC Level 2 (Self). It is not marked ITAR, and the topic mentions support for handling CUI data.
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Official topic text (verbatim from DSIP)
The text in this section is copied word for word from the official DSIP topic as published on November 4, 2026. DSIP is the official source and topics can be amended. Always confirm the current version at dodsbirsttr.mil before you submit. Our commentary above is BW&CO's interpretation, not Government guidance.
Title
B-52 Multi-Factor AI Performance Charts
Objective
Develop and validate a capability that modernizes B-52 AI Performance Chart capability that automates approved performance calculations, supports disconnected EFB operations, and provides accurate, auditable outputs for aircrew decision-making.
Description
The B-52 Stratofortress has been the backbone of the United States Air Force's long-range strategic bomber capability for more than six decades. Despite multiple upgrades to its avionics and mission systems, aircrews continue to rely on labor-intensive, manually interpreted paper Technical Order performance charts and multi-step worksheets to calculate critical operational calculations such as takeoff and landing distances, brake energy limits, crosswind restrictions, runway condition impacts, directional control limits, and degraded-performance scenarios. These manual processes are time-consuming, increase cognitive workload and introduce opportunities for human error, and require aircrews to make high-stakes decisions under challenging time constraints, often during dynamic operational scenarios. Continued reliance on manual processes reduces operational efficiency, increases crew workload, and can negatively impact decision-making, especially in contested or degraded environments.
The current state of the art within commercial aviation utilizes Electronic Flight Bag (EFB)-based performance planning tools capable of rapidly calculating aircraft performance using approved aircraft data and validated calculation methodologies. While these solutions demonstrate the feasibility of digital performance-planning capabilities, they do not incorporate B-52-specific TO data, military performance procedures, cybersecurity requirements, airworthiness considerations, or Department of the Air Force EFB integration requirements. As a result, a capability gap exists between current B-52 performance-planning processes and modern digital aviation decision-support systems. The proposed effort will begin at approximately TRL 3-4, leveraging prior Phase-I-like work including operational requirements analysis, commercial market research, chart digitization methodologies, deterministic algorithm concepts, and successful demonstrations of related technologies in both government and commercial aviation sectors. Commercial aviation performance applications, technical-order digitization technologies, and deterministic calculation engines have demonstrated feasibility and provide a strong foundation for this effort. The objective of the Phase II effort is to advance the capability to TRL 6 through development, validation, and operational demonstration of a prototype capability suitable for transition into the Air Force EFB ecosystem. The Government is particularly interested in approaches that leverage secure research infrastructure, advanced computing resources, AI-assisted data extraction capabilities, and development of government-owned intellectual property to accelerate chart digitization, data validation, and long-term sustainment. Additionally, the Government seeks to leverage existing federally funded research investments such as those within U.S. National Laboratories or other Federally Funded Research & Development Centers (FFRDCs) to the maximum extent possible. Leveraging existing investments around AI security, vulnerability assessments, red teaming, model assurance, secure deployment of advanced models, and evaluation of AI systems supporting national security missions will help accelerate technology maturation and transition.
To meet this goal, the Phase II D2P2 effort is expected to include the following activities:
- Integration of the AI-driven solution for chart mining and digitization to extract data; Refinement of deterministic algorithms to calculate performance metrics (e.g., takeoff/landing distances, brake energy limits, crosswind tolerances) based on TO and other authoritative data sources.
- Development of intuitive user interface and operational workflows that align with aircrew feedback and mission requirements, ensuring a transparent and user-friendly application.
- Rigorous testing and validation of algorithm accuracy, decision-support reliability, and adherence to airworthiness and compliance standards, incorporating inputs from operational stakeholders and SMEs.
- Coordination with USG relevant Program Offices and certification authorities to support EFB system accreditation, operational testing, and fielding - this would translate into the existing U.S. Government EFB system with offline capabilities to ensure reliable operation in contested, degraded, or denied environments.
- Coordination with U.S. National Laboratories or FFRDCs already investing in similar capabilities to enable rapid technology advancement, alignment of development roadmaps towards broader national security priorities, and aid in the transition into operational systems.
This STTR effort stands to vastly improve the DoW's technological state by eliminating the inefficiencies of current manual performance calculations and transitioning the B-52 platform into a new era of data-driven, AI-enhanced operations that rapidly generate transparent and auditable aircraft performance assessments using approved source data. The capability shall not generate unvalidated performance estimates, extrapolated values, or inferred outputs when required data are unavailable or outside approved limits. The resulting solution will enable faster, more reliable decision-making, directly enhancing safety and mission outcomes while reducing aircrew workload. Moreover, the offline functionality ensures the tool's efficacy in a range of operational environments, including denied, degraded, and contested settings. Minimum acceptable deliverables include:
- A prototype closed loop EFB-compatible software capability suitable for operational evaluation.
- Digitized and validated performance datasets derived from approved B-52 source material.
- Verified and validated calculation methodologies demonstrating agreement with approved reference calculations.
- User workflows and interfaces suitable for operational aircrew use.
- Test and validation reports documenting accuracy, reliability, and operational suitability.
- Sustainment and transition documentation supporting future EFB integration, cybersecurity accreditation, airworthiness review, and operational fielding.
Through these efforts, the B-52 will evolve to keep pace with the Air Force’s operational imperatives, including enhanced readiness to deploy and fight (Operational Imperative 7) and modernization priorities such as trusted AI and autonomy. Success in Phase II will position this innovation for further development and transition under Phase III, facilitating its adoption as a fielded capability that aligns with the U.S. Air Force’s modernization goals and long-term sustainment strategy for the B-52 fleet.
Phase I
As this is a STTR Direct-to-Phase-II (D2P2) topic, no Phase I awards will be made. To qualify, applicants must demonstrate feasibility via prior "Phase I-type" efforts, providing data validating deterministic algorithms. Documentation should include validated algorithm outputs, prototype results, proving accuracy and offline functionality.
Phase II
Phase II will produce a validated, EFB-integrated AI Performance Chart prototype for the B-52, delivering automated, traceable outputs for critical performance metrics like takeoff/landing distances and brake energy limits. Achievements include TO-compliant algorithm validation, rigorous offline testing, and a prototype demonstration in an operationally relevant environment to enhance mission safety, efficiency, and readiness in contested environments. The prototype should be cyber-compliant for EFB use, and demonstrate feasibility of digitized, automated performance decision support—without attempting full-scale automation, certification, or fleet deployment.
Phase II Technical Efforts include:- Integration of the AI-driven solution for chart mining and digitization to extract data; software model optimization techniques and high performance computing desired.
- Must be closed loop in flight application without reliance on internet, cloud, AI inference, or other external sources
- Compatible with existing Government Hardware.- Approaches that leverage secure research environments, high-performance computing (HPC) resources, deep scientific expertise, cutting-edge research facilities, AI-assisted data extraction capabilities, and expertise in model optimization and reduction are highly desirable to accelerate chart digitization, improve validation processes, support handling of CUI data, and enable deployment on resource-constrained EFB platforms.
- Refinement of deterministic algorithms to calculate performance metrics (e.g., takeoff/landing distances, brake energy limits, crosswind tolerances) based on TO and other authoritative data sources.
- Development of intuitive user interface and operational workflows that align with aircrew feedback and mission requirements, ensuring a transparent and user-friendly application.
- Rigorous testing and validation of algorithm accuracy, decision-support reliability, and adherence to airworthiness and compliance standards, incorporating inputs from operational stakeholders and SMEs.
- Prevent generation of unvalidated performance data/estimates when inputs are unclear, outside performance parameters, or unknown.
- Coordination with USG relevant Program Offices and certification authorities to support EFB system accreditation, and operational test. This would translate into the existing U.S. Government EFB system with offline capabilities to ensure reliable operation in contested, degraded, or denied environments.
- Coordination with U.S. National Laboratories or FFRDCs already investing in similar capabilities to enable rapid technology advancement, alignment of development roadmaps towards broader national security priorities, and aid in the transition into operational systems.
Phase III Dual Use Applications
Expected TRL at entry is approximately TRL 6. The Phase III effort will field a certified, TO-compliant AI-driven EFB system for B-52 performance calculations, enhancing mission readiness and safety. Expected activities in Phase 3 include: operational test and evaluation, USG certification activities, fielding, and training. In addition, a long term sustainment and funding model should be in place for continuous revisions and updates to the TOs. Commercial aviation is well advanced in this arena so will not benefit. Other industries that rely on legacy data, however, may benefit from chart digitization and software model reduction techniques. Transition strategies include collaboration with the B-52 SPO for deployment and aligning with broader Air Force AI modernization goals. Coordination is already in place with the B-52 and EFB program offices to include long term funding plans. Following successful fielding on the B-52 platform, the framework may be expanded to additional Air Force aircraft communities that continue to rely on manual TO performance-chart calculations, including mobility, tanker, ISR, training, and other legacy aircraft platforms. This could also be leveraged DoW wide for all organizations that continue to rely on legacy data.
References
Technical Order 1B-52H-1-1, "Flight Manual, B-52H Airplane Performance Data," United States Air Force, latest update available
United States Air Force Operational Imperatives, with emphasis on Operational Imperative 7: "Readiness to Deploy and Fight," Air Force Modernization Priorities, 2023
Department of Defense Directive 3000.09, "Autonomy in Weapon Systems," 2023, outlining trusted autonomy and AI implementation guidelines
"B-52 Stratofortress Sustainment Program: Advancing Operational Efficiency through Technology Modernization," US Air Force Global Strike Command Report, 2022
Federal Aviation Administration (FAA) Advisory Circular 61-23C, "Pilot's Handbook of Aeronautical Knowledge," highlighting best practices for performance factors, calculations, and operational safety in aviation-related decision-making
IEEE 12207:2017, "Systems and software engineering – Software life cycle processes," addressing standards for software development lifecycles, relevant to the EFB software integration
"Applications of Deterministic Algorithms in Aviation Performance Metrics," Journal of Aerospace Information Systems, Vol. 15, No. 4, April 2022
AFWERX Technology Transition Strategy Best Practices Guidelines, relevant for transitioning innovative capabilities into operational use, 2023.National Defense Strategy of the United States of America, 2022, emphasizing modernization priorities for strategic bombers and integration of advanced technologies like AI
"Optimizing Large Aircraft Performance in Contested Environments Using Deterministic Algorithms," Proceedings of the NATO Science & Technology Organization Workshop on Mission-Specific Aircraft Enhancements, 2021.
"National labs work to optimize AI infrastructure amid Genesis Mission"https://fedscoop.com/doe-national-labs-ai-infrastructure-data-center/,"ORNL and VA Use Security Framework on Summit for Health Research"https://www.hpcwire.com/off-the-wire/ornl-and-va-use-security-framework-on-summit-for-health-research/
Keywords
B-52; AI Performance Charts; Electronic Flight Bag; Deterministic Algorithms; Aircraft Performance; Offline Capability; Operational Efficiency; Technical Orders; Aircrew Decision Support; Trusted AI; AI for National Security, AI Centers of Excellence
Frequently asked questions
What is DAF27TZ02-DV004?
The B-52 Multi-Factor AI Performance Charts topic from the Department of the Air Force in the DoW STTR 2027 BAA. It seeks an EFB-compatible capability that automates approved B-52 performance calculations, works offline, and produces accurate, auditable outputs for aircrew.
When is DAF27TZ02-DV004 due?
Proposals are due January 6, 2027, 12:00 PM ET. DSIP pre-release began November 4, 2026, and direct contact with the topic authors (TPOCs) is allowed only until the topic opens on December 2, 2026, 8:00 AM ET.
Is DAF27TZ02-DV004 Phase I or Direct-to-Phase II?
Direct-to-Phase II STTR. No Phase I awards will be made. Applicants must show feasibility through prior Phase I-type work, including data validating their deterministic algorithms.
Can the EFB application use AI to compute performance in flight?
No. AI is used for chart mining and digitization. The flight application must be closed loop without reliance on internet, cloud, AI inference, or other external sources, and performance values come from deterministic algorithms based on Technical Order and other authoritative data.