Starfighters Space Validates StarLaunch 1 Separation in Critical Wind Tunnel Tests

Starfighters Space, Inc. (NYSE American: FJET) has successfully completed a comprehensive wind tunnel testing campaign for its StarLaunch 1 vehicle, a significant step forward in the company’s efforts to develop a responsive, air-launched suborbital rocket. The tests, conducted at the FAMU/FSU Joint College of Engineering Polysonic Wind Tunnel, validated the aerodynamic safety of releasing the launch vehicle from the company’s supersonic F-104 Starfighter aircraft.

According to the company’s announcement, the campaign focused on verifying “clean separation” characteristics across multiple flight regimes. The data gathered confirms that the StarLaunch 1 can safely detach from the F-104 carrier aircraft without adverse aerodynamic interactions, a fundamental requirement for air-launched systems.

Validating the Separation Dynamics

The primary objective of this testing phase was to ensure that the launch vehicle would separate predictably from the aircraft under high-speed conditions. Starfighters Space reported that tests were conducted at both subsonic (Mach 0.85) and supersonic (Mach 1.3) speeds. These specific velocity points are critical to the company’s launch profile, which leverages the high-speed capabilities of the F-104 to impart significant initial energy to the rocket.

Company officials noted that the experimental results showed a strong correlation with pre-test Computational Fluid Dynamics (CFD) models. This alignment between simulation and physical testing reduces technical risk as the program moves toward flight hardware.

“Demonstrating clean, predictable separation across these flight regimes is a foundational requirement for an air-launched system. The close alignment between our simulations and the wind tunnel results gives us confidence in the underlying design and allows us to proceed methodically to the next phase of testing.”

, Rick Svetkoff, CEO of Starfighters Space

The F-104 Platform Advantage

Starfighters Space utilizes a unique launch platform: the Lockheed F-104 Starfighter. As the only commercial operator of a fleet of flight-ready F-104s, the company aims to exploit the aircraft’s high performance for commercial space access and defense testing. Unlike modified commercial airliners used by other air-launch proponents, the F-104 is a supersonic interceptor capable of sustained speeds over Mach 2 and altitudes exceeding 50,000 feet.

By launching from a supersonic condition (Mach 1.3+), the StarLaunch 1 vehicle requires less onboard propellant to reach hypersonic velocities (Mach 5+) or suborbital space compared to ground-launched or subsonic air-launched systems. This capability positions the vehicle as a potential testbed for high-speed research and microgravity experiments.

AirPro News Analysis: The Niche of High-Speed Air Launch

While the heavy-lift air-launch market has faced significant headwinds, most notably with the bankruptcy of Virgin Orbit, Starfighters Space appears to be targeting a different operational niche. Rather than competing for large satellite constellations, the F-104 platform is sized for smaller, “tactical” payloads and high-cadence hypersonic testing.

In our view, the validation of supersonic separation is the key differentiator here. Most air-launch systems drop from subsonic carrier aircraft (like a Boeing 747 or L-1011). Starfighters’ ability to release at Mach 1.3 offers a kinematic advantage that is particularly relevant for the defense sector’s insatiable demand for hypersonic test targets. If the company can translate these wind tunnel results into successful flight tests, they may secure a defensible position in the “test and evaluation” market, distinct from the crowded commercial launch sector.

Path to Flight Testing

With aerodynamic validation complete, Starfighters Space has outlined the immediate next steps for the StarLaunch 1 program. The company stated it will now move toward the procurement and Manufacturing of instrumented drop test articles.

The upcoming phase will involve physical drop tests, where unpowered test vehicles will be released from the F-104 in flight to verify the wind tunnel data in a real-world environment. Successful completion of these drop tests is the final major milestone required before the company attempts powered suborbital flights.

The development of StarLaunch 1 is being conducted in Partnerships with Innoveering, LLC, a GE Aerospace company known for its expertise in high-speed gas dynamics and advanced propulsion, further underscoring the program’s focus on high-performance flight regimes.

Sources

• Starfighters Space Press Release

Boeing Team Prototypes Onboard AI to Revolutionize Space Missions

This article is based on an official report from Boeing.

A Boeing engineering team has successfully prototyped new “Onboard AI” capabilities designed to fundamentally change how satellites and spacecraft operate. According to a recent internal report from the company, this advancement marks a significant shift from traditional ground-based data processing to “Edge Computing” in orbit. The new technology aims to allow spacecraft to process complex data locally, significantly reducing latency and enabling autonomous decision-making in deep space.

The initiative addresses a critical bottleneck in modern space exploration: the reliance on “bent pipe” architectures where satellites capture raw data, such as images or signals, and transmit the entire volume to Earth for analysis. As sensor capabilities grow, the volume of data has become unmanageable for standard downlinks. Boeing’s prototype system runs directly on spacecraft hardware, filtering data in real-time and transmitting only high-value information to ground stations.

The Shift to Edge Computing in Space

Traditional satellite operations have long been constrained by bandwidth limitations and transmission delays. In its report, Boeing highlights that the new onboard AI system is designed to overcome these hurdles by moving the “brain” of the mission from the ground to the satellite itself.

The prototype technology reportedly focuses on three core capabilities:

• Data Filtering: The AI analyzes imagery and sensor data in real-time, discarding low-value data (such as cloud-obscured images) to conserve bandwidth.
• Autonomous Diagnostics: The system can detect anomalies caused by space weather or hardware faults and initiate repairs or adjustments without waiting for human commands.
• Real-Time Reaction: For defense and tracking applications, the system enables immediate identification and tracking of targets, bypassing the latency of ground-based loops.

Key Missions and Strategic Applications

The development of this onboard AI is linked to several major Boeing initiatives scheduled for the near future. According to the company’s project details, the technology is expected to play a vital role in the upcoming Q4S Quantum Satellite mission.

Q4S Quantum Satellite

Scheduled for launch in 2026, the Q4S mission aims to demonstrate quantum entanglement swapping in orbit. While the primary goal is to advance secure quantum networking, the mission requires sophisticated onboard processing to manage the quantum network autonomously. The AI prototype developed by the Boeing team provides the necessary control logic to handle these complex tasks without constant ground intervention.

Self-Healing Satellites and NASA Collaboration

Boeing is also leveraging this technology in collaboration with partners like Saber Astronautics. The joint effort focuses on deploying diagnostic AI tools, such as “Sentient,” which monitor thousands of telemetry points to predict failures before they occur. This “self-healing” capability allows satellites to automatically mitigate damage from solar flares or radiation.

Furthermore, the technology aligns with NASA’s push for “Cognitive Spacecraft.” As part of the “Advance Science Team” initiative, Boeing’s AI allows probes to act as autonomous scientists. Instead of waiting for instructions, a spacecraft could independently decide which geological features to analyze on Mars or which ocean plumes to sample on Europa.

AirPro News Analysis

The move toward Onboard AI represents a necessary evolution for the aerospace industry, driven by what experts call the “Data Deluge.” Modern satellites generate terabytes of data daily, making it physically impossible to downlink every byte. By processing data at the edge, Boeing is addressing both a logistical necessity and a strategic imperative.

From a defense perspective, the implications are profound. In contested space environments, the time required to send data to Earth, process it, and send a command back is a vulnerability. Onboard AI reduces this reaction time from minutes to milliseconds. Additionally, the integration of platforms like Palantir’s Foundry into Boeing’s defense programs suggests a broader strategy to modernize legacy hardware with cutting-edge software, ensuring that future constellations are not just data relays, but intelligent, autonomous assets.

Frequently Asked Questions

What is “Edge AI” in the context of space?
Edge AI refers to running artificial intelligence algorithms locally on the device (the satellite) rather than sending data to a central server (Earth) for processing. This reduces the time it takes to make decisions.

When will this technology launch?
Elements of this technology are associated with the Q4S Quantum Satellite, which is scheduled to launch in 2026.

How does this help with deep space missions?
Communication with Mars or the outer planets involves significant time delays (up to 20 minutes or more). Onboard AI allows spacecraft to make safety and science decisions instantly without waiting for instructions from Earth.

Sources: Boeing

This article is based on an official press release from the European Space Agency (ESA).

ESA and MT Aerospace Deploy AI to Slash Rocket Inspection Times by 95%

The European Space Agency (ESA) has announced a significant leap forward in the manufacturing of launch vehicles, revealing that the integration of artificial intelligence (AI) into its production lines has drastically reduced quality assurance timelines. In a statement released on January 21, 2026, ESA detailed how its collaboration with German manufacturing partner MT Aerospace has successfully applied machine learning to the production of the Ariane 6 rocket.

The initiative, conducted under ESA’s Future Launchers Preparatory Programme (FLPP), focuses on automating the complex analysis of metal forming and welding. According to the agency, the most immediate impact has been observed in the inspection of friction stir welds, where the introduction of AI has cut analysis time by 95% compared to traditional manual methods.

By shifting from labor-intensive human inspection to data-driven algorithmic monitoring, ESA aims to increase production rates and reduce costs, critical factors in an increasingly competitive global launch market.

Revolutionizing Friction Stir Welding

The core of this manufacturing update centers on Friction Stir Welding (FSW), a solid-state joining technique used to construct the massive fuel tanks for the Ariane 6. Unlike traditional welding, which melts materials to fuse them, FSW uses a rotating pin to generate friction and heat, joining metals without reaching their melting point. While this produces exceptionally strong joints, verifying their integrity has historically required time-consuming analysis.

Under the new system, machine learning algorithms monitor digital telemetry directly from the welding equipment. This includes data points such as weld force, torque, and temperature. The system processes this data to automatically verify the shape and quality of the final weld seam.

Daniel Chipping, ESA Project Manager for Software-Centred and Digitalisation Activities, highlighted the operational impact of this technology:

“Artificial intelligence, such as machine learning, in combination with new digital technologies is transforming launcher manufacturing… from automating complex analysis tasks to reducing tedious machine stop-starts, we are starting to see the benefits across all materials and shaping processes.”

, Daniel Chipping, ESA Project Manager (FLPP)

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Precision in Shot Peen Forming

Beyond welding, the initiative has applied AI to “shot peen forming,” a chaotic process used to shape the dome heads of the Ariane 6 fuel tanks. This technique involves blasting metal sheets with small spherical shots to bend them into specific curves without applying heat, which preserves the material’s structural integrity.

Predicting the Unpredictable

Historically, shot peening has been difficult to model precisely because the impact of thousands of individual shots is physically unpredictable. This often necessitated a trial-and-error approach to achieve the correct geometry. ESA reports that MT Aerospace has now trained machine learning models to predict exactly how the metal will deform under specific bombardment patterns.

This predictive capability allows manufacturers to achieve the desired dome shape with a tolerance of just 2 millimeters, significantly reducing the time required to set up and calibrate the machinery.

Advancing Carbon-Fibre Composites

The FLPP initiative also extends to the “Phoebus” project, a collaboration aimed at replacing heavy metallic upper-stage tanks with lightweight carbon-fibre reinforced plastic (CFRP). Reducing the mass of the upper stage is a priority for ESA, as every kilogram saved on the structure translates to additional payload capacity.

In this application, laser sensors combined with machine learning models are used to detect and classify manufacturing defects “on the fly” during the automated fibre placement process. By identifying issues immediately as layers are applied, the system prevents long production stoppages associated with manual checks, streamlining the fabrication of these complex composite parts.

AirPro News Analysis

The integration of AI into the Ariane 6 supply chain represents a necessary evolution for the European space sector. While new entrants like Relativity Space have garnered headlines for 3D-printing entire rockets, ESA’s approach demonstrates how legacy manufacturers can modernize established industrial processes to achieve similar efficiency gains.

The 95% reduction in weld analysis time is more than a technical statistic; it addresses a primary bottleneck in rocket production. In an era where launch cadence is dictated by how quickly vehicles can roll off the assembly line, removing manual “stop-starts” is essential for Ariane 6 to meet its commercial and institutional targets. By validating these technologies through the FLPP, ESA is effectively de-risking the transition to a more automated, data-centric future for European aerospace.

Sources

Sources: ESA (Primary Source)