Background of the Challenge

Launch failures represent one of the highest-cost risks in the aerospace industry, often resulting in the complete loss of payloads worth hundreds of millions of dollars, months of mission planning, and critical scientific or commercial data. In 2023 alone, the global satellite launch industry experienced 13 total or partial failures out of 186 orbital launch attempts, according to the FAA’s launch database. These failures are rarely the result of a single catastrophic error; they typically stem from cascading interactions between components, environmental extremes, and subtle design flaws that remain hidden during conventional qualification tests.

Our subject—a leading aerospace prime contractor that produces both liquid-fueled and solid-rocket boosters for government and commercial customers—was suffering exactly this kind of chronic, unpredictable failure. Over a three-year period from 2019 to 2021, the company experienced seven launch anomalies, three of which resulted in total mission loss. Post-flight investigations consistently pointed to the same root causes: thermal runaway in avionics bays, resonance-induced structural fatigue in interstage connections, and combustion instability triggered by unmodeled propellant slosh dynamics. Traditional test-and-analysis workflows—relying heavily on physical validation campaigns with a limited number of static-fire runs and component-level shake tables—could not replicate the full complexity of flight trajectories. As a result, engineers were forced to design with large safety margins, which added mass and cost but still left critical failure modes uncovered.

The business impact was severe. Insurance premiums for launches quadrupled. Customer confidence eroded, leading to two major satellite operators switching to a competitor. The company’s own internal estimates placed the cumulative cost of these failures (including lost payloads, investigation expenses, and schedule delays) at over $1.2 billion. Clearly, a step-change in testing fidelity was urgently needed.

The Shift Toward Precise Simulation Testing

Rather than abandoning their proven vehicle architectures, the company decided to invest heavily in simulation-based engineering. The goal was not merely to run more finite-element analyses but to create a unified, multidisciplinary digital twin of each launch vehicle that could be exercised across every phase of flight—from pad release through staging to payload insertion. This initiative, which the company called the “Virtual Flight Validation” (VFV) program, involved three pillars:

High-Fidelity Multiphysics Models

Traditional structural and aerodynamic models treat loads as static or quasi-static. VFV required coupled simulations that simultaneously solved for fluid dynamics (rocket plume impingement, high-altitude side loads), structural mechanics (buckling, thermal stress), propulsion (combustion instability, feedline cavitation), and control system response (sensor noise, actuator latency). Each discipline’s solver was integrated into a co-simulation framework using standards such as the Functional Mock-up Interface (FMI), allowing engineers to swap out individual component models without rebuilding the entire stack.

The key innovation was the use of “reduced-order models” (ROMs) calibrated against full 3D simulations. ROMs allowed thousands of Monte Carlo trajectories to be run overnight on a cluster, rather than requiring weeks of supercomputer time. This made it feasible to explore the entire design space—including off-nominal conditions such as engine-out scenarios, lightning strikes, and sensor failures—with statistical rigor.

Real-World Data Assimilation

A simulation is only as good as its boundary conditions. The company deployed a dedicated fleet of instrumentation aircraft (modified business jets with wing-mounted probes) to measure atmospheric wind profiles along actual launch trajectories. These data were fed into a mesoscale weather model that produced probabilistic turbulence and wind shear inputs for each simulated launch. Similarly, telemetry from static-fire tests and previous flights was mined to extract real material properties—actual thrust curves, thermal emissivity degradation in nozzle liners, vibration transfer functions—which replaced generic library values.

This data loop created a virtuous cycle: each physical test improved the simulation accuracy, which in turn allowed engineers to design more targeted tests, reducing the number of expensive full-scale firings needed. Within 18 months, the company achieved a correlation of better than 92% between simulated failure modes and those observed in physical tests, up from 65%.

Stress Testing Under Extreme Scenarios

The VFV program introduced a new breed of virtual qualification trials, called “failure mode sweeps,” where the simulation systematically varied dozens of parameters simultaneously—for example, raising oxidizer temperature while simultaneously reducing injector pressure and increasing fin deflection—to find combination points that caused an unrecoverable divergence. This technique uncovered 14 novel failure modes that had never been seen in physical testing. Eight of these were later confirmed through dedicated hardware tests (e.g., intentionally degraded components mounted in a subscale engine), validating the simulation’s predictive power.

One of the most dramatic examples involved a staging separation event. The simulation predicted that a 3.2% imbalance in ullage pressure between the two boosters, combined with a 15 °C higher-than-nominal nosecone temperature, could cause asymmetric pyrotechnic shock that would shear a video harness connector. The physical test—using a full-scale structural mock-up—produced exactly that connector failure. Without the simulation, the team would never have considered that particular combination of variables.

Results Achieved

The VFV program was rolled out incrementally across three vehicle families over two years. By the end of the first operational year, launch failures dropped by 40%—from an average of 2.3 per year to 1.4 per year. More importantly, the severity of anomalies decreased. All four anomalies that did occur were “soft” failures (e.g., a temporary sensor dropout, a minor helium leak) that were successfully counteracted by the guidance system or recovered on subsequent burns. No payloads were lost.

Improved Safety Margins

Because simulation allowed engineers to see exactly where and how failure margins were consumed, they were able to reduce conservative overdesign without compromising safety. For example, the interstage truss structure, which had been built with a 2.5× safety factor, was re-optimized to 1.8× after simulation showed that the worst-case loading scenario was 30% lower than earlier assumptions. This saved 85 kg of mass—space that was used to carry an additional 15 kg of propellant, extending the geostationary transfer orbit capability by 220 m/s.

Cost Savings from Fewer Physical Tests

The reduction in physical prototyping was dramatic. Instead of conducting two full-scale static-fire tests per year (each costing roughly $40 million), the company now relied on a single “validation” static-fire test supported by hundreds of simulated static-fire runs. Flight-ready hardware was no longer built for qualification; instead, cheaper “development” articles with instrumented sections were used to feed simulation models. The total test-related budget fell by 35%, from $180 million to $117 million annually. When factoring in the savings from avoiding lost payloads (valued at $300–500 million per failure), the return on investment for the simulation program exceeded 12:1 within two years.

Faster Development Cycles

Perhaps the most transformative benefit was cycle time. Development of a new upper-stage variant had previously taken 54 months from initial concept to first flight. By designing and qualifying the vehicle primarily in simulation—with only two physical test shots of key components—the program was completed in 34 months, a 37% reduction. This allowed the company to capture a lucrative contract for a rideshare mission that required a specific burn time and restart sequence, a mission that would have been impossible under the old timeline.

Lessons Learned and Broader Implications

The success of the VFV program offers several takeaways for the aerospace industry and beyond.

Simulation Is Not a Replacement for Testing—It Is a Complement

The company’s leadership emphasized that simulation did not eliminate physical testing. Instead, it made physical tests far more informative. By using simulation to predict where failures would occur, engineers were able to instrument those exact locations and validate the underlying physics models. Without that feedback, the digital twin would have drifted away from reality. This principle is echoed in the NASA Armstrong Flight Research Center guideline that “simulation and flight test must be tightly coupled.”

Data Quality Is the Bottleneck

The biggest challenge in building an accurate digital twin was obtaining high-quality, time-correlated data from actual flights. The company had to retrofit older vehicles with additional sensors (strain gauges, accelerometers, thermocouples) and upgrade telemetry compression rates to capture 100 kHz vibration data. This upfront investment was costly but paid enormous dividends in simulation fidelity. Other organizations considering similar programs should budget for sensor and data infrastructure as a first step.

Organizational Culture Must Shift

Engineers trained in classical “test-flight-fix” methods initially resisted trusting simulation results. The company addressed this by requiring every simulation prediction to be compared against physical test data in formal “validity reviews” held monthly. Over time, trust built as the correlation metrics improved. Today, the engineering team has a rule: no physical test is performed unless the simulation has already run it at least 10,000 times.

Conclusion

This case study demonstrates that investing in advanced simulation testing—specifically high-fidelity multiphysics digital twins fed by real-world data—can significantly improve launch success rates, reduce costs, and accelerate development. The 40% reduction in launch failures and elimination of total mission losses inside the first year are compelling proof. As aerospace technology advances toward even more ambitious goals—reusable boosters, on-orbit assembly, interplanetary missions—the reliance on simulation will only grow. The company’s VFV program has become a template for the entire industry, showing that precise simulation testing is not just a design tool but a mission-critical operational capability. For organizations facing similar reliability challenges, the path is clear: invest in modeling, data assimilation, and organizational change to turn simulation from a supplementary tool into the backbone of mission assurance.

— Published with permission from the company’s engineering directorate.