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From the voyages of spacecraft to the Moon and beyond, to the launches of satellites that help us navigate, communicate, and understand our planet and the universe, the use of liquid-fueled rockets has been key to humanity’s use and exploration of space. Today marks 100 years since the first successful test of this technology.

On March 16, 1926, physicist and inventor Dr. Robert H. Goddard achieved a small but significant success when he launched a liquid-fueled rocket for the first time. His rocket, fueled by liquid oxygen and gasoline, was tested at his Aunt Effie’s farm in Auburn, Massachusetts.

While unimpressive by most measures—the rocket flew for just 2.5 seconds, reaching 41 feet (12.5 meters) in altitude and landing in a cabbage patch 184 feet (56 meters) away—it was a breakthrough that heralded the exploration of space.

Over his lifetime, Goddard improved on his design and went on to create other technologies for space travel, including systems to steer rockets, pumps for rocket fuels, and engines that could pivot for better control. His pioneering work laid an important foundation for our achievements in space today.

Photo Credit: Esther Goddard, from the Clark University archive.

Download PDF: A Combination of Techniques Leads to Improved Friction Stir Welding

The NESC developed several innovative tools and techniques during an assessment to find the root cause of poor tensile strength and low topography anomalies (LTA) in welds formed using a solid-state welding process called self-reacting friction stir welding (SRFSW).   

Using a combination of machine learning, statistical modeling, and physics-based simulations, the assessment team helped improve the weld process and solve both issues, lifting constraints that had been placed on flight hardware.  

Developing Techniques for LTA Detection 

Determining the root cause of poor tensile strength welds and LTA observed on the weld fracture surfaces involved several techniques: 

• Deep Learning for LTA Detection: The NESC team developed a machine-learning model to detect and segment LTA in weld images. The model was trained on images annotated by metallurgy experts, with a majority-vote consensus to resolve disagreements. The team then developed an accompanying standard operating procedure for image capture to improve robustness and reduce bias. This model was built on previous NASA work to develop specialty microscopy analysis foundation models by pretraining on 100,000+ microscopy images. This step was crucial to linking process parameters with LTA occurrence in an objective, nonbiased way. 

• Integrated Data-Ingestion Framework: SRFSW is a complex process with many interacting variables. The weld process produces a large amount of data with diverse data types that include dozens of tabular process parameters, dozens of sequential data streams from the production tool, fracture and weld cross-section images, and mechanical-test lab data. A Python-based framework was developed to automatically ingest and validate these diverse data and compile them into a single master spreadsheet and a database. This tool reduced manual effort, minimized transcription errors, and improved data quality for downstream analysis. The team delivered the tool to stakeholders for their ongoing use. 

• Data Analysis Web Application: A new web-based visualization and analysis tool allowed engineers and subject matter experts to quickly explore the integrated dataset for faster hypothesis testing and more intuitive insight generation throughout the investigation

• Space-Filling Design of Experiments: Because SRFSW involves complex, nonlinear relationships between process parameters, the team found traditional factorial designs were insufficient and implemented a space-filling design of experiments (DOE) to efficiently explore the full parameter space. These data-trained machine-learning models capture the underlying weld behavior. The team also developed a software tool for generating such designs and shared it with stakeholders.

• Physics-Based SRFSW Simulation: Creating a computational model of the SRFSW process simulated weld conditions, microstructure evolution, and resulting properties, offering insight into aspects of the weld process that are inaccessible to physical sensors. This enhanced understanding and guided improvements. 

Determining LTA Root Cause 

Using these tools and analyses, the team identified two root causes for the LTA and poor tensile strength: 

1. Overly aggressive post-weld surface preparation in production reduced weld strength. 

2. Weld power input outside the optimal range led to inconsistent welds and increased risk of LTA. 

The process models helped define a target weld power input window and recommended how to adjust primary control parameters to reliably achieve that target. Follow-up production tests confirmed that these adjustments could be implemented with high precision, eliminating both low-strength welds and LTA.  

Friction Stir Welding 

In SRFSW, a rotating pin is plunged into the seam between two metal plates, generating heat through friction that fuses the sheets together without melting the material. This technique produces stronger joints than traditional welding and enables the use of high-performance but traditionally non-weldable alloys like Aluminum 2219. 

The SRFSW technique uses no blowtorches or solder because friction stirs the materials together at a molecular level.

 

NASA’s Friction Stir Welding lab resides inside NASA’s Michoud Vertical Assembly Center in New Orleans and is being used to join major components of the SLS rocket. 

For information, contact Donald S. Parker.  donald.s.parker@nasa.gov 

References: NASA/TM-20240016466 and NASA/TM-20230010624 

Download PDF: NESC Develops Method for Estimating Risk When Reducing NDE 

Performing nondestructive evaluation (NDE) can have both cost and schedule impacts, leading some to question whether descoping (i.e., reducing or eliminating) NDE inspections on certain spaceflight hardware could be possible. However, this approach would be counter to NASA’s Technical Standard NASA-STD-5019A, which outlines the spaceflight system requirements for establishing a fracture control plan—one that relies on design, analysis, testing, NDE, and tracking of fracture-critical parts to verify damage tolerance and mitigate catastrophic failure. 

Under the 5019A framework, damage smaller than the NDE detection capability is assumed to exist, but through analysis or test, the part being evaluated must be shown to survive the required service life. In practice, NDE’s role is to screen out flaws that otherwise may result in failure. However, in some cases, descoping NDE from the damage tolerance verification process could be useful and still provide the required level of safety.   

The NESC conducted an assessment to help answer the question of whether rationale could be found for achieving an equivalent risk posture without using the traditional 5019A approach to damage tolerance. The objective was to develop a probabilistic analysis method that would allow NASA programs and projects to estimate risk associated with descoping the NDE requirements of single-wrought materials. This effort included using historical data to demonstrate the method, performing sensitivity studies, and identifying the minimum supporting data that would be required for approving a descoping request. 

Descoping NDE from Damage Tolerance 

Damage tolerance is typically treated as deterministic: an NDE detection threshold is established as a fixed flaw size with an associated binary outcome (flaw exists/does not exist), and failure is based on a conservative analysis or test with a binary result (pass/fail). However, damage tolerance is rooted in the following probabilities:  

•  P(A): Probability that a flaw of a given size exists, 

•  P(D0│A): Probability that this flaw will be missed by NDE, and  

•  P(F│D0,A): Probability that a flaw results in failure given that it exists and was missed by NDE.  

These are combined into the joint failure probability: P(F,D0,A) = P(F│D0,A)P(D0│A)P(A) 

Damage tolerance is based on the idea that analysis and testing suggests a near-zero probability of failure below a critical initial flaw size (aCIFS) shown by the green (lower) arrow in Figure 1, and NDE results in a near-zero probability of missing a flaw above some detectability threshold (aNDE) shown by the yellow (upper) arrow in Figure 1. If these two areas overlap, then the part is damage tolerant, with a near-zero failure probability regardless of underlying probability of flaw existence, i.e., conservatively assuming that P(a>aCIFS)=1 for any flaw size does not impact the conclusion. However, if NDE is descoped, it removes the right arrow from Figure 1, and risk will increase to a value proportional to the probability P(a>aCIFS). 

Estimating P(a>aCIFS) may be intractable without expensive, high-resolution methods to characterize the frequency of flaw occurrence at a particular size for a given part. Alternatively, it may be possible to estimate P(a> aNDE), the probability of a detectable flaw existing. Assuming that a part of interest is shown to be damage tolerant prior to any NDE descope (i.e., satisfying NASA-STD-5019A), it can be assumed that (1) historical inspection data are available, and (2) aNDE > aCIFS, due to the required overlap in Figure 1. As such, it was proposed that the frequency of historical finds could be used to estimate a 95% upper confidence bound on P(a> aNDE) and thus an estimate of the risk associated with descoping. 

To demonstrate the risk-evaluation framework, the NESC gained access to a historical NDE database comprising 33,630 bolt-hole inspections over a 3-year period. In total, six crack-like features were found by NDE. Accounting for uncertainty due to sample size yielded a 95% confidence upper bound of P(a> aNDE) = 0.04% for each hole. In the proposed method, it is conservatively assumed that if a flaw exceeding the CIFS exists, then it will lead to structural failure. While conservative, this assumption was necessary based on the limitations of the database in that it lacked detected flaw sizing. Based on this assumption, P(a> aNDE) = 0.0004 yields a structural reliability of approximately 0.9996 (expressed as 3.4 “nines”).  

The results are illustrated graphically in Figure 2. In this case study, increasing the number of inspections in the dataset to 100,000 (i.e., multiplying by a factor of 3) marginally increases the number of nines to 3.5. At the observed NDE rejection rate, 4 nines of reliability are not achievable even with infinite samples and zero uncertainty. It is expected that the rejection rates and sample sizes in this case study are on the order of magnitude of what would be observed and available in practice. Since 2 nines or less would equate to a significant increase relative to the baseline risk for NASA Human Spaceflight Programs, a minimum sample size of 5,000 inspections is needed at an NDE rejection rate of 0.04%. 

There are necessary assumptions underpinning this methodology. First, time-invariant process control is required to ensure that estimated probabilities from historical inspections are predictive of future probabilities after descope. Ensuring consistency during the data collection period is a first step in verifying existing controls, and continued monitoring is necessary to verify that the process remains time-invariant. Second, while aggregating data across multiple parts can increase the inspection sample size and decrease uncertainty in estimated rejection rates, it requires aggregation rationale via qualitative and quantitative assessments of similitude. The methodology developed by the NESC is intended to be a component of a comprehensive fracture control evaluation by the NASA Fracture Control Board and the responsible Technical Authority.  

For information, contact Patrick E. Leser.  patrick.e.leser@nasa.gov 

Reference: NASA/TM-20250004074 

For most of the year, ice blankets the waterways of the northern Canadian Arctic Archipelago. But during the brief summer melt season, the stark white and gray landscape transforms into a colorful, dynamic environment. On a particularly striking day in 2022, sediment plumes and fractured sea ice traced swirling eddies in a branch of the Nansen Sound fjord system.

These satellite images show a section of Cañon Fiord, located about 115 kilometers (70 miles) southeast of the Eureka research station on west-central Ellesmere Island. Waters from the fjord flow into Greely Fiord, which connects to Nansen Sound and ultimately the Arctic Ocean. The images were acquired by the OLI (Operational Land Imager) on Landsat 8 on August 9, 2022.

Igor Dmitrenko, a physical oceanographer at the Centre for Earth Observation Science at the University of Manitoba, has studied eddies in the fjord system and notes that the water’s turbidity, a measure of its cloudiness, remains low during the ice-covered season. Freshwater runoff—and the sediment it carries—drops sharply this time of year, and the formation of 2-meter-thick sea ice shields the surface from wind, suppressing mixing that would otherwise resuspend particles.

Summer presents a contrasting scenario. The detailed image below (top) shows that the sea ice in this part of the fjord has broken up, free to drift with the currents and wind. Note that some of the pieces are likely icebergs that have broken off from nearby outlet glaciers. The second detailed image shows a similar scenario; however, in this case, it is sediment suspended in the water that is tracing the flow.

Alex Gardner and Chad Greene, glaciologists at NASA’s Jet Propulsion Laboratory, pointed out that the sediment plume is mostly glacial flour—rock that has been pulverized by a glacier. Surface meltwater that gets under the glacier ultimately flushes the glacial flour into the fjord, making the water appear turquoise. Glacial flour is a critical source of nutrients, specifically iron. Soluble iron is a vital nutrient in marine ecosystems because most phytoplankton—the foundation of marine food webs—depend on it to grow. 

The glacial ice visible in these scenes comes from the Agassiz Ice Cap, one of five major ice caps on Ellesmere Island. Using data from NASA’s ICESat and the DLR-NASA GRACE missions, scientists have shown that glaciers in the Canadian Arctic Archipelago began shrinking rapidly in the mid-2000s and that the trend has persisted.

NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen.

References & Resources

• Dmitrenko, I.A., et al. (2025) Following a half-century oceanographic data gap in the northern Canadian Arctic Archipelago: multidecadal variability of the Pacific water throughflow. Frontiers in Marine Science, 12, 1602485.
• Gardner, A.S., et al. (2011) Sharply increased mass loss from glaciers and ice caps in the Canadian Arctic Archipelago. Nature, 473, 357-360.
• Wouters, B., et al. (2019) Global Glacier Mass Loss During the GRACE Satellite Mission (2002-2016). Frontiers in Earth Science, 7, 96.

Patches of the Sun’s surface often show strong magnetic fields. These fields can emerge within a matter of hours, and can decay slowly or quickly, sometimes over days, weeks, or even months. Thanks to a new study about these long-lived active regions, we now know much more about the patches where these strong magnetic fields take at least a month to decay.

This study relied on inputs from NASA’s Solar Active Region Spotter citizen science project, which asked volunteers to answer a series of questions about pairs of active region images from NASA’s Solar Dynamics Observatory.

Project leads Emily Mason (Predictive Science Inc.) and Kara Kniezewski (Air Force Institute of Technology) looked at the data and the analysis done by volunteers. They found that the long-lived active regions produce disproportionately more flares than the shorter-lived regions and are 3-6 times more likely than other active regions to be the source of the most intense kinds of solar flares. These results are a strong indication that long-lived active regions are crucial for predicting space weather and could provide critical information on magnetic fields deeper inside the Sun. 

The Solar Active Region Spotter project is now complete, but you can learn more about the results here: https://www.zooniverse.org/projects/eimason/solar-active-region-spotter/about/results

Explore NASA Citizen Science projects you can join today to help advance our understanding of space weather: https://go.nasa.gov/3ZK6nvE.

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