Antoinette Tordesillas

Tordesillas, in collaboration with NASA, led a team studying the soil of Mars and the moon with the aim of understanding how their surfaces would respond to attempts to build, mine, or drill.[9][10] NASA approached Tordesillas at the recommendation of the US Army, who named her as the authority to consult about sand.[9] To tackle the project, Tordesillas and her team at the University of Melbourne used data about extraterrestrial soil and photos collected by orbiters and rovers in conjunction with a study of granule dynamics.[11][9] This approach involved testing simulated space soil, computer modeling the effects of added pressure, and considering simpler models of highly idealized particles adjusted for differences in the strength of gravity.[9][10] Tordesillas contemplated highly idealized particles that were round and uniform while questioning how the unique shapes observed on Mars and the moon formed.[9] She recognized that many different local conditions would need to be understood prior to any future landings.[9] She also noted the potential application of this research to combat problems arising from the unpredictable nature of stored granular materials.[11]These materials include the products of important Australian export industries such as wheat, iron ore, and coal.[11]

Tordesillas received a $750,000 grant from the Australian research fund and the US Army Research Office to develop a model that generates a high-resolution picture of individual granules and allows the visualization of the shear-band microstructures that precede disruptions in granular materials.[12] This represents an important step towards predicting earthquakes and mitigating soil erosion from heavy off-road military vehicles.[12] Tordesillas and her team developed a software tool using applied mathematics and big data analytics to predict the time and location of landslides up to two weeks in advance.[12][13] This model analyzes large quantities of data to identify sites of future failures, or sites of seismic activity.[14] It flags locations where patterns of motion become ordered because, preceding seismic activity, particles begin moving in similar ways approaching the site of what will become a failure.[14] It uses the expanding capabilities of computer programming and memory to decode big data and convert it into a network conducive to the recognition of hidden patterns.[14] The early detection of subtle changes is key to predicting failures and allows existing data to inform risk assessment and management.[14][13] This effort culminated after five years of work when Tordesillas and Robin Batterham developed, tested, and patented the Spatiotemporal Slope Stability Analytics for Failure Estimate (SSAFE) model.[15][16] This model analyzes slope stability data over time to predict the time and location of future failures, combining remote seismic data with the physics of granular failure.[16] The model can be used to predict failures in mines where precise measurements concerning the movement along rock faces.[16] It can also monitor rural areas where satellites collect radar data every few days or weeks, but its ultimate goal is improving early warning systems and mitigating the dangers of landslides in the context of climate change.[16]

On July 13, 2012, Tordesillas filed a patent application with Peter Joseph Scales, Anthony Dirk Stickland, Robin John Battheram, and the University of Melbourne for the comminution and/or removal of liquid from a material.[17] The World Intellectual Property Organization published that this patent covers a material processing method developed by the four scientists in which a material is fed between oppositely moving surfaces with the result of shearing the material parallel to its direction of flow between the two surfaces. [17]