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NASA NTRS2018

An Improved UAV-Based Fog Collector for Local-Scale Aerobiological Sampling

The use of unmanned air vehicles (UAVs) to passively collect fog for microbiological analysis is being investigated at NASA Ames, contributing to our understanding of the dynamics and limits of the biosphere. Current improvements to a preliminary payload design include physically modifying the mesh of the passive impactor collection unit (PICU) to increase fog-capture efficiency and a quick- mounting system to improve sample turnaround and contamination control. Increasing the water-capture efficiency of the PICU involved changing the surface roughness of the polypropylene mesh by abrasive sanding. Four scale models of the PICU were created, with three of those models sanded with 80-, 150-, and 240-grit sand paper for a minimum of thirty seconds on each side. Each mesh was individually tested in a fog-simulation chamber to measure collection efficiency. Tests showed that sanding the mesh will increase the water-capture efficiency by at least 3-fold for the 150- and 240-grit. Next steps are to determine the scaling laws and minimum amount of exposure time as a function of the fog density, to acquire enough microorganisms for analysis; a light-weight ground module for the PICU has been developed to allow rapid, easy field testing. Developing a mounting system that will ensure sterile deployment, as well as prompt changes of PICUs, for collection of fog water, utilized computational fluid dynamics (CFD) for the UAV to ensure that the location of the impactors will not be contaminated by particulates in the UAV downwash. The original design allowed the impactors to remain within sterile packaging until mounted on the UAV but did not address the concerns of contamination from aerodynamic ground effects and particulate lofting during takeoff. Design for a raised launch pad would allow for the sterile PICU to be attached to UAV and launched from outside the region of projected particulate lofting. California coastal sites have been scouted for appropriate testing locations. The improvements to the PICU design and operations will allow for multiple sampling points within a single fog event. The returned fog samples will undergo ion chromatography, qPCR, ATP quantification, and other tests to characterize microbial species and relevant biochemistry.

Wong, Amber↗

NASA NTRS2009

Findings from the Supersonic Qualification Program of the Mars Science Laboratory Parachute System

In 2012, the Mars Science Laboratory Mission (MSL) will deploy NASA's largest extra-terrestrial parachute, a technology integral to the safe landing of its advanced robotic explorer on the surface. The supersonic parachute system is a mortar deployed 21.5 m disk-gap-band (DGB) parachute, identical in geometric scaling to the Viking era DGB parachutes of the 1970's. The MSL parachute deployment conditions are Mach 2.3 at a dynamic pressure of 750 Pa. The Viking Balloon Launched Decelerator Test (BLDT) successfully demonstrated a maximum of 700 Pa at Mach 2.2 for a 16.1 m DGB parachute in its AV4 flight. All previous Mars deployments have derived their supersonic qualification from the Viking BLDT test series, preventing the need for full scale high altitude supersonic testing. The qualification programs for Mars Pathfinder, Mars Exploration Rover, and Phoenix Scout Missions were all limited to subsonic structural qualification, with supersonic performance and survivability bounded by the BLDT qualification. The MSL parachute, at the edge of the supersonic heritage deployment space and 33% larger than the Viking parachute, accepts a certain degree of risk without addressing the supersonic environment in which it will deploy. In addition, MSL will spend up to 10 seconds above Mach 1.5, an aerodynamic regime that is associated with a known parachute instability characterized by significant canopy projected area fluctuation and dynamic drag variation. This aerodynamic instability, referred to as "area oscillations" by the parachute community has drag performance, inflation stability, and structural implications, introducing risk to mission success if not quantified for the MSL parachute system. To minimize this risk and as an alternative to a prohibitively expensive high altitude test program, a multi-phase qualification program using computation simulation validated by subscale test was developed and implemented for MSL. The first phase consisted of 2% of fullscale supersonic wind tunnel testing of a rigid DGB parachute with entry-vehicle to validate two high fidelity computational fluid dynamics (CFD) tools. The computer codes utilized Large Eddy Simulation and Detached Eddy Simulation numerical approaches to accurately capture the turbulent wake of the entry vehicle and its coupling to the parachute bow-shock. The second phase was the development of fluid structure interaction (FSI) computational tools to predict parachute response to the supersonic flow field. The FSI development included the integration of the CFD from the first phase with a finite element structural model of the parachute membrane and cable elements. In this phase, a 4% of full-scale supersonic flexible parachute test program was conducted to provide validation data to the FSI code and an empirical dataset of the MSL parachute in a flight-like environment. The final phase is FSI simulations of the full-scale MSL parachute in a Mars type deployment. Findings from this program will be presented in terms of code development and validation, empirical findings from the supersonic testing, and drag performance during supersonic operation.

EDL↗

DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗