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From Apollo to Artemis: How Processing ANGSA Core Samples 73001/2 Can Help to Prepare for Future Sample Return Missions to the Moon and Beyond.

Introduction: Apollo Sample 73001/2 is a ~71cm long double drive tube consisting of an upper part (73002) and a lower part (73001) that contains regolith collected near Lara Crater at the Apollo 17 site, Station 3. The double drive tube is believed to have penetrated a lunar landslide deposit that was transported from the slope of the South Massif into the Taurus-Littrow Valley [1]. As part of the ANGSA (Apollo Next Generation Sample Analyses) initiative, preparing a preliminary examination (PE) catalog of 73001/2 is a crucial first step for the early identification of material types such as rock fragments and potential stratigraphy within the core. Many new curation and scientific tools such as X-ray computed tomography (XCT) [3], multi-spectral imaging [4], and gas extraction manifold with piercing tool [5-7], have been applied to the ANGSA core to benefit curation strategy, PE efforts, sample allocation to the planetary science community, and ultimately help to prepare for future sample return missions like Artemis. 73001/2 Preliminary Examination and Processing: Sample 73002 was successfully opened and extruded in Nov. 2019 and fully dissected at the end of 2021. Sample 73001 (Fig. 1) was successfully extruded in March 2022 after careful planning before opening the Core Sample Vacuum Container (CSVC) that was holding the drive tube of 73001. XCT, as part of PE, was used to scan the bottom and top part of the 73001 core tube within the CSVC prior to opening it to 1) facilitate non-destructive, rapid detection of any contamination potentials due to piercing of the CSVC during gas extraction [7]; and 2) to aid in the Artemis sample tool development and provide data on the knife edge seal of the CSVC. This knowledge will help us connect the mechanics of the implemented design (i.e., XCT data) to the performance of the seal (i.e., data on the gas samples will tell us how well the seal preserved the volatile record of lunar samples). Both type of information will feed forward into Artemis tool and storage strategies for future samples. Results and Lessons learned: The XCT data of the CSVC and core tube within showed that the bottom Teflon cap was not pierced during gas extraction (Fig. 1c) and thus, the sample integrity remained guaranteed during piercing and subsequent gas extraction. However, the XCT scan of the top of the core (Fig. 1b) revealed that the drive tube was overfilled with lunar soil and the tool that keeps the soil constrained within the drive tube was not fully deployed. These preliminary data allowed us to implement the necessary steps to prevent loss of sample integrity, including any potential stratigraphy shifts during extrusion. Processing Apollo core 73001/2, creating an informative PE catalog, and applying new and refined tools and technologies for sample analyses are invaluable activities that will assist in circumventing any potential pitfalls, aid in the characterization of samples, and help in the assessment of how well any lunar material has been collected and preserved in the past. This will aid in designing future sample collections and curation procedures and help to prepare for future human exploration and sampling missions such as Artemis. References: [1] Schmitt H. (2017) Icarus 298, 2-33. [3] Zeigler et al. (2021) LPSC 52nd, #2632; [4] Sun et al. (2021), LPSC 52nd, #1789; [5] Parai et al. (2021), LPSC 52nd #2665; [6] Schild et al. (2021) LPSC 52nd #1888; [7] McDonald (2022) ESL 2022.

ANGSA↗

Program ELM: A tool for rapid thermal-hydraulic analysis of solid-core nuclear rocket fuel elements

This report reviews the state of the art of thermal-hydraulic analysis codes and presents a new code, Program ELM, for analysis of fuel elements. ELM is a concise computational tool for modeling the steady-state thermal-hydraulics of propellant flow through fuel element coolant channels in a nuclear thermal rocket reactor with axial coolant passages. The program was developed as a tool to swiftly evaluate various heat transfer coefficient and friction factor correlations generated for turbulent pipe flow with heat addition which have been used in previous programs. Thus, a consistent comparison of these correlations was performed, as well as a comparison with data from the NRX reactor experiments from the Nuclear Engine for Rocket Vehicle Applications (NERVA) project. This report describes the ELM Program algorithm, input/output, and validation efforts and provides a listing of the code.

Walton, James T.↗

Spot and Runway Departure Advisor (SARDA)

Spot and Runway Departure Advisor (SARDA) is a decision support tool to assist airline ramp controllers and ATC tower controllers to manage traffic on the airport surface to significantly improve efficiency and predictability in surface operations. The core function of the tool is the runway scheduler which generates an optimal solution for runway sequence and schedule of departure aircraft, which would minimize system delay and maximize runway throughput. The presentation also discusses the latest status of NASA's current surface research through a collaboration with an airline partner, where a tool is developed for airline ramp operators to assist departure pushback operations. The presentation describes the concept of the SARDA tool and results from human-in-the-loop simulations conducted in 2012 for Dallas-Ft. Worth International Airport and 2014 for Charlotte airport ramp tower.

surface management↗

Overview: Spot and Runway Departure Advisor (SARDA)

Spot and Runway Departure Advisor (SARDA) is a decision support tool to assist airline ramp controllers and ATC tower controllers to manage traffic on the airport surface to significantly improve efficiency and predictability in surface operations. The core function of the tool is the runway scheduler which generates an optimal solution for runway sequence and schedule of departure aircraft, which would minimize system delay and maximize runway throughput. The presentation describes the concept of the SARDA tool and results from human-in-the-loop simulations conducted in 2012 for DallasFt. Worth International Airport. The presentation also explains the latest status of NASA's current surface research through a collaboration with an airline partner. The presentation concludes with a discussion on other on-going as well as future surface research.

decision support tool↗

Spot and Departure Runway Advisor (SARDA)

Spot and Runway Departure Advisor (SARDA) is a decision support tool to assist airline ramp controllers and ATC tower controllers to manage traffic on the airport surface to significantly improve efficiency and predictability in surface operations. The core function of the tool is the runway scheduler which generates an optimal solution for runway sequence and schedule of departure aircraft, which would minimize system delay and maximize runway throughput. The presentation describes the concept of the SARDA tool and results from human-in-the-loop simulations conducted in 2012 for Dallas-Ft. Worth International Airport. The presentation also discusses the latest status of NASA's current surface research through a collaboration with an airline partner, where a tool is developed for airline ramp operators to assist departure pushback operations.

Departure Schedule↗

Development of Monte Carlo Capability for Orion Parachute Simulations

Parachute test programs employ Monte Carlo simulation techniques to plan testing and make critical decisions related to parachute loads, rate-of-descent, or other parameters. This paper describes the development and use of a MATLAB-based Monte Carlo tool for three parachute drop test simulations currently used by NASA. The Decelerator System Simulation (DSS) is a legacy 6 Degree-of-Freedom (DOF) simulation used to predict parachute loads and descent trajectories. The Decelerator System Simulation Application (DSSA) is a 6-DOF simulation that is well suited for modeling aircraft extraction and descent of pallet-like test vehicles. The Drop Test Vehicle Simulation (DTVSim) is a 2-DOF trajectory simulation that is convenient for quick turn-around analysis tasks. These three tools have significantly different software architectures and do not share common input files or output data structures. Separate Monte Carlo tools were initially developed for each simulation. A recently-developed simulation output structure enables the use of the more sophisticated DSSA Monte Carlo tool with any of the core-simulations. The task of configuring the inputs for the nominal simulation is left to the existing tools. Once the nominal simulation is configured, the Monte Carlo tool perturbs the input set according to dispersion rules created by the analyst. These rules define the statistical distribution and parameters to be applied to each simulation input. Individual dispersed parameters are combined to create a dispersed set of simulation inputs. The Monte Carlo tool repeatedly executes the core-simulation with the dispersed inputs and stores the results for analysis. The analyst may define conditions on one or more output parameters at which to collect data slices. The tool provides a versatile interface for reviewing output of large Monte Carlo data sets while preserving the capability for detailed examination of individual dispersed trajectories. The Monte Carlo tool described in this paper has proven useful in planning several Crew Exploration Vehicle parachute tests.

Moore, James W.↗

Vacuum Sealable Container (VSC) and Astronaut Lunar Drill (ALD) for Artemis

Introduction: NASA’s Artemis Program is under development to send first woman and next man to the Moon. Artemis will utilize a suite of new technology for Lunar exploration, including new space vehicles, new space suits, and new Astronaut Tools. Honeybee Robotics has been working with NASA JSC to develop a new Vacuum Sealable Container (VSC) and new Astronaut Lunar Drill (ALD) for the upcoming Artemis missions. Vacuum Sealable Container: Sample return continues to be the “Holy Grail” of space exploration, allowing for the analysis of materials using Earth-based laboratories instead of needing to miniaturize and ruggedize instrumentation for space. The Apollo missions to the Moon had several kinds of Sealable Containers which brought back Lunar samples for analysis [1]. These samples are still being analyzed, fifty years later. The VSC requirements are different from that for Apollo containers and as such, new development was required. One major difference between Artemis samples and those from Apollo is the desire to bring back volatiles which may be part of lunar regolith. The VSC is designed to withstand a high-pressure differential caused by sublimating volatiles. Because of the new, stricter sealing requirements, additional features have been added to the VSC. For example, the seal on the container is required to be more robust, thus required more force to actuate, and the seal must be locked in place with a secondary mechanism. Astronaut Lunar Drill: The ALD is designed to be a multi-functional platform for Lunar sample acquisition. The drill builds on lessons learned from the Apollo Lunar Surface Drill (ALSD), as well as Honeybee’s long history of mechanized sample acquisition devices for space [2]. The main functionality of the ALD is Deep Core Regolith Drilling. Additional functionality includes Surface Rock Coring (SRC), and GeoTech Tools (GTT). The ALD is a rotary-percussive drill designed with deep drilling in mind. The ALD is currently designed to have decoupled rotary and percussion subsystems to allow for maximum battery life and reduced fatigue on the crewmember. Honeybee drill technology will automatically engage the percussion when needed to drill at maximum efficiency. The mechanized drill stand helps improve drilling efficiency; the system utilizes advanced drilling algorithms which only require the crewmember to hold a single switch. Additionally, the stand aids in extraction of deep cores, something which was a problem on Apollo. The SRC functionality of the ALD utilizes Honeybee’s Eccentric Tube Core Breakoff technology to collect and retain rock core samples. This technology has also been infused into the Perseverance rover mission. The ALD is removable from the stand to allow crewmembers to collect samples from large boulders. Bringing back rock cores samples instead of full rocks allows for a wider variety of samples to be returned to Earth for study and puts them in a uniform form-factor for effective sealing and analysis. SRC bits will utilize the power of the drill’s percussion system to drill hard Lunar rocks and expedite sample acquisition. The mechanized stand on the ALD allows for additional attachments for taking geotechnical measurements with a Static Cone Penetrometer (SCP) and a Shear Vane (SV). With the stand, the ALD can take SCP measurements with the touch of a button, storing data for return to Earth. SV measurements utilize the ALD’s Rotary motor to spin the vanes in a controlled manner, getting clean data untampered by human error. References: [1] Bar Cohen and Zacny (2009), Drilling in Extreme Environments - Penetration and Sampling on Earth and Other Planets, Wiley. [2] Bar-Cohen and Zacny, Advances in Terrestrial and Extraterrestrial Drilling, CRC Press. [3] Myrick (2003), Core Break-off Mechanism. US Patent No. 6,550,549 Acknowledgements: This work has been supported by NASA via SBIR Phase 3.

Artemis↗

Advanced composites: Fabrication processes for selected resin matrix materials

This design note is based on present state of the art for epoxy and polyimide matrix composite fabrication technology. Boron/epoxy and polyimide and graphite/epoxy and polyimide structural parts can be successfully fabricated. Fabrication cycles for polyimide matrix composites have been shortened to near epoxy cycle times. Nondestructive testing has proven useful in detecting defects and anomalies in composite structure elements. Fabrication methods and tooling materials are discussed along with the advantages and disadvantages of different tooling materials. Types of honeycomb core, material costs and fabrication methods are shown in table form for comparison. Fabrication limits based on tooling size, pressure capabilities and various machining operations are also discussed.

Welhart, E. K.↗

CFD Multiphysics Tool

The recent bold initiatives to expand the human presence in space require innovative approaches to the design of propulsion systems whose underlying technology is not yet mature. The space propulsion community has identified a number of candidate concepts. A short list includes solar sails, high-energy-density chemical propellants, electric and electromagnetic accelerators, solar-thermal and nuclear-thermal expanders. For each of these, the underlying physics are relatively well understood. One could easily cite authoritative texts, addressing both the governing equations, and practical solution methods for, e.g. electromagnetic fields, heat transfer, radiation, thermophysics, structural dynamics, particulate kinematics, nuclear energy, power conversion, and fluid dynamics. One could also easily cite scholarly works in which complete equation sets for any one of these physical processes have been accurately solved relative to complex engineered systems. The Advanced Concepts and Analysis Office (ACAO), Space Transportation Directorate, NASA Marshall Space Flight Center, has recently released the first alpha version of a set of computer utilities for performing the applicable physical analyses relative to candidate deep-space propulsion systems such as those listed above. PARSEC, Preliminary Analysis of Revolutionary in-Space Engineering Concepts, enables rapid iterative calculations using several physics tools developed in-house. A complete cycle of the entire tool set takes about twenty minutes. PARSEC is a level-zero/level-one design tool. For PARSEC s proof-of-concept, and preliminary design decision-making, assumptions that significantly simplify the governing equation sets are necessary. To proceed to level-two, one wishes to retain modeling of the underlying physics as close as practical to known applicable first principles. This report describes results of collaboration between ACAO, and Embry-Riddle Aeronautical University (ERAU), to begin building a set of level-two design tools for PARSEC. The "CFD Multiphysics Tool" will be the propulsive element of the tool set. The name acknowledges that space propulsion performance assessment is primarily a fluid mechanics problem. At the core of the CFD Multiphysics Tool is an open-source CFD code, HYP, under development at ERAU. ERAU is renowned for its undergraduate degree program in Aerospace Engineering the largest in the nation. The strength of the program is its applications-oriented curriculum, which culminates in one of three two-course Engineering Design sequences: Aerospace Propulsion, Spacecraft, or Aircraft. This same philosophy applies to the HYP Project, albeit with fluid physics modeling commensurate with graduate research. HYP s purpose, like the Multiphysics Tool s, is to enable calculations of real (three-dimensional; geometrically complex; intended for hardware development) applications of high speed and propulsive fluid flows.

Perrell, Eric R.↗

No-Oven, No-Autoclave Composite Processing

Very large composite structures, such as those used in NASA's Space Launch System, push the boundaries imposed by current autoclaves. New technology is needed to maintain composite performance and free manufacturing engineers from the restraints of curing equipment size limitations. Recent efforts on a Phase II project by Cornerstone Research Group, Inc. (CRG), have advanced the technology and manufacturing readiness levels of a unique two-part epoxy resin system. Designed for room-temperature infusion of a dry carbon preform, the system includes a no-heat-added cure that delivers 350 F composite performance in a matter of hours. This no-oven, no-autoclave (NONA) composite processing eliminates part-size constraints imposed by infrastructure and lowers costs by increasing throughput and reducing capital-specific, process-flow bottlenecks. As a result of the Phase II activity, NONA materials and processes were used to make high-temperature composite tooling suitable for further production of carbon-epoxy laminates and honeycomb/ sandwich-structure composites with an aluminum core. The technology platform involves tooling design, resin infusion processing, composite part design, and resin chemistry. The various technology elements are combined to achieve a fully cured part. The individual elements are not unusual, but they are combined in such a way that enables proper management of the heat generated by the epoxy resin during cure. The result is a self-cured carbon/ epoxy composite part that is mechanically and chemically stable at temperatures up to 350 F. As a result of the successful SBIR effort, CRG has launched NONA Composites as a spinoff subsidiary. The company sells resin to end users, fabricates finished goods for customers, and sells composite tooling made with NONA materials and processes to composite manufacturers.

Rauscher, Michael D.↗

Long range inhabited surface transportation system power source for the exploration of Mars (manned Mars mission)

A hydrogen-oxygen fuel cell system is identified as a viable power source for a long range inhabited surface transportation system for the exploration of Mars. Power system weights and power requirements are determined as a function of vehicle weight. For vehicles weighing from 2700 to 7300 kg in LEO, the total power system weight ranges from 1140 to 1860 kg, with the reactants and energy conversion hardware (fuel cells, reactant storage, and radiator) weighing 430 to 555 kg and 610 to 1110 kg, respectively. Vehicle power requirements range from 45 kW for a 2700 kg vehicle to 110 kW for a 7300 kg vehicle. Power system specific weights and power profiles for housekeeping and the operation of scientific equipment such as coring drills and power tools are also specified.

Lisa Kohout↗

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and automation, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Tong, Michael T.↗

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and data science, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Core Size↗

The SunPy Project: An Interoperable Ecosystem for Solar Data Analysis

The SunPy Project is a community of scientists and software developers creating an ecosystem of Python packages for solar physics. The project includes the sunpy core package as well as a set of affiliated packages. The sunpy core package provides general purpose tools to access data from different providers, read image and time series data, and transform between commonly used coordinate systems. Affiliated packages perform more specialized tasks that do not fall within the more general scope of the sunpy core package. In this article, we give a high-level overview of the SunPy Project, how it is broader than the sunpy core package, and how the project curates and fosters the affiliated package system. We demonstrate how components of the SunPy ecosystem, including sunpy and several affiliated packages, work together to enable multi-instrument data analysis workflows. We also describe members of the SunPy Project and how the project interacts with the wider solar physics and scientific Python communities. Finally, we discuss the future direction and priorities of the SunPy Project.

Solar physics↗

In Vitro Selections with RNAs of Variable Length Converge on a Robust Catalytic Core

In vitro selection is a powerful tool that can be used to understand basic principles of molecular evolution. We used in vitro selection to understand how changes in length and the accumulation of point mutations enable the evolution of functional RNAs. Using RNA populations of various lengths, we performed a series of in vitro experiments to select for ribozymes with RNA ligase activity. We identified a core ribozyme structure that was robust to changes in RNA length, high levels of mutagenesis, and increased selection pressure. Elaboration on this core structure resulted in improved activity which we show is consistent with a larger trend among functional RNAs in which increasing motif size can lead to an exponential improvement in fitness. We conclude that elaboration on conserved core structures is a preferred mechanism in RNA evolution. This conclusion, drawn from selections of RNAs from random sequences, is consistent with proposed evolutionary histories of specific biological RNAs. More generally, our results indicate that modern RNA structures can be used to infer ancestral structures. Our observations also suggest a mechanism by which structural outcomes of early RNA evolution would be largely reproducible even though RNA fitness landscapes consist of disconnected clusters of functional sequences.

In vitro evolution↗

Techniques and Tools for Estimating Ionospheric Effects in Interferometric and Polarimetric SAR Data

The InSAR Scientific Computing Environment (ISCE) is a flexible, extensible software tool designed for the end-to-end processing and analysis of synthetic aperture radar data. ISCE inherits the core of the ROI_PAC interferometric tool, but contains improvements at all levels of the radar processing chain, including a modular and extensible architecture, new focusing approach, better geocoding of the data, handling of multi-polarization data, radiometric calibration, and estimation and correction of ionospheric effects. In this paper we describe the characteristics of ISCE with emphasis on the ionospheric modules. To detect ionospheric anomalies, ISCE implements the Faraday rotation method using quadpolarimetric images, and the split-spectrum technique using interferometric single-, dual- and quad-polarimetric images. The ability to generate co-registered time series of quad-polarimetric images makes ISCE also an ideal tool to be used for polarimetric-interferometric radar applications.

Ionosphere↗

Development of an Inertial Sensor-based Methodology for Spacesuited Geology Task Assessments during Simulated Lunar Extravehicular Activities

Lunar surface exploration during Artemis missions will require the specific skill set of geology sampling. Apollo astronauts had extensive training and used specialized tools to collect lunar rocks, core samples, pebbles, sand, and dust. The inflexibility of the pressurized Apollo spacesuits forced sampling to be taken at a standstill posture. However, new exploration spacesuits are expected to incorporate advanced materials and joint bearings, allowing for greater mobility and a wider range of functional postures. Thus, science and exploration during Artemis missions will likely involve a variety of standing, squatting, and kneeling postures. In preparation for future lunar exploration missions, NASA provides geologic training to astronauts and other mission personnel. This professional training with a spacesuit in simulated lunar environments will enhance performance and reduce risk of injury to astronauts on the lunar surface. However, anecdotally, untrained or newly trained people wearing prototype planetary spacesuits have been observed to performing motions differently than a trained geologist would when conducting the same geology sampling tasks. Therefore, a tool for evaluating geology postures at extravehicular activity (EVA) training facilities becomes required. In this paper, we introduce a novel inertial measurement unit (IMU)-based method of geology task assessments in spacesuited conditions during simulated lunar EVAs. As a case study, two subjects (one geologist and one non-geologist) participated and donned the Mark III prototype planetary spacesuit during offloading with the spreader bar gimbal in NASA’s Active Response Gravity Offload System (ARGOS). For automated geology task assessments, the spacesuit was instrumented with three wireless IMUs (APDM Opal, OR, USA): one on the chest and one each on the left and right ankle bearings. Then subjects performed geology tasks using various tools (rake, trench, hammer chisel, scoop, and drive tube) for 45 minutes each. The chest IMU measured the torso tilt angle in the sagittal plane. We used an ensemble learning method with the ankle IMUs to discriminate between standing and kneeling activities. IMU data were processed using custom MATLAB (Mathworks, MA, USA) software. In our case study, the developed method was able to discriminate differences in standing and kneeling activity levels between subjects who were all highly experienced with spacesuited testing. Our preliminary data showed one subject maintained the constant and lower range of the upper body tilt angle while both standing and kneeling, while the other subject showed more variation of the upper body tilt angle and preferred bending the upper body rather than changing from standing to kneeling posture and vice versa. While geology experience may be a factor, these results need further investigation as suit sizing and ARGOS offloading configurations have been proven to have a significant influence on suited ARGOS tasks. Also, more subjects will be needed to complete these tasks for validation. IMU-based geology task assessments can provide useful information for geology training programs. Additionally, our IMU-based posture analysis can provide new insights into how to evaluate spacesuited geology task characteristics of astronauts during simulated lunar EVAs.

Kyoung Jae Kim↗