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NASA NTRS2022

Epoxy Resins with Reduced Viscoelastic Relaxation

The payloads of current space launch systems, e.g. rockets, are constrained to a small size due to aerodynamic, weight, and cost limitations. Deployable structures allow for a folded or stowed configuration during launch and deployment at the mission location. Polymer composites have shown both high weight savings over current metallic structure, as well as improved specific mechanical strengths overall. One issue encountered with polymer composite deployables, however, is stress relaxation of the polymer matrix in the stowed configuration. In this study, a range of different epoxy formulations were evaluated as potential matrix resins for deployable composites. A new multifunctional epoxy resin with a fortifying additive is predicted to show 70% less stress relaxation after 1 year as compared to a state-of-the-art aerospace epoxy matrix.

Ray S. Peterson↗

NASA NTRSDate not supplied

Pterodactyl: Effects of 3D Thermal Analysis on Thermal Protection System Design for a Flap Control System

NASA’s Pterodactyl project has investigated the use of a novel multi-flap control system to facilitate precision targeting of Deployable Entry Vehicles (DEVs) during atmospheric re-entry [1]. DEVs can be folded to fit within the limiting cross-sectional area of current launch systems. Once deployed, the vehicle expands and settles into a pre-determined blunt body shape. While DEVs provide a more efficient solution to increased payload sizes, the absence of a back shell does not allow for easy integration of reaction control systems, which have historically been used for guidance and control (G&C) of rigid aeroshells during entry. One DEV solution, called the Adaptable, Deployable Entry Placement Technology (ADEPT), employs mechanically deployed gores. This provided the Pterodactyl project with the opportunity to incorporate a rib-mounted, 8-flap control system for G&C. Each flap can deflect independently in and out of the hypersonic flow, requiring TPS for the flaps. Previous work within the Pterodactyl project utilized 1D thermal analysis to design the TPS. However, the Pterodactyl project was concerned that the base level tools for 1D thermal analysis were not accounting for the 3D effects of the large heating gradients along and across the flaps, the in-depth in-plane conductivity of the TPS and internal structure, and the effects of the small edge radii at the neck of the flaps. This paper discusses the methodology and results of a 3D thermal analysis of the flap control system. Additionally, the results from the 1D and 3D thermal analyses are compared. It is found that elements of the original design that resulted from the 1D analysis may be overly conservative, and implementation of a 3D thermal analysis indicated a reduction in TPS thickness is feasible. This result helps to reduce the mechanical integration complexity of the flap at the rib tip and realizes potential mass savings.

Sarah N D'Souza↗

NASA NTRSDate not supplied

Application and Use of Lunar Node-Derived Beacons for Lunar Surface Navigation

To maximize the scientific return and safety of operations on the lunar surface, multiple civil organizations are investing in Position, Navigation, and Timing infrastructure. This approach mimics the deployment of Global Navigation Satellite Systems around the Earth and aims to enable a similar robust capability around the moon. With these satellites, it will be possible to maintain high- fidelity knowledge of positioning and timing both on the surface and in orbit. For initial deployments, this capability is focused on high need areas, such as the Lunar South Pole, the target of the currently in-planning Artemis surface missions to high accuracy. Similar to GNSS systems, this capability will be implemented over time to build up to a level of global access for real-time navigation. For early missions, this means a limited capability in terms of coverage. To provide increased performance, ground augmentation can be leveraged. This not only provides an additional reference signal but helps to supply timing to enable a high accuracy real-time position solutions. This paper discusses the path towards evolving the Lunar Node 1 platform to augment these early constellation deployments. Analysis of notional performance with and without surface aids is provided, as well as discussion and paths towards deployment and operation. Given the analysis results, the benefit of surface navigation aids to both provide additional surface-based signals to fill in coverage gaps helps to provide additional robustness and an early-on capability.

Evan J. Anzalone↗

NASA NTRSDate not supplied

TechEdSat 7, 10, 13, 15: Exo-brake Experiments on the ISS, First Virgin Orbit, and First Firefly-Alpha Test Flights.

The TechEdSat flight series (TES-n), developed by the Nano Orbital Workshop (NOW) group at NASA Ames, has been studying cube satellite re-entry technologies with a focus on Exo-Brake drag device research. An exo-atmospheric braking device, the Exo-Brake uses the tenuous upper atmosphere acting in a free molecular flow regime to enable controllable adjustment of the drag profile of the host spacecraft, currently enabling rapid disposal of small spacecraft after mission conclusion, and eventual targeted de-orbit to a desired entry point at the Von Karman line, or approximately 100km in altitude. The TES-7 mission, flown on the first successful Virgin Orbit flight January 17, 2021, was injected into a 500km, 61-degree inclination orbit, with an expected orbital lifetime of approximately ten years. After successful deployment of a unique hydrogen gas-cell inflation design ‘disposal’-type Mylar Exo-Brake intended to rapidly de-orbit the spacecraft, the maximum expected orbital lifetime of the TES-7 spacecraft was successfully reduced from ten years to 1.3 years, an 87% reduction in orbit lifetime via a passive, fuel-less system. The subsequent TES-13 mission utilized a new, less complex ‘disposal’-type Exo-Brake design made from collapsible rigid struts rather than inflated struts to reduce the engineering, construction, and safety complexities introduced by the prior hydrogen gas inflation design. The rigid strut Exo-Brake design is stowed via rotational compression and deploys using only stored spring energy once released via electronic actuator. This new rotation compression storage design has become the new Exo-Brake design standard used on TES missions thanks to its simplicity. Following on the success of this new Exo-Brake design demonstrated by TES-13, the design was further refined to enabled active manipulation of the Exo-Brake effective drag area though a winch-like system. Both the TES-10 and TES-15 spacecraft were equipped with geometrically similar Exo-Brake devices capable of such active drag modulation, with the intent of studying the operational impact of having such devices on cube satellite missions. To increase the survival time of the Exo-Brake at low altitudes and thus gain more experiment time and better guidance capability, TES-15 was equipped with a modulated Exo-Brake constructed from 3M™ Nextel™ 440 ceramic oxide fiber, the same fabric material used in the Space Shuttle TPS system, rather than Mylar. After launching on Alpha Flight 2 October 1st, 2022, Firefly’s first successful launch, TES-15 met the same modulation restrictions as TES-10, with modulation prohibited above 200km to avoid possible collision with other spacecraft. In this case, a low deployment orbit caused an extremely short mission life, which, coupled with delayed spacecraft identification, exacerbated collision concerns and caused performance data of the high-temperature Exo-Brake design to be inconclusive. As such, the Nano Orbital Workshop has taken a new approach of only conducting modulated Exo-Brake research on missions deploying below ISS altitude to avoid collision avoidance restrictions, and is working to improve spacecraft identification and location reporting techniques to increase experiment durations. Upcoming TES mission will therefore use one of two classes of Exo-Brake depending on their target altitude, as to be described in the submission.

M Murbach↗

NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗

NASA NTRS2022

Formal Methods for Trusted Space Autonomy, Boon or Bane?

Trusted Space Autonomy is challenging in that space systems are complex artifacts deployed in a high stakes environment with complicated operational settings. Thus far these challenges have been met using the full arsenal of tools: formal methods, informal methods, testing, runtime techniques, and operations processes. Using examples from previous deployments of autonomy to the Remote Agent on DS-1, Autonomous Sciencecraft on EO-1, WATCH on MER, IPEX, AEGIS on MER, MSL, and M2020, and the M2020 Onboard planner, we discuss how each of these approaches have been used to enable successful deployment of autonomy. We next focus on relatively limited use of formal methods (both prior to deployment and runtime methods). From the needs perspective, formal methods represent the best chance for reliable autonomy as testing, informal methods, and operations accommodations do not scale well with increasing complexity of the autonomous system. However from the practice perspective, formal methods have been limited in their application due to difficulty in eliciting formal specifications and challenges in representing complex constraints such as metric time and resources. We discuss some of these challenges as well as the opportunity to extend formal and informal methods into runtime validation systems.

Chien, Steve↗

NASA NTRSDate not supplied

TechEdSat 7, 10, 13, 15: Exo-brake Experiments on the ISS, First Virgin Orbit, and First Firefly-Alpha Test Flights

Marcus Murbach↗

NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of trained machine-learning models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a Windows app that has been created to deploy trained machine-learning models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of machine-learning application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). Current version of the app focuses on the performance prediction of conventional turbofans. The app gets user input for a turbofan design, preprocesses the input data, and deploys trained machine-learning models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The machine-learning predictive models were built by employing supervised deep-learning algorithm to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these machine-learning models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.

machine learning↗

NASA NTRS2023

Sea Ice Drift Tracks From Autonomous Buoys in the MOSAiC Distributed Network

A network of autonomous, ice-tethered buoys was deployed around the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) experiment in late September 2019 for a year-long drift in the Arctic Transpolar Drift Stream. The buoys were deployed as part of the MOSAiC distributed network (DN) which included 12 multi-instrumented ice stations and an additional 116 GPS buoys distributed primarily within a 40 km radius of the MOSAiC Central Observatory. Buoy coverage within the DN was maintained with additional deployments throughout the year-long drift allowing for collection of data over a full sea ice growth and melt cycle. All GPS position data from buoys deployed within the DN have been assembled and processed into the collection of 216 quality-controlled buoy drift tracks presented in this dataset covering the period 26 September 2019 – 23 May 2021. The drift tracks in this collection are ideal for studies of dynamic sea ice motion around the MOSAiC experiment at cascading spatial scales ranging from 100s of meters to 100s of km.

Angela C. Bliss↗

NASA NTRSDate not supplied

Springtime SEA Air Is Consistently Both Smoky and Humid, but This Relationship (and Its Radiative Effects) Varies Spatially and Seasonally

The atmosphere over the southeast Atlantic Ocean (SEA) sees a consistent springtime biomass burning (BB) smoke from widespread agricultural fires on the African continent. This smoke layer is initially lofted high in a continental mixed layer (~5-6km) and is then transported westward in the free troposphere, where it overlies and ultimately mixes into the SEA stratocumulus-topped oceanic boundary layer. Coincident with this smoke is an elevated humidity signal which is present from the time a given airmass is over the continental source region. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) was a NASA Earth Venture Suborbital mission with the goal of measuring aerosol, cloud, and atmospheric properties over this region during three deployments in September 2016, August 2017, and October 2018. A previous study discussed the spatial characteristics of the water vapor-BB plume observations in the September 2016 flight data and their degree of agreement with several atmospheric reanalyses and models. In the present work, we first discuss atmospheric chemistry, structure, and aerosol results from all three ORACLES deployments to place these results in a broader seasonal context. Despite the differing locations and season of each deployment, we show that there continues to be good agreement between the airborne ORACLES dataset and large-scale reanalyses, specifically the ECMWF ERA5 and CAMS reanalyses. Other reanalyses (specifically NASA's MERRA-2) preserved the observed correlation between meteorology and biomass burning conditions, but, as was seen for 2016, was frequently displaced spatially relative to the observations. The CAMS reanalysis performs better with water vapor than with CO. Results comparing CAMS column AOD to the field measurements show slight overestimates, consistent with previous analysis, but the good relationship observed between AOD and inlet-based aerosol extinction demonstrates this metric's utility to broader studies. The good ERA5/CAMS/ORACLES agreement allows us to next examine the multi-year seasonal patterns and trends beyond the three ORACLES deployment periods. Looking at seven years of reanalysis data for the BB season, we find distinct variations between each month/deployment in terms of vertical smoke distribution and correlation to atmospheric specific humidity, due to changing conditions through the BB season. Using k-means clustering of these climatological reanalysis, we identify six canonical atmospheric profile types of varying total atmospheric humidity and vertical structure and describe their changing incidence spatially and throughout the season, and six analogous profile types for carbon monoxide, allowing us to characterize the atmospheric structure of both vapor and BB over time throughout the SEA region. This classification will allow for a more complete analysis of the broader radiative and dynamical effects of humid aerosols overlying stratocumulus clouds.

Kristina Marie Myers Pistone↗

NASA NTRSDate not supplied

Jettison and Disposal From Near Rectilinear Halo Orbits, Part 2: Applications

Objects deployed from a Near Rectilinear Halo Orbit (NRHO) experience simultaneous gravitational forces from the Moon, the Earth, and the Sun, and post-deployment behavior is complex. The current investigation applies the theories explored in Part 1 of the study to examine the dynamics of objects deployed from the Gateway NRHO to heliocentric space. Examples include jettison of cubesats and logistics modules as well as end-of-life options for the Gateway itself. Recontact risk with the Gateway as each object departs the lunar vicinity is explored, and both the immediate destination and the long-term fate of the deployed objects are assessed.

Diane C. Davis↗

NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗

NASA NTRSDate not supplied

Maryland Space Weather UnderGround (SWUG) Educational Outreach Program for Solar Eclipse Study

Space Weather UnderGround (SWUG) is an educational outreach program that provides hands-on experiences in Science, Technology, Engineering, and Mathematics (STEM) to high school and undergraduate students. It has three ultimate goals: 1. Educating the future STEM workforce. 2. Building a cost-effective, research-capable ground 3. magnetometer array across the United States. Collecting geomagnetic field data at high spatial resolution for heliophysics research. SWUG students build, test, and deploy the Simple Aurora Monitor (SAM)-III kit, purchased by Reeve Engineers. The SAM-III kit is a fluxgate magnetometer that measures geomagnetic field changes at a resolution of 1 nT/sec. Dr. Charles Smith initiated the SWUG program at the University of New Hampshire, and it has since expanded to Alaska and Maryland. This presentation introduces the Maryland SWUG activities. The MD-SWUG program was started in the Fall of 2022 with the goal of deploying magnetometers to the solar eclipse sites in 2023 and 2024. Two student-built magnetometers were deployed to the Southwest Research Institute and the Los Alamos National Laboratory for the 2023 annular solar eclipse, with plans to deploy at least four magnetometers to the 2024 total solar eclipse sites, including the University of Texas at Dallas. During a solar eclipse, reduced solar irradiance weakens ionospheric currents and reduces geomagnetic fields by up to 30 nT at the eclipse sites. A recent study suggested that solar eclipses impact geomagnetic fields not only along the eclipse path but also at their magnetic conjugate locations. The SWUG program will help us understand this electrodynamic coupling between the conjugate locations during a solar eclipse by providing better data coverage along the solar eclipse path.

Hyunju Connor↗

NASA NTRSDate not supplied

Mineralogical, Elemental, and Tomographic Reconnaissance Investigation for CLPS (METRIC): A Payload Designed for Exploration of Terrestrial Planetary Bodies

Geological materials (indeed, all solid objects) are characterized by their crystal structure, elemental composition, and morphology. The Mineralogical, Elemental, and Tomographic Reconnaissance Investigation for CLPS (METRIC) instrument suite quantifies all three. These measurements address fundamental science questions (e.g., the origin and evolution of planetary bodies) and support the human exploration of space (e.g., the characterization of regolith for ISRU and the constraint of its geotechnical properties). METRIC comprises an X-ray Diffraction/X-ray Fluorescence instrument (XRD: mineral structure and XRF: elemental composition), an X-ray micro-Computed Tomography instrument (XCT: 3D internal micromorphology), and a hyperspectral imaging infrared spectrometer (IRS) to provide local/regional mineralogic context for these measurements. METRIC XRD/F draws heritage from the highly successful Mars Science Laboratory CheMin instrument. The METRIC XRD/F employs two separate sample cells, one optimized for XRD and one for XRF, resulting in more rapid XRD analysis (tens of minutes vs. tens of hours for CheMin) and an orders-of-magnitude improvement in XRF detection. XCT has not been deployed in space, so the METRIC XCT represents a new capability for solar system exploration. The XCT uses the same basic high-TRL components as METRIC XRD/F, decreasing its development cost for flight. The METRIC IRS is a derivative of the NASA Earth Science Technology Office funded Hyperspectral Thermal Imager instrument and utilizes the NASA Technology Transfer Program to incorporate a commercial-of-the-shelf infrared camera ruggedized for space by NASA Marshall Space Flight Center. The IRS spectral range (8–14 µm) and resolution (10.8 cm -1 ) are tailored to quantify mineralogy in rocks using their characteristic Reststrahlen bands and to characterize mineralogy of soils using the position of the Christensen Feature. The METRIC payload is currently designed for deployment to the Moon on a Commercial Lunar Payload Services (CLPS) mission, where the XRD/F and XCT would be located on a lander and the IRS would be on deployed on a companion rover to evaluate the mineralogical diversity of the landing site. A pneumatic drill designed by Honeybee Robotics would excavate regolith up to 50 cm below the lander and deliver multiple aliquots of regolith to the XRD/F and XCT. The METRIC payload could also be deployed on a rover. In this case, a sample handling system on a robotic arm could scoop regolith and/or drill rocks and deliver powder to the XRD/F and XCT located in the rover’s interior. Alternatively, METRIC instruments could be used singly or in combination on human space missions. The XRD/F and XCT could be used to characterize samples in a rover or in a science laboratory within a habitat. These data could help astronauts identify resource-enriched rocks and regolith and triage geologic samples to return samples of high interest for analysis in terrestrial laboratories. The IRS could be attached to a human-navigated rover to collect mineralogical data along a traverse and identify high-priority science samples.

E. B. Rampe↗

NASA NTRSDate not supplied

Advanced Supersonic Parachute Inflation Research Experiment 2 (ASPIRE2) Flight Mechanics and Parachute Performance

The Advanced Supersonic Parachute Inflation Re-search Experiment-2 (ASPIRE2) program consists of a sounding rocket flight test to be launched at Wallops Flight Facility (WFF) in 2025. This effort seeks to qualify the supersonic parachute for the Sample Retrieval Lander (SRL) under the Mars Sample Return (MSR) program, building off the successes of its predecessor program, ASPIRE, which launched in both 2017 and 2018. The conops for the ASPIRE2 mission are shown in Figure 1 relative to its predecessor. While the ASPIRE pro-gram qualified a 21.5 m diameter disk-gap band parachute deployed at Mach 1.7, the SRL parachute will be increased to 24 m in diameter and target a Mach 2.1 parachute deployment condition, the largest diameter supersonic parachute deployed at the highest Mach number to date. These requirements are driven by the increased landing mass of the sample retrieval lander, which is roughly a 50% increase over that of the Mars2020 entry vehicle. These changes necessitate ASPIRE2 to certify the parachute performance under similar deployment conditions. This work will cover the 6-DoF para-chute model implementation and flight mechanics performance for the ASPIRE2 campaign and discuss how these results will impact the design of the test article. The ASPIRE2 trajectory is simulated using a multi-body flight dynamics tool, Program to Optimize Simulated Trajectories II (POST2). This work will summarize both the flight mechanics and parachute modeling (from payload separation until splashdown) and performance for this mission. Various design trades will be assessed such as para-chute tuning parameters as well as vehicle center of gravity location.

Evan Roelke↗

NASA NTRSDate not supplied

Uneven Inflation Load Share Trends in Clusters

The use of parachute clusters for payload recovery is still seeing widespread use ever since the early days of WWII. By involving the (near) simultaneous deployment of several smaller and identical canopies connected to the payload, cluster systems offer flexibility in tailoring to needed descent rates and load management, as well as providing robustness against individual canopy deployment or opening failure. Their downside, of course, resides in the possibility of differing inflation rates by each cluster member as caused by deployment variability, canopy-to-canopy interference, etc. Such variability leads to the lead-lag phenomenon, which causes uneven loading among the parachutes, often times leaving a single canopy to take up a significant portion of the system’s inflation loads. Herein we investigate how serious such an effect can be in terms of the number N of cluster members, underinflation drag of the lagging canopies, inflation swiftness of the leader in comparison to the laggards’, disreefing cutter activation staggering and pre-disreefing drag area. Two new metrics are used to highlight load share unevenness, namely, the peak and average leader canopy riser load in comparison to the leader’s drag during no-lead-lag; and leader peak load, as compared to total peak load. Results are calculated from data collected in NASA’s Orion/CPAS test program, as well as from simple algebraic expressions informing leader drag as sustained in different cluster systems (i.e., of different N) and varying leader inflation time relative to the laggards. Generally, using large-N cluster systems confers better load sharing among canopies. However, and in deployments where significant lead-lag occur, large-N systems may feature greater leader overload excursions, oftentimes in excess of 50% the no-lead-lag levels. These excursions are particularly made worse when cutter activation among the members are far from simultaneous and the laggards’ pre-disreefing drag area is small in comparison to the leader’s.

parachutes↗

NASA NTRSDate not supplied

Machine Learning Application in Aircraft Engine Conceptual Design

In the current competitive environment, the successful creation and application of machine learning (ML) technologies have become crucial across multiple industries. This study outlines the process of creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models use supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. Key focus areas include crucial engine parameters such as thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While developing ML models is fundamental, ensuring their seamless deployment is equally important. To address this, a conversational AI chatbot is constructed using natural language processing (NLP) techniques to facilitate the deployment of these ML models. The comprehensive workflow includes several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine conceptual design.

Aircraft Engine↗

NASA NTRSDate not supplied

Experimental Characterization of Additively Manufactured Nickel-Titanium Shape Memory Alloy Heat Pipes

Shape memory alloys (SMA) have been identified for use in spacecraft components as replacement for conventional deployment mechanisms. They may be used in thermal management components such as radiators to create self-deploying radiators. One SMA, NiTi, has also been developed for additive manufacturing processes. Heat pipes are a common way to create highly effective and lightweight spaceflight radiators, and heat pipes can also be made from NiTi and related alloys. The wick is the critical element of a functioning heat pipe, and recent progress over the past years has led to the development of additively manufactured heat pipe wicks in various materials. The combination of these efforts is the focus of this project: creating an additively manufactured, shape memory alloy self-deploying heat pipe radiator. This paper will focus on the experimental characterization of these additively manufactured NiTi heat pipes. The heat pipe coupons were additively manufactured by direct metal laser sintering (DMLS), with an integral liquid cooled condenser. Heat is input to the heat pipe via a thin film heater. Thermocouples were spot welded to the heat pipes to measure temperature at several axial locations. The heat pipes were tested with two working fluids: water and ethanol. Ethanol is not an ideal working fluid for heat pipes but is useful in characterizing them because it wets well to a wide variety of surfaces. Water is in general a superior working fluid for heat pipes, but its contact angle and therefore wicking performance strongly depends on the surface chemistry of the surface it is in contact with. A particular measurement of interest in this test is the evaporator to condenser thermal conductance, which will be compared in the full paper to recently published correlations for additively manufactured heat pipes. Experimental results for two straight geometry and one bellows geometry heat pipe will be presented. The bellows geometry is of interest for condenser of the self-deploying radiator design.

Additive manufacturing↗