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At least 1,027 records · Page 57

Static Torsion Testing and Modeling of a Variable Thickness Hybrid Composite Bull Gear

Torsional strength of a variable thickness hybrid gear web was measured by performing static testing on the part in a large torsion test frame. The outer rim of the hybrid gear web was fixed to the bottom of the test frame and loading was applied to the web through a shaft. The test setup included the installation of digital image correlation (DIC) systems to obtain deformation and strain measurements from the surfaces of the hybrid gear web and the mechanical test equipment to ensure reliability of the test. The results indicated that the variable thickness hybrid gear web achieved approximately twice the torsional strength compared to that of previous hybrid gear designs. The DIC analysis showed significantly more straining of the loading shaft than the actual test article. Additionally, the results demonstrated the importance and affect that the metallic, lobed interlock features had on the principal strain and out-of-plane displacement fields. The analysis revealed that the fixed outer rim was in fact rotating and a rigid body motion compensation (RBMC) function was computed to determine the actual rotation of the hub and composite web relative to the outer rim. Modeling simulations were performed for the variable thickness hybrid gear web and correlated well with the RBMC rotational deformation seen in the DIC analysis. In addition to benchmarking the load capacity of the hybrid gear web, measuring its strength is useful information to define the parameters needed for dynamic, endurance, and other testing of the part.

Johnston, Joel P.↗

A Method and Model to Predict Initial Failure Rates

It has long been well known that actual system reliability typically falls well short of early estimates. Failure rates are often ten or more times higher than anticipated. Many reasons have been given for this, but over-optimism is the fundamental cause of too-favorable reliability predictions. Most forecasts of reliability are essentially best-case scenarios, as are predictions of budget and schedule. Confident engineers assemble estimates bottom-up, including the known factors and ignoring problems that they hope won’t happen. Traditional reliability estimation is based on simply summing up the component failure rates. This ignores most actual failure causes. The way to reduce over-optimism is to use the historical system level failure rate from similar projects. Adjustments should not be made based purely on engineering judgment, but only if there is so logical quantitative justification. The traditional component-based reliability estimate is useful as a lower bound on the system failure rate. The difference between this lower bound component-based reliability and the historical system level reliability indicates how much of the total failure rate is due to system level problems rather than component failures.

Jones, Harry W.↗

A Method and Model to Predict Initial Failure Rates

It has long been well known that actual system reliability typically falls well short of early estimates. Failure rates are often ten or more times higher than anticipated. Many reasons have been given for this, but over-optimism is the fundamental cause of too-favorable reliability predictions. Most forecasts of reliability are essentially best-case scenarios, as are predictions of budget and schedule. Confident engineers assemble estimates bottom-up, including the known factors and ignoring problems that they hope won't happen. Traditional reliability estimation is based on simply summing up the component failure rates. This ignores most actual failure causes. The way to reduce over-optimism is to use the historical system level failure rate from similar projects. Adjustments should not be made based purely on engineering judgment, but only if there is so logical quantitative justification. The traditional component-based reliability estimate is useful as a lower bound on the system failure rate. The difference between this lower bound component-based reliability and the historical system level reliability indicates how much of the total failure rate is due to system level problems rather than component failures

Jones, Harry W.↗

Blind Validation Study of Parametric Cost Estimation Tool SEER-H for NASA Space Missions

One of the primary parametric cost modeling tools used by NASA and the aerospace industry to estimate the development and production cost of future spacecraft hardware is SEER-H by Galorath. To date, no independent validation of this tool for cost estimation of space missions has been reported in the literature. In the present validation study, cost estimators used SEER-H to estimate the cost of twelve different past NASA science missions. The estimators were prevented from knowing the actual cost of the missions in an effort to minimize cognitive biases. The point estimates of SEER-H had an average error of 23%, median error of -0.3%, and a standard deviation of 43%. Nine of the twelve mission's actual costs fell within the 80% confidence interval of SEER's probabilistic estimates. Several factors independent of SEER that may have affected the accuracy of the results have been identified and are discussed; these include: uncertainty in the technical data used for the estimates, the methods used to estimate uncertainty in spacecraft component mass and numbers of prototypes, and the experience of the estimators.

SEER-H↗

NASA Lunabotics Robotic Mining Competition 10th Anniversary (2010-2019): Taxonomy and Technology Review

Space Mining for resources such as water ice, and regolith, which contain many elements in the form of metals, minerals, volatiles and other compounds, is a necessary step for In-Situ Space Resource Utilization (ISRU). One of the primary goals is to extract propellants from the regolith and water ice, such as oxygen and hydrogen which could then be used for in-space transportation. In addition, the space mining system can be used for various construction tasks that can benefit human and robotic exploration as well as scientific investigations based on excavated exposed topography, such as the side walls of trenches. The National Aeronautics & Space Administration (NASA) "Lunabotics" Robotic Mining Competition (RMC) is a university-level competition designed to engage and retain students in science, technology, engineering and mathematics (STEM). NASA has directly benefited from the competition by encouraging the development of innovative lunar excavation concepts from universities which has resulted in clever ideas and solutions which could be applied to an actual lunar excavation device or payload. The challenge is for students to design and build a remote controlled or autonomous excavator, called a "lunabot", which can collect and deposit a minimum of 10 kilograms of lunar simulant within 15 minutes. In recent years the goal has been changed to excavate a minimum of 1 kg of simulated icy regolith which is found under an overburden of regolith simulant. The complexities of the challenge include the abrasive characteristics of the lunar regolith simulant, the weight and size limitations of the lunabot, and the ability to control the lunabot from a remote-control center or operate it autonomously. This paper will present the results of the ten Lunabotics Robotic Mining Competitions held between May 2010 and May 2019. Each year over 50 university teams have attended, resulting in over 500 lunabot designs and subsequent prototypes. Over 6,000 university students have been part of the on-site competition at KSC. Even more students and the public were engaged via internet broadcasting and social networking media. The various designs have been cataloged and categorized here to provide information to future Lunabotics RMC mining robot designers and competitors. Categories will focus on both the mechanical design as well as the autonomy architecture/design. It is also expected to be of value for actual future space missions, as knowledge is gained from testing many innovative prototypes in simulated lunar regolith. A taxonomy of robotic excavator designs has been presented. In addition, the paper will discuss changes in learning paradigms occurring in the current generation of students, and how this competition leverages those changes to challenge students to develop skills in graduate level concepts and apply them. Examples of how this translates to hiring opportunities for commercial sponsors has also been discussed.

Robert P Mueller↗

NASA LUNABOTICS ROBOTIC MINING COMPETITION 10TH ANNIVERSARY (2010 – 2019): TAXONOMY AND TECHNOLOGY REVIEW

Space Mining for resources such as water ice, and regolith, which contain many elements in the form of metals, minerals, volatiles and other compounds, is a necessary step for In-Situ Space Resource Utilization (ISRU). One of the primary goals is to extract propellants from the regolith and water ice, such as oxygen and hydrogen which could then be used for in-space transportation. In addition, the space mining system can be used for various construction tasks that can benefit human and robotic exploration as well as scientific investigations based on excavated exposed topography, such as the side walls of trenches. The National Aeronautics & Space Ad-ministration (NASA) Lunabotics Robotic Mining Competition (RMC) is a university-level competition designed to engage and retain students in science, technology, engineering and mathematics (STEM). NASA has directly benefited from the competition by encouraging the development of innovative lunar excavation concepts from universities which has resulted in clever ideas and solutions which could be applied to an actual lunar excavation device or payload. The challenge is for students to design and build a remote controlled or autonomous excavator, called a lunabot, that can collect and deposit a minimum of 10 kilograms of lunar simulant within 15 minutes. In recent years the goal has been changed to excavate a minimum of 1 kg of simulated icy regolith which is found under an over-burden of regolith simulant. The complexities of the challenge include the abrasive characteristics of the lunar simulant, the weight and size limitations of the lunabot, and the ability to control the lunabot from a remote control center. This paper will present the results of the 10 Lunabotics Robotic Mining Competitions held between May 2010 and May 2019. Each year over 50 university teams have attended, resulting in over 500 lunabot designs and subsequent prototypes. Over 5,000 university students have been part of the competition on-site at KSC. More students and the public were en-gaged via internet broadcasting and social net-working media. The various designs will be cataloged and categorized to provide information to future Lunabotics mining robot designers and competitors. It is also expected to be of value for actual future space missions, as knowledge is gained from testing many innovative prototypes in simulated lunar regoith. A taxonomy of robotic excavator designs will be presented.

Competition↗

The Impacts of Fossil Fuel Emission Uncertainties and Accounting for 3-D Chemical CO2 Production on Inverse Natural Carbon Flux Estimates from Satellite and In Situ Data

Atmospheric carbon dioxide (CO2) inversions for estimating natural carbon fluxes typically do not allow for adjustment of fossil fuel CO2 emissions, despite significant uncertainties in emission inventories and inadequacies in the specification of international bunker emissions in inversions. Also, most inversions place CO2 release from fossil fuel combustion and biospheric sources entirely at the surface. However, a non-negligible portion of the emissions actually occurs in the form of reduced carbon species, which are eventually oxidized to CO2 downwind. Omission of this ‘chemical pump’ can result in a significant redistribution of the inferred total carbon fluxes among regions. We assess the impacts of different prescriptions of fossil fuel emissions and accounting for the chemical pump on flux estimation, with a novel aspect of conducting both satellite CO2 observation-based and surface in situ-based inversions. We apply 3-D carbon monoxide (CO) loss rates archived from a state-of-the-art GEOS chemistry and climate model simulation in a forward transport model run to simulate the distribution of CO2 originating from oxidation of carbon species. We also subtract amounts from the prior surface CO2 fluxes that are actually emitted in the form of fossil and biospheric CO, methane, and nonmethane volatile organic compounds (VOCs). We find that the posterior large-scale fluxes are generally insensitive to the finer-scale spatial differences between the ODIAC and CDIAC fossil fuel CO2 gridded datasets and assumptions about international bunker emissions. However, accounting for 3-D chemical CO2 production and the surface correction shifts the global carbon sink, e.g., from land to ocean and from the tropics to the north, with a magnitude and even direction that depend on assumptions about the surface correction. A GOSAT satellite-based inversion is more sensitive to the chemical pump than one using in situ observations, exhibiting substantial flux impacts of 0.28, 0.53, and -0.47 Pg C yr(exp -1) over tropical land, global land, and oceans, due to differences in the horizontal and vertical sampling of the wo observation types. Overall, the biases from neglecting the chemical pump appear to be minor relative to the flux estimate uncertainties and the differences between the in situ and GOSAT inversions, but their relative importance will grow in the future as observational coverage further increases and satellite retrieval biases decrease.

James S Wang↗

Reconstructing Subgrid Cloud Variability Guided by CloudSat/CALIPSO Observations

Predicting realistic cloud subgrid variability remains a challenge for Global Climate Models (GCMs) even though it can play an important role for proper representation of processes pertaining to cloud microphysics, precipitation, and radiation, but also for comparisons with satellite observations which are of much higher resolution than model grids. The diagnostic approach of subgrid cloud variability is commonly handled by subcolumn cloud generators. For a specific GCM, one ideally wants the subgrid variability used for comparisons with satellite observations to be created by the same generator and with the same rules as the one used for model integration. With this in mind, we have embarked in an effort to test and improve cloud subcolumn generators appropriate for GCMs. For this purpose, we use cloud (hydrometeor) products from active observations by the CloudSat radar (CPR) and the CALIPSO lidar (CALIOP). Cloud products from active sensors while suffering significant sampling and coverage drawbacks have the advantage of resolving both horizontal and vertical variability. The main question is: given a profile of cloud condensate mean and variance, can we create a subgrid cloud field that is statistically similar to the observed subgrid cloud field? By “statistically”, we suggest that we do not aspire to reproduce “well” each individual subgrid cloud field of a GCM-scale region, but that our generator performs well for a large ensemble of cases. We simulate radar, passive imager, and radiation flux fields from the observed 2D cloud fields and create one-point statistics; we use the profiles of cloud condensate mean and variance as input to the generator to create subgrid cloud fields; we compare the statistics; we adjust the rules of the generator to create the best possible agreement between the radar, imager and radiation field statistics. In this process, the active observations have actually a dual role: they provide the actual subgrid cloud field which can be reduced to a profile of mean and variance used by the subcolumn generator, but they also provide the rules needed by the generator, such as measures (e.g, decorrelation length) of the vertical overlap of cloud fraction and of the condensate horizontal variability. This presentation will show our progress using Cloudsat and CALIPSO products in this dual fashion.

cloud↗

Reconstructing Subgrid Cloud Variability Guided by CloudSat/CALIPSO Observations

Predicting realistic cloud subgrid variability remains a challenge for Global Climate Models (GCMs) even though it can play an important role for proper representation of processes pertaining to cloud microphysics, precipitation, and radiation, but also for comparisons with satellite observations which are of much higher resolution than model grids. The diagnostic approach of subgrid cloud variability is commonly handled by subcolumn cloud generators. For a specific GCM, one ideally wants the subgrid variability used for comparisons with satellite observations to be created by the same generator and with the same rules as the one used for model integration. With this in mind, we have embarked in an effort to test and improve cloud subcolumn generators appropriate for GCMs. For this purpose, we use cloud (hydrometeor) products from active observations by the CloudSat radar (CPR) and the CALIPSO lidar (CALIOP). Cloud products from active sensors while suffering signi􀂦cant sampling and coverage drawbacks have the advantage of resolving both horizontal and vertical variability. The main question is: given a profile of cloud condensate mean and variance, can we create a subgrid cloud field that is statistically similar to the observed subgrid cloud field? By “statistically”, we suggest that we do not aspire to reproduce “well” each individual subgrid cloud field of a GCM-scale region, but that our generator performs well for a large ensemble of cases. We simulate radar, passive imager, and radiation flux fields from the observed 2D cloud fields and create one-point statistics; we use the profiles of cloud condensate mean and variance as input to the generator to create subgrid cloud fields; we compare the statistics; we adjust the rules of the generator to create the best possible agreement between the radar, imager and radiation field statistics. In this process, the active observations have actually a dual role: they provide the actual subgrid cloud field which can be reduced to a profile of mean and variance used by the subcolumn generator, but they also provide the rules needed by the generator, such as measures (e.g, decorrelation length) of the vertical overlap of cloud fraction and of the condensate horizontal variability. This presentation will show our progress using Cloudsat and CALIPSO products in this dual fashion.

clouds↗

Inverse Compton Cooling in the Coronae of Simulated Black Hole Accretion Flows

We present a formulation for a local cooling function to be employed in the diffuse, hot corona region of 3D GRMHD simulations of accreting black holes. This new cooling function calculates the cooling rate due to inverse Compton scattering by considering the relevant microphysics in each cell in the corona and approximating the radiation energy density and Compton temperature there by integrating over the thermal seed photon flux from the disk surface. The method either assumes ion and electron tem- peratures are equal (1T), or calculates them separately (2T) using an instantaneous equilibrium approach predicated on the actual relevant rate equations (Coulomb and Compton). The method is shown to be consistent with a more detailed ray-tracing calculation where the bulk of the cooling occurs, but is substantially less costly to per- form. As an example, we apply these methods to a harm3d simulation of a 10M⊙, non-spinning black hole, accreting at nominally 1% the Eddington value. Both 1T and 2T approaches lead to increased radiative efficiency and a larger fraction of total cooling in the corona as compared to the original target-temperature cooling function used by harm3d, especially in the 1T case. Time-averaged post-processing reveals that the continuum spectral observations predicted from these simulations are qualitatively similar to actual X-ray binary data, especially so for the 1T approach which yields a harder power-law component (Γ = 2.25) compared to the 2T version (Γ = 2.53).

Brooks E. Kinch↗

(abstract) The Impactor Flux in the Pluto-Charon System

The impactor flux on Pluto and Charon consists of long-period comets from the Oort cloud passing through the region, and intermediate-period comets evolving inward to (or outward from) short-period orbits. The source of the intermediate-period comets is likely an extended disk of remnant planetesimals beyond the orbit of Neptune, commonly referred to as the Kuiper belt, though the Oort cloud may be an additional source of some short-period comets. The Pluto-Charon system actually circulates within the Kuiper belt, and with an aphelion of 49.3 AU, moves through a region where remnant planetesimal orbits would likely be stable over the age of the solar system. The recent discovery of two objects at Kuiper belt distances, 1992 QB 1 and 1993 FW, and the fact that the orbit determined for 1992 QB 1 is likely of low eccentricity and low inclination, have provided strong observational evidence for the Kuiper belt. Weisman et al (1989) showed that, for the expected number of objects in the Kuiper belt necessary to provide the short-period comet flux, cometary cratering on Pluto and Charon is dominated by Kuiper belt comets. We will provide updated estimates of the numbers, based on an improved understanding of Oort cloud and Kuiper belt dynamics since 1989, and on improved estimates for the radii and masses for Pluto and Charon. Because Pluto and Charon actually penetrate the stable region of the Kuiper belt beyond 40 AU, expected cratering rates for the pair are substantially higher than for Triton, which is located at the inner edge of the Kuiper belt. However, the Pluto-Charon system is tidally evolved and this may have led to resurfacing events at various times in its history. Thus, detailed predictions of the integrated crater density or comparisons with Triton are not very meaningful without a knowledge of that history. Differences in the suspected compositions of Pluto and Charon may also lead to different surface rheologies and thus, different retention times for cratered surfaces.

Pluto↗

Characteristics of a high-power ion beam deflection system necessary to deflect the hypothetical asteroid 2017 PD

The July 2027 impact date for the hypothetical asteroid 2017 PDC, that is the subject of an emergency response exercise, leaves just over ten years to implement a deflection approach. The analyses herein allocates four years to the design, fabrication, assembly, test and launch of a notional high-power Ion Beam Deflection (IBD) vehicle to meet a launch readiness date no later than May 2021. Using this launch date along with estimates for the vehicle mass and performance characteristics of the electric propulsion system, low-thrust trajectory analyses indicate a 2.56-year flight time to rendezvous with 2017 PDC. This would leave 3.6 years to execute the actual deflection phase. A 160-kW IBD vehicle could deflect 2017 PDC by at least one Earth radius within this time provided the asteroid’s actual diameter was less than about 140 m and its density was 2 g/cm3 or less. Larger diameters and/or higher densities would require a higher power IBD vehicle, multiple IBD vehicles, or a longer deflection phase. Ion beam deflection is largely independent of the characteristics of the threat object, but its effectiveness, like all deflection approaches, is sensitive to both the asteroid mass and the time available for deflection. The characteristics of IBD, i.e., large standoff distance between the spacecraft and the asteroid surface, as well as ion beam divergence angles of a few degrees, facilitates the possible simultaneous use of multiple IBD vehicles to improve the performance and robustness of the asteroid deflection. The 65-cm diameter, 20-kW NEXIS ion thruster developed in support of the Jupiter Icy Moons Orbiter mission concept is particularly well suited for application to a high-power IBD system. It has demonstrated an ion beam divergence angle of approximately 2 degrees and operation at 20 kW for over 2,000 hours. Completion of the development and flight qualification of this technology would significantly reduce the risk and time necessary to respond to the discovery of potentially hazardous asteroid in the size range of 50- to 100-m diameter.

Reeves, David↗

An Approach for Defining IASMS Services, Functions, and Capabilities

Assuring safety in the NAS with the inclusion of new entrants, such as Advanced Air Mobility (AAM), will require overcoming unique safety challenges that result from combining innovative technologies with novel airspace concepts for moving people and cargo using autonomous vehicles. The focus of the In-time Aviation Safety Management System (IASMS) is to overcome AAM’s safety assurance challenges. The IASMS Concept of Operations (ConOps) describes an interconnected set of services, functions, and capabilities (SFCs) designed to manage operational risks, identify unknown risks, and inform system designs. This paper describes an approach for defining SFCs based on technology trends in research, assessment of known and unknown risks in voluntary safety reports, and causal and contributing factors in aviation accidents and incidents. This approach would identify potential SFCs that further expand the Monitor, Assess, and Mitigate (M-A-M) functionality that represents the enabling framework of the IASMS. Safety implications that will result from integration of AAM in the transformation of the National Airspace System (NAS) were addressed in National Academies committees reports on AAM and IASMS. Development of a ConOps for IASMS was a top recommendation and can be represented as a reframing of safety assurance that builds on real-time alerting such as the Traffic Alert and Collision Avoidance System, and adds the more encompassing in-time temporal parameter in recognition of the different timelines for collecting and assessing safety data for risk mitigations. For example, mining for safety trends from data sources such as the Aviation Safety Information Analysis and Sharing system occurs over a longer time period. Research on AAM operations poses that SFCs can be designed to monitor the safety margin appropriate for AAM including with regards to the distance between current flight parameters and nominal ideal conditions. These in-time comparisons will become more complex as the density of operations increases at least in certain areas and can include planned and actual 4D trajectory, and in-time comparisons having implications on conflict modeling and prediction including expected and actual departure time, fix/waypoint crossing times, and arrival time. These comparisons would be integrated as part of SFCs that redefine and inform new safety margin. An increased safety margin improves management of operational risks while reducing the potential for anomalies. An increased safety margin also has implications for operator confidence in the certainty of its operations and trust in automation. Technology trends in research could be used to refine existing SFCs and define needs for additional SFCs that provide safety improvements to the design and operation of vehicles, airspace design, and operator performance requirements. NASA is developing innovative approaches to safeguard against major accidents and incidents that have occurred in the NAS and those anticipated with the inclusion of envisioned AAM operations. The innovations use operational performance data to monitor, detect, and predict flight variations exceeding safe nominal patterns, such as would be caused by navigational error, severe weather complications, or hijacking of UAS controls. These innovative approaches have high potential to prevent accidents and incidents in the new AAM era. It is anticipated that elements of the innovations will evolve into SFCs for the IASMS. Voluntary safety reports can be monitored to identify anomalies related to design or operational performance risks. Reports could be periodically monitored and assessed for specific topics. Reports might serve as weak signals or precursors indicative of emergent risk such as when combined with other safety information. The architecture could include SFCs that are based on voluntary safety reports recognizing the periodic temporal nature of data analysis. As previously mentioned, aviation accidents with their causal and contributing precursors can inform the need for SFCs in the IASMS. Accidents and incidents at San Francisco International Airport such as Asiana 214 and Air Canada 759 illustrate how combinations of different factors lead to increased risk. These types of precursors and different factors have implications on the types of SFCs that could be needed to monitor and manage different sources and types of design and operational risk. Continuing to assure the safety of AAM as designs and operations gain in complexity can be accompanied by defining SFCs that also increase in complexity. These SFCs can leverage information from findings and recommendations synthesized across on-going research, voluntary safety reports, and accident and incident reports. These SFCs can serve to refine accuracy of algorithms and resolve limitations with current practices. The IASMS architecture represents the framework for the SFCs and their critical role in safety assurance.

In-Time Aviation Safety Management System↗

LOFTID Heat Flux Gauge Calibration: What is Truth?

The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) is a demonstration of Hypersonic Inflatable Aerodynamic Decelerator (HIAD) technology, which may enable the delivery of heavy payloads to Mars, Venus, and Titan, as well as return to Earth. Unlike rigid aeroshells that are constrained by the size of the rocket’s shroud, inflatable aeroshells can be deployed to a much larger scale, thus allowing a spacecraft to begin its deceleration earlier and experience less heating. On LOFTID, there will be 4 total heat flux gauges (HFG) with a range of 70 W/cm2 and 1 radiometer with a range of 3 W/cm2, arranged as shown in Fig. 1. Both the radiometer and total HFGs are Schmidt-Boelter gauges purchased from an external vendor. Radiative calibrations were performed in-house at NASA Ames’ Sensors and TPS Advanced Research Laboratories (STAR Labs) before and after environmental testing to investigate how the testing affected the sensors' response. Additional rounds of radiative calibration at STAR Labs were also performed in order to investigate the large uncertainties associated with these tests. For example, a survey of multiple calibration facilities concluded that the uncertainty within a given facility was +/-3% [1]. An additional NIST study that calibrated heat flux gauges at 7 different facilities also found the variation in calibration coefficients to be up to ~3% within a given facility, but up to 15% between facilities, suggesting systematic differences between test setups [2]. Finally, the response of heat flux gauges to radiative versus convective heat flux has shown to differ by up to 20% [3],[4]. Because the heat flux gauges on LOFTID will predominantly experience convective heat flux during flight, a convective calibration study was performed at Boeing's Large-Core Arc Tunnel (LCAT) facility. Radiative Calibration Procedure The calibrations performed at STAR Labs utilize a quartz lamp bank (QLB) that provides a maximum heat flux of 50 W/cm2, which bounds the expected LOFTID flight environment. The calibration involves exposing a water-cooled Gardon gauge (reference) and then the unit-under-test (UUT) to 5 different heat fluxes multiple times for 10 seconds each, and then calculating a linear fit. The test setup is shown in Fig. 2. The total HFGs were calibrated at STAR Labs 3 times, denoted as STAR 1 (before environmental testing), STAR 2 (after protoflight vibration and thermal-vacuum testing), and STAR 3 (no change from previous test). All 8 flight-lot total HFGs showed a decrease in full-scale output from STAR 1 to STAR 2 by between 0.5% and 10. The first portion of this investigation was to determine whether the change could be due to differences in temperature between the two calibration runs. A typical linear fit to the calibration data was performed using Eq. 1 where q’ is the heat flux in W/cm2, c is the calibration coefficient, and mV is the sensor output. To account for temperature, the data were fit to a nonlinear function that included both the sensor output (mV) and the temperature from the thermocouple embedded inside the HFG near the surface (T): q'=mV/(c1* T + c0}. The residuals between the fits and the actual data points were calculated for every point, and proven to be much smaller for the temperature-compensated fits than for the linear fits for all sensors. An example is shown in Fig. 3. When the temperature-compensated fits from STAR 1 were applied to the STAR 2 data, the residuals did not improve, suggesting that the change in sensitivity between these two calibration runs was not due to temperature. A third round of calibration (STAR 3) was conducted to further address the temperature dependence of the total HFGs, and the resulting sensitivities matched closely to STAR 2 (within 2%). Temperature-compensated calibration curves were once again fit to the data. In this case, when the temperature-compensated fits from STAR 3 were applied to STAR 2 data, the residuals between the fits and STAR 2 data were much lower than the residuals due to the linear fits. This suggests that the changes seen between STAR 1 and STAR 2 were likely due to actual changes in the sensors caused by the environmental testing between the two calibrations. A modification of the original calibration process, in which the UUT was exposed to each heat flux for just 3 seconds (instead of 10) to reduce the temperature increase during the test, was additionally performed on several of the HFGs. In general, the sensitivities were 1-1.5% lower than from the 10-second tests, but the temperatures were also significantly lower. When the temperature-compensated fits from the 10-second tests were applied to the 3-second test data, the residuals were greatly improved than when just using the linear fits, further suggesting that the temperature-compensated fits may lead to better accuracy than the linear fits in flight. Convective Calibration The second portion of this study was to create a mapping between the radiative and convective calibration coefficients. The majority of the heating during flight will be convective, so it is important to understand how the HFG response differs under these conditions. However, there are no standardized methods for convective calibration [5]. Because the TPS aerothermal response models were validated at LCAT, the same facility was chosen for convective calibration of two of the total HFGs (Fig. 4). Preliminary results showed that the full-scale output was 3% and 8% higher in convective heat flux as compared to radiative heat flux. However, tunnel variation may have contributed to noise and uncertainty in the measurements, and more testing and analysis remains to be done. Scope of Presentation The presentation will include an overview of the changes seen in HFG calibration before and after environmental testing, differences between radiative and convective calibrations, the modeling work done to aid in understanding the sensor response to varying environments, and recommended future work.

H S Alpert↗

Estimation With Range Depended Sensor Model

This paper focuses on the improvement of object detection accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. The tracking problem is considered in a mixed continuous-discrete time domain, where the target dynamics is in continuous-time and the measurements are in discrete-time, which is an optimal choice in many tracking and navigation applications. We adopt a commonly used continuous time coordinate-uncoupled white-noise acceleration model for a point object to describe the target motion, and use Extended Kalman Filter (EKF) framework to estimate the target's state and the unknown proportionality coefficient.

Vahram Stepanyan↗

The Role of Tactile Cueing in Multimodal Displays: Application in Complex Task Environments for Space Exploration

This paper reviews the potential role of tactile cueing in multimodal displays that will need to be developed to enable Extravehicular Activity (EVA) and telerobotic exploration for both Moon and Mars missions. Specific missions to be accomplished during EVA surface and space operations will include construction and assembly, surface and geologic exploration, and excavation for protective shelter from radiation and significant solar particle events. EVA crews have limited time, resources, mobility, visibility and dexterity, and activities take place in a perceptually impoverished environment. Limitations include a loss of spatial reference frame leading to spatial disorientation, a loss of information normally provided by the auditory and somato-sensory systems, and a restricted field of view. In addition, in non-surface operations crew must be able to navigate and operate in a fully six degree of freedom (DoF) environment. Fatigue resulting from suited EVA tasks will also critically affect performance and safety. In order to mitigate these factors, it is important that crews be well trained prior to executing actual missions. There is a critical need to design multimodal interfaces to optimize performance under these conditions for both training and actual operations. Because of the high level of complexity of the EVA environment on the one hand, and the “sensory restrictions” on the other, it makes sense to envisage enhancements to the astronauts’ visual environment. Such an enriched environment may include virtual auditory as well as tactile inputs.

multimodal displays↗

Transforming Science Prioritization Processes Using Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning (ML) have potential to augment significantly the current labor-intensive processes of science prioritization, specifically by the National Academies’ Decadal Survey on behalf of NASA and NSF. Here we summarize what we believe to be the first exploratory demonstration-of-concept results from an application of AI/ML to Survey science prioritization. Specifically, we applied Latent Dirichlet Allocation (LDA) and Natural Language Processing (NLP) to reveal trends in published astrophysics research that may indicate science priorities and which could be applied to strategic planning. For the purpose of the work that we summarize here, AI/ML is able to analyze – that is, to “understand,” in a manner of speaking – a vast amount of text to reveal complex relationships among research topics, including the growth or decline of science community activities in those topics over time. We trained ourselves and AI/ML algorithms by using ~400,000 abstracts in the period 1998 to 2010 to “forecast” the Academies’ Astro2010 recommendations and compare with the solicited white papers. Comparing our results with actual Astro2010 recommendations allowed us to identify candidate metrics that better predicted the actual results of the Survey. We found, for example, that Compound Annual Growth Rate (CAGR) of papers published in a topic area is a good proxy measure for importance of this topic area of research. With this training complete, we identified candidate astrophysics astrophysics science priorities for the 2021+ period using the research during 2007 - 2019 . We conclude that appropriate application of AI can potentially significantly reduce the current workload of the Decadal Survey processes and reveal otherwise unrecognized characteristics in the body of astronomical research. We emphasize throughout the exploratory nature of our work, encouraging colleagues to pursue promising results further. Our most critical governing assumption was that increased (or decreased) research activity can be used to identify scientific or technology topic areas worthy of increased (or decreased) future emphasis. We discuss advantages, limitations, and recognize the “black box” nature of our technique. We note ethics issues associated, for example, with using AI/ML to reveal “hidden” meanings and biases in published work. Furthermore, inevitable improvements in AI may soon enable widespread and welcome identification of and advocacy for science and technology priorities by disparate and diverse groups and organizations. Consequently, we continue to urge a near-term, in-depth evaluation of appropriate applications of AI, including implications and consequences, as well as support for multiple follow-on assessments, of which ours is only a beginning.

Artificial Intelligence↗

In-Flight Turbulence Emulation for Fixed-Wing Aircraft using Equivalent Multisine Excitations

A procedure is discussed for designing control-equivalent turbulence input (CETI) excitations to emulate a fixed-wing aircraft response to turbulence while flying through calm air. These excitations are added to the actuator commands to produce the same motion of the aircraft that would have resulted from flying in actual turbulence. Although CETI models are usually shaping filters driven by white noise inputs, the equivalent control inputs in this report were constructed as multisine excitations, which enables mimicking the response to turbulence with arbitrary power spectra, within hardware limitations. Practical aspects of the approach are discussed, such as actuator dynamics, model uncertainty, actual in-flight turbulence, and others. The procedure is demonstrated using a flight dynamics simulation model of a subscale generic jet transport aircraft.

control-equivalent turbulence inputs (CETI)↗