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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Constraints on the Volatile Distribution Within Shackleton Crater at the Lunar South Pole

Shackleton crater is nearly coincident with the Moon's south pole. Its interior receives almost no direct sunlight and is a perennial cold trap, making Shackleton a promising candidate location in which to seek sequestered volatiles. However, previous orbital and Earth-based radar mapping and orbital optical imaging have yielded conflicting interpretations about the existence of volatiles. Here we present observations from the Lunar Orbiter Laser Altimeter on board the Lunar Reconnaissance Orbiter, revealing Shackleton to be an ancient, unusually well-preserved simple crater whose interior walls are fresher than its floor and rim. Shackleton floor deposits are nearly the same age as the rim, suggesting that little floor deposition has occurred since the crater formed more than three billion years ago. At a wavelength of 1,064 nanometres, the floor of Shackleton is brighter than the surrounding terrain and the interiors of nearby craters, but not as bright as the interior walls. The combined observations are explicable primarily by downslope movement of regolith on the walls exposing fresher underlying material. The relatively brighter crater floor is most simply explained by decreased space weathering due to shadowing, but a one-micrometre-thick layer containing about 20 per cent surficial ice is an alternative possibility.

Zuber, Maria T.↗
NASA NTRS2016

Summary of the Results from the Lunar Orbiter Laser Altimeter after Seven Years in Lunar Orbit

In June 2009 the Lunar Reconnaissance Orbiter (LRO) spacecraft was launched to the Moon. The payload consists of 7 science instruments selected to characterize sites for future robotic and human missions. Among them, the Lunar Orbiter Laser Altimeter (LOLA) was designed to obtain altimetry, surface roughness, and reflectance measurements. The primary phase of lunar exploration lasted one year, following a 3-month commissioning phase. On completion of its exploration objectives, the LRO mission transitioned to a science mission. After 7 years in lunar orbit, the LOLA instrument continues to map the lunar surface. The LOLA dataset is one of the foundational datasets acquired by the various LRO instruments. LOLA provided a high-accuracy global geodetic reference frame to which past, present and future lunar observations can be referenced. It also obtained high-resolution and accurate global topography that were used to determine regions in permanent shadow at the lunar poles. LOLA further contributed to the study of polar volatiles through its unique measurement of surface brightness at zero phase, which revealed anomalies in several polar craters that may indicate the presence of water ice. In this paper, we describe the many LOLA accomplishments to date and its contribution to lunar and planetary science.

LRO↗
NASA NTRS2017

Evidence for Surface Water Ice in the Lunar Polar Regions Using Reflectance Measurements from the Lunar Orbiter Laser Altimeter and Temperature Measurements from the Diviner Lunar Radiometer Experiment

We find that the reflectance of the lunar surface within 5 deg of latitude of theSouth Pole increases rapidly with decreasing temperature, near approximately 110K, behavior consistent with the presence of surface water ice. The North polar region does not show this behavior, nor do South polar surfaces at latitudes more than 5 deg from the pole. This South pole reflectance anomaly persists when analysis is limited to surfaces with slopes less than 10 deg to eliminate false detection due to the brightening effect of mass wasting, and also when the very bright south polar crater Shackleton is excluded from the analysis. We also find that south polar regions of permanent shadow that have been reported to be generally brighter at 1064 nm do not show anomalous reflectance when their annual maximum surface temperatures are too high to preserve water ice. This distinction is not observed at the North Pole. The reflectance excursion on surfaces with maximum temperatures below 110K is superimposed on a general trend of increasing reflectance with decreasing maximum temperature that is present throughout the polar regions in the north and south; we attribute this trend to a temperature or illumination-dependent space weathering effect (e.g. Hemingway et al. 2015). We also find a sudden increase in reflectance with decreasing temperature superimposed on the general trend at 200K and possibly at 300K. This may indicate the presence of other volatiles such as sulfur or organics. We identified and mapped surfaces with reflectances so high as to be unlikely to be part of an ice-free population. In this south we find a similar distribution found by Hayne et al. 2015 based on UV properties. In the north a cluster of pixels near that pole may represent a limited frost exposure.

Emily A Fisher↗
NASA NTRSDate not supplied

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗
NASA NTRSDate not supplied

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗
NASA NTRSDate not supplied

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗
NASA NTRSDate not supplied

Direct Reconstruction of Ablative Thermal Protection System Aeroheating Using A Green's Function Approach

A Green’s function inverse heat transfer (IHT) approach is used to reconstruct the surface heating conditions on ablative thermal protection system (TPS) materials from embedded heat flux sensor and temperature probe measurements. The approach models the temperature time-history at the measurement location as a discrete linear system, allowing for the heat flux boundary condition to be recovered directly without the need for time-marching schemes. The effects of material decomposition and pyrolysis gas transport are modeled using an energy source/sink analogue. The performance of the reconstruction approach is analyzed on a 1D test case representative of an atmospheric entry heating scenario. The approach can recover the TPS surface heat flux to within 4% of the input heating condition with a computation time of 2-3 seconds (>3 orders of magnitude faster than current time-marching IHT methods). As a byproduct of the surface heating reconstruction, the algorithm also captures the surface pyrolysis gas mass flux and solid decomposition at multiple through-thickness locations within the TPS.

Kenneth McAfee↗
NASA NTRSDate not supplied

Reconstruction of Thermal Protection System Aeroheating using a Green’s Function Approach

Inverse heat transfer (IHT) techniques are often used to reconstruct the surface heating conditions on spacecraft thermal protection systems (TPS) during atmospheric entry. Current IHT techniques for entry spacecraft applications, however, demand substantial computational resources, and are impractical for analyses such as uncertainty quantification and real-time health monitoring. In this paper, a Green’s function sensor fusion approach is used to reconstruct the TPS surface aeroheating conditions on experimental spaceflight and ground test systems from collocated temperature and heat flux sensors embedded in the TPS. The algorithm leverages Green’s functions to model the heat conduction within the spacecraft TPS and stabilizes the recovery of the surface heating condition using the direct heat flux sensor measurement. The algorithm is validated using arc-jet ground test data and applied to the reconstruction of the Mars 2020 backshell heating during Martian atmospheric entry. The performance of the algorithm is benchmarked against a current state-of-the-art IHT framework, FIAT_Opt. The Green’s function-based reconstruction algorithm recovers the net hot-wall heat flux absorbed by the TPS and the incident heat flux from the atmospheric entry environment in close agreement with FIAT_Opt. Notably, computation of the surface heating condition is completed in three orders of magnitude less time with the Green’s function sensor fusion approach using a consumer-grade PC, versus with FIAT_Opt running on a high performance computer cluster. The efficiency of the algorithm is leveraged to compute the uncertainty contributions of input parameters to the total uncertainty in reconstructed Mars 2020 backshell heating for the full atmospheric entry heat pulse. The sensitivity analysis uncovers that, at different times throughout the entry heat pulse, uncertainties in the TPS specific heat, thermal conductivity, and emissivity are all dominant drivers of the reconstruction uncertainty. These results demonstrate Green’s functions and sensor-fusion techniques as promising IHT approaches to reconstruct atmospheric entry environments from TPS-embedded measurements, and highlight how these techniques may give access to post-flight analyses previously hindered by the prohibitive cost of current methods.

Kenneth McAfee↗
NASA NTRSDate not supplied

Reconstruction of Thermal Protection System Aeroheating using a Green’s Function Approach

Inverse heat transfer (IHT) techniques are often used to reconstruct the surface heating conditions on spacecraft thermal protection systems (TPS) during atmospheric entry. Current IHT techniques for entry spacecraft applications, however, demand substantial computational resources, and are impractical for analyses such as uncertainty quantification and real-time health monitoring. In this paper, a Green’s function sensor fusion approach is used to reconstruct the TPS surface aeroheating conditions on experimental spaceflight and ground test systems from collocated temperature and heat flux sensors embedded in the TPS. The algorithm leverages Green’s functions to model the heat conduction within the spacecraft TPS and stabilizes the recovery of the surface heating condition using the direct heat flux sensor measurement. The algorithm is validated using arc-jet ground test data and applied to the reconstruction of the Mars 2020 backshell heating during Martian atmospheric entry. The performance of the algorithm is benchmarked against a current state-of-the-art IHT framework, FIAT_Opt. The Green’s function-based reconstruction algorithm recovers the net hot-wall heat flux absorbed by the TPS and the incident heat flux from the atmospheric entry environment in close agreement with FIAT_Opt. Notably, computation of the surface heating condition is completed in three orders of magnitude less time with the Green’s function sensor fusion approach using a consumer-grade PC, versus with FIAT_Opt running on a high performance computer cluster. The efficiency of the algorithm is leveraged to compute the uncertainty contributions of input parameters to the total uncertainty in reconstructed Mars 2020 backshell heating for the full atmospheric entry heat pulse. The sensitivity analysis uncovers that, at different times throughout the entry heat pulse, uncertainties in the TPS specific heat, thermal conductivity, and emissivity are all dominant drivers of the reconstruction uncertainty. These results demonstrate Green’s functions and sensor-fusion techniques as promising IHT approaches to reconstruct atmospheric entry environments from TPS-embedded measurements, and highlight how these techniques may give access to post-flight analyses previously hindered by the prohibitive cost of current methods.

Kenneth McAfee↗
DOE OSTI2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, heterogeneous quantum-probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability.

Mohseni, Masoud↗