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The X2000 Power System Electronics (PSE) is a Jet Propulsion Laboratory (JPL) task to develop a new generation of power system building blocks for potential use on future deep space missions. The effort includes the development of electronic components and modules that can be used as building blocks in the design of generic spacecraft power systems.
The Descent Assisted Split Habitat (DASH) lunar lander concept utilizes a disposable braking stage for descent and a minimally sized pressurized volume for crew transport to and from the lunar surface. The lander can also be configured to perform autonomous cargo missions. Although a braking-stage approach represents a significantly different operational concept compared with a traditional two-stage lander, the DASH lander offers many important benefits. These benefits include improved crew egress/ingress and large-cargo unloading; excellent surface visibility during landing; elimination of the need for deep-throttling descent engines; potentially reduced plume-surface interactions and lower vertical touchdown velocity; and reduced lander gross mass through efficient mass staging and volume segmentation. This paper documents the conceptual study on various aspects of the design, including development of sortie and outpost lander configurations and a mission concept of operations; the initial descent trajectory design; the initial spacecraft sizing estimates and subsystem design; and the identification of technology needs
The existence of the Saharan air layer (SAL), a layer of warm, dry, dusty air that frequently moves westward off of the Saharan desert of Africa and over the tropical Atlantic Ocean, has long been appreciated. As air moves over the desert, it is strongly heated from below, producing a very hot air mass at low levels. Because there is no moisture source over the Sahara, the rise in temperature causes a sharp drop in relative humidity, thus drying the air. In addition, the warm air produces a very strong jet of easterly flow in the middle troposphere called the African easterly jet that is thought to play a critical role in hurricane formation. In recent years, there has been an increased focus on the impact that the SAL has on the formation and evolution of hurricanes in the Atlantic. However, the nature of its impact remains unclear, with some researchers arguing that the SAL amplifies hurricane development and with others arguing that it inhibits it. The argument for positively influencing hurricane development is based upon the fact that the African easterly jet produces the waves that eventually form hurricanes and that it leads to rising motion south of the jet that favors the development of deep thunderstorm clouds. The potential negative impacts of the SAL include 1) low-level vertical wind shear associated with the African easterly jet; 2) warm SAL air aloft, which increases thermodynamic stability and suppresses cloud development; and 3) dry air, which produces cold downdrafts in precipitating regions, thereby removing energy needed for storm development. As part of this recent focus on the SAL and hurricanes (which motivated a 2006 NASA field experiment), there has been little emphasis on the SAL s potential positive influences and almost complete emphasis on its possible negative influences, almost to the point of claims that the SAL is the major suppressing influence on hurricanes in the Atlantic. Multiple NASA satellite data sets (TRMM, MODIS, and AIRS/AMSU) and National Centers for Environmental Prediction global analyses are used to characterize the SAL s properties and evolution in relation to developing hurricanes. The results show that storms generally form on the southern side of the jet, where favorable background rotation is high. The jet often helps to form the northern side of the storms and rarely moves over their inner cores, so jet-induced vertical wind shear does not appear to be a negative influence on developing storms. Warm SAL air is confined to regions north of the jet and generally does not impact the tropical cyclone precipitation south of the jet. Of the three proposed negative influences, dry air appears to be the key influence; however, the presence of dry SAL air is not a good indicator of whether a storm will weaken since many examples of intensifying storms surrounded by such dry air can be found. In addition, a global view of relative humidity shows moisture distributions in other ocean basins that are almost identical to the Atlantic. The dry zones correspond to regions of descending air on the eastern and equatorward sides of semi-permanent oceanic high pressure systems. Thus, the dry air over the Atlantic appears to be primarily a product of the large-scale flow, but with enhanced drying at low levels associated with the Sahara. As a result, we conclude that the SAL is not a major negative influence on hurricanes. It is just one of many possible influences and can be both positive and negative.
No abstract provided
The need for databases for the distributed loading on launch vehicles during the early portion of flight necessitates the use of expensive computational flows in regimes where wake effects dominate. While also being expensive, this is a regime that computational tools tend to historically have problems simulating accurately. To help tackle this problem, a method of data fusion to combine computational results to wind tunnel derived force and moment data is developed. Using this method, significant reduction in computational costs and increases in confidence of the final product is possible and has been used to generate several databases for the Space Launch System (SLS) at NASA. While the full details of database generation are not part of this work, the crucial method at its core is developed here. Two SLS geometries are used throughout the work to demonstrate the techniques. These are two of the larger geometries and represent both planned crewed missions to the Moon as well as potential cargo missions to deep space. The method uses principal component analysis (PCA) to generate a reduced ordered model (ROM) to help fill in the full parameter space. Other similar techniques are explored, but were not found to have a significant result on the predictions of the ROM. Because the full number of components are kept to generate the model, this lack of difference is expected. This method is then extended to ensure that predicted surfaces match trusted force and moment data derived from wind tunnel testing. This extension is done by setting up a constrained optimization problem in order to minimize the deviation from the surface resolved computational data while still integrating to the desired values. When generating the constrained optimization problem, a weighting factor to balance these competing needs is introduced. The work compares previously introduced weighting terms from similar work to the proposed terms and shows that the previously used terms do not have as desirable behavior in this flow regime. This method is then expanded by developing a technique to incorporate uncertainty quantification into the developed data fusion methodology. This expansion takes a two pronged approach. One examines transferring the uncertainties in the force and moment database and characterizes how those adjustments change the predicted lineloads. The second looks at model form error and looks how rebuilding the model using slightly different data changes the predictions. These two terms are then combined in order to create an uncertainty model that takes both effects into account. The limitations of the proposed methods is then discussed as well as possible techniques to address these shortcomings.
Abstract A fast, robust pipeline for strain mapping of crystalline materials is important for many technological applications. Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions, but this technique is limited when the electron beam undergoes multiple scattering. Deep-learning methods have the potential to invert these complex signals, but require a large number of training examples. We implement a Fourier space, complex-valued deep-neural network, FCU-Net, to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images. FCU-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures, orientations, thicknesses, and microscope parameters, which are augmented with experimental artifacts. We evaluated FCU-Net against simulated and experimental datasets, where it substantially outperforms conventional analysis methods. Our code, models, and training library are open-source and may be adapted to different diffraction measurement problems.
INTRODUCTION Advances in Earth Independent Medical Operations (EIMO) focused capabilities will be crucial for completing successful beyond low-earth-orbit (LEO) missions, where traditional Earth-based medical support and near real-time communication are limited, intermittent, or absent. In such restricted or limited communication environments, emerging extended reality (ER or XR) technologies may play a vital role in ensuring crew health and safety. Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) are common terms used to describe technologies that "generate or modify" while “extended reality” (ER or XR) is often used as an umbrella term for these technologies. This review explores how such cutting-edge “extended reality” information delivery systems are currently applied across various disciplines within NASA. The review focuses on their capabilities, limitations, and prospective future applications as they pertain to Earth-Independent Medical Operations and its components of pre-mission planning, medical decision making, resource management, and task load management. METHODS/DESIGN The review began with a literature search to identify relevant disciplines (e.g., medical education, austere environments) where XR technologies have been studied to highlight areas where evidence is lacking. Previous and current NASA XR projects and applications, as well as commercial partnerships, such as those with Small Business Innovation Research (SBIR), were identified. Discussions with technical experts involved in the design, development, and application of these technologies allowed for classification based on their respective spaceflight applications. RESULTS AND CONCLUSION This review highlights current and emerging XR technologies that have been developed, tested, and implemented both terrestrially and in spaceflight by NASA, its international partners, the U.S. Armed Forces, and various relevant commercial entities. While these novel information delivery systems vary in their respective technology readiness levels (TRL), they have the potential to be implemented on Earth, in LEO, and during deep space exploration. These systems have the potential for impacting and optimizing many aspects of EIMO.
The procedures used by the Jet Propulsion Laboratory to select and recommend frequencies to be used for deep space telecommunications are explained. The frequency selection process described deals only with the potential for radio frequency interference between deep space telecommunication links. Channel plans based on bandwidth, hardware implementation, and frequency ratio considered are used. The channel selection process is based on calculations and analysis of interference-to-signal power ratios as a function of time for each possible pair of missions. The modes of interference and the interference protection ratio are explained, and the interference analysis procedure described. Equations used in calculating the downlink and uplink interference analyses are presented.
A novel fiber-optic system with dynamic range of up to 150 dB-Hz for transmission of microwave analog signals is described. The design, analysis, and laboratory evaluations of this system are reported, and potential applications in the NASA/JPL Deep Space Network are discussed.
Europa, a Moon-sized, ice-covered satellite of Jupiter, is second only to Mars in its astrobiological potential. Beneath the icy surface, an ocean up to 150 km deep is thought to exist, providing a potential habitat for life,and a tempting target for future space missions. The Galileo mission to the Jovian system recently ended, but there are already long-range plans to send much more capable spacecraft,such as the proposed Jupiter Icy Moons Orbiter (JIMO), to take a closer look at Europa and her siblings, Ganymede and Callisto, some time in the next two decades. Europak outer icy shell is the only interface between this putative ocean and the surface, but many aspects of this shell are presently poorly understood; in particular, its composition, thickness, deformational history, and mechanical properties. To discuss the ice shell and our current understanding of it, 78 scientists from the terrestrial and planetary science communities in the United States and Europe gathered for a 3-day workshop hosted by the Lunar and Planetary Institute in Houston in February. A key goal was to bring researchers from disparate disciplines together to discuss the importance and limitations of available data on Europa with a post-Galileo perspective. The workshop featured 2 days of reviews and contributed talks on the composition, physical properties, stratigraphy, tectonics, and future exploration of the ice shell and underlying ocean. The final morning included an extended discussion period, moderated by a panel of noted experts, highlighting outstanding questions and areas requiring future research.
The Zenifim Formation is being considered as a potential disposal formation for a deep borehole nuclear repository concept in Israel. Site selection and repository construction are intended to ensure that waste is separated from circulating groundwater, but long-term deformation of the wellbore could potentially create fluid flow pathways. To understand how time-dependent rock strength could affect wellbore stability, here we conducted creep tests under low to moderate confining pressures on retrieved core from the Zenifim formation. During creep, samples strain slowly as gradual damage accumulation progressively weakens the samples. Failure eventually occurred through the near-instantaneous formation of a shear fracture. Experimental results were used to calibrate a continuum damage poro-elastic model for sandstones. The calibrated damage-poro-elastic model successfully simulates different types of loading experiments including quasi-static and creep. The state of strain in experiments is close to yield during loading as the yield cap continuously evolves with damage accumulation. For creep tests, most damage occurs during triaxial loading. Minor damage accumulation occurs under constant load until the final stage of creep, where damage accelerates and promotes unstable fracturing.
Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure–property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information from 3D microstructure images, they often require a large network architecture and substantial training time. In this research, we trained a graph neural network (GNN) using phase field generated microstructures of Ni-Al alloys to predict the evolution of mechanical properties. We found that a single GNN is capable of accurately predicting the strengthening of Ni-Al alloys with microstructures of varying sizes and dimensions, which cannot otherwise be done with a CNN. Additionally, GNN requires significantly less GPU utilization than CNN and offers more interpretable explanation of predictions using saliency analysis as features are manually defined in the graph. We also utilize explainable artificial intelligence tool Bayesian Inference to determine the coefficients in the power law equation that governs coarsening of precipitates. Overall, our work demonstrates the ability of the GNN to accurately and efficiently extract relevant information from material microstructures without having restrictions on microstructure size or dimension and offers an interpretable explanation.
The COVID-19 pandemic demonstrated the insufficiency of a reactive approach to emerging zoonotic pathogens. With spillover increasing in frequency as environments change and the human footprint continues to grow, pandemic prevention will require predictive models that can identify (i) potential zoonoses with a high likelihood of emergence and (ii) environmental or other features that may trigger a shift in host, vector, or pathogen baselines associated with emergence and/or spillover. Artificial intelligence (AI), and particularly its machine learning and deep learning branches, holds enormous potential for detecting shifts in large-scale biodiversity and disease datasets (genomic, ecological, geospatial, etc.). Such algorithms can be trained to identify subtle patterns in large volumes of data to yield insights into complex phenomena for which we have limited knowledge of the true cause(s) or predictor(s), as is the case for emerging infectious diseases.
Ablative thermal protection systems (TPS) are essential for high speed entry of planetary atmospheres, such as those of Earth and Mars. Upon entry the kinetic energy of the spacecraft is converted into thermal energy, leading to high heat fluxes at the wall of the craft. Because of this extreme heating, a robust ablative TPS material must be selected. A common material selection today is phenolic-impregnated carbon ablator (PICA), which is a low-density carbon material known for producing dust that is not suitable to a cleanroom environment. To mitigate dust created by a PICA heatshield, a silicone-based spray called NuSil is applied to the surface of the TPS, creating PICA-NuSil (PICA-N). PICA-N has been observed to have a different material response from regular PICA during high enthalpy flow testing, producing surface temperatures up to 200K less than those seen for PICA [1]. To better understand this phenomenon, it is critical that robust methods of PICA-N material characterization are developed. The purpose of this project is to investigate Object Research Systems’ (ORS) Dragonfly deep learning tools as a means of accurately segmenting and characterizing PICA-N. Systematic testing of this software has shown that Dragonfly deep learning tools have strong potential for accurate segmentation/ characterization of PICA-N and other TPS materials.
The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.
Microbial ecosystems based on the energy supplied by water-rock chemistry carry particular significance in the context of geo- and astrobiology. With no direct dependence on solar energy, lithotrophic microbes could conceivably penetrate a planetary crust to a depth limited only by temperature or pressure constraints (several kilometers or more). The deep lithospheric habitat is thereby potentially much greater in volume than its surface counterpart, and in addition offers a stable refuge against inhospitable surface conditions related to climatic or atmospheric evolution (e.g., Mars) or even high-energy impacts (e.g., early in Earth's history). The possibilities for a deep microbial biosphere are, however, greatly constrained by life s need to obtain energy at a certain minimum rate (the maintenance energy requirement) and of a certain minimum magnitude (the energy quantum requirement). The mere existence of these requirements implies that a significant fraction of the chemical free energy available in the subsurface environment cannot be exploited by life. Similar limits may also apply to the usefulness of light energy at very low intensities or long wavelengths. Quantification of these minimum energy requirements in terrestrial microbial ecosystems will help to establish a criterion of energetic habitability that can significantly constrain the prospects for life in Earth's subsurface, or on other bodies in the solar system. Our early work has focused on quantifying the biological energy quantum requirement for methanogenic archaea, as representatives of a plausible subsurface metabolism, in anoxic sediments (where energy availability is among the most limiting factors in microbial population growth). In both field and laboratory experiments utilizing these sediments, methanogens retain a remarkably consistent free energy intake, in the face of fluctuating environmental conditions that affect energy availability. The energy yields apparently required by methanogens in these sediment systems for sustained metabolism are about half that previously thought necessary. Lowered energy requirements would imply that a correspondingly greater proportion of the planetary subsurface could represent viable habitat for microorganisms.
Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.
Hydrous and nominally anhydrous minerals are a fundamental class of solids of enormous significance to geophysics. They are the water carriers in the deep geological water cycle and impact structural, elastic, plastic, and thermodynamic properties and phase relations in Earth's forming aggregates (rocks). They play a critical role in the geochemical and geophysical processes that shape the planet. Their complexity has prevented predictive calculations of their properties, but progress in materials simulations ushered by machine-learning potentials is transforming this state of affairs. Here, we adopt a hybrid approach that combines deep learning potentials (DPs) with the strongly constrained and appropriately normed meta-generalized gradient approximation functional to simulate a prototypical hydrous system. We illustrate the success of this approach to simulate δ − AlOOH ( δ ) , a phase capable of transporting water down to near the core-mantle boundary of the Earth ( ∼ 2900 km depth and ∼ 135 GPa ) in subducting slabs. A high-throughput sampling of phase space using molecular dynamics simulations with DPs sheds light on the hydrogen-bond behavior and proton diffusion at geophysical conditions. These simulations provide a pathway for a deeper understanding of these crucial components that shape Earth's internal state. Published by the American Physical Society 2024