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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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Reference data for advanced microwave and laser communication and tracking systems - Vol. 3
Advanced energy algorithms running at big-data scale will be necessary to identify, realize, and verify energy savings to meet government and utility goals of building energy efficiency. Any algorithm must be well characterized and validated before it is trusted to run at these scales. Smart meter data from real buildings will ultimately be required for the development, testing, and validation of these energy algorithms and processes. However, for initial development and testing, smart meter data are difficult to work with due to privacy restrictions, noise from unknown sources, data accessibility, and other concerns which can complicate algorithm development and validation. This paper describes a new methodology to generate synthetic smart meter data of electricity use in buildings using detailed building energy modeling, which aims to capture the variability and stochastics of real energy use in buildings. The methodology can create datasets tailored to represent specific scenarios with known truth and controllable amounts of synthetic noise. Knowledge of ground truth also allows the development and validation of enhanced processes which leverage building metadata, such as building type or size (floor area), in addition to smart meter data. The methodology described in this paper includes the key influencing factors of real-world building energy use including weather data, occupant-driven loads, building operation and maintenance practices, and special events. Data formats to support workflows leveraging both synthetic meter data and associated metadata are proposed and discussed. Finally, example use cases of the synthetic meter data are described to illustrate potential applications.
Contractor data requirements relating to advanced missions and potential follow-up programs of Voyager project
Along with the smart grid development, modern power systems are entering a ‘data-intensive’ era. A vast volume of data from power grids is being collected through advanced sensing and communication technologies, such as smart metering data, phasor measurement data, as well as meteorological data (e.g., wind speed and solar irradiance) related to renewable power generation. Such data contains comprehensive information about the power system covering equipment's health status, power grid's static and dynamic characteristics, renewable power generation, customers’ electricity usage pattern, etc. Therefore, advanced data-analytics techniques are needed to convert such data to knowledge for practical applications. In line with the trend of widespread data-driven applications in power systems, this Special Issue aims to present state-of-the-art research works on advanced data-analytics for power system's operation, control, and situational awareness. There are in total twenty-six papers accepted for publication in this Special Issue through careful peer reviews and revisions. Under the overarching theme of data-driven applications in power systems, the selected papers are broadly categorised into five topics. The summary of every topic is given below. You are, however, strongly encouraged to read the full paper if interested.
The Sun lies at the center of space weather and is the source of its variability. The primary input to coronal and solar wind models is the activity of the magnetic field in the solar photosphere. What is needed is to develop physics-based models for the dynamics of the magnetic field from the deep convection zone of the Sun to the corona with the goal of providing robust near real-time boundary conditions at the base of space weather forecast models. NASA strategic capabilities efforts toward this goal will be summarized during the talk. These have the objective to enable characterizing and predicting the magnetic field structure and flow dynamics of the Sun by assimilating data from helioseismology and magnetic field observations into physics-based realistic magnetohydrodynamics (MHD) simulations. It is argued that an effort in integrating first-principle modeling of solar magnetism and flow dynamics with real-time observational data via advanced data assimilation methods is needed for transformative steps in space weather research and prediction.
Advanced multirotor vertical flight aircraft concepts are emerging faster than rigorous individualized tests can investigate their utility and performance. There are several analysis tools that predict multirotor performance and flow characteristics, but the accuracy of these predictions is still being debated due to lack of experimental data from multirotor tests that are needed to validate the analyses. The objective of this paper is to simulate multirotor configurations using two different mid-fidelity rotorcraft analysis tools, Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM) and Rotorcraft Computational Fluid Dynamics (RotCFD), and compare the simulation results to experimental data from a wind tunnel test of the Multirotor Test Bed (MTB). The MTB, developed by NASA Ames Research Center, is a new capability for testing a wide array of advanced vertical take-off and landing (VTOL) rotor configurations, with a primary focus on testing in the U.S. Army 7-by 10-Foot Wind Tunnel at NASA Ames Research Center. The MTB was designed to allow adjustment of the vertical, lateral, and longitudinal placement of up to six rotors, as well as allow tilt adjustment of each rotor and pitch adjustment of the whole assembly. The six-axis load cells under each rotor give the MTB the capability of measuring the rotor performance in a wide array of configurations. The overall goal of the MTB project is to help gain a better understanding of the performance, control, interactional aerodynamics, and acoustics of multirotor and tilting-rotor systems. For the work presented here, the MTB data were used to validate RotCFD and CHARM results for several multirotor test configurations. With confidence in both analyses established by the validation exercise, additional simulations were performed to explore quadrotor configurations that will be tested during the MTB’s second wind tunnel entry planned for 2022. This second tunnel entry will examine quadrotor configurations that represent published NASA reference designs for urban air mobility concept vehicles. Results from this paper confirm the ability of RotCFD and CHARM to simulate multirotor aerodynamic interactions on individual rotor performance under edgewise forward-flight conditions.
The development of large scale data processing systems for remote sensing is studied by evaluating: (1) the suitability of several sensor types with regard to producing data required for multispectral machine analysis; (2) various types of data preprocessing necessary to prepare such data for analysis; and (3) transfer of machine processing techniques for earth resources data to user community.
Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genomewide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.
Advances in the use of autoregressive models, pattern recognition methods, and hidden Markov models for on-line health monitoring of dynamic systems (such as DSN antennas) have recently been reported. However, the algorithms described in previous work have the significant drawback that data acquired under fault conditions are assumed to be available in order to train the model used for monitoring the system under observation. This article reports that this assumption can be relaxed and that hidden Markov monitoring models can be constructed using only data acquired under normal conditions and prior knowledge of the system characteristics being measured. The method is described and evaluated on data from the DSS 13 34-m beam wave guide antenna. The primary conclusion from the experimental results is that the method is indeed practical and holds considerable promise for application at the 70-m antenna sites where acquisition of fault data under controlled conditions is not realistic.
Focal Area: This white paper proposes advanced data-driven and computing techniques to improve the predictability of extreme climate events (precipitation) and focuses on comprehending their impact dynamics to enhance resilience of urban systems. Science Challenge: In urban systems, climate change has presented significant challenges related to accurate spatiotemporal prediction of extreme climate events (heavy precipitation) and their impacts, i.e. flooding. The occurrence of extreme climate events and their compounding repercussions on the built environment make urban systems increasingly more vulnerable. The interactions between extreme climate events, urban systems, and the corresponding impacts are irregular, non-linear, and dynamic, making it difficult to standardize resilience. As it stands, the management of complex urban systems is affected by uncertainties related to the nature of short- and long-term consequences caused by extreme events.
Advances in scanning probe microscopy (SPM) methods such as time-resolved electrostatic force microscopy (trEFM) now permit the mapping of fast local dynamic processes with high resolution in both space and time, but such methods can be time-consuming to analyze and calibrate. Here, we design and train a regression neural network (NN) that accelerates and simplifies the extraction of local dynamics from SPM data directly in a cantilever-independent manner, allowing the network to process data taken with different cantilevers. We validate the NN’s ability to recover local dynamics with a fidelity equal to or surpassing conventional, more time-consuming, calibrations using both simulated and real microscopy data. We apply this method to extract accurate photoinduced carrier dynamics on n = 1 butylammonium lead iodide, a halide perovskite semiconductor film that is of interest for applications in both solar photovoltaics and quantum light sources. Lastly, we use SHapley Additive exPlanations to evaluate the robustness of the trained model, confirm its cantilever-independence, and explore which parts of the trEFM signal are important to the network.
This viewgraph presentation gives an overview of the requirements and uses of non-volatile memory (NVM) in advanced onboard data processing systems. Supercomputing in space presents the only viable approach to the bandwidth problem (can't get data down to Earth), controlling constellations of cooperating satellites, reducing mission operating costs, and real-time intelligent decision making and science data gathering. Details are given on the REE vision and impact on NASA and Department of Defense missions, objectives of REE, baseline architecture, and issues. NVM uses and requirements are listed.
Advancements in liquid propellant management science and technologies are key to increasing safety, decreasing cost, and increasing payload mass of NASA space missions. Liquid propellant usually comprises a large portion of the total mass of launch vehicles and spacecraft, so predicting and controlling the motion of it are important. Computational fluid dynamics (CFD) programs are critical to predicting slosh dynamics, but CFD programs require experimental validation with physically relevant test data before the results can be trusted. Low-gravity slosh test data is lacking, and most of what exists is inadequate for CFD validation. A potential slosh risk for Orion, and the lack of validated low-gravity slosh models, led to performing on-orbit slosh tests during the Artemis 1 mission to assess the impacts of slosh on the guidance, navigation, and control of Orion. This work details the validation of a coupled slosh-motion CFD model using the Orion orbital slosh test data set.
Advancements in liquid propellant management science and technologies are key to increasing safety, decreasing cost, and increasing payload mass of NASA space missions. Liquid propellant usually comprises a large portion of the total mass of launch vehicles and spacecraft, so predicting and controlling the motion of it are important. Computational fluid dynamics (CFD) programs are critical to predicting slosh dynamics, but CFD programs require experimental validation with physically relevant test data before the results can be trusted. Low-gravity slosh test data is lacking, and most of what exists is inadequate for CFD validation. A potential slosh risk for Orion, and the lack of validated low-gravity slosh models, led to performing on-orbit slosh tests during the Artemis 1 mission to assess the impacts of slosh on the guidance, navigation, and control of Orion. This work details the validation of a coupled slosh-motion CFD model using the Orion orbital slosh test data set.
Understanding the Earth's climate and how it responds to climate perturbations requires knowledge of how atmospheric moisture, clouds, latent heating, the large-scale circulation and energy fluxes vary with changing climatic conditions. The physical process linking these climate elements is precipitation. Accurate knowledge of how precipitation varies in space and time and how it couples with other atmospheric variables is essential for understanding the global water and energy cycle. In recent years, TRMM data products have played a key role in advancing the field of data assimilation to provide better global analyses for climate research and numerical weather prediction. TRMM research has demonstrated the effectiveness of microwave-based rainfall and total precipitable water (TPW) observations in improving the quality of assimilated datasets and upgrading forecast skills. TRMM latent heating products have also stimulated experimentation with innovative techniques to use this type of information to improve global analyses. We discuss strategies of assimilating TRMM observations at NASA s Data Assimilation Office and present results on the impact assimilating TRMM data on the Goddard Earth Observing System (GEOS) analyses and forecast capabilities.
Advanced airbreathing propulsion systems used in Mach 4-6 mission scenarios, usually consist of a single integrated turboramjet or as in this study, a turbojet housed in an upper bay with a separate ramjet housed in a lower bay. As the engines transition from turbojet to ramjet, there is an operational envelope where both engines operate simultaneously. One nozzle concept under consideration has a common nozzle, where the plumes from the turbojet and ramjet interact with one another as they expand to ambient conditions. In this paper, the two plumes interact at the end of a common 2-D cowl, when they both reach an approximate Mach 3.0 condition and then jointly expand to Mach 3.6 at the common nozzle exit plane. At this condition, the turbojet engine operated at a higher NPR than the ramjet, where the turbojet overpowers the ramjet plume, deflecting it approximately 12 degrees downward and in turn the turbojet plume is deflected 6 degrees upward. In the process, shocks were formed at the deflections and a shear layer formed at the confluence of the two jets. This particular case was experimentally tested and the data used to compare with the PARC3D code with k-kl two equation turbulence model. The 2-D and 3-D centerline CFD solutions are in good agreement, but as the CFD solutions approach the outer sidewall, a slight variance occurs. The outer wall boundary layers are thin and do not present much of an interaction, however, where the confluence interaction shocks interact with the thin boundary layer on the outer wall, strong vortices run down each shock causing substantial disturbances in the boundary layer. These disturbances amplify somewhat as they propagate downstream axially from the confluence point. The nozzle coefficient (CFG) is reduced 1/2 percent as a result of this sidewall interaction, from 0.9850 to 0.9807. This three-dimensional reduction is in better agreement with the experimental value of 0.9790.
Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.
Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive bimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a bimodal visualization. Here, we propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large bimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization techniques. We demonstrate our approach using synthetic examples, industrial phantom objects created to stress bimodal scanning techniques, and real-world objects, and we discuss expert feedback.