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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cybersecurity Framework Profile for Electric Vehicle Extreme Fast Charging Infrastructure

This document is the Cybersecurity Framework Profile (Profile) developed for the Electric Vehicle Extreme Fast Charging (EV/XFC) ecosystem, including the four domains that relies on the ecosystem (i) Electric Vehicles (EV); (ii) Extreme Fast Charging (XFC); (iii) XFC Cloud or Third-Party Operations; and (iv) Utility and Building Networks. This Profile utilizes the NIST Cybersecurity Framework Version 1.1 and provides voluntary guidance to help relevant parties develop Profiles specific to their organization to understand, assess, and communicate their cybersecurity posture as a part of their risk management process. The Profile is intended to supplement, not replace, an existing risk management program or cybersecurity standards, regulations, and industry guidelines that are in current use by the EV/XFC industry.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. Here, the proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Overview of the ICRF heating system in SPARC

Ion Cyclotron Range of Frequencies (ICRF) heating is a critical system for the SPARC mission of demonstrating net positive fusion gain. In the first campaign, 20 MW of installed power at 120 MHz will be supplied to 10 four-strap antennas, arranged in poloidal pairs. The matching network is designed to accommodate for a range of loading conditions and a fast controller adjusts the matching during a pulse with frequency modulation of ±1 MHz. For the main L-mode scenario of the first experimental campaign, the power coupling and absorption is analysed in this work. Several feeding schemes are compared and limits on the amount of the total coupled power are evaluated. Coupling of the full 20 MW power is possible well within the limit of the peak electric field in the transmission line of 15 kV/cm. The 3 He minority and 2 nd harmonic T heating scheme is validated in full-wave modelling to be efficient for heating plasma in the SPARC L-mode Q>1 scenario, with dominant ion heating and efficient single-pass absorption. A range of ICRF wave parameters and minority concentration would be suitable for operation and allow tailoring the heating properties for plasma conditions and experimental objectives.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1980

Pioneer Venus Overview

A Pioneer Venus Overview is discussed. Pioneer Venus consists of two basic spacecraft: Orbiter and Multiprobe. The latter was separated into five separate vehicles near Venus. These were the probe transporter (called the Bus), a large atmospheric entry probe (dubbed Sounder) and three identical smaller probes (called North , Day, and Night in accordance with their entry locations). At Venus all six spacecraft communicated directly back to the Earth-based Deep Space Network (DSN) and, in the case of the Multiprobe mission, to two special receiving sites near Guam and Santiago (Chile). At the time this Special Issue was submitted for publication the nominal mission was complete and the Orbiter was continuing into an extended mission phase. It appears so that sufficient fuel remains to permit full operation through calendar year 1980, at least. The scientific payload, Principle Investigator, and his affiliation are listed for each Pioneer Venus spacecraft. This special issue is primarily devoted to short descriptions of the instruments listed with the exception of the Orbitor Cloud Photopolarimeter. Detailed instrument descriptions for this experiment have been published. Before proceeding with descriptions of the individual instruments, four special archival-type reports are included. The first deals with spacecraft design and operation. The Pioneer Venus spacecraft were unique and very special design features and operational modes needed to be incorporated. These are summarized therein. It should be noted that neither the scientific objectives nor the scientific results of the Pioneer Venus program are described or discussed in detail. The objectives have been published elsewhere.

Colin, Lawrence↗
NASA NTRS2015

A Journey with MOM

In late 2013, the Indian Space Research Organization (ISRO) launched its "Mars Orbiter Mission" (MOM). ISRO engaged NASA's Jet Propulsion Laboratory (JPL) for navigation services to support ISRO's objectives of MOM achieving and maintaining Mars orbit. The navigation support included planning, documentation, testing, orbit determination, maneuver design /analysis, and tracking data analysis. Several of MOM's attributes had an impact on navigation processes, e.g., S -band telecommunications, Earth Orbit Phase maneuvers, and frequent angular momentum desaturation s (AMDs). The primary source of tracking data was NASA/ JPL's Deep Space Network (DSN); JPL also conducted a performance assessment of Indian Deep Space Network (IDSN) tracking data. Planning for the Mars Orbit Insertion (MOI) was complicated by a pressure regulator failure that created uncertainty regarding MOM's main engine and raised potential planetary protection issues. A successful main engine test late on approach resolved these issues; it was quickly followed by a successful MOI on 24-September - 2014 at 02:00 UTC. Less than a month later, Comet Siding Spring's Mars flyby necessitated plans to minimize potential spacecraft damage. At the time of this writing, MOM's orbital operations continue, and plans to extend JPL 's support are in progress. This paper covers the JPL 's support of MOM through the Comet Siding Spring event.

Mars orbiter↗
NASA NTRS2003

A Framework for Intelligent Rocket Test Facilities with Smart Sensors

A long-term center goal at the John C. Stennis Space Center (SSC) is the formulation and implementation of a framework for an Intelligent Rocket Test Facility (IRTF), which incorporates distributed smart sensor elements. The IRTF is to provide reliable, high-confident measurements. Specific objectives include: 1. Definition of a framework and architecture that supports implementation of highly autonomous methodologies founded on basic physical principles and embedded knowledge. 2. Modeling of autonomous sensors and processes as self-sufficient, evolutionary elements. 3. Development of appropriate communications protocols to enable the complex interactions that must take place to allow timely and high-quality flow of of information among all the autonomous elements of the system. 4. Development of lab-scale prototypes of key system elements. Though our application is next-generation rocket test facilities, applications for the approach are much wider and include monitoring of shuttle launch operations, air and spacecraft operations and health monitoring, and other large-scale industrial system operations such as found in processing and manufacturing plans. Elements of prototype IRTF have been implemented in preparation for advanced development and validation using rocket test stand facilities as SSC. This work has identified issues that are important to further development of complex network and should be of interest to other working with sensor networks.

Figueroa, Fernando↗
NASA NTRS2021

A Geodetic and Positioning Thematic Layer - Identifying tools to connect the GGRF and IGIF

Effective and sustainable modernization of a nation’s geodetic framework relies on the ability of the relevant organizations and other stakeholders to communicate, integrate, and align both their strategic objectives and operational planning with the United Nations Committee of Experts on Global Geospatial Information Management (UN GGIM) Sub-Committee on Geodesy’s (SCoG) roadmap for a Global Geodetic Reference Frame (GGRF) for Sustainable Development. In addition, to implement geodetic modernization initiatives through multiple government agencies, a holistic country action plan (CAP) for geospatial information management must incorporate pertinent geodetic outcomes and outputs. Presently, the CAP framework and principles used by nations is the UN GGIM Integrated Geospatial Information Framework (IGIF). From engagement with geospatial and survey communities across emerging nations in the Asia Pacific region, it is evident more assistance and coordination is necessary to articulate, integrate and connect geodetic organizational strategies with the requirements of the GGRF roadmap; the strategic pathways of the IGIF; and resourcing for a meaningful and relevant multi-faceted CAP. One of the supporting mechanisms for such planning or preparation, which representatives of the International Federation of Surveyors (FIG) Asia Pacific Capacity Development Network (AP CDN) and the UN SCoG Education, Training and Capacity Building (ETCB) working group are considering, is a policy framework and guide for a “Geodetic and Positioning Thematic Layer” (GPTL). Essentially, this “thematic layer”, in terms of the GGRF and IGIF, aims to provide a comprehensive understanding of, and a toolbox for, the “why, what, how, and who” of geodesy and positioning. This discussion paper will provide background and insights for a GPTL dedicated to recognizing, and aligning the geodetic capacity development needs with broader geospatial information management issues and applications. The paper will also outline a rigorous, participatory, and inclusive consultation process for the design and development of a thematic layer. Furthermore, as the intention of the authors is to prepare a forthcoming “white paper” that will concisely describe the issues of a GPTL and initiatives for an appropriate guide and/or policy framework, this paper will seek feedback on: the potential scientific, social, environmental and political benefits of modernizing geodetic infrastructure and systems; the challenges associated with the GGRF roadmap; leveraging the geodesy-relevant elements of each IGIF strategic pathway; the importance of collaborative efforts; and the capacity development needs and resources in relation to governance, technology and people.

SARIB, Rob↗
DOE OSTI2023

Machine learning-based real-time kinetic profile reconstruction in DIII-D

Abstract Kinetic equilibrium reconstruction plays a vital role in the physical analysis of plasma stability and control in fusion tokamaks. However, the traditional approach is subjective and prone to human biases. To address this, the consistent automatic kinetic equilibrium reconstruction (CAKE) method was introduced, providing objective results. Nonetheless, its offline nature limits its application in real-time plasma control systems (PCSs). To address this limitation, we present RTCAKENN, a machine learning model that approximates 7 CAKE-level output profiles, namely pressure, inverse q , toroidal current density, electron temperature and density, carbon ion impurity temperature and rotation profiles, using real-time available inputs. The deep neural network consists of an encoder layer, where the scalars and interdependent inputs such as plasma boundary coordinates and motional Stark effect data are encoded using multi-layer perceptrons (MLPs), while profile inputs are encoded by 1D convolutional layers. The encoded data is passed through a MLP for latent feature extraction, before being decoded in the decoding layers, which consist of upsampling and convolutional layers. RTCAKENN has been implemented in the DIII-D PCS and our model achieves accuracy comparable to CAKE and surpasses existing real-time alternatives. Through clever dropout training, RTCAKENN exhibits robustness and can operate even in the absence of Thomson scattering data or charge exchange recombination data. It executes in under 8 ms in the real-time environment, enabling future application in real-time control and analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Scalable algorithms for physics-informed neural and graph networks

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

42 ENGINEERING↗
NASA NTRS1988

Using AGNESS (A Generalized Network-based Expert System Shell) for matching images

The image correspondence problem is considered the most difficult step in both stereo and motion analysis. Stereo vision is useful in determining the 3-D positions of points on visible surface in a scene. Motion analysis is useful in determining the spatial and temporal relationships of objects in an environment. Besides stereo and motion analysis, there is the image correspondence problem. Most of this work is based on point or local area properties of the observed gray level values in 2-D images. A global and general approach to this problem is described by using a knowledge-based system. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions extracted from the given images. The physical component depends on features of the edge or region) in isolation. The spatial component involves the set of edges and regions adjacent to a given edge (or region) of the first image and the set of edges and regions adjacent to each potentially matching edge (or region) of the second image; thus the spatial context of each edge or region is considered. A computational network is used to represent this knowledge, it allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. An expert system shell called AGNESS (A Generalized Network-based Expert System Shell) is used to build a prototype system.

Pong, Ting-Chuen↗
NASA NTRS2011

Design, Development and Pre-Flight Testing of the Communications, Navigation, and Networking Reconfigurable Testbed (Connect) to Investigate Software Defined Radio Architecture on the International Space Station

The Communication Navigation and Networking Reconfigurable Testbed (CoNNeCT) is a NASA-sponsored mission, which will investigate the usage of Software Defined Radios (SDRs) as a multi-function communication system for space missions. A softwaredefined radio system is a communication system in which typical components of the system (e.g., modulators) are incorporated into software. The software-defined capability allows flexibility and experimentation in different modulation, coding and other parameters to understand their effects on performance. This flexibility builds inherent redundancy and flexibility into the system for improved operational efficiency, real-time changes to space missions and enhanced reliability/redundancy. The CoNNeCT Project is a collaboration between industrial radio providers and NASA. The industrial radio providers are providing the SDRs and NASA is designing, building and testing the entire flight system. The flight system will be integrated on the Express Logistics Carrier (ELC) on the International Space Station (ISS) after launch on the H-IIB Transfer Vehicle in 2012. This paper provides an overview of the technology research objectives, payload description, design challenges and pre-flight testing results.

Over, Ann P.↗
DOE OSTI2024

Intelligent Manufacturing for Extreme Environments Conference Proceedings

The Intelligent Manufacturing for Extreme Environments (IMEE) workshop was held at the Center for Advanced Energy Studies (CAES) in Idaho Falls, Idaho, May 2–3, 2023, in support of the United States (U.S.) National Science Foundation (NSF) Established Program to Stimulate Competitive Research: Workshop Opportunities (EPSCoR-WO) program. This workshop featured keynote speakers, panels, and breakout sessions with 58 participants. Nuclear reactors need to operate under extreme service conditions, such as high temperatures, corrosive environments, and high-radiation doses. Hence, reactor components must be able to withstand those conditions. The participants envision a future where on demand manufacture of components for small modular reactors (SMRs), microreactors, and other advanced reactor designs are possible. In this future, regulatory bodies accept validated manufacturing processes and standardized feedstocks, thus eliminating the need for individual component testing. However, the necessary technologies and regulatory policies needed for this future do not exist today. Successful innovation would revolutionize the nuclear power sector, enable fast commercial development, create economic opportunities in the U.S., reduce the carbon footprint and associated risks, and promote a skilled and highly competitive workforce. The objective of the workshop was to convene world-class experts, researchers, educators, and students to identify gaps and envision solutions for five interrelated challenges for intelligent manufacturing in extreme environments. The key outcomes of the conference were: (1) to take the opportunity for researchers and educators to network and form collaborations; and (2) to produce a full report to inform policy-makers, industry, and the academic community of various challenges and opportunities in the nuclear energy sector.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Scientific investigations at a lunar base

Scientific investigations to be carried out at a lunar base can have significant impact on the location, extent, and complexity of lunar surface facilities. Among the potential research activities to be carried out are: (1) Lunar Science: Studies of the origin and history of the Moon and early solar system, based on lunar field investigations, operation of networks of seismic and other instruments, and collection and analysis of materials; (2) Space Plasma Physics: Studies of the time variation of the charged particles of the solar wind, solar flares and cosmic rays that impact the Moon as it moves in and out of the magnetotail of the Earth; (3) Astronomy: Utilizing the lunar environment and stability of the surface to emplace arrays of astronomical instruments across the electromagnetic spectrum to improve spectral and spatial resolution by several orders of magnitude beyond the Hubble Space Telescope and other space observatories; (4) Fundamental physics and chemistry: Research that takes advantage of the lunar environment, such as high vacuum, low magnetic field, and thermal properties to carry out new investigations in chemistry and physics. This includes material sciences and applications; (5) Life Sciences: Experiments, such as those that require extreme isolation, highly sterile conditions, or very low natural background of organic materials may be possible; and (6) Lunar environmental science: Because many of the experiments proposed for the lunar surface depend on the special environment of the Moon, it will be necessary to understand the mechanisms that are active and which determine the major aspects of that environment, particularly the maintenance of high-vacuum conditions. From a large range of experiments, investigations and facilities that have been suggested, three specific classes of investigations are described in greater detail to show how site selection and base complexity may be affected: (1) Extended geological investigation of a complex region up to 250 kilometers from the base requires long range mobility, with transportable life support systems and laboratory facilities for the analysis of rocks and soil. Selection of an optimum base site would depend heavily on an evaluation of the degree to which science objectives could be met. These objectives could include lunar cratering, volcanism, resource surveys or other investigations; (2) An astronomical observatory initially instrumented with a VLF radio telescope, but later expanding to include other instruments, requires site preparation capability, "line shack" life support systems, instrument maintenance and storage facilities, and sortie mode transportation. A site perpetually shielded from Earth is optimum for the advanced stages of a lunar observatory; (3) an experimental physics laboratory conducting studies requiring high vacuum facilities and heavily instrumented experiments, is not highly dependent on lunar location, but will require much more flexibility in experiment operation and EVA capability, and more sophisticated instrument maintenance and fabrication facilities.

Extraterrestrial Environment↗
DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A dataset of eco-evidence tools to inform early-stage environmental impact assessments of hydropower development

The datasets described herein provide the foundation for a decision support prototype (DSP) toolkit aimed at assisting stakeholders in determining evidence of which aspects of river ecosystems have been impacted by hydropower. The DSP toolkit and its application are presented and described in the article “Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development” [1]. Development of the DSP and the output for decision support centralize around 42 river function indicators describing the dimensionality of river ecosystems through six main categories: biota and biodiversity, water quality, hydrology, geomorphology, land cover, and river connectivity. Three main tools are represented in the DSP: A science-based questionnaire (SBQ), an environmental envelope model (EEM), and a river function linkage assessment tool (RFLAT). The SBQ is a structured survey-style questionnaire whose objective is to provide evidence of which indicators have been impacted by hydropower. Based on a global literature review, 140 questions were developed from general hypotheses regarding the impacts of dams on rivers. The EEM is a model to predict the likelihood of hydropower impacting indicators based on a several variables. The intended use of the EEM is for situations of new hydropower development where results of the SBQ are incomplete or highly uncertain. The EEM was developed through the compilation of a dataset containing attributes of dams, reservoirs, and geospatial information on environmental concerns, which was combined with data on ecological indicators documented at those sites through literature review. The model operates through 247 “envelopes” and weighting factors, representing the individual effect of each variable on each indicator, all available through spreadsheets. Finally, the RFLAT is a tool to examine causal relationships amongst indicators. Inter-indicator relationships were hypothesized based on literature review and summarized into node and edge datasets to represent the structure of a graphical network. Bayes theorem was used estimate conditional probabilities of inter-indicator relationships based on the output of the SBQ. Nodes and edges were imported into R programming environment to visualize ecological indicator networks. The datasets can be expanded upon and enriched with more detailed questions for the SBQ, building upon the EEM with to develop more sophisticated models, and identifying new relationships for the RFALT. Additionally, once the tools are applied to numerous hydropower developments, the output of the tools (e.g. evidence of impacted indicators) becomes a very useful dataset for meta-analyses of hydropower impacts.

13 HYDRO ENERGY↗
DOE OSTI2023

Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning (RAPID)

X-ray ptychographic tomography is a nondestructive method for three dimensional (3D) imaging with nanometer-sized resolvable features. The size of the volume that can be imaged is almost arbitrary, limited only by the penetration depth and the available scanning time. Here we present a method that rapidly accelerates the imaging operation over a given volume through acquiring a limited set of data via large angular reduction and compensating for the resulting ill-posedness through deeply learned priors. The proposed 3D reconstruction method “RAPID” relies initially on a subset of the object measured with the nominal number of required illumination angles and treats the reconstructions from the conventional two-step approach as ground truth. It is then trained to reproduce equal fidelity from much fewer angles. After training, it performs with similar fidelity on the hitherto unexamined portions of the object, previously not shown during training, with a limited set of acquisitions. In our experimental demonstration, the nominal number of angles was 349 and the reduced number of angles was 21, resulting in a x140 aggregate speedup over a volume of 4.48 x 93.18 x 3.92 μm 3 and with (14nm) 3 feature size, i.e. ~ 10 8 voxels. RAPID’s key distinguishing feature over earlier attempts is the incorporation of atrous spatial pyramid pooling modules into the deep neural network framework in an anisotropic way. We found that adjusting the atrous rate improves reconstruction fidelity because it expands the convolutional kernels’ range to match the physics of multi-slice ptychography without significantly increasing the number of parameters.

47 OTHER INSTRUMENTATION↗
NASA NTRS2015

Standardized Modular Power Interfaces for Future Space Explorations Missions

Earlier studies show that future human explorations missions are composed of multi-vehicle assemblies with interconnected electric power systems. Some vehicles are often intended to serve as flexible multi-purpose or multi-mission platforms. This drives the need for power architectures that can be reconfigured to support this level of flexibility. Power system developmental costs can be reduced, program wide, by utilizing a common set of modular building blocks. Further, there are mission operational and logistics cost benefits of using a common set of modular spares. These benefits are the goals of the Advanced Exploration Systems (AES) Modular Power System (AMPS) project. A common set of modular blocks requires a substantial level of standardization in terms of the Electrical, Data System, and Mechanical interfaces. The AMPS project is developing a set of proposed interface standards that will provide useful guidance for modular hardware developers but not needlessly constrain technology options, or limit future growth in capability. In 2015 the AMPS project focused on standardizing the interfaces between the elements of spacecraft power distribution and energy storage. The development of the modular power standard starts with establishing mission assumptions and ground rules to define design application space. The standards are defined in terms of AMPS objectives including Commonality, Reliability-Availability, Flexibility-Configurability and Supportability-Reusability. The proposed standards are aimed at assembly and sub-assembly level building blocks. AMPS plans to adopt existing standards for spacecraft command and data, software, network interfaces, and electrical power interfaces where applicable. Other standards including structural encapsulation, heat transfer, and fluid transfer, are governed by launch and spacecraft environments and bound by practical limitations of weight and volume. Developing these mechanical interface standards is more difficult but an essential part of defining physical building blocks of modular power. This presentation describes the AMPS projects progress towards standardized modular power interfaces.

Spacecraft Power↗