Search NASA⌕ Search

SEARCH · Search NASA

Results for “Software defined networking”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

370 records · Page 21

Multiple Uplinks Per Antenna (MUPA) Signal Acquisition Schemes

The Deep Space Network (DSN) currently makes use of the technique of Multiple Spacecraft per Antenna (MSPA) where a single antenna is used to track multiple spacecraft downlinks within its beam, such as in the case of multiple spacecraft orbiting Mars at 8.4 GHz (X-band). It is desired to extend this technique to the uplink where a single station is used to send a signal to multiple spacecraft in order to make more efficient use of ground resources. This would be applicable to numerous smallsat constellations being considered for future missions or to future spacecraft at Venus, Mars, or more distant destinations that are all within the half-power beamwidth of a single 34-m diameter antenna. In one scheme, each spacecraft’s command sequences would be time multiplexed onto a single uplink frequency. Each spacecraft would lock onto the uplink signal and would accept only commands intended for it via special identifier codes. Each spacecraft would also emit a downlink signal to the ground that is coherent with the uplink signal but would have its own allocated frequency channel and identifier information. A couple of key challenges associated with using this technique need to be addressed. Because of the single uplink frequency, coherent turnaround for two-way Doppler and ranging would not conform to established ratios, thus the radios employed by the spacecraft would need to be capable of variable turnaround ratios. In addition, because of the different orbits or spacecraft trajectories, the relative Doppler shifts and rates can be large with respect to the common uplink signal whose frequency would lie at the centroid of the frequencies of the expected received signals of the constellation. This would be problematic with standard analog spacecraft radios whose acquisition bandwidths are relatively small (~1.7 kHz) relative to the large frequency offsets (~100 kHz) expected using the single frequency uplink technique. With the advent of software defined radios (SDRs), signal frequency search algorithms can be utilized within the flight software and/or programmable hardware (e.g., FPGAs) that can easily acquire and track signals with large frequency offsets and varying dynamics. Such techniques could include FFT search algorithms, step-and-sweep search algorithms, or onboard frequency steering making use of trajectory vectors uplinked to each member spacecraft. Other challenges include mitigation of potential interference between received signals. We have identified several software defined radios that are in different stages of development and whose key parameters have been tabulated. We have examined each radio’s capabilities with respect to acquiring and tracking signals with large frequency offsets. Such analyses made use of previous studies supplemented with specially designed tests using both simulation tools and/or existing testbeds. We have compared signal acquisition times computed from provided algorithms along with measured values derived from tests using existing hardware and simulation tools for the purpose of conducting tradeoff studies between the various radio designs and software/firmware programming approaches.

Abraham, Douglas S.↗

VLSI neuroprocessors

Electronic and optoelectronic hardware implementations of highly parallel computing architectures address several ill-defined and/or computation-intensive problems not easily solved by conventional computing techniques. The concurrent processing architectures developed are derived from a variety of advanced computing paradigms including neural network models, fuzzy logic, and cellular automata. Hardware implementation technologies range from state-of-the-art digital/analog custom-VLSI to advanced optoelectronic devices such as computer-generated holograms and e-beam fabricated Dammann gratings. JPL's concurrent processing devices group has developed a broad technology base in hardware implementable parallel algorithms, low-power and high-speed VLSI designs and building block VLSI chips, leading to application-specific high-performance embeddable processors. Application areas include high throughput map-data classification using feedforward neural networks, terrain based tactical movement planner using cellular automata, resource optimization (weapon-target assignment) using a multidimensional feedback network with lateral inhibition, and classification of rocks using an inner-product scheme on thematic mapper data. In addition to addressing specific functional needs of DOD and NASA, the JPL-developed concurrent processing device technology is also being customized for a variety of commercial applications (in collaboration with industrial partners), and is being transferred to U.S. industries. This viewgraph p resentation focuses on two application-specific processors which solve the computation intensive tasks of resource allocation (weapon-target assignment) and terrain based tactical movement planning using two extremely different topologies. Resource allocation is implemented as an asynchronous analog competitive assignment architecture inspired by the Hopfield network. Hardware realization leads to a two to four order of magnitude speed-up over conventional techniques and enables multiple assignments, (many to many), not achievable with standard statistical approaches. Tactical movement planning (finding the best path from A to B) is accomplished with a digital two-dimensional concurrent processor array. By exploiting the natural parallel decomposition of the problem in silicon, a four order of magnitude speed-up over optimized software approaches has been demonstrated.

Kemeny, Sabrina E.↗

Custom-trained Machine-learning Interatomic Potentials: ZnCl2 Aqueous Solution

This dataset was generated using an iterative active-learning strategy implemented in the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials for aqueous ZnCl2 solutions. Each active-learning cycle consisted of three stages: training, exploration, and labeling. The initial training set combined configurations generated in this work from enhanced-sampling ab initio molecular dynamics simulations with configurations from a previously reported neural-network-potential study of aqueous ZnCl2. The enhanced-sampling ab initio molecular dynamics simulations involved Zn–Cl separation and the chloride coordination number around Zn²? as collective variables. These configurations served as the seed dataset. Subsequent active-learning cycles expanded the training set by identifying and labeling configurations that were poorly represented by the current models, thereby improving coverage of ion-association states and changes in local coordination and charge-state environments relevant to the solution free-energy landscape. For all selected configurations, single-point calculations of the total energies and atomic forces were performed within density functional theory using the CP2K Quickstep module. Reference calculations employed the revPBE-D3 and r2SCAN exchange-correlation functionals. Motivated by recent work on aqueous Zn²?, the main revPBE calculations omitted D3 dispersion contributions involving Zn²?, while retaining the D3 correction for water and chloride. For comparison, fully dispersion-corrected revPBE-D3 reference calculations were also performed, with D3 applied to all species, including Zn²?. Valence electrons were treated explicitly, while core electrons were represented using norm-conserving Goedecker–Teter–Hutter pseudopotentials. The wave functions were expanded using the mixed Gaussian-and-plane-wave scheme with TZV2P-MOLOPT basis sets for all elements and a 600 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent-field convergence was accelerated using the orbital-transformation and Direct Inversion in the Iterative Subspace algorithms, with a convergence threshold of 10?6. All single-point calculations were performed in periodic orthorhombic cells. The CELL_REF keyword in CP2K was used to define a fixed reference cell with a box length of 25 Å. This treatment ensured a consistent reference for configurations extracted from NpT trajectories with fluctuating cell dimensions. The resulting DFT energies and atomic forces constitute the ground-truth labels used to train the MLIPs. The resulting MLIP was trained for aqueous ZnCl2 solutions spanning concentrations from 0 to 30 molal and a broad pH range, from strongly acidic to strongly basic conditions. Representative examples of configurations included in the MLIP training dataset are provided below. These include 1) Representative configurations from the dataset labeled at the revPBE-D3 level, with D3 dispersion interactions involving Zn2+ excluded (revPBE-wo-D3). 2) Representative configurations from the dataset labeled at the fully dispersion-corrected revPBE-D3 level, with D3 interactions applied to all species, including Zn2+ (revPBE-D3). 3) Representative configurations from the dataset labeled at the r2SCAN level of theory (r2SCAN).

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗

Evolution and Reengineering of NASA's Flight Dynamics Facility (FDF)

The NASA Goddard Space Flight Center's Flight Dynamics Facility (FDF) is a multimission support facility that performs ground navigation and spacecraft trajectory design services for a wide range of scientific satellites. The FDF also supports the NASA Space Network by providing orbit determination and tracking data evaluation services for the Tracking Data Relay Satellite System (TDRSS). The FDF traces its history to early NASA missions in the 1960's, including navigation support to the Apollo lunar missions. Over its 40 year history, the FDF has undergone many changes in its architecture, services offered, missions supported, management approach, and business operation. As a fully reimbursable facility (users now pay 100% of all costs for FDF operations and sustaining engineering activities), the FDF has faced significant challenges in recent years in providing mission critical products and services at minimal cost while defining and implementing upgrades necessary to meet future mission demands. This paper traces the history of the FDF and discusses significant events in the past that impacted the FDF infrastructure and/or business model, and the events today that are shaping the plans for the FDF in the next decade. Today's drivers for change include new mission requirements, the availability of new technology for spacecraft navigation, and continued pressures for cost reduction from FDF users. Recently, the FDF completed an architecture study based on these drivers that defines significant changes planned for the facility. This paper discusses the results of this study and a proposed implementation plan. As a case study in how flight dynamics operations have evolved and will continue to evolve, this paper focuses on two periods of time (1992 and the present) in order to contrast the dramatic changes that have taken place in the FDF. This paper offers observations and plans for the evolution of the FDF over the next ten years. Finally, this paper defines the mission model of the future for the FDF based on NASA's current mission list and planning for the Constellation Program. As part of this discussion the following are addressed: the relevance and benefits of a multi-mission facility for NASA's navigation operations in the future; anticipated technologies affecting ground orbit determination; continued incorporation of Commercial Off-the-shelf (COTS) software into the FDF; challenges of a business model that relies entirely on user fees to fund facility upgrades; anticipated changes in flight dynamics services required; and considerations for defining architecture upgrades given a set of cost drivers.

Stengle, Thomas↗

Enhancing Cloud Cybersecurity: Prescriptive Controls for Operational Technology

This whitepaper provides strategic insights and recommendations into security cloud-based solutions for electric utilities, encompassing operational technology (OT), virtual power plants (VPP), distributed energy resources (DERs), applications, networks, and data storage as they transition to and leverage cloud infrastructure through managed service providers (MSPs) and cloud service providers (CSPs). Principles derived from established frameworks serve as a foundation for best practices across cybersecurity projects and remove the constraints of settling on a single framework. For organizations that prefer not to integrate a specific framework altogether, elements of the proposed approach could be adopted or tailored to best fit defined requirements and expected functionalities. The Cirrus assessment, a utility cloud feasibility tool, and the roadmap it provides serve as a precursor to this paper, which seeks to be a valuable resource for defining next steps following cloud technology integration feasibility appraisal. With its comprehensive approach to adoption, the Cirrus framework offers strategic guidance on responsibly preparing for or deploying a utility cloud solution. The previously published whitepaper, “Use Case-Informed Framework for Utility Cloud Migration,” details the guiding strategy, research, and deployment of cloud solutions within electric and interconnected grid systems. Before implementing the controls suggested in this document, it is recommended that stakeholders complete Cirrus's cloud integration assessment and pair the results with their unique cybersecurity controls to form a comprehensive cloud-based utility cybersecurity plan. The Cirrus outcome will consider a series of future architectures for the grid before and after the energy transition and evaluate the arguments for and against cloud applications for each electric and interconnected grid layer. This document is a companion to the original whitepaper, "Use Case-Informed Framework for Utility Cloud Migration" to further identify and recommend security controls based on Cirrus’s cloud integration assessment output. The following whitepaper outlines the cybersecurity controls that secure cloud-service models pertinent to the electric sector using the predefined categories identify, protect, detect, and respond and recover. The objective is to outline prescriptive security controls based on the type of architecture and data stored in the cloud. The focus includes dissecting the shared responsibility model and elucidating what on-premises Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) entail. A pivotal consideration in this context is allocating responsibility for foundational cybersecurity aspects—having used Cirrus for the cloud integration assessment. The ensuing controls detailed herein also represent a checklist of controls necessary for a secure cloud transition, equipping utilities with the knowledge to navigate this digital transformation with confidence and strategic foresight in a safe and responsible manner.

42 ENGINEERING↗

DINGO: Digital assistant to grid operators for resilience management of power distribution system

With increasing adverse weather events and disasters, enabling resiliency of the power distribution system (PDS) is becoming increasingly important. Here in this work, resiliency is defined as the systems ability to keep supplying critical loads even with multiple contingencies. Resiliency may depend on: (a) advanced tools to assist operators in situational awareness and decision making with the increasing volume of data generated by the PDS, (b) visualization and ease of interaction with system resources and information, especially during extreme events and resulting human operator stress, and (c) flexible resources and autonomous control. Operators and support engineers need to interact with the system for key information and take action under stress, given the requirement for decisions in a short time. Integrated technological solutions are prevailing steps to support the most appropriate decision during critical times to serve essential loads. In order to meet the required goals, a Real-time Resiliency Monitoring and Operational Decision Support (RT-RMOD) tool have been developed. It supports various functionalities, including real-time monitoring, resilience assessment, and proactive decision support. However, this work makes advanced feature additions to the tool by developing data-enabled resilience management algorithms for (i) outage detection and localization, (ii) Resiliency-metric driven restoration and reconfiguration, and (iii) NLP based digital assistant for operators called DINGO (DIgital assistaNt to Grid Operators) to interact with Advanced Distribution Management System (ADMS) and RT-RMOD. The developed algorithm was validated for multiple cases of weather events using a real-world, off-grid microgrid system modeled in a real-time simulator, sensor data, and software tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Prototyping Operational Autonomy for Space Traffic Management

Current state of the art in Space Traffic Management (STM) relies on a handful of providers for surveillance and collision prediction, and manual coordination between operators. Neither is scalable to support the expected 10x increase in spacecraft population in less than 10 years, nor does it support automated manuever planning. We present a software prototype of an STM architecture based on open Application Programming Interfaces (APIs), drawing on previous work by NASA to develop an architecture for low-altitude Unmanned Aerial System Traffic Management. The STM architecture is designed to provide structure to the interactions between spacecraft operators, various regulatory bodies, and service suppliers, while maintaining flexibility of these interactions and the ability for new market participants to enter easily. Autonomy is an indispensable part of the proposed architecture in enabling efficient data sharing, coordination between STM participants and safe flight operations. Examples of autonomy within STM include syncing multiple non-authoritative catalogs of resident space objects, or determining which spacecraft maneuvers when preventing impending conjunctions between multiple spacecraft. The STM prototype is based on modern micro-service architecture adhering to OpenAPI standards and deployed in industry standard Docker containers, facilitating easy communication between different participants or services. The system architecture is designed to facilitate adding and replacing services with minimal disruption. We have implemented some example participant services (e.g. a space situational awareness provider/SSA, a conjunction assessment supplier/CAS, an automated maneuver advisor/AMA) within the prototype. Different services, with creative algorithms folded into then, can fulfil similar functional roles within the STM architecture by flexibly connecting to it using pre-defined APIs and data models, thereby lowering the barrier to entry of new players in the STM marketplace. We demonstrate the STM prototype on a multiple conjunction scenario with multiple maneuverable spacecraft, where an example CAS and AMA can recommend optimal maneuvers to the spacecraft operators, based on a predefined reward function. Such tools can intelligently search the space of potential collision avoidance maneuvers with varying parameters like lead time and propellant usage, optimize a customized reward function, and be implemented as a scheduling service within the STM architecture. The case study shows an example of autonomous maneuver planning is possible using the API-based framework. As satellite populations and predicted conjunctions increase, an STM architecture can facilitate seamless information exchange related to collision prediction and mitigation among various service applications on different platforms and servers. The availability of such an STM network also opens up new research topics on satellite maneuver planning, scheduling and negotiation across disjoint entities.

space traffic management↗

Soft costs and knowledge pathways in the US solar photovoltaic ecosystem

An enduring puzzle in the US solar industry is that despite substantial reductions in the cost of photovoltaic hardware, soft costs (defined here as all costs not directly driven by hardware) have not done the same (US DOE, 2012). In fact, soft costs now account for the majority of the cost of total cost of installed solar systems, despite decades of efforts by firms and policy-makers to reduce these costs. In this study, we address two questions: a) what drives soft costs within the US solar industry, and b) how do firms gain the knowledge required to address these costs? We address this question through an explorative inductive multiple-case study (e.g., Eisenhardt, 1989; Appleyard, 1996). Our sample consists of 15 actors from across various roles within the industry, including residential and commercial installers (8), distribution intermediaries (3), and business software vendors (4). We conducted extensive interviews with executives at each firm (76 interviews in total), and supplemented this information with archival resources such as press releases, news coverage, analyst reports, and internet articles. This data was synthesized into a comprehensive case for each actor, detailing the activities conducted within each firm, the drivers of soft costs within that firm, and knowledge flows (e.g., new technologies, best practices, etc) within and across firms. We then compared across these cases to identify common patterns in cost and knowledge dynamics. Our study contributes a nuanced perspective on the drivers of soft costs within the solar industry as well as the patterns and mechanisms of knowledge flows within the industry. We find for example that the majority of the soft costs occur at the interface between firms (e.g., utilities, suppliers). We also observe substantive variation in costs across different types of actors and even within firm types (e.g., small vs. large installers). We also provide insight into how firms acquire knowledge and when they are able to apply it in order to reduce these costs. Our data reveal heterogeneity in a) the types of knowledge, and b) the pathways of knowledge flow between the actors in the industry. For example, we observe that distributors and suppliers play a key role in the dissemination of new technologies as well as general management knowledge for smaller firms. In contrast, informal personal networks and internal experimentation appear to be more important for larger firms. We also reveal critical distinctions across markets – e.g., differences in organizational culture that facilitate learning and experimentation more readily in residential vs. commercial markets. Overall, these results contribute preliminary insights into the knowledge dynamics of the solar industry, the persistence of soft costs over time, heterogeneity across firm types and regions, and the effective design of policy to support the solar industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Real-Time High-Accuracy Digital Wireless Time, Frequency, and Phase Calibration for Coherent Distributed Antenna Arrays

his work presents a fully-digital high-accuracy real-time calibration procedure for frequency and time alignment of open-loop wirelessly coordinated coherent distributed antenna array (CDA) modems, enabling radio frequency (RF) phase coherence of spatially separated commercial off-the-shelf (COTS) software-defined radios (SDRs) without cables or external references such as the global navigation satellite system (GNSS). Building on previous work using high-accuracy spectrally-sparse time of arrival (ToA) waveforms and a multistep ToA refinement process, a high-accuracy two-way time transfer (TWTT)-based time–frequency coordination approach is demonstrated. Due to the two-way nature of the high-accuracy TWTT approach, the time and frequency estimates are Doppler and multipath tolerant, so long as the channel is reciprocal over the synchronization epoch. This technique is experimentally verified using COTS SDRs in a lab environment in static and dynamic scenarios and with significant multipath scatterers. Time, frequency, and phase stability were evaluated by beamforming over coaxial cables to an oscilloscope which achieved time and phase precisions of ~60– 70 ps , with median coherent gains above 99% using optimized coordination parameters, and a beamforming frequency root-mean-square error (RMSE) of 3.73 ppb in a dynamic scenario. Finally, experiments were conducted to compare the performance of this technique with previous works using an analog continuous-wave two-tone (CWTT) frequency reference technique in both static and dynamic settings.

Clock synchronization↗

Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Engineering Experiment Station, Temple University, and Quanta Technology, LLC. The in-kind support came from OSIsoft (acquired by AVEVA), which provided their PI Historian software to demonstrate the use case of streaming PMU data. The first section of the report describes the project goals and objectives related to the development of Machine Learning (ML) models capable of detecting and classifying events by processing phasor measurements captured in the field by Phasor Measurement Units (PMUs). The data for this study was contributed by the utilities/ISOs from the Western and Eastern interconnects and ERCOT, further referred to as Interconnect B (IC B), Interconnect A (IC A), and Interconnect C (IC C), respectively. The approach that the BDSMART Research Team proposed and the key research tasks defined by the team are outlined in this section. The next section describes the technical approach. We first discuss the data constraints related to the PMU measurements and data interpretation constraints imposed by the data contributors. They provided neither the topological information of the grid nor PMU placement locations and captured recorded data at very few locations in the system with the reporting rate of either 30 or 60 fps. The recordings are mostly positive sequence voltage, frequency, and ROCOF, and in some limited cases, three-phase voltages and currents. We then reflect on the bad data issues that stem from poor recording practices and vague definitions of the PMU status bits to supposedly be used for bad data identification. Finally, the data discovery points to imprecise time stamps with incomplete event start/end time, as well as inconsistent and incomplete event labeling, which combined make the implementation of the data models using supervising learning quite challenging. Following the data discovery study, we hypothesize that because the IC B data has the most complete labels, we should focus our model development on that data and then test it on data from other interconnects. We also define the common metrics used to evaluate the results from the ML algorithm tests. We concluded this section by summarizing the common ML models we used and explaining how we implemented and tested them. The issues from this section are expanded in the Training Dataset Report from this project. The final section of this report deals with the accomplishments and conclusions. As the accomplishments, we formulate the problem we are solving and what is achieved by solving the problem. We then reflect on each of the analytics tools we developed and point out the performance of each tool when applied to solving the mentioned problems. We reference this work for further details to the papers we published on each tool. In the conclusions, we give recommendations on how to improve future PMU recording practices to facilitate the ML algorithm implementation and guidance for the future standardization work aimed at clarifying the ambiguities associated with the PMU status bits. We finally list future tasks that can bring about further improvements in the proposed algorithms. The issues from this section are expanded in the Training, and Test Dataset Report filed at the project completion date.

97 MATHEMATICS AND COMPUTING↗