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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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The Space Interferometry Mission, slated to launch in 2008, will require simultaneous operation of tens of control loops running at kiloHertz rates. The task of designing a computer control system to run this complex, distributed system will require careful requirements analysis and design flow.
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The CCSDS space link protocols i.e., Telemetry (TM), Telecommand (TC), Advanced Orbiting Systems (AOS) were developed in the early growth period of the space program. They were designed to meet the needs of the early missions, be compatible with the available technology and focused on the specific link environments. Digital technology was in its infancy and spacecraft power and mass issues enforced severe constraints on flight implementations. Therefore the Telecommand protocol was designed around a simple Bose, Hocquenghem, Chaudhuri (BCH) code that provided little coding gain and limited error detection but was relatively simple to decode on board. The infusion of the concatenated Convolutional and Reed-Solomon codes5 for telemetry was a major milestone and transformed telemetry applications by providing them the ability to more efficiently utilize the telemetry link and its ability to deliver user data. The ability to significantly lower the error rates on the telemetry links enabled the use of packet telemetry and data compression. The infusion of the high performance codes for telemetry was enabled by the advent of digital processing, but it was limited to earth based systems supporting telemetry. The latest CCSDS space link protocol, Proximity-1 was developed in early 2000 to meet the needs of short-range, bi-directional, fixed or mobile radio links characterized by short time delays, moderate but not weak signals, and short independent sessions. Proximity-1 has been successfully deployed on both NASA and ESA missions at Mars and is planned to be utilized by all Mars missions in development. A new age has arisen, one that now provides the means to perform advanced digital processing in spacecraft systems enabling the use of improved transponders, digital correlators, and high performance forward error correcting codes for all communications links. Flight transponders utilizing digital technology have emerged and can efficiently provide the means to make the next leap in performance for space link communications. Field Programmable Gate Arrays (FPGAs) provide the capability to incorporate high performance forward error correcting codes implemented within software transponders providing improved performance in data transfer, ranging, link security, and time correlation. Given these synergistic technological breakthroughs, the time has come to take advantage of them in applying them to both on going (e.g., command, telemetry) and emerging (e.g., space link security, optical communication) space link applications. However one of the constraining factors within the Data Link Layer in realizing these performance gains is the lack of a generic transfer frame format and common supporting services amongst the existing CCSDS link layer protocols. Currently each of the four CCSDS link layer protocols (TM, TC, AOS, and Proximity-1) have unique formats and services which prohibits their reuse across the totality of all space link applications of CCSDS member space agencies. For example, Mars missions. These missions implement their proximity data link layer using the Proximity-1 frame format and the services it supports but is still required to support the direct from Earth (TC) protocols and the Direct To Earth (AOS/TM) protocols. The prime purpose of this paper, is to describe a new general purpose CCSDS Data Link layer protocol, the NGSLP that will provide the required services along with a common transfer frame format for all the CCSDS space links (ground to/from space and space to space links) targeted for emerging missions after a CCSDS agency-wide coordinated date. This paper will also describe related options that can be included for the Coding and Synchronization sub-layer of the Data Link layer to extend the capacities of the link and additionally provide an independence of the transfer frame sub-layer from the coding sublayer. This feature will provide missions the option of running either the currently performed synchronous coding and transfer frame data link or an asynchronous coding/frame data link, in which the transfer frame length is independent of the block size of the code. The benefits from the elimination of this constraint (frame synchronized to the code block) will simplify the interface between the transponder and the data handling equipment and reduce implementation costs and complexities. The benefits include: inclusion of encoders/decoders into transmitters and receivers without regard to data link protocols, providing the ability to insert latency sensitive messages into the link to support launch, landing/docking, telerobotics. and Variable Coded Modulation (VCM). In addition the ability to transfer different sized frames can provide a backup for delivering stored anomaly engineering data simultaneously with real time data, or relaying of frames from various sources onto a trunk line for delivery to Earth.
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The solar investment community is in need of up-to-date and data-derived system degradation rates to use in financial models that calculate the risk and expectation of energy revenue. To date, financial models choose degradation rates from past studies that are limited and may not be representative of solar projects in development today. As the solar industry expands in all sectors (residential, commercial, and utility), there is a growing amount of energy generation data available to analyze. With the help of the open source degradation analysis code (RdTools) it is now possible to derive degradation on an ongoing basis and continuously provide up-to-date durability statistics to the investment community. Ongoing and accurate statistics of fielded photovoltaic systems allows financial stakeholders to constrain energy revenue projections, lower financial risk, and increase the bankability of solar. This presentation will provide a mid-term progress update on a 2 year SETO-funded degradation project that involves applying RdTools analysis on kWh Analytics’ industry database. With results from the RdTools analysis in hand, machine learning methods are employed to quantify how much variance of system performance degradation can be explained by predictor variables such as environmental factors, equipment bill of materials characteristics, and system design information. The goal of this presentation is to promote the usage of RdTools on an ongoing to data owners and to solicit feedback from researchers and stakeholders on how to maximize impact of the SETO-funded kWh Analytics degradation project.
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This project addresses a fundamental flaw in solar PV research and solar project financing; the assumed rate of degradation for solar plants. The solar industry currently relies on an out-dated report that observed a 0.5% degradation rate based on a small sample size of systems (~100). While the research conducted at the time was new and innovative, the solar community has not updated this research and universally applies this 0.5% degradation assumption in financial models. Our project updates this assumption by analyzing observed degradation from the industry’s largest dataset of operating solar assets (>10,000 systems) and creating the first machine-learning model based on these observed results to quantify and identify features that drive degradation. There are two strategic goals for this award: reduce the cost of capital (enable solar to attract more capital) and improve the reliability of solar itself. These dual goals are achieved by leveraging an industry dataset to observe system degradation on a large scale, deploying advanced data analysis and machine learning methods to quantify and predict system reliability, and engaging with industry stakeholders to help them accurately price degradation in financial models.
Abstract not provided.
The proliferation of low-cost sensors in smartphones has facilitated numerous applications; however, large-scale deployments often encounter performance issues. Sensing heterogeneity, which refers to varying data quality due to factors such as device differences and user behaviors, presents a significant challenge. In this research, we perform an extensive analysis of 3-axis accelerometer data from the MyShake system, a global seismic network utilizing smartphones. We systematically evaluate the quality of approximately 22 million 3-axis acceleration waveforms from over 81 thousand smartphone devices worldwide, using metrics that represent sampling rate and noise level. We explore a broad range of factors influencing accelerometer data quality, including smartphone and accelerometer manufacturers, phone specifications (release year, RAM, battery), geolocation, and time. Our findings indicate that multiple factors affect data quality, with accelerometer model and smartphone specifications being the most critical. In addition, we examine the influence of data quality on earthquake parameter estimation and show that removing low-quality accelerometer data enhances the accuracy of earthquake magnitude estimation.
The Central Nevada Test Area, Nevada, was the site of an underground nuclear test in 1968, which resulted in residual contamination at and near the detonation depth of 975 meters (3200 feet). The environmental corrective action strategy for the site was conducted in accordance with the Federal Facility Agreement and Consent Order and all applicable Nevada Division of Environmental Protection (NDEP) policies and regulations. The corrective action strategy initiated in 1999 included developing a conceptual site model, preparing a numerical flow and transport model, calculating contaminant boundaries, negotiating compliance boundaries with NDEP, performing model validation, and monitoring groundwater. This strategy was executed through the Corrective Action Decision Document/Corrective Action Plan phase, when it was determined that the flow and transport model could not be validated because it failed to adequately predict groundwater elevations in the monitoring/validation (MV) wells (MV-1, MV-2, and MV-3) drilled subsequent to the modeling effort. This led to a change in the corrective action strategy that is now designed to validate the compliance boundary through monitoring and institutional controls, rather than relying predominantly on a numerical flow and transport model. This new approach included enhancements to the monitoring well network and additional monitoring to confirm that data were sufficient to proceed to closure. The corrective action activities were completed in 2015, and the Closure Report, which established the long-term postclosure monitoring program, was approved in 2016. The postclosure monitoring program is designed to (1) assess the effectiveness of the compliance boundary by monitoring for the radionuclides of interest and (2) evaluate the effectiveness of monitoring locations within the groundwater flow system by monitoring water elevations to ensure that monitoring wells are located along the potential migration pathways. (authors)
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Modeling strong gravitational lenses is prohibitively expensive for modern and next-generation cosmic survey data. Neural posterior estimation (NPE), a simulation-based inference (SBI) approach, has been studied as an avenue for efficient analysis of strong lensing data. However, NPE has not been demonstrated to perform well on out-of-domain target data -- e.g., when trained on simulated data and then applied to real, observational data. In this work, we perform the first study of the efficacy of NPE in combination with unsupervised domain adaptation (UDA). The source domain is noiseless, and the target domain has noise mimicking modern cosmology surveys. We find that combining UDA and NPE improves the accuracy of the inference by 1-2 orders of magnitude and significantly improves the posterior coverage over an NPE model without UDA. We anticipate that this combination of approaches will help enable future applications of NPE models to real observational data.