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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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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Autonomous 3D and Radiation Mapping in Tunnel Environments Using Aerial Robots - 18156

In this paper we present the algorithms and systems developed to enable autonomous 3D and radiation mapping, as well as radiological source localization in tunnel environments using aerial robots. The developed technologies aim to address the need of autonomous robotic inspection of nuclear sites such as facilities in the process of being decommissioned. As a motivational example, the PUREX tunnels at the Hanford Site are considered and the robot is designed to be able to conduct multi-modal 3D/radiation characterization of the tunnel environment without any prior knowledge or map provided beforehand. In order to enable the necessary GPS-denied simultaneous localization and mapping in dark, visually-degraded tunnel environments, a multi-modal mapping unit was developed which fuses the data from stereo vision cameras synchronized with flashing LEDs, inertial sensors, and three time-of-flight 3D depth sensors. The robot's pose and surrounding map are estimated at 20 Hz through a depth-enhanced visual- inertial localization and mapping framework. Provided this ability to estimate the robot's pose and develop a dense map in real-time, a receding horizon sampling-based autonomous exploration path planning algorithm was utilized and equips the robot with the ability to explore its environment given no prior map. The aforementioned key robotic functionalities are further extended through the integration of miniaturized fully solid-state scintillation detectors, utilizing advanced scintillators and Silicon Photomultipliers. The derived sensor data is employed for radiation mapping, which when fused with the 3D maps and the spectroscopic source localization methods, allow the aerial robotic platform to localize the radiation source and identify the specific source isotope. The source location is estimated through a geometric algorithm which can localize a source given only three measurements and short dwell times. The developed systems were tested using two small aerial robot platforms of the F450 and F550 frame classes with weights of 2.6 kg and 2.9 kg respectively. All localization and planning as well as high-level control algorithms are executed onboard. The scintillation detector is integrated on the robot and its data is collected through a dedicated microcontroller. A set of field experiments were conducted inside the dark and GPS-denied environment of a 4.25 m wide, 7.5 m tall, and 137 m long railroad tunnel. A Cs-137 source was placed within the tunnel, the associated 3D/radiation maps were derived, and the source location was estimated. Through these efforts, the goal is to develop the robotic technology that would enable the autonomous systematic inspection or emergency response in nuclear waste decommissioning facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction

Snow derived water is a critical component of the US water supply. Measurements of the Snow Water Equivalent (SWE) and associated predictions of peak SWE and snowmelt onset are essential inputs for water management efforts. This paper aims to develop an integrated framework for real-time data ingestion, estimation, prediction and visualization of SWE based on daily snow datasets. In particular, we develop a data-driven approach for estimating and predicting SWE dynamics using the Long Short-Term Memory neural network (LSTM) method. Our approach uses historical datasets (precipitation, air temperature, SWE, and snow thickness) collected at NRCS Snow Telemetry (SNOTEL) stations to train the LSTM network and current year data to predict SWE behavior. The performance of our prediction was compared for different prediction dates and prediction training datasets. Our results suggest that the proposed LSTM network can be an efficient tool for forecasting the SWE timeseries, as well as Peak SWE and snowmelt timing. Results showed that the window size impacts the model performance (where the Nash Sutcliffe efficiency (NSE) ranged from 0.96 to 0.85 and the Rooted Mean Square Error (RMSE) ranged from 0.038 to 0.07) with an optimum number that should be calibrated for different stations and climate conditions. In addition, by implementing the LSTM prediction capability in a cloud based site-monitoring platform, we automate model-data integration. By making the data accessible through a graphical web interface and an underlying API which exposes both training and prediction capabilities. The associated results can be made easily accessible to a broad range of stakeholders.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Microbial Vessel for Impedance Spectroscopy and Electrochemistry (Mvise): an Extensible, Interoperable Data Acquisition Platform for Liquid Culture Studies in Space Biology Research

The White House Office of Science and Technology Policy (OSTP) has declared 2023 to be the Year of Open Science following an initiative to democratize scientific knowledge. Simultaneously, new sensor technologies have broadened the experimental space available to bioastronautics research. With these open-science goals and technological advances in mind, we have designed and constructed a data acquisition platform for high-precision, real-time monitoring of liquid culture systems. The vessel rig is fitted with six Atlas Scientific probes (micro pH, electrical conductivity, dissolved oxygen, oxidation-reduction potential, liquid temperature, air CO2) and a custom optical density probe similar to the one on BioSentinel’s BioSensor payload. A custom dielectric spectroscopy probe is also planned. The structure of the vessel is resin 3-D printed on a hobbyist-level machine, reducing the production cost and iteration time by over 60% each while increasing extensibility. Data acquisition and storage is controlled with a standalone C state machine-based program running on a Raspberry Pi 3 Model B. When not running headless, an additional program automatically generates and updates plots for live data visualization. Validation of the rig as a data collection system was performed with a yeast liquid culture experiment. While the vessel rig is currently used for standalone experiments, it can also be used as the base perception unit in a self-driving laboratory (SDL). SDLs are high-throughput data collection systems that employ automation and artificial intelligence to conduct and manage routine experiments. Here, we envision an SDL driven by several vessel rigs in which an automated script compares key results, informing the design of future experiments. A vessel rig SDL would streamline many operations, including 1) strain selection for the Lunar Explorer Instrument for space biology Applications (LEIA) investigation and 2) the study of bioregenerative life support systems (BLSS). Ultimately, the datasets that can now be acquired will provide crucial information for accelerating bioastronautics application development in the era of commercial space.

Stephen Lantin↗
DOE OSTI2025

EVs@Scale Next-Gen Profiles - EV Profile Capture 2024

As part of the U.S. DOE EVs@Scale consortium Next-Gen Profiles (NGP) project, the profile capture and analysis of production electric vehicles undergoing high power charging (HPC) is conducted over a wide range of conditions to explore variance and performance. Charge session parameters are collected from both the electric vehicle (EV) and electric vehicle supply equipment (EVSE) at a rate of 10Hz and entered into a time-series database for analysis. These charge profiles are captured under nominal and off-nominal conditions, exploring the impact of battery state of charge (SOC), battery temperature, vehicle condition, smart charge management (SCM), and EVSE limitations. Nominal conditions are defined to be ideal conditions that should transfer the maximum allowable energy in the minimum possible amount of time. Nominal condition profiles are compared across EVs to characterize state-of-the-art EV charging performance against one another. Off-nominal condition profiles are compared against its nominal condition profile counterpart to highlight the variance across less desirable starting conditions within a single EV. This EV Profile Capture 2024 report stands as an update from the EV Profile Capture 2023 report to include the additional EV & EVSE assets tested and analyzed in 2024. The major updates within this report include the addition of three next-generation electric vehicles, added test cases, and further analysis. This expansion of analysis includes power profiles, power distribution, quantifying SOC, energy and range performance, EVSE limitation impacts, boost converter performance, etc. Additionally, NGP time-series data has been used as input towards three national laboratory-led grid modelling efforts: ANL’s IEEE-37 HIL model, INL’s Caldera model, and NREL’s EVI-X model. A summary of these platforms and how NGP has worked to improve their effectiveness has also been added to this years’ report.

Thurston, Sam↗
DOE OSTI2016

Aquifer recharge, depletion, and connectivity: Inferences from GRACE, land surface models, and geochemical and geophysical data

We used data from the Gravity Recovery and Climate Experiment (GRACE) and outputs of the CLM4.5 model to estimate recharge and depletion rates for large aquifers, investigate the connectivity of an aquifer's subbasins, and identify barriers and preferred pathways for groundwater flow within an aquifer system. The Nubian Sandstone Aquifer System and its subbasins (Dakhla, Northern Sudan Platform, and Kufra) in northeast Africa were used for demonstration purposes, and findings were tested and verified against geological, geophysical, remote sensing, geochronologic, and geochemical data. There are four major findings. (1) The average annual precipitation data over recharge areas in the southern Kufra section and the Northern Sudan Platform subbasin were estimated at 54.8 km 3 , and 32.8 km 3 , respectively, and knowing the annual extraction rates over these two areas (similar to 0.40 ± 0.20 km 3 ), recharge rates were estimated at 0.78 ± 0.49 km 3 /yr and 1.44 ± 0.42 km 3 /yr, respectively. (2) GRACEderived groundwater depletion rates over the Dakhla subbasin and the Northern Kufra section were estimated at 4.44 ± 0.42 km 3 /yr and 0.48 ± 0.32 km 3 /yr, respectively. (3) The observed depletion in the southern parts of the Dakhla subbasin is apparently caused by the presence of the east- west- trending Uweinat-Aswan basement uplift, which impedes the south-to-north groundwater flow and hence reduces replenishment from recharge areas in the south. (4) A major northeast- southwest- trending shear zone (Pelusium shear system) is apparently providing a preferred groundwater flow pathway from the Kufra to the Dakhla subbasin. Thus, inte-grated approach provides a replicable and cost-effective model for better understanding of the hydrogeologic setting of large aquifers worldwide and for optimum management of these groundwater resources.

58 GEOSCIENCES↗
DOE OSTI2016

Data catalog project—A browsable, searchable, metadata system

Modern experiments are typically conducted by large, extended groups, where researchers rely on other team members to produce much of the data they use. The experiments record very large numbers of measurements that can be difficult for users to find, access and understand. Here. we are developing a system for users to annotate their data products with structured metadata, providing data consumers with a discoverable, browsable data index. Machine understandable metadata captures the underlying semantics of the recorded data, which can then be consumed by both programs, and interactively by users. Collaborators can use these metadata to select and understand recorded measurements. The data catalog project is a data dictionary and index which enables users to record general descriptive metadata, use cases and rendering information as well as providing them a transparent data access mechanism (URI). Users describe their diagnostic including references, text descriptions, units, labels, example data instances, author contact information and data access URIs. The list of possible attribute labels is extensible, but limiting the vocabulary of names increases the utility of the system. The data catalog is focused on the data products and complements process-based systems like the Metadata Ontology Provenance project [Greenwald, 2012; Schissel, 2015]. This system can be coupled with MDSplus to provide a simple platform for data driven display and analysis programs. Sites which use MDSplus can describe tree branches, and if desired create ‘processed data trees’ with homogeneous node structures for measurements. Sites not currently using MDSplus can either use the database to reference local data stores, or construct an MDSplus tree whose leaves reference the local data store. A data catalog system can provide a useful roadmap of data acquired from experiments or simulations making it easier for researchers to find and access important data and understand the meaning of the data and how it was obtained. This is particularly useful in research facilities that study the results of many different experiments or simulations and may not know the intricacies of the data organization in use where the data was generated. It is also possible to store a local copy of key data items in local MDSplus trees and then add processed data to the local catalog.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

MARBLE: A Multi-GPU Aware Job Scheduler for Deep Learning on HPC Systems

Deep learning (DL) has become a key tool for solving complex scientific problems. However, managing the multi-dimensional large-scale data associated with DL, especially atop extant multiple graphics processing units (GPUs) in modern supercomputers poses significant challenges. Moreover, the latest high-performance computing (HPC) architectures bring different performance trends in training throughput compared to the existing studies. Existing DL optimizations such as larger batch size and GPU locality-aware scheduling have little effect on improving DL training throughput performance due to fast CPU-to-GPU connections. Additionally, DL training on multiple GPUs scales sublinearly. Thus, simply adding more GPUs to a system is ineffective. To this end, we design MARBLE, a first-of-its-kind job scheduler, which considers the non-linear scalability of GPUs at the intra-node level to schedule an appropriate number of GPUs per node for a job. By sharing the GPU resources on a node with multiple DL jobs, MARBLE avoids low GPU utilization in current multi-GPU DL training on HPC systems. Our comprehensive evaluation in the Summit supercomputer shows that MARBLE is able to improve DL training performance by up to 48.3% compared to the popular Platform Load Sharing Facility (LSF) scheduler. Compared to the state-of-the-art of DL scheduler, Optimus, MARBLE reduces the job completion time by up to 47%.

Han, Jingoo↗
NASA NTRS2005

Parachute Testing for the NASA X-38 Crew Return Vehicle

NASA's X-38 program was an in-house technology demonstration program to develop a Crew Return Vehicle (CRV) for the International Space Station capable of returning seven crewmembers to Earth when the Space Shuttle was not present at the station. The program, managed out of NASA's Johnson Space Center, was started in 1995 and was cancelled in 2003. Eight flights with a prototype atmospheric vehicle were successfully flown at Edwards Air Force Base, demonstrating the feasibility of a parachute landing system for spacecraft. The intensive testing conducted by the program included testing of large ram-air parafoils. The flight test techniques, instrumentation, and simulation models developed during the parachute test program culminated in the successful demonstration of a guided parafoil system to land a 25,000 Ib spacecraft. The test program utilized parafoils of sizes ranging from 750 to 7500 p. The guidance, navigation, and control system (GN&C) consisted of winches, laser or radar altimeter, global positioning system (GPS), magnetic compass, barometric altimeter, flight computer, and modems for uplink commands and downlink data. The winches were used to steer the parafoil and to perform the dynamic flare maneuver for a soft landing. The laser or radar altimeter was used to initiate the flare. In the event of a GPS failure, the software navigated by dead reckoning using the compass and barometric altimeter data. The GN&C test beds included platforms dropped from cargo aircraft, atmospheric vehicles released from a 8-52, and a Buckeye powered parachute. This paper will describe the test program and significant results.

Stein, Jenny M.↗
NASA NTRS2006

X-Windows PVT Widget Class

The X-Windows Process Validation Table (PVT) Widget Class ( Class is used here in the object-oriented-programming sense of the word) was devised to simplify the task of implementing network registration services for Information Sharing Protocol (ISP) graphical-user-interface (GUI) computer programs. Heretofore, ISP PVT programming tasks have required many method calls to identify, query, and interpret the connections and messages exchanged between a client and a PVT server. Normally, programmers have utilized direct access to UNIX socket libraries to implement the PVT protocol queries, necessitating the use of many lines of source code to perform frequent tasks. Now, the X-Windows PVT Widget Class encapsulates ISP client server network registration management tasks within the framework of an X Windows widget. Use of the widget framework enables an X Windows GUI program to interact with PVT services in an abstract way and in the same manner as that of other graphical widgets, making it easier to program PVT clients. Wrapping the PVT services inside the widget framework enables a programmer to treat a PVT server interface as though it were a GUI. Moreover, an alternate subclass could implement another service in a widget of the same type. This program was written by Matthew R. Barry of United Space Alliance for Johnson Space Center. For further information, contact the Johnson Technology Transfer Office at (281) 483-3809. MSC-23582 Shuttle Data Center File- Processing Tool in Java A Java-language computer program has been written to facilitate mining of data in files in the Shuttle Data Center (SDC) archives. This program can be executed on a variety of workstations or via Web-browser programs. This program is partly similar to prior C-language programs used for the same purpose, while differing from those programs in that it exploits the platform-neutrality of Java in implementing several features that are important for analysis of large sets of time-series data. The program supports regular expression queries of SDC archive files, reads the files, interleaves the time-stamped samples according to a chosen output, then transforms the results into that format. A user can choose among a variety of output file formats that are useful for diverse purposes, including plotting, Markov modeling, multivariate density estimation, and wavelet multiresolution analysis, as well as for playback of data in support of simulation and testing.

Barry, Matthew R.↗
DOE OSTI2026

Arctic Deployment of a Fully Integrated Self-Powered Drifting Buoy Harvesting Wave Energy via a Triboelectric Nanogenerator

The Arctic Ocean remains one of the most poorly sampled regions on Earth, where improved in situ environmental monitoring is vital for advancing oceanographic and atmospheric studies. However, data collection efforts are constrained by the short operational lifespans and high costs of conventional systems. Drifting buoys powered by pendulum-driven wave energy harvesters offer a cost-effective alternative, yet earlier designs have neither been optimized for real-world wave conditions nor validated in the Arctic. In this study, we develop a self-powered drifting buoy that integrates a pendulum-driven triboelectric nanogenerator (TENG) system with a mechanical motion rectifier, a high-gear-ratio transmission, and power management circuits. Through coupled buoy–pendulum dynamic simulations and laboratory testing using a motion simulator, we identify an optimal pendulum mass of 1.6 kg (12.7% of total buoy weight) that maximizes energy output while maintaining buoy stability. Laboratory experiments achieved average power outputs of 12.7 mW under Arctic-like wave and temperature conditions. The system was successfully deployed in the Bering Sea, where it generated 11 J of energy in 3.1 m waves, marking the first Arctic deployment of a TENG-based drifting buoy for sea surface temperature monitoring. This work establishes a cost-effective framework for designing self-powered Arctic monitoring platforms and advances the feasibility of long-term environmental observations in real Arctic waters.

marine enerby↗
DOE OSTI2019

Life cycle assessment of emerging technologies: Evaluation techniques at different stages of market and technical maturity

Life cycle assessment (LCA) analysts are increasingly being asked to conduct life cycle-based systems level analysis at the earliest stages of technology development. While early assessments provide the greatest opportunity to influence design and ultimately environmental performance, it is the stage with the least available data, greatest uncertainty, and a paucity of analytic tools for addressing these challenges. While the fundamental approach to conducting an LCA of emerging technologies is akin to that of LCA of existing technologies, emerging technologies pose additional challenges. In this paper, we present a broad set of market and technology characteristics that typically influence an LCA of emerging technologies and identify questions that researchers must address to account for the most important aspects of the systems they are studying. The paper presents: (a) guidance to identify the specific technology characteristics and dynamic market context that are most relevant and unique to a particular study, (b) an overview of the challenges faced by early stage assessments that are unique because of these conditions, (c) questions that researchers should ask themselves for such a study to be conducted, and (d) illustrative examples from the transportation sector to demonstrate the factors to consider when conducting LCAs of emerging technologies. The paper is intended to be used as an organizing platform to synthesize existing methods, procedures and insights and guide researchers, analysts and technology developer to better recognize key study design elements and to manage expectations of study outcomes.

42 ENGINEERING↗
DOE OSTI2017

A global Fine-Root Ecology Database to address below-ground challenges in plant ecology

Variation and tradeoffs within and among plant traits are increasingly being harnessed by empiricists and modelers to understand and predict ecosystem processes under changing environmental conditions. And while fine roots play an important role in ecosystem functioning, fine-root traits are underrepresented in global trait databases. This has hindered efforts to analyze fine-root trait variation and link it with plant function and environmental conditions at a global scale. This Viewpoint addresses the need for a centralized fine-root trait database, and introduces the Fine-Root Ecology Database (FRED, http://roots.ornl.gov) which so far includes > 70 000 observations encompassing a broad range of root traits and also includes associated environmental data. FRED represents a critical step toward improving our understanding of below-ground plant ecology. For example, FRED facilitates the quantification of variation in fine-root traits across root orders, species, biomes, and environmental gradients while also providing a platform for assessments of covariation among root, leaf, and wood traits, the role of fine roots in ecosystem functioning, and the representation of fine roots in terrestrial biosphere models. There has been a continued input of observations into FRED to fill gaps in trait coverage will improve our understanding of changes in fine-root traits across space and time.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Thoroughly testing and integrating hundreds of Pull Requests per month: ROOT’s new Cost-efficient and Feature Rich GitHub-based CI

ROOT is an open source framework, freely available on GitHub, at the heart of data acquisition, processing and analysis of HE(N)P experiments, and beyond. It is developed collaboratively: contributions are not authored only by ROOT team members, but also by the user community at large: developers and scientists from universities, labs as well as the private sector. More than 1500 GitHub Pull Requests are merged on average per year. It is in this context that code integration acquires a primary role. The review of code contributions isn’t enough: not only they need to be thoroughly reviewed, they also need to be thoroughly tested through a powerful CI infrastructure on several different platforms to comply with the high code quality standards of the project. Since the end of 2023, ROOT moved its continuous integration system from Jenkins to GitHub Actions. In this contribution, we characterise the transition to the GitHub CI, focussing on our strategy, its implementation and the lessons learned, as well as the advantages the new system offers with respect to the previous one. Particular emphasis will be given to the evaluation of the cost-benefit ratio for Jenkins and GitHub Actions for the ROOT project. We also describe how we manage to run in less than one hour thousands of unit, integration, functional and end-to-end tests on different flavours of Windows, four versions of macOS, as well as about ten of the most used Linux distributions, taking advantage of the CERN computing infrastructure.

Piparo, Danilo [CERN]↗
DOE OSTI2023

Vectorization of CMSSW offline software

The CMS experiment has been utilizing vectorization, or SIMD, in parts of its data processing applications for over a decade. On x86 platforms the vectorization level is still SSE3. In the past attempts to use wider vector instruction sets such as AVX or AVX-512 have, in practice, not resulted in improvements in the overall event processing throughput, because the CPUs scale down their frequency when processing AVX instructions. In addition, a notable part of the global pool of CMS’ resources has been old systems either not supporting AVX, or where the CPU frequency downscaling impacts all cores of the CPU. CMS has nevertheless continued to vectorize more of its application code, and in this work we review profiling methods we have found effective to find out pieces of code that would benefit from vectorization, and techniques to transform those codes such that the GCC compiler is able to auto-vectorize those codes. The build system used for CMSSW, Scram, has also been enha nced to be able to build code for multiple CPU microarchitectures such that the shared libraries of desired microarchitecture level can be loaded based on the CPU of the system. This multi-microarchitecture setup is invisible to the workflow management system, which makes its deployment straightforward. We describe in detail how this multi-microarchitecture build is set up, and measure the impact of using wider vector units than SSE3 on the event processing throughput of CMS applications such as simulation and reconstruction on recent x86 CPUs.

Gartung, Patrick↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness - An Introduction to the Open-Source Tool and Use Cases

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an open-source tool for visualizing variable renewable resource forecasts and ramp alerts for significant up/down ramps in renewable resource and the consequent net-load. The modular dashboard of RAVIS contains configurable panes for viewing- probabilistic time series forecasts, ramp event alerts on the look-ahead timeline, spatially resolved resource sites and forecasts, and system simulation and market clearing data such as transmission lines utilization, nodal prices and available generation flexibility. RAVIS uses a technology suite that is assembled to provide optimum visualization facility while maintaining a wide pool of potential deployment and client environments. The tool is designed to take advantage of web application technologies, open source visualization libraries and tooling. Utilizing this technology will enable deployment in any environment, using any operating system, and is scalable to much higher spatial and temporal scales of visualization. As a prototype of the tool and demonstrating a use case of variable renewable integration, RAVIS currently integrates site-specific solar power forecasts in the California Independent System Operator (CAISO) and Mid-continent ISO (MISO) footprint from the IBM WattSun forecasting platform, and also superimposes market simulation data for CAISO footprint from as in-house NREL market clearing tool. The tool has the ability to alert the viewer for excessive up or down ramps for both individual solar sites as well as regionally aggregated net-load ramps, and alerts can also be qualified with respect to available flexible generation. This report will provide an introduction to the RAVIS tool, and summarize the above mentioned capabilities, typical use cases and possible extensions of the tool. The RAVIS development team believes there are likely to be high economic and reliability benefits of integrating probabilistic forecasts of variable renewables into control center visualizations and improved ramp events situational awareness for system operators and forecasting teams in the ISOs and electric utilities in comparison with their business as usual practices.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRSDate not supplied

Transcribing Air Traffic Control System Command Center Planning Telecons Using Cloud-Based Automatic Speech Recognition

This paper addresses the challenge of using Automatic Speech Recognition (ASR) technology to transcribe regular teleconferences that happen between FAA Air Traffic Control System Command Center (ATCSCC) planners, stakeholders and air users. These planning teleconferences (aka telecons or planning webinars) are an integral part of managing air traffic in the U.S. National Airspace System (NAS). In particular, the meetings facilitate the creation and modification of various traffic management initiatives (TMIs), that are used to regulate the flow of air traffic. This is typically a human intensive process, requiring specialists to listen to the entire meeting audio (10-20 minutes duration) and inferring the state of the NAS (e.g., weather phenomenon) that was discussed. It would be advantageous to have digital transcripts of the audio and have useful information (e.g., related to TMIs) automatically extracted from the transcripts. In this regard, we are exploring the adoption of state-of-the-art speech to text and Natural Language Processing (NLP) tools that will achieve our objective of digitizing the webinar audio. Unfortunately, the highly technical phraseology present in the audio and limited data availability for model building make ASR difficult. To overcome this challenge, we have taken the critical first step in creating a human transcription dataset from ~20 hours of speech in the ATCSCC audio with the help of subject matter experts. A novelty of our work is the creation of a ground truth transcription dataset for ATCSCC teleconference webinars, which is particularly important for Aviation domain-specific NLP tasks. Using Microsoft Speech Studio, a cloud-based ASR platform, we have fine-tuned the English pre-trained ASR models (available in speech studio) and achieved an average word error rate (WER) of 6.81%. The baseline ASR also provides a digital version of each planning webinar, making it accessible and text-searchable for future references. Additionally, the transcriptions can serve as a bridge between raw audio data and a range of text-based NLP tasks, such as named entity recognition (NER) and intent classification, potentially enhancing the digital footprint of the webinars and other connected data sources. Our work has several potential applications. Firstly, the transcriptions can be analyzed to understand the complex decision process of creating, implementing and modifying TMIs and may also contribute to TMI prediction services. Secondly, our dataset and model can be used to develop more accurate ASR systems for aviation-specific language, which can bring about digital communication in the aviation industry (and aid current “voice only” communications, which are inherently error-prone). Lastly, the transcriptions themselves can be used as a valuable resource for training other NLP models.

Stephen S. B. Clarke↗
NASA NTRS1993

Optimizing digital 8mm drive performance

The experience of attaching over 350,000 digital 8mm drives to 85-plus system platforms has uncovered many factors which can reduce cartridge capacity or drive throughput, reduce reliability, affect cartridge archivability and actually shorten drive life. Some are unique to an installation. Others result from how the system is set up to talk to the drive. Many stem from how applications use the drive, the work load that's present, the kind of media used and, very important, the kind of cleaning program in place. Digital 8mm drives record data at densities that rival those of disk technology. Even with technology this advanced, they are extremely robust and, given proper usage, care and media, should reward the user with a long productive life. The 8mm drive will give its best performance using high-quality 'data grade' media. Even though it costs more, good 'data grade' media can sustain the reliability and rigorous needs of a data storage environment and, with proper care, give users an archival life of 30 years or more. Various factors, taken individually, may not necessarily produce performance or reliability problems. Taken in combination, their effects can compound, resulting in rapid reductions in a drive's serviceable life, cartridge capacity, or drive performance. The key to managing media is determining the importance one places upon their recorded data and, subsequently, setting media usage guidelines that can deliver data reliability. Various options one can implement to optimize digital 8mm drive performance are explored.

Schadegg, Gerry↗
DOE OSTI2021

An overview of data tools for representing and managing building information and performance data

Building information modeling (BIM) has been widely adopted for representing and exchanging building data across disciplines during building design and construction. However, BIM's use in the building operation phase is limited. With the increasing deployment of low-cost sensors and meters, as well as affordable digital storage and computing technologies, growing volumes of data have been collected from buildings, their energy services systems, and occupants. Such data are crucial to help decision makers understand what, how, and when energy is consumed in buildings—a critical step to improving building performance for energy efficiency, demand flexibility, and resilience. However, practical analyses and use of the collected data are very limited due to various reasons, including poor data quality, ad-hoc representation of data, and lack of data science skills. To unlock value from building data, there is a strong need for a toolchain to curate and represent building information and performance data in common standardized terminologies and schemas, to enable interoperability between tools and applications. This study selected and reviewed 24 data tools based on common use cases of data across the building life cycle, from design to construction, commissioning, operation, and retrofits. The selected data tools are grouped into three categories: (1) data dictionary or terminology, (2) data ontology and schemas, and (3) data platforms. The data are grouped into ten typologies covering most types of data collected in buildings. This study resulted in five main findings: (1) most data representation tools can represent their intended data typologies well, such as Green Button for smart meter data and Brick schema for metadata of sensors in buildings and HVAC systems, but none of the tools cover all ten types of data; (2) there is a need for data schemas to represent the basis of design data and metadata of occupant data; (3) standard terminologies such as those defined in BEDES are only adopted in a few data tools; (4) integrating data across various stages in the building life cycle remains a challenge; and (5) most data tools were developed and maintained by different parties for different purposes, their flexibility and interoperability can be improved to support broader use cases. Finally, recommendations for future research on building data tools are provided for the data and buildings community based on the FAIR principles to make data Findable, Accessible, Interoperable, and Reusable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗