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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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C. thermocellum is an important candidate for sustainable and cost-effective production of bioethanol through consolidated bioprocessing. In addition to unsurpassed cellulose deconstruction, industrial application and fundamental studies would benefit from improvement of glucose and fructose consumption.
The dataset was created to train machine learning models for the task of identifying the (1) cavity and (2) fault type from C100-type cryomodules at the Thomas Jefferson National Accelerator Facility (Jefferson Lab), thereby replacing the time-consuming efforts of a subject matter expert. Superconducting radio-frequency (SRF) cavity trips represent a significant source of accelerator downtime. Real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type would give control room operators valuable feedback for corrective action planning. The anticipated benefit is increased beam-on-target time for users and provides performance metrics that can be used to improve future cavity designs. A series of 17 RF signals are recorded for each of the 8 cavities in a C100 cryomodule every time a cavity trips. These time-series signals are written to file using a specially designed data acquisition system. The dataset represents fault events recorded during Continuous Electron Beam Accelerator Facility (CEBAF) beam operations between January 18, 2019 and March 9, 2020. The following filtering steps were applied to collected data; (1) only 4 of the 17 signals per cavity are retained (GMES, GASK, CRFP, DETA2) (2) only events with data from each of the eight cavities in the cryomodule are kept, (3) only events that were sampled at 5 kHz were kept, (4) events from cryomodule 0L04 were neglected, (5) events occurring between February 4, 3PM and February 5, 12PM were neglected. As a result of preprocessing, the dataset is comprised of 2,375 unique events. The full dataset is comprised of three files: features.csv, cavity_labels.csv, fault_labels.csv. Each instance in faults.csv includes a timestamp (“date_time”), a label for the cryomodule which experienced the trip (“zone_label”), and 192 features (“feature_1”, “feature_2”... “feature_192”). The features correspond to 6 autoregressive features for each of 4 signals per cavity for each of the 8 cavities (6 × 4 signals/cavity × 8 cavities/cryomodule = 192). To deal with the large variation of signal amplitudes, time-series standardization via the z-score (standard score) function was applied prior to computing the features. For each instance, there is an associated label for the (1) cavity which faulted first (cavity_labels.csv) and (2) the type of fault that caused the trip (fault_labels.csv). The cavity identification can take values of [0, 1, 2, 3, 4, 5, 6, 7, 8] and the fault type can take values of [‘Microphonics’, ‘Quench_100ms’, ‘Controls_Fault’, ‘E_Quench’, ‘Quench_3ms’, ‘Single_Cav_Turn_Off’ , ‘Heat_Riser_Choke’, ‘Multi_Cav_Turn_Off’].
This presentation includes discussion of the integration of High Energy Density Physics (HEDP) and astrophysics experiment.
Task 5 consisted of two parts, wave energy converter (WEC) arrays and current energy converter (CEC) arrays. The two parts of the project were executed independently, and this report covers only the work on WECs. In this part of the task, the team characterized the behavior of and developed control schemes for wave energy converter (WEC) arrays that improved performance (i.e., maximized aggregate power generation and reduced the levelized cost of energy) over baseline, non-coordinated control approaches. Specifically, for WEC arrays: the team developed optimal layouts of arrays that considered the effect of WEC placement within an array on coordinated array control with real-time estimation schemes. The numerical codes developed through this task were made available to users for array design. These tools utilized existing commercial software as well as software developed within the project team.
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Large duration energy storage is the key to couple renewable energy generation with power supply. This project aimed to advance the durability of a low-cost and eco-friendly flow battery technology based on iron chemistry to speed up the technology readiness level rapidly and radically. A novel device which is called “an artificial kidney” was innovated through disruptive research to integrate with the flow battery for rebalancing capacity. Key results at 50 cm2 scale demonstrates that artificial kidney enabled retaining the storage capacity of the flow battery over 100 cycles. Comparison of outcome of this work showing 0% capacity degradation to the state-of-the-art technology corroborates that this novel system is a game changing innovation.
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Sandia is a federally funded research and development center (FFRDC) focused on developing and applying advanced science and engineering capabilities to mitigate national security threats. This is accomplished through the exceptional staff leading research at the Labs and partnering with universities and companies. Sandia’s LDRD program aims to maintain the scientific and technical vitality of the Labs and to enhance the Labs’ ability to address future national security needs. The program funds foundational, leading-edge discretionary research projects that cultivate and utilize core science, technology, and engineering (ST&E) capabilities. Per Congressional intent (P.L. 101-510) and Department of Energy (DOE) guidance (DOE Order 413.2C, Chg 1), Sandia’s LDRD program is crucial to maintaining the nation’s scientific and technical vitality.
A SNL summary slide deck of GUIDE (Generative Unconstrained Intelligent Drug Engineering) project to be delivered to sponsor JPEO.
Abstract not provided.
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Target Diagnostic’s Engineering Science (TDES) currently supports over 80 diagnostic systems, all of which draw from a fixed budget that covers staff time and procurement. As the division continues to add new diagnostics, demand on these limited resources increases, yet the budget remains unchanged. Shot Responsible Individuals (RIs) have unrestricted freedom to select any number or type of diagnostics for each experiment, often by copying previous shot configurations with only minor modifications. This practice makes it easy to generate new shots but does not encourage critical evaluation of diagnostic necessity. As a result, many diagnostics are routinely included in a shot without a clear justification, and in practice, much of the data collected is not analyzed. The ever-growing arsenal of diagnostics also increases the burden on TDES personnel, requiring more staff for support, maintenance, and service. This environment creates challenges in focusing resources on diagnostics that provide the greatest value to the NIF mission and raises concerns about the continued justification for maintaining all existing systems. The static TDES budget cannot sustain the ongoing growth in demand for new diagnostic systems. Shot RIs currently face no constraints on the number or type of diagnostics they assign to each experiment, often defaulting to previous configurations without critically assessing the necessity of each diagnostic. This results in the routine inclusion of diagnostics whose data may not be analyzed or may no longer directly support the NIF mission. Consequently, TDES is required to maintain and support an expanding set of diagnostics, straining limited resources and risking inefficient use of staff and funding. There is a need for TDES to efficiently allocate limited resources by establishing a process that requires shot RIs to critically evaluate and justify the inclusion of each diagnostic in their experiments. This process should discourage the routine, unexamined inclusion of diagnostics, support the identification of underutilized or obsolete systems, and ensure that only those diagnostics providing the greatest value to the NIF mission are maintained and supported. Ultimately, this approach must enable TDES to operate within budget constraints while maximizing scientific impact and operational efficiency.
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The goal of this integration is to replicate the results from the original paper Autonomous Utility Pole Identification on different camera hardware and integrate the model into a live video stream provided by the unmanned aerial system (UAS) itself while in operation. This involves retraining the original model and validating its efficacy on multiple camera modules to select the most effective device for installation. Moreover, this integration requires writing software to handle the reception of a real-time streaming protocol stream from the UAS and run each frame through the model while allowing a user to monitor the camera feed.
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Explore the source record for details and available documents.