Charliecloud’s Successful Prototype Integration with Slurm: A Promising Approach with Some Strings Attached [Slides]
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WINDOW (LBNL 2022) and AERCalc (LBNL 2018) software developed by Lawrence Berkely National Laboratory are needed for running the tool. The database that in installed along with WINDOW software is utilized and modified to develop database for AERCalc simulation in generic case. However, any other custom WINDOW database created by users can be utilized as well. Another requirement for use of the tool is the availability of the shading fabric in the complex glazing database (CGDB). The CGDB id of the fabric was used for database generation discussed in next section.
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The project aimed to develop a Virtual Sensor Digital Twin (VS-DT) to monitor rotor rim float displacement (RRFD) in legacy hydropower turbines. RRFD, if undetected, can cause rotor and stator damage, leading to costly repairs and safety concerns. The purpose of the VS-DT was to predict RRFD signals using existing conventional sensors instead of installing dedicated, costly physical sensors, thereby reducing operational costs and enhancing safety.
We present a case for low batch-size inference with the potential for adaptive training of a lean encoder model. We do so in the context of a paradigmatic example of machine learning as applied in data acquisition at high data velocity scientific user facilities such as the Linac Coherent Light Source-II x-ray Free-Electron Laser. We discuss how a low-latency inference model operating at the data acquisition edge can capitalize on the naturally stochastic nature of such sources. We simulate the method of attosecond angular streaking to produce representative results whereby simulated input data reproduce high-resolution ground truth probability distributions. By minimizing the mean-squared error between the decoded output of the latent representation and the ground truth distributions, we ensure that the encoding layers and resulting latent representation maintains full fidelity for any downstream task, be it classification or regression. We present throughput results for data-parallel inference of various batch sizes, some with throughput exceeding 100 k images per second. We also show in situ training below 10 s per epoch for the full encoder–decoder model as would be relevant for streaming and adaptive real-time data production at our nation’s scientific light sources.
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Heat transfer and pressure on a flat plate and on protuberances in a supersonic turbulent boundary layer at mach 2.65, 3.51, and 4.44
A low-power microscope or telescope provides a simple means of autocollimation. This is done by fitting the instrument with a light source to permit alignment from a reflecting surface normal to the optic axis of the instrument.