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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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We describe the design and implementation of a motion control server that proved crucial to the safe assembly and validation of Mars 2020 hardware in the absence of flight avionics.
The beam commissioning software framework of NSLS-II project adopts a client/server based architecture to replace the more traditional monolithic high level application approach. The server software under development is available via an open source sourceforge project named epics-pvdata, which consists of modules pvData, pvAccess, pvIOC, and pvService. Examples of two services that already exist in the pvService module are itemFinder, and gather. Each service uses pvData to store in-memory transient data, pvService to transfer data over the network, and pvIOC as the service engine. The performance benchmarking for pvAccess and both gather service and item finder service are presented in this paper. The performance comparison between pvAccess and Channel Access are presented also. For an ultra low emittance synchrotron radiation light source like NSLS II, the control system requirements, especially for beam control are tight. To control and manipulate the beam effectively, a use case study has been performed to satisfy the requirement and theoretical evaluation has been performed. The analysis shows that model based control is indispensable for beam commissioning and routine operation. However, there are many challenges such as how to re-use a design model for on-line model based control, and how to combine the numerical methods for modeling of a realistic lattice with the analytical techniques for analysis of its properties. To satisfy the requirements and challenges, adequate system architecture for the software framework for beam commissioning and operation is critical. The existing traditional approaches are self-consistent, and monolithic. Some of them have adopted a concept of middle layer to separate low level hardware processing from numerical algorithm computing, physics modelling, data manipulating and plotting, and error handling. However, none of the existing approaches can satisfy the requirement. A new design has been proposed by introducing service oriented architecture technology, and client interface is undergoing. The design and implementation adopted a new EPICS implementation, namely epics-pvdata [9], which is under active development. The implementation of this project under Java is close to stable, and binding to other language such as C++ and/or Python is undergoing. In this paper, we focus on the performance benchmarking and comparison for pvAccess and Channel Access, the performance evaluation for 2 services, gather and item finder respectively.
GlusterFS is a Linux based distributed file system, designed to be highly scalable and serve many clients. Some reasons to use GlusterFS are: No centralized metadata server, Scalability, Open Source, Dynamic and live service modifications, Can be used over Infiniband or Ethernet, Can be tuned for speed and/or resilience and Flexible administration. It's useful for enterprise environments - virtualization; high performance computing (HPC) and it works with Mac, Linux and Windows clients. Conclusions are: (1) GlusterFS proved to have widespread capabilities as a virtual file system; (2) Scalability is very dependent upon the underlying hardware; (3) Lack of built-in encryption and security paradigm; and (4) Best suited in a general purpose computing environment.
This software is contains reference implementations for how to secure and harden Linux Web Servers for compliance with OMB M-15-13 compliance requirements.
Systems and methods for simulating and controlling a power system in real time, using a controller, are provided. The controller includes a simulation layer to simulate an operation of the power system, a disturbance generation layer to provide data to the simulation layer to disturb the simulated operation of the power system, an application layer to display the simulated operation of the power system and generate a control signal to control, based on the simulated operation of the power system, at least one element of the power system, and a distributed data streaming server (D2S2) to allow interoperability among the simulation layer, the disturbance layer, and the application layer.
Machine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called “emerging jets” is performed, using graph neural networks to greatly increase sensitivity to the signal’s signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory’s computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users’ analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment’s real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle’s “track quality” in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.
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This is a the code and data for a website created to display collision cross section data for metabolites and small molecules. This repository hosts two things. First is the source code (js) for a website created to display collision cross section data for metabolites and other small molecules. The second is the underlying CCS dataset. The growing interest in metabolomic and exposomic studies is also inciting a need for new techniques to analyze these diverse molecules. Mass spectrometry-based studies are often preferred for the confident identification of small molecules. Ion mobility spectrometry (IMS) is capable of separating molecules that have the same m/z but different conformational arrangements, which is very useful in metabolomic and exposomic analyses. As the desire for IMS measurements of metabolites continues to grow, so does the need for high quality collision cross section (CCS) values.
This tool helps red teamers and penetration testers to conduct post exploitation activities through various Configuration Management tools such as Puppet, Chef, Ansible, and Salt.
This application monitors all LCLS BPMs, and emits a PV Access table of synchronized orbit data.
This software is a plug-in that interfaces between the Phoenix Integration's Model Center and the Base SAS 9.2 applications. The end use of the plug-in is to link input and output data that resides in SAS tables or MS SQL to and from "legacy" software programs without recoding. The potential end users are users who need to run legacy code and want data stored in a SQL database.
Gaggle Tool Creator (GTC) is a web application which provides access to public annotation, interaction, orthology, and genomic data for hundreds of organisms, and enables instant analysis of the data using many popular web-based and desktop applications.