Engineering Papers⌕ Search

Engineering topics

Lawrence, David

Publications and source records attributed to Lawrence, David.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Control and Calibration of GlueX Central Drift Chamber Using Gaussian Process Regression

The Gluonic Excitations (GlueX) experiment is designed to search for exotic hybrid mesons using photoproduction, and to study the hybrid meson spectrum predicted from Lattice Quantum Chromodynamics. For the first time, the GlueX Central Drift Chamber was controlled autonomously using machine learning (ML) to calibrate in real time while recording cosmic ray tracks. We demonstrate the ability of a Gaussian Process to predict the gain correction calibration factor used to determine a high voltage setting that will stabilize the CDC gain in response to changing environmental conditions; this is in contrast to the traditional, computationally expensive method of calibrating raw data after data collection is complete.

McSpadden, Helen↗
DOE OSTI2022

Permafrost and Climate Change: Carbon Cycle Feedbacks From the Warming Arctic

Rapid Arctic environmental change affects the entire Earth system as thawing permafrost ecosystems release greenhouse gases to the atmosphere. Understanding how much permafrost carbon will be released, over what time frame, and what the relative emissions of carbon dioxide and methane will be is key for understanding the impact on global climate. In addition, the response of vegetation in a warming climate has the potential to offset at least some of the accelerating feedback to the climate from permafrost carbon. Temperature, organic carbon, and ground ice are key regulators for determining the impact of permafrost ecosystems on the global carbon cycle. Together, these encompass services of permafrost relevant to global society as well as to the people living in the region and help to determine the landscape-level response of this region to a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Real-time Implementation of Grid Code Compliant Grid Edge Energy Management System

Integrated distributed energy resources (DER) in a distribution system need to follow grid codes to avoid violations that result in DER/circuit segment disconnection. To comply with grid code requirements at the grid edge level, network constrained grid edge energy management system (EMS) can be deployed. The objective of grid edge EMS is to provide economic solution for active and reactive power DER setpoints at each dispatch interval and ensure voltage regulation to support secure interconnection of the grid edge segment to the distribution system with multiple inverter based DER units. In this work, real-time simulation of grid code compliant grid edge EMS is deployed in a realistic feeder circuit segment. For real-time simulation, communication between the grid edge EMS and DERs is done exploiting IEC 61850-7-420. It enables interoperability among different DERs and grid edge EMS. No prior art has deployed IEC 61850-7-420 GOOSE communication protocol for grid edge EMS. Conversion of IEC 61850 GOOSE messages to Modbus communication protocol is also performed to communicate with grid edge EMS in commodity-off the shelf embedded boards in this work. The real-time simulation in OPAL-RT real-time digital simulator shows the out-performance of grid edge EMS by reducing the voltage violation in the distribution circuit.

Energy management system↗
DOE OSTI2022

Gaussian process for calibration and control of GlueX Central Drift Chamber

We have developed and implemented a machine learning based system to calibrate and control the GlueX Central Drift Chamber at Jefferson Lab, VA, in near real-time. The system monitors environmental and experimental conditions during data taking and uses those as inputs to a Gaussian process (GP) with learned prior. The GP predicts calibration constants in order to recommend a high voltage (HV) setting for the detector that maintains consistent detector performance (gain and resolution) throughout data taking. This approach is in stark contrast to traditional detector operations in which the detector operates at fixed HV and its calibration parameters vary quite considerably with time. Additionally, the ML based system utilizes uncertainty quantification to correct the recommended control parameters when appropriate. We will present results from the ML system autonomously during the Charged Pion Polarizability (CPP) experiment conducted in Hall D at Jefferson Lab.

McSpadden, Helen↗
DOE OSTI2022

AI Data Quality Monitoring with Hydra

Hydra is an extensible framework for training and managing AI for near real time monitoring that aims to replace the tedious and repetitive data quality monitoring activities the shift crew and online monitoring coordinator typically perform. It continuously scans incoming data in the form of monitoring plots for signs of problems, flagging them for human review. A web app was developed such that experts can efficiently label images for training. Labels are stored in a database for use in training and model validation. Backed up by a comprehensive database, it utilizes an additional web based front-end for viewing the current monitoring status from anywhere in the world. The system has been in production use for the GlueX experiment at Jefferson Lab for more than 2 years with new features still under active development.

Britton, Thomas↗
DOE OSTI2022

EJFAT: Towards Intelligent Compute Destination Load Balancing

To handle increased data flow, Jefferson Lab (JLab) is partnering with ESnet for development of an AI/ML directed compute work Load Balancer (LB) of UDP streamed data. The LB is FPGA based featuring dynamically configurable, low latency and high throughput destination address switching. The LB provides integration of edge and core computing to support JLab experimental programs, the Electron-Ion Collider, as well as data centers of the future. In the ESnet/JLab FPGA Accelerated Transport (EJFAT) initiative, the function of the LB Data Plane (DP) is to redirect data streams to selectable (but unknown to sender) destination hosts based on current worload and within that host to destination ports as a function of sub- stream id. This effects hierarchical scaling, first across compute machines for processing over a series of events and second, across ports so different data source sub-streams may be assigned to different processors for further parallelization. The LB Control Plane (CP) programs the DP using compute farm telemetry to direct and balance workloads across a compute cluster as the operating conditions require. While Proportional/Integrative/Derivative (PID) controllers are often seen in similar applications, here we investigate the feasibility of a Reinforcement Learning (RL) based schedule manager running in the CP to provide dynamic updates to the DP scheduling policy.

Lawrence, David↗
DOE OSTI2022

Gaussian process for calibration and control of GlueX Central Drift Chamber

We have developed and implemented a machine learning based system to calibrate and control the GlueX Central Drift Chamber at Jefferson Lab, VA, in near real-time. The system monitors environmental and experimental conditions during data taking and uses those as inputs to a Gaussian process (GP) with learned prior. The GP predicts calibration constants in order to recommend a high voltage (HV) setting for the detector that maintains consistent detector performance (gain and resolution) throughout data taking. This approach is in stark contrast to traditional detector operations in which the detector operates at fixed HV and its calibration parameters vary quite considerably with time. Additionally, the ML based system utilizes uncertainty quantification to correct the recommended control parameters when appropriate. We will present results from the ML system autonomously during the Charged Pion Polarizability (CPP) experiment conducted in Hall D at Jefferson Lab.

McSpadden, Helen↗
DOE OSTI2022

Streaming readout for next generation electron scattering experiments

Current and future experiments at the high-intensity frontier are expected to produce an enormous amount of data that needs to be collected and stored for offline analysis. Thanks to the continuous progress in computing and networking technology, it is now possible to replace the standard ‘triggered’ data acquisition systems with a new, simplified and outperforming scheme. ‘Streaming readout’ (SRO) DAQ aims to replace the hardware-based trigger with a much more powerful and flexible software-based one, that considers the whole detector information for efficient real-time data tagging and selection. Considering the crucial role of DAQ in an experiment, validation with on-field tests is required to demonstrate SRO performance. In this paper, we report results of the on-beam validation of the Jefferson Lab SRO framework. In this work, we exposed different detectors (PbWO-based electromagnetic calorimeters and a plastic scintillator hodoscope) to the Hall-D electron-positron secondary beam and to the Hall-B production electron beam, with increasingly complex experimental conditions. By comparing the data collected with the SRO system against the traditional DAQ, we demonstrate that the SRO performs as expected. Furthermore, we provide evidence of its superiority in implementing sophisticated AI-supported algorithms for real-time data analysis and reconstruction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Machine learning on FPGA for event selection

Real-time data processing is a frontier field in experimental particle physics. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented Machine Learning (ML) elements for real-time data processing with a triggered readout system that runs most of the ML algorithms on a computer farm. The work described in this article aims to test the ML-FPGA algorithms for streaming data acquisition. Herein, there are many experiments working in this area and they have a lot in common, but there are many specific solutions for detector and accelerator parameters that are worth exploring further. This report describes the purpose of the work and progress in evaluating the ML-FPGA application.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

AI for Experimental Controls at Jefferson Lab

We report the AI for Experimental Controls project is developing an AI system to control and calibrate detector systems located at Jefferson Laboratory. Currently, calibrations are performed offline and require significant time and attention from experts. This work would reduce the amount of data and the amount of time spent calibrating in an offline setting. The first use case involves the Central Drift Chamber (CDC) located inside the GlueX spectrometer in Hall D. We use a combination of environmental and experimental data, such as atmospheric pressure, gas temperature, and the flux of incident particles as inputs to a sequential Neural Network (NN) to recommend a high voltage setting and the corresponding calibration constants in order to maintain consistent gain and optimal resolution throughout the experiment. Utilizing AI in this manner represents an initial shift from offline calibration towards near real time calibrations performed at Jefferson Laboratory.

47 OTHER INSTRUMENTATION↗
NASA NTRS2022

Nickel Accounting for the Psyche Spacecraft

(16) Psyche is a metallic asteroid located in the asteroid belt between Mars and Jupiter. The composition of Psyche is unknown, but the scientific community believes it is composed primarily of iron, nickel, and silicates. The Psyche mission will be the first space mission to study this asteroid in close proximity, and as such, determining the elemental composition of the asteroid is one of the primary objectives of this mission. The spacecraft measures this elemental composition using the Gamma Ray Spectrometer (GRS), which detects cosmic-ray induced gamma rays from Psyche’s surface. In addition to gamma rays from Psyche, the GRS will also detect background gamma rays from spacecraft material nearby the GRS. Because Ni is such a key element for achieving the mission science objectives, a special effort has been made to minimize background gamma rays from Ni, thus maximizing sensitivity to the Ni gamma rays from Psyche. The Psyche mission approached this potential Ni background issue with two methods, locating the GRS away from the spacecraft/source of Ni and setting a threshold requirement against the total Ni mass of the spacecraft. Verifying that the total amount of Ni on the spacecraft is within this threshold is thus a key part of ensuring the GRS can meet its science objectives. Ni alloys are commonly used on spacecraft, but the mass and location of elemental Ni is not typically accounted for. Ni can be found in many materials all over the spacecraft – in fasteners, optical assemblies, magnetic shields, electromagnetic interference (EMI) tape, under gold plating, in connectors, and so on. The task proved further challenging because of the distributed nature of the build of this spacecraft and the use of vendors external to JPL. While the analysis was atypical and source information difficult to cull, the approach described in this paper used tools and data available to the project in some form to achieve this goal. For Psyche, current analysis shows there is margin in meeting the Ni mass requirement. This paper explains the systematic approach used to accurately and precisely record Ni content on the Psyche spacecraft. While this approach was defined for Ni on the Psyche mission, it can be used for any future project with the need to accurately estimate the total mass of a single element or material on a spacecraft.

Cullinan, Joe↗
NASA NTRS2022

Nickel Accounting for the Psyche Spacecraft

(16) Psyche is a metallic asteroid located in the asteroid belt between Mars and Jupiter. The composition of Psyche is unknown, but the scientific community believes it is composed primarily of iron, nickel, and silicates. The Psyche mission will be the first space mission to study this asteroid in close proximity, and as such, determining the elemental composition of the asteroid is one of the primary objectives of this mission. The spacecraft measures this elemental composition using the Gamma Ray Spectrometer (GRS), which detects cosmic-ray induced gamma rays from Psyche’s surface. In addition to gamma rays from Psyche, the GRS will also detect background gamma rays from spacecraft material nearby the GRS. Because Ni is such a key element for achieving the mission science objectives, a special effort has been made to minimize background gamma rays from Ni, thus maximizing sensitivity to the Ni gamma rays from Psyche. The Psyche mission approached this potential Ni background issue with two methods, locating the GRS away from the spacecraft/source of Ni and setting a threshold requirement against the total Ni mass of the spacecraft. Verifying that the total amount of Ni on the spacecraft is within this threshold is thus a key part of ensuring the GRS can meet its science objectives. Ni alloys are commonly used on spacecraft, but the mass and location of elemental Ni is not typically accounted for. Ni can be found in many materials all over the spacecraft – in fasteners, optical assemblies, magnetic shields, electromagnetic interference (EMI) tape, under gold plating, in connectors, and so on. The task proved further challenging because of the distributed nature of the build of this spacecraft and the use of vendors external to JPL. While the analysis was atypical and source information difficult to cull, the approach described in this paper used tools and data available to the project in some form to achieve this goal. For Psyche, current analysis shows there is margin in meeting the Ni mass requirement. This paper explains the systematic approach used to accurately and precisely record Ni content on the Psyche spacecraft. While this approach was defined for Ni on the Psyche mission, it can be used for any future project with the need to accurately estimate the total mass of a single element or material on a spacecraft.

Cullinan, Joe↗
DOE OSTI2021

GMLC 1.5.03: Increasing Distribution System Resiliency using Flexible DER and Microgrid Assets Enabled by OpenFMB (Duke-RDS Final Report)

This is the final project report for the Grid Modernization Laboratory Consortium (GMLC) Resilient Distribution System (RDS) project titled “Increasing Distribution System Resiliency using Flexible DER and Microgrid Assets Enabled by OpenFMB”. The primary goal of this project was to increase the resiliency of distribution systems at utilities around the nation by deploying flexible operating strategies that engage end-use assets as a resource.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

HOSS!

The Hall-D Online Skim System (HOSS) was developed to simultaneously solve two issues for the high intensity GlueX experiment. One was to parallelize the writing of raw data files to disk in order to improve bandwidth. The other was to distribute the raw data across multiple compute nodes in order to produce calibration skims of the data online. The highly configurable system employs RDMA, RAM disks, and zeroMQ driven by Python to simultaneously store and process the full high intensity GlueX data stream.

Lawrence, David↗
DOE OSTI2021

AI Enabled Data Quality Monitoring with Hydra

Data quality monitoring is critical to all experiments impacting the quality of any physics results. Traditionally, this is done through an alarm system, which detects low level faults, leaving higher level monitoring to human crews. Artificial Intelligence is beginning to find its way into scientific applications, but comes with difficulties, relying on the acquisition of new skill sets, either through education or acquisition, in data science. This paper will discuss the development and deployment of the Hydra monitoring system in production at Gluex. It will show how “off-the-shelf” technologies can be rapidly developed, as well as discuss what sociological hurdles must be overcome to successfully deploy such a system. Early results from production running of Hydra will also be shared as well as a future outlook for development of Hydra.

Britton, Thomas↗
DOE OSTI2021

Streaming Readout of the CLAS12 Forward Tagger Using TriDAS and JANA2

An effort is underway to develop streaming readout data acquisition system for the CLAS12 detector in Jefferson Lab’s experimental Hall-B. Successful beam tests were performed in the spring and summer of 2020 using a 10GeV electron beam from Jefferson Lab’s CEBAF accelerator. The prototype system combined elements of the TriDAS and CODA data acquisition systems with the JANA2 analysis/reconstruction framework. This successfully merged components that included an FPGA stream source, a distributed hit processing system, and software plugins that allowed offline analysis written in C++ to be used for online event filtering. Details of the system design and performance are presented.

Ameli, Fabrizio↗
DOE OSTI2021

AI-Constrained Bottom-Up Ecohydrology and Improved Prediction of Seasonal, Interannual, and Decadal Flood and Drought Risks

Focal Areas: (2) Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system composed of a hierarchy of models (3)Insight gleaned from complex data (both observed & simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI

54 ENVIRONMENTAL SCIENCES↗