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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Development of real-time rotating waveplate Stokes polarimeter using multi-order retardation for ITER poloidal polarimeter

The rotating waveplate Stokes polarimeter was developed for ITER (International Thermonuclear Experimental Reactor) poloidal polarimeter. The generalized model of the rotating waveplate Stokes polarimeter and the algorithm suitable for real-time field-programmable gate array (FPGA) processing were proposed. Since the generalized model takes into account each component associated with the rotation of the waveplate, the Stokes parameters can be accurately measured even in unideal condition such as non-uniformity of the waveplate retardation. Experiments using a He-Ne laser showed that the maximum error and the precision of the Stokes parameter were 3.5% and 1.2%, respectively. The rotation speed of waveplate was 20 000 rpm and time resolution of measuring the Stokes parameter was 3.3 ms. Software emulation showed that the real-time measurement of the Stokes parameter with time resolution of less than 10 ms is possible by using several FPGA boards. Evaluation of measurement capability using a far-infrared laser which ITER poloidal polarimeter will use concluded that measurement error will be reduced by a factor of nine.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Monitoring system for testing the radiation hardness of a KINTEX-7 FPGA

A much more efficient Ring Imaging Cherenkov sub-detector system will be rebuilt in the second long shutdown of Large Hadron Collider for the LHCb experiment. Radiation-hard electronic components together with Commercial Off-The-Shelf ones will be used in the new Cherenkov photon detection system architecture. An irradiation program was foreseen to determine the radiation tolerance for the new electronic devices, including a Field Programmable Gate Array from KINTEX-7 family of XILINX. An automated test bench for online monitoring of the XC7K70T KINTEX-7 device operation in radiation conditions was designed and implemented by the LHCb Romanian group.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Note: Digital laser frequency auto-locking for inter-satellite laser ranging

We present a prototype of a laser frequency auto-locking and re-locking control system designed for laser frequency stabilization in inter-satellite laser ranging system. The controller has been implemented on field programmable gate arrays and programmed with LabVIEW software. The controller allows initial frequency calibrating and lock-in of a free-running laser to a Fabry-Pérot cavity. Since it allows automatic recovery from unlocked conditions, benefit derives to automated in-orbit operations. Program design and experimental results are demonstrated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Super-transient scaling in time-delay autonomous Boolean network motifs

Autonomous Boolean networks are commonly used to model the dynamics of gene regulatory networks and allow for the prediction of stable dynamical attractors. However, most models do not account for time delays along the network links and noise, which are crucial features of real biological systems. Concentrating on two paradigmatic motifs, the toggle switch and the repressilator, we develop an experimental testbed that explicitly includes both inter-node time delays and noise using digital logic elements on field-programmable gate arrays. We observe transients that last millions to billions of characteristic time scales and scale exponentially with the amount of time delays between nodes, a phenomenon known as super-transient scaling. We develop a hybrid model that includes time delays along network links and allows for stochastic variation in the delays. Using this model, we explain the observed super-transient scaling of both motifs and recreate the experimentally measured transient distributions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Note: Fully integrated 3.2 Gbps quantum random number generator with real-time extraction

We present a real-time and fully integrated quantum random number generator (QRNG) by measuring laser phase fluctuations. The QRNG scheme based on laser phase fluctuations is featured for its capability of generating ultra-high-speed random numbers. However, the speed bottleneck of a practical QRNG lies on the limited speed of randomness extraction. To close the gap between the fast randomness generation and the slow post-processing, we propose a pipeline extraction algorithm based on Toeplitz matrix hashing and implement it in a high-speed field-programmable gate array. Further, all the QRNG components are integrated into a module, including a compact and actively stabilized interferometer, high-speed data acquisition, and real-time data post-processing and transmission. The final generation rate of the QRNG module with real-time extraction can reach 3.2 Gbps.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Modifications to the synthetic aperture microwave imaging diagnostic

The synthetic aperture microwave imaging diagnostic has been operating on the MAST experiment since 2011. It has provided the first 2D images of B-X-O mode conversion windows and showed the feasibility of conducting 2D Doppler back-scattering experiments. The diagnostic heavily relies on field programmable gate arrays to conduct its work. Recent successes and newly gained experience with the diagnostic have led us to modify it. The enhancements will enable pitch angle profile measurements, O and X mode separation, and the continuous acquisition of 2D DBS data. Finally, the diagnostic has also been installed on the NSTX-U and is acquiring data since May 2016.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

A novel technique for real-time estimation of edge pedestal density gradients via reflectometer time delay data

A new model-based technique for fast estimation of the pedestal electron density gradient has been developed. The technique uses ordinary mode polarization profile reflectometer time delay data and does not require direct profile inversion. Because of its simple data processing, the technique can be readily implemented via a Field-Programmable Gate Array, so as to provide a real-time density gradient estimate, suitable for use in plasma control systems such as envisioned for ITER, and possibly for DIII-D and Experimental Advanced Superconducting Tokamak. The method is based on a simple edge plasma model with a linear pedestal density gradient and low scrape-off-layer density. By measuring reflectometer time delays for three adjacent frequencies, the pedestal density gradient can be estimated analytically via the new approach. Using existing DIII-D profile reflectometer data, the estimated density gradients obtained from the new technique are found to be in good agreement with the actual density gradients for a number of dynamic DIII-D plasma conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Note: A versatile radio-frequency source for cold atom experiments

A radio-frequency (RF) source designed for cold atom experiments is presented. The source uses AD9858, a direct digital synthesizer, to generate the sine wave directly, up to 400 MHz, with sub-Hz resolution. An amplitude control circuit consisting of wideband variable gain amplifier and high speed digital to analog converter is integrated into the source, capable of 70 dB off isolation and 4 ns on-off keying. A field programmable gate array is used to implement a versatile frequency and amplitude co-sweep logic. Owing to modular design, the RF sources have been used on many cold atom experiments to generate various complicated RF sequences, enriching the operation schemes of cold atoms, which cannot be done by standard RF source instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

FPGA based demodulation of laser induced fluorescence in plasmas

We present a field programmable gate array (FPGA)-based system that counts photons from laser-induced fluorescence (LIF) on a laboratory plasma. This is accomplished with FPGA-based up/down counters that demodulate the data, giving a background-subtracted LIF signal stream that is updated with a new point as each laser amplitude modulation cycle completes. We demonstrate using the FPGA to modulate a laser at 1 MHz and demodulate the resulting LIF data stream. Finally, this data stream is used to calculate an LIF-based measurement sampled at 1 MHz of a plasma ion fluctuation spectrum.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Enhancing SRF cavity stability and minimizing detuning with data-driven resonance control based on dynamic mode decomposition

Effective resonance control of superconducting radio frequency (SRF) cavities is critical for large machines like LCLS-II, as failure to achieve proper control can result in increased RF power consumption, higher cryogenic heat loads, and increased costs. To address this challenge, we have developed a machine learning (ML) model based on the dynamic mode decomposition method to represent the forced cavity dynamics. Using this model, we designed a model predictive controller (MPC) and demonstrated through simulation that the MPC can effectively stabilize the amplitude and phase of SRF cavities using only a frequency actuator, even in the presence of multiple mechanical modes. The lightweight and explicit ML model makes the controller suitable for direct implementation on field-programmable gate arrays, unlocking the full potential of SRF linacs like LCLS-II, enabling higher beam power and energy, and also serving as an advanced motion controller for various applications, such as photon beamlines and storage rings.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. Here, in this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A hybrid neural architecture: Online attosecond x-ray characterization

The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 μs inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2017

Optimization on fixed low latency implementation of the GBT core in FPGA

We present that in the upgrade of ATLAS experiment, the front-end electronics components are subjected to a large radiation background. Meanwhile high speed optical links are required for the data transmission between the on-detector and off-detector electronics. The GBT architecture and the Versatile Link (VL) project are designed by CERN to support the 4.8 Gbps line rate bidirectional high-speed data transmission which is called GBT link. In the ATLAS upgrade, besides the link with on-detector, the GBT link is also used between different off-detector systems. The GBTX ASIC is designed for the on-detector front-end, correspondingly for the off-detector electronics, the GBT architecture is implemented in Field Programmable Gate Arrays (FPGA). CERN launches the GBT-FPGA project to provide examples in different types of FPGA. In the ATLAS upgrade framework, the Front-End LInk eXchange (FELIX) system is used to interface the front end electronics of several ATLAS subsystems. The GBT link is used between them, to transfer the detector data and the timing, trigger, control and monitoring information. The trigger signal distributed in the down-link from FELIX to the front-end requires a fixed and low latency. In this paper, several optimizations on the GBT-FPGA IP core are introduced, to achieve a lower fixed latency. For FELIX, a common firmware will be used to interface different front-ends with support of both GBT modes: the forward error correction mode and the wide mode. The modified GBT-FPGA core has the ability to switch between the GBT modes without FPGA reprogramming. Finally, the system clock distribution of the multi-channel FELIX firmware is also discussed in this paper.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

The Octopus processor for the CMS L1 muon trigger for High Luminosity LHC

The upgraded L1 muon trigger system of the CMS experiment in the High Luminosity Large Hadron Collider is based on custom processors featuring large Field Programmable Gate Arrays (FPGAs) connected by large numbers of optical links. These provide the I/O bandwidth and power necessary to process the complex algorithms used during the collection of physics data. The design and performance requirements of these processors creates significant challenges in signal integrity, power delivery, and thermal management. In this paper we describe the Octopus processor, featuring a large Xilinx Virtex Ultrascale+ FPGA and up to 128 links interfaced to optics through high quality twin-ax copper cables. Results on signal integrity at 25 Gb/s and the first demonstration of 50+ Gb/s links with pluggable optics in CMS are also shown, demonstrating bit error rates below 10 –15 at a 95% confidence level. The thermal performance is measured inside an Advanced-TCA crate with acceptable thermal margins up to 200 W of chip power. Future improvements are mentioned, potentially allowing operation at up to 300 W.

Instruments & Instrumentation↗
DOE OSTI2022

High performance FPGA embedded system for machine learning based tracking and trigger in sPhenix and EIC

We present a comprehensive end-to-end pipeline to classify triggers versus background events in this paper. This pipeline makes online decisions to select signal data and enables the intelligent trigger system for efficient data collection in the Data Acquisition System (DAQ) of the upcoming sPHENIX and future EIC (Electron-Ion Collider) experiments. Starting from the coordinates of pixel hits that are lightened by passing particles in the detector, the pipeline applies three-stage of event processing (hits clustering, track reconstruction, and trigger detection) and labels all processed events with the binary tag of trigger versus background events. The pipeline consists of deterministic algorithms such as clustering pixels to reduce event size, tracking reconstruction to predict candidate edges, and advanced graph neural network-based models for recognizing the entire jet pattern. In particular, we apply the message-passing graph neural network to predict links between hits and reconstruct tracks and a hierarchical pooling algorithm (DiffPool) to make the graph-level trigger detection. We obtain an impressive performance (≥70% accuracy) for trigger detection with only 3200 neuron weights in the end-to-end pipeline. We deploy the end-to-end pipeline into a field-programmable gate array (FPGA) and accelerate the three stages with speedup factors of 1152, 280, and 21, respectively.

Instruments & Instrumentation↗
DOE OSTI2022

100 Gb/s High Throughput Serial Protocol (HTSP) for data acquisition systems with interleaved streaming

Demands on Field-Programmable Gate Array (FPGA) data transport have been increasing over the years as frame sizes and refresh rates increase. As the bandwidths requirements increase the ability to implement data transport protocol layers using "soft" programmable logic becomes harder and start to require harden IP blocks implementation. To reduce the number of physical links and interconnects, it is common for data acquisition systems to require interleaving of streams on the same link (e.g. streaming data and streaming register access). Further, this paper presents a way to leverage existing FPGA harden IP blocks to achieve a robust, low latency 100 Gb/s point-to-point link with minimal programmable logic overhead geared towards the needs of data acquisition systems with interleaved streaming requirements.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics

We present a novel application of the machine learning / artificial intelligence method called boosted decision trees to estimate physical quantities on field programmable gate arrays (FPGA). The software package fwXmachina features a new architecture called parallel decision paths that allows for deep decision trees with arbitrary number of input variables. It also features a new optimization scheme to use different numbers of bits for each input variable, which produces optimal physics results and ultraefficient FPGA resource utilization. Problems in high energy physics of proton collisions at the Large Hadron Collider (LHC) are considered. Estimation of missing transverse momentum (E T miss ) at the first level trigger system at the High Luminosity LHC (HL-LHC) experiments, with a simplified detector modeled by Delphes, is used to benchmark and characterize the firmware performance. The firmware implementation with a maximum depth of up to 10 using eight input variables of 16-bit precision gives a latency value of $\mathcal{O}$(10) ns, independent of the clock speed, and $\mathcal{O}$(0.1)% of the available FPGA resources without using digital signal processors.

Instruments & Instrumentation↗
DOE OSTI2024

Investigating resource-efficient neutron/gamma classification ML models targeting eFPGAs

There has been considerable interest and resulting progress in implementing machine learning (ML) models in hardware over the last several years from the particle and nuclear physics communities. A big driver has been the release of the Python package, hls4ml, which has enabled porting models specified and trained using Python ML libraries to register transfer level (RTL) code. So far, the primary end targets have been commercial field-programmable gate arrays (FPGAs) or synthesized custom blocks on application specific integrated circuits (ASICs). However, recent developments in open-source embedded FPGA (eFPGA) frameworks now provide an alternate, more flexible pathway for implementing ML models in hardware. These customized eFPGA fabrics can be integrated as part of an overall chip design. In general, the decision between a fully custom, eFPGA, or commercial FPGA ML implementation will depend on the details of the end-use application. In this work, we explored the parameter space for eFPGA implementations of fully-connected neural network (fcNN) and boosted decision tree (BDT) models using the task of neutron/gamma classification with a specific focus on resource efficiency. We used data collected using an AmBe sealed source incident on Stilbene, which was optically coupled to an OnSemi J-series silicon photomultiplier (SiPM) to generate training and test data for this study. We investigated relevant input features and the effects of bit-resolution and sampling rate as well as trade-offs in hyperparameters for both ML architectures while tracking total resource usage. The performance metric used to track model performance was the calculated neutron efficiency at a gamma leakage of 10 -3 . The results of the study will be used to aid the specification of an eFPGA fabric, which will be integrated as part of a test chip.

47 OTHER INSTRUMENTATION↗