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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Characterizing TES Power Noise for Future Single Optical-Phonon and Infrared-Photon Detectors

In this letter, we present the performance of a 100 μm × 400 μm × 40 nm W Transition-Edge Sensor with a critical temperature of 40 mK. This device has a noise equivalent power of 1.5×10-18 W/Hz, in a bandwidth of 2.6 kHz, indicating a resolution for Dirac delta energy depositions of 40 ± 5 meV (rms). The performance demonstrated by this device is a critical step toward developing a O(100) meV threshold athermal phonon detector for low-mass dark matter searches.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Transient absorption spectroscopy to explore cellular pathways to photobiomodulation

Photobiomodulation (PBM) describes the use of low irradiance light in the red to near-infrared wavelength range to stimulate biological effects in tissue, and many biological and spectroscopic techniques are used to study PBM. However, these techniques focus on the products or downstream effects rather than the electronic transitions that initiate the PBM processes. This study presents a novel approach to studying low irradiance light exposures on individual proteins and/or protein complexes by combining a continuous wave (CW) laser diode with femtosecond transient absorption spectroscopy (TAS), coined here as CW-TAS, and tests the system on reduced cytochrome c (Cyt c) for proof of principle. TAS was conducted using a 532-nm excitation pump beam and a 350-600 nm supercontinuum probe. In this work, CW laser diodes with wavelengths of 450 nm, 635 nm, and 808 nm were interchangeably fiber coupled into the HELIOS Fire. Samples of Cyt c were tested by TAS using a pump power of 15 µW, both with and without CW exposure. CW exposures were carried out with irradiances of 1.60 and 3.20 mW/cm 2 , except for 808 nm, which was only tested at 1.60 mW/cm 2 . Both kinetic and global analyses were performed on the TAS data and the time constants for sets with and without CW exposures were compared. The TAS data for Cyt c with the full dosage of CW exposures did not alter the TAS data distinguishably from the control data. No new electronic transient signals were observed beyond the background when testing Cyt c with the CW exposures. Kinetic analysis confirmed that existing transients did not deviate beyond uncertainty. Global time constants for Cyt c were calculated to be 0.25 ± 0.03 ps and 5.1 ± 0.3 ps for the control study, and the time constants for the CW exposed Cyt c were not significantly different. This study concludes that CW irradiation, at doses delivered, does not alter the transient absorption data of Cyt c. The CW-TAS method provides a new tool for studying PBM effects in other proteins and protein complexes, such as Complex IV, in future studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

General signals for charged lepton flavor violating decays

We explore the most general phenomenology of charged lepton flavor violating (CLFV) decays of muon and tau leptons to the three body final states ($\bar{e}ee, \bar{μ}μμ, \bar{e}μμ, \bar{μ}μe, \bar{μ}ee, \bar{e}eμ$). By constructing a complete basis of operators at each dimension, we derive the most general amplitudes for these decay processes. By considering constraints from unitarity and Large Electron-Positron Collider (LEP), we show that operators of mass dimension 6 and 7 are the most likely to be observed in next generation experiments. Focusing on these dimensions, we compute the results of unpolarized (spin-averaged) decays parametrized in terms of the invariant masses of the daughter particles. We also compute the differential decay rates for polarized decays, in anticipation of the experimental search Mu3e, which expects to have a muon beam with ∼ 90% polarization, and the chiral Belle proposal, which aims to have a 70% polarized electron beam. To determine the extent to which the operators may be distinguished experimentally, we plot the differential distributions for each operator, showing that they leave only a few possible degenerate explanations. Through a statistical analysis, we estimate the number of events needed to break the degeneracies using the angular information. These results are adapted to treat ℓ → ℓ′⁢$v\bar{v}$, where the angular distribution of the outgoing charged lepton has enhanced distinguishing power. With many Standard Model extensions predicting these CLFV decays, these results will better enable upcoming searches to identify and/or constrain physics beyond the Standard Model.

phenomenology↗
DOE OSTI2023

Time‐Lapse Image Classification Using a Diffractive Neural Network

Diffractive deep neural networks (D 2 NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all‐optical object classification and perform universal linear transformations. Herein, a “time‐lapse” image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super‐resolution applications; inspired by their success, a time‐lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time‐lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR‐10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR‐10 dataset. Time‐lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all‐optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In-Situ Laser Diagnostics to Understand Plasma-Surface Interactions in Titanium Thin Film Deposition

During the current project period the following tasks were completed: (1) We demonstrated the feasibility of a new way of measuring CH3 radicals in a plasma environment using a single, femtosecond (fs) pulsed laser for the first time, in a CH4 + Ar flow. A single fs pulsed Ti:Sa laser was split into two beams and with beam conversion using crystals we generatedtwo beams of 275 nm and 205 nm. The 275 nm was used to predissociate the CH3 radicals to CH2+H, followed by 205 nm Two Photon Laser Induced Fluorescence (TALIF) detection of H atoms, with a 11 ns delay from 275 nm predissociation pulse. (2) We demonstrated preliminary CH3 concentration measurements with spatial (< 100 µm) and time resolution (< 10 ns) by image processing of H and Kr TALIF signal images. (3) The methodology to quantify CH3 concentrations was developed. (4) Preliminary measurements of laser dissociation cross sections for CH4 and CH3 to (CH3+H), and (CH2+H), respectively, were conducted. (5) 275 nm laser dissociation of CH4 is a multiphoton process, and the CH4 dissociation cross section is a more sensitive function of the laser pulse energy than CH4 concentration. In the coming months, we will make more precise measurements of the dissociation cross sections, 𝑘275, 𝐶𝐻4 and 𝑘275, 𝐶𝐻3, by varying the laser energy while keeping the flow composition and conditions constant. This will enable more precise quantification of CH3 radical concentrations. More detailed measurements of CH3 radicals will be conducted in the following months of 2025 (Sept-Dec) in a Titanium Isopropoxide (TIP) + H2 + Ar flow meant for pure phase Ti thin film deposition by Chemical Vapor Deposition (CVD). More precise measurements of 275 nm laser dissociation cross sections for CH4 (to CH3 + H) and CH3 (to CH2 + H) will be conducted in the future. Detailed 275 nm laser predissociation cross sections for CH4 and CH3 dissociation to (CH3+H), and (CH2+H), respectively, as a function of gas heating, plasma power, and laser pulse energy will be evaluated. This will enable quantification of CH3 concentrations in the precursor flow over a substrate with time and spatial resolution. Comparison of the concentrations with 2D numerical simulations can lead to understanding of plasma surface interactions during pure metallic Ti thin film deposition using environmentally safer non-halogen TIP precursor. Flow conditions for pure Ti thin film deposition using non-halogen precursor (TIP) will be identified in the following months.

Uddi, Mruthunjaya [Advanced Cooling Technologies, ↗
DOE OSTI2020

Ghost imaging second harmonic generation microscopy

Second harmonic generation (SHG) microscopy is useful for visualizing interfaces and sub-structures within a wide range of materials due to the propensity for SHG to occur in non-centrosymmetric environments. However, since SHG is a nonlinear process generally necessitating small focal sizes for higher peak powers, a raster scanning approach is usually needed to build an SHG image over a significant sample size. While raster scanning is effective, there is a cost in terms of the time needed to acquire the image and, also, some materials cannot withstand the higher optical intensities within the small focal volume. As such, we describe a SHG microscopy approach based on ghost imaging (GI), which enables imaging data to be collected in parallel rather than sequentially as in raster scanning techniques. We experimentally demonstrate the approach and combine GI-SHG with compressive sensing to make further substantial gains in reducing the amount of sampling required for image reconstruction. Furthermore, GI-SHG is shown to have significant advantages for imaging in highly scattering environments, partly because GI is a background-free approach requiring spatial correlations between photons that travel two paths, with one path entirely devoid of sample interaction. This basic property of GI means that only the photons that travel unimpeded through the sample preserve the spatial correlations needed to reconstruct the image, while more scattered photons do not contribute to the overall GI signal. Finally, we compare the image quality and sampling properties of three different reconstruction algorithms used for compressive sensing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Dynamic time-warping correction for shifts in ultrahigh resolving power ion mobility spectrometry and structures for lossless ion manipulations

Detection of arrival time shifts between ion mobility spectrometry (IMS) separations can limit achievable resolving power (Rp), particularly when multiple separations are summed or averaged, as commonly practiced. Such variations are more apparent in higher Rp measurements, and are particularly evident in long path length traveling wave structures for lossless ion manipulations (SLIM) IMS due to their typically much longer separation times. Here we explore the utility of a data processing approaches employing linear alignment (LA) and nonlinear dynamic time warping (DTW) of IMS separations to correct for variations between separations, such as due to pressure fluctuations. For multipass SLIM IMS separations, where narrow mobility range measurements have arrival times that can extend to several seconds, the LA approach effectively corrected for such variations, and significantly improvement Rp for summed separations. However, LA was much less effective for high Rp broad mobility range separations, such as obtained with multilevel SLIM IMS. Changes in IMS arrival times ions were observed to be correlated with small pressure changes, with approximately 0.6% relative arrival time shifts being common, sufficient to result in a loss of Rp for summed separations. Comparison of the approaches showed DTW alignment performed similarly to LA when used over a narrow mobility range, but was significantly better (providing narrower peaks and higher signal intensities) for wide mobility range data. We found the DTW approach increased Rp by as much as 115% for measurements in which 50 IMS separations over 2 seconds were summed, and leading to a large improvement in effective Rp. We conclude that DTW is superior to LA for ultrahigh resolution broad mobility range SLIM IMS separations, correcting for ion arrival time shifts regardless of the cause. Our tool is publicly available for use with universal ion mobility format (.UIMF) and text (.txt) files.

Data alignment, dynamic time warping, ion mobility↗
DOE OSTI2024

High‐speed 4‐dimensional scanning transmission electron microscopy using compressive sensing techniques

Abstract Here we show that compressive sensing allows 4‐dimensional (4‐D) STEM data to be obtained and accurately reconstructed with both high‐speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4‐D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal‐to‐noise ratio in the recovered phase using 0.3% of the total data. Lay abstract : Four‐dimensional scanning transmission electron microscopy (4‐D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2‐dimensional signal is acquired at each 2‐D probe location, equating to a 4‐D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4‐D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100‐1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4‐D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10‐100 times. The acquired data is then processed to fill‐in or inpaint the missing data, taking advantage of the inherently low‐complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4‐D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4‐D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100‐300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.

Robinson, Alex W.↗
DOE OSTI2024

Ultrafast Exciton Dynamics of CH 3 NH 3 PbBr 3 Perovskite Nanoclusters

Exciton dynamics of perovskite nanoclusters has been investigated for the first time using femtosecond transient absorption (TA) and time-resolved photoluminescence (TRPL) spectroscopy. The TA results show two photoinduced absorption signals at 420 and 461 nm and a photoinduced bleach (PB) signal at 448 nm. The analysis of the PB recovery kinetic decay and kinetic model uncovered multiple processes contributing to electron-hole recombination. The fast component (~8 ps) is attributed to vibrational relaxation within the initial excited state, and the medium component (~60 ps) is attributed to shallow carrier trapping. The slow component is attributed to deep carrier trapping from the initial conduction band edge (~666 ps) and the shallow trap state (~40 ps). The TRPL reveals longer time dynamics, with modeled lifetimes of 6.6 and 93 ns attributed to recombination through the deep trap state and direct band edge recombination, respectively. In conclusion, the significant role of exciton trapping processes in the dynamics indicates that these highly confined nanoclusters have defect-rich surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Real-Time ANPC Inverter Digital Twin with Integrated Design-For-Trust

The demand for renewable energy has increased over the last few years, and so has the demand for greater expectations within the energy market. This increasing trend has been accompanied by more significant usage of internet-connected devices (IoT), leading to critical electrical infrastructure being connected to the internet. Implementing internet connectivity with such devices and systems provides benefits such as improving the system's performance, facilitating irregularity and anomaly mitigation, and providing additional situational awareness for enhanced decision-making. However, enhancing the connected system with IoT introduces a drawback – a greater vulnerability to cyber-attacks. Cyber-attacks targeting critical infrastructure in the electrical sector have occurred in the United States and Ukraine. These cyber-attacks highlight and expose vulnerabilities that a system inherits when connecting to the internet. These attacks left thousands of customers without electricity for hours until operators could regain control of the electric utility grid. Therefore, to address the vulnerabilities of an internet-connected power electronic device, this work focused on the hardware layer of the system. Implementing a cyber-control system inside the hardware layer can significantly reduce the possibility of an attacker patching malicious controller firmware into a photovoltaic grid-connected inverter, thus mitigating the likelihood that the inverter becomes inactive a cyber-attack scenario. With this mitigation technique, if a cyberattack is successful and an attacker gains control of the network, a cyber-defense technique is in place to mitigate the impact of the cyber-attack. This additional protection layer was developed based on an innovative concept known as Digital Twin (DT). A DT, in this case, replicates an Active-Neutral Point Clamped (ANPC) inverter and was designed using a hardware language known as VHDL (Very High-Speed Integrated Circuit Hardware Description Language) and applied to Field-Programmable-GateArray (FPGA). The DT is embedded within the FPGA and contained in a controller board, the UCB (Unified Controller Board), developed by the University of Arkansas electrical engineering team. This UCB also contains two Digital Signal Processors (DSPs) responsible for generating associated signals to control an authentic physical inverter. These DSP signals are received and processed by the FPGA that implements the DT of an ANPC; in other words, it simulates in realtime the expected output of an actual ANPC inverter using the signals from the DSP. When a new firmware is ready to be patched, the DT provides output signals simulating behavior that a real ANPC inverter would generate with the new firmware. The new firmware is tested to check if it meets all the operational requirements established using a Design-For-Trust technique (DFTr). If the new firmware fails in at least one of the DFT tests, it is considered malicious and must be rejected. This work is divided into sections, such as Background, which explains the pieces that were used and the strategy behind this work; Process and Procedure, which explains the methodology that was adopted to prove the reliability and effectiveness of this work; Results and Discussion, where the simulations and results are described and explained; followed by Conclusion and Future work section, which concludes this work and adds possible future projects to continue this work further.

do Amaral Custodio, Paulo Vitor↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2026

Learned adaptive properties for mitigation of weight perturbations in embedded spiking networks

Recent years have seen an increased importance of neural network inference in edge-based scenarios, which impose size and power constraints requiring novel computing devices. These same edge scenarios may require operating over long periods of time, or exposure to extreme environments, resulting in a drift of neural network weights that cause degraded performance. In searching for ways to develop neural network approaches that perform robustly under these conditions, we propose a biologically-inspired mechanism for the dynamic adaptation of within-neuron parameters that is guided by a global context signal carrying information about perturbations and variability in incoming stimuli. Specifically, we demonstrate that adaptive voltage thresholds or neuronal time constants, when informed by a global context signal, can enable network-level mechanisms to recover from perturbed synaptic weights. Consistent with prior literature, the context-modulated approach is effective for recurrent, but not feedforward networks, by modulating network level dynamics. We demonstrate this approach successfully recovers performance in image classification tasks and spatiotemporal tracking tasks under idealized and Gaussian noise as well as for realistic perturbations from a memristive device when exposed to ionizing radiation. Finally, we discuss how this approach enables the design of robust and energy-efficient neuromorphic systems that perform well, even in resource-constrained scenarios with extreme environments such as edge processing.

context modulation↗
DOE OSTI2021

MicroPulse DIAL (MPD) – a diode-laser-based lidar architecture for quantitative atmospheric profiling

Abstract. Continuous water vapor and temperature profiles are critically needed for improved understanding of the lower atmosphere and potential advances in weather forecasting skill. Ground-based, national-scale profiling networks are part of a suite of instruments to provide such observations; however, the technological method must be cost-effective and quantitative. We have been developing an active remote sensing technology based on a diode-laser-based lidar technology to address this observational need. Narrowband, high-spectral-fidelity diode lasers enable accurate and calibration-free measurements requiring a minimal set of assumptions based on direct absorption (Beer–Lambert law) and a ratio of two signals. These well-proven quantitative methods are known as differential absorption lidar (DIAL) and high-spectral-resolution lidar (HSRL). This diode-laser-based architecture, characterized by less powerful laser transmitters than those historically used for atmospheric studies, can be made eye-safe and robust. Nevertheless, it also requires solar background suppression techniques such as narrow-field-of-view receivers with an ultra-narrow bandpass to observe individual photons backscattered from the atmosphere. We discuss this diode-laser-based lidar architecture's latest generation and analyze how it addresses a national-scale profiling network's need to provide continuous thermodynamic observations. The work presented focuses on general architecture changes that pertain to both the water vapor and the temperature profiling capabilities of the MicroPulse DIAL (MPD). However, the specific subcomponent testing and instrument validation presented are for the water vapor measurements only. A fiber-coupled seed laser transmitter optimization is performed and shown to meet all of the requirements for the DIAL technique. Further improvements – such as a fiber-coupled near-range receiver, the ability to perform quality control via automatic receiver scanning, advanced multi-channel scalar capabilities, and advanced processing techniques – are discussed. These new developments increase narrowband DIAL technology readiness and are shown to allow higher-quality water vapor measurements closer to the surface via preliminary intercomparisons within the MPD network itself and with radiosondes.

Spuler, Scott M.↗
DOE OSTI2023

A New Plasma Radar Concept for Simultaneous Magnetic and Density Measurements

An innovative, compact 288GHz interferometer has been fabricated, tested, installed and successfully demonstrated on the LAPD-U magnetized plasma at UCLA. The system takes advantage of frequency modulated (FM) radar techniques to deliver a compact heterodyne system. In addition, the reflected power from the source is taken advantage of to eliminate the need for additional quasi-optical components. Electron density in LAPD-U plasma has recently been increased substantially thereby requiring a higher frequency/shorter wavelength interferometer to avoid deleterious refractive effects. This system satisfies those needs. The system uses a 96GHz varactor tuned Gunn oscillator which passes to a passive tripler. This tripler has ~3% conversion efficiency. The 288GHz radiation is then coupled to free space using a so-called dual-mode or Pickett horn. The output 288GHz beam is then coupled to an aspheric lens manufactured from low-loss, high-density polyethylene. This lens is employed to collimate the emerging beam. Small axial adjustment of the lens position can also be used to create a slowly focusing beam so as to optimize the measured signal. In addition, up-down or side-to-side adjustment of the lens can be utilized to steer the beam vertically or horizontally – again to optimize alignment. The propagating beam passes through a beam splitter and then through a water-free, bubble-free fused quartz window into the LAPD-U vacuum vessel. The beam-splitter is a thin sheet of G10 which reflects a small fraction of the incident power (~5 %) towards a zero-bias detector optimized for the frequency range from 220 to 300GHz. Note that waveguides at this frequency have dimensions of ~0.9mm x 0.45mm and so have very large conductive losses. This drives the use of quasi-optical propagation. The detector requires no DC bias and is very responsive (> 1V/mW into 1MΩ). Radiation is coupled to the detector via a similar lens-horn arrangement used for the launch. This reflected beam acts as the local oscillator or reference millimeter-wave beam for the detector. The remainder of the launched source beam then enters the LAPD-U vacuum vessel and passes through the plasma at the mid-plane until reaching the opposing port which is closed off with an aluminum flange. This flange is used as a mirror to retroreflect the incident 288GHz beam back along its path. The retroreflected beam exits the input port but does NOT couple directly into the zero-bias detector. Instead, the majority of the return power continues towards the 288GHz source. As mentioned above the transmitted beam enters the source a second time. This would appear undesirable. However, at these frequencies multipliers are highly non-linear elements which results in a significant portion of the return beam (~20%) re-emerging from the multiplier and horn and then coupling via the G10 beam-splitter to the zero-bias detector. This approach eliminated the need for a second quasi-optical beam-splitter. The system is extremely compact measuring approximately 28 inches x 20 inches. The above did not explain how heterodyne operation was achieved. As mentioned above the Gunn oscillator is able to be varactor tuned. This allows a low voltage to be applied to control the operating frequency of the Gunn oscillator. During heterodyne operation a sawtooth shaped voltage is applied to the varactor at 750kHz using an 80MHz Arbitrary Waveform Generator (AWG). This voltage changes the Gunn frequency linearly during the up-sweep which is then reset abruptly at the sawtooth crash to be immediately followed by another linear sweep. Passage through the 288GHz multiplier triples the frequency change experienced by the electromagnetic wave. These frequency changes are small – tens of megahertz. This FM radar approach results in the launched electromagnetic wave frequencies at the detector for the reference and plasma wave to be different. The approximately 10 ns delay propagation delay for the plasma beam results in the local oscillator and plasma beams NOT having an identical frequency – there is in fact a fixed difference frequency. The frequency tuning level of the Gunn oscillator is then adjusted so that there is ONE cycle of this difference frequency during each linear ramp. During the sawtooth crash or downward re-sweep this one cycle replays in reverse but on a very fast timescale. The process then repeats. Low-pass filtering eliminates the fast re-sweep to leave a pure sine wave heterodyne signal. When the plasma is present it introduces a phase delay in the sine wave (caused by the extremely small Doppler shift resulting from the optical path length change). Of course, to measure this phase change we need a reference. This is simply obtained from an arbitrary waveform generator which provides a synchronized output pulse train which again is low pass filtered to obtain a 750kHz sinusoidal voltage reference for the interferometer. The interferometer was installed on LAPD-U where it has worked reliably and has established that electron densities exceeding 1x10 13 cm -3 are routinely achieved. In addition, the system sensitivity was able to easily observe density fluctuation at frequencies up to 50kHz. FM Radar techniques have enabled a full demonstration of a compact, sensitive, high frequency (288GHz/1mm) heterodyne interferometer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Detecting Process Equipment Failures Using Acoustic Data and Machine Learning

Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2021

Identifying insects, clouds, and precipitation using vertically pointing polarimetric radar Doppler velocity spectra

Abstract. This study presents a method to identify and distinguish insects, clouds, and precipitation in 35 GHz (Ka-band) vertically pointing polarimetric radar Doppler velocity power spectra and then produce masks indicating the occurrence of hydrometeors (i.e., clouds or precipitation) and insects at each range gate. The polarimetric radar used in this study transmits a linear polarized wave and receives signals in collinear (CoPol) and cross-linear (XPol) polarized channels. The measured CoPol and XPol Doppler velocity spectra are used to calculate linear depolarization ratio (LDR) spectra. The insect–hydrometeor discrimination method uses CoPol and XPol spectral information in two separate algorithms with their spectral results merged and then filtered into single value products at each range gate. The first algorithm discriminates between insects and clouds in the CoPol Doppler velocity power spectra based on the spectra texture, or spectra roughness, which varies due to the scattering characteristics of insects vs. cloud particles. The second algorithm distinguishes insects from raindrops and ice particles by exploiting the larger Doppler velocity spectra LDR produced by asymmetric insects. Since XPol power return is always less than CoPol power return for the same target (i.e., insect or hydrometeor), fewer insects and hydrometeors are detected in the LDR algorithm than the CoPol algorithm, which drives the need for a CoPol based algorithm. After performing both CoPol and LDR detection algorithms, regions of insect and hydrometeor scattering from both algorithms are combined in the Doppler velocity spectra domain and then filtered to produce a binary hydrometeor mask indicating the occurrence of cloud, raindrops, or ice particles at each range gate. Forty-seven summertime days were processed with the insect–hydrometeor discrimination method using US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar observations in northern Oklahoma, USA. For these 47 d, over 70 % of the hydrometeor mask column bottoms were within ±100 m of simultaneous ceilometer cloud base heights. All datasets and images are available to the public on the DOE ARM repository.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Resolving ECRH deposition broadening due to edge turbulence in DIII-D

Microwave heat pulse propagation experiments have demonstrated a correlation between millimeter-scale turbulence and deposition profile broadening of electron cyclotron (EC) waves on the DIII-D tokamak. In a set of discharges in DIII-D, a variation in edge density fluctuations on the mm-scale is associated with 40%–150% broader deposition profiles, expressed in terms of normalized minor radius, as compared with equilibrium ray tracing. The 1D power profile is determined from transport analysis of the electron temperature response to EC power modulation using perturbative analysis with a square wave power modulation at 20–70 Hz, producing a series of Fourier harmonics that are fit collectively to resolve transport. Fitting an integrated heat flux expressed in the Fourier basis of the modulation to diffusive, convective, and coupled transport terms in a linear model can resolve the broadened EC deposition width from the power perturbation to resolve a broadening in each case. The best fit degree of beam broadening observed scales approximately linearly with the Doppler backscattering measured fluctuation level in the steep gradient region. Quantifying the effect of edge fluctuation broadening on EC current drive power needs of future devices will require 3D full-wave codes that can be validated on the current generation of machines. These DIII-D experiments provide a quantitative measure of fluctuation effects and a dataset to benchmark full-wave simulations that can model and eventually predict nonlinear effects neglected by 1D equilibrium beam and ray tracing.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Low-Energy Ion Scattering Intensities from Supported Nanoparticles: The Spherical Cap Model

Supported nanoparticles are of great importance to many technologies like fuel processing and chemical synthesis using catalysts and electrocatalysts, energy storage and generation using fuel cells and batteries, electrochemistry, magnetic information storage, and more. Low-energy ion scattering spectroscopy (LEIS) with noble gas ions like He+ is a powerful tool for the characterization of nanoparticles dispersed across flat support surfaces due to its ability to probe the elemental composition in the topmost atomic layer of a surface, providing quantitative information regarding the size and number density of nanoparticles. In this work, we present a derivation of the LEIS intensities expected from nanoparticles and the support material as a function of the average particle size, their number per unit area, and their contact angle with the support when modeled as spherical caps of the nanoparticle material dispersed over the surface of a flat support. The model assumes that the ion intensities are determined only by the physical blocking of linear ion trajectories and independent of the tilt angle of the local surface relative to the incident and scattered ion directions, an assumption we support by quantitative modeling of published data which tested tilt-angle effects. The model is a generalization to arbitrary contact angles of the hemispherical cap model, which assumes 90° contact angle and has been widely used to model spectroscopic signals in LEIS (and also in Auger and photoelectron spectroscopies) during nanoparticle growth. Here, this new model quantitatively reveals how LEIS signals are sensitive not only to the diameter and number density of the nanoparticle but also to their contact angle (or height/diameter ratio). With the use of additional data (e.g., from microscopy or adsorption microcalorimetry), the model presented here will enable more accurate determination of the average size, shape, and number density of supported nanoparticles based on LEIS intensity measurements.

99 GENERAL AND MISCELLANEOUS↗