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At least 973 records · Page 54

California’s 2023 snow deluge: Contextualizing an extreme snow year against future climate change

The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term “snow deluge” and use anomalously high snowpack in California’s Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California’s 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges—which we define as the 20 y April 1 SWE event—are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation.

Marshall, Adrienne M.↗

Dynamics of N 2 O in vicinity of plant residues: a microsensor approach

Plant residues decomposing within the soil matrix are known to serve as hotspots of N 2 O production. However, the lack of technical tools for microscale in-situ N 2 O measurements limits understanding of hotspot functioning. Our aim was to assess performance of microsensor technology for evaluating the temporal patterns of N 2 O production in immediate vicinity to decomposing plant residues. We incorporated intact switchgrass leaves and roots into soil matrix and monitored O 2 depletion and N 2 O production using electrochemical microsensors along with N 2 O emission from the soil. We also measured residue’s water absorption and b-glucosidase activity on the surface of the residue - the characteristics related to microenvironmental conditions and biological activity near the residue. N 2 O production in the vicinity of switchgrass residues began within 0–12 h after the wetting, reached peak at ~0.6 day and decreased by day 2. N 2 O was higher near leaf than near root residues due to greater leaf N contents and water absorption by the leaves. However, N 2 O production near the roots started sooner than near the leaves, in part due to high initial enzyme levels on root surfaces. Electrochemical microsensor is a useful tool for in-situ micro-scale N 2 O monitoring in immediate vicinity of soil incorporated plant residues. Monitoring provided valuable information on N 2 O production near leaves and roots, its temporal dynamic, and the factors affecting it. The N 2 O production from residues measured by microsensors was consistent with the N 2 O emission from the whole soil, demonstrating the validity of the microsensors for N 2 O hotspot studies.

59 BASIC BIOLOGICAL SCIENCES↗

Integration of a DER Management System in Riverside. Final report

The tasks in this project covered various aspects, including algorithm development, algorithm integration into a commercial Active Network Management (ANM) platform, hardware-in-the-loop (HIL) testing in an industry-standard testing platform, pilot demonstration in Riverside, California, and also cost and benefit analysis. The DERMS platform in this project can host different algorithms developed on different platforms (e.g., MATLAB and Python) and it can interact with different hardware devices (e.g., different PV inverters, battery inverters, and different sensors). The DER control solution are based on an advanced model-free, layered, and clustered DER control paradigm. At the core of the DER control algorithms was the concept of Extremum Seeking (ES), which is a model-free probing-based control technique. The ES-based control algorithms were tested on major real-world inverters; both individually and in a cluster. It was shown that even legacy equipment (or when paired with a few additional advanced equipment) can support such advanced control. The monitoring algorithms utilize a heterogeneous set of legacy and advanced sensor measurements, such as behind-the-meter DER sensors, distribution-level Phase Measurement Units, distribution-substation Supervisory Control and Data Acquisition (SCADA), and line current sensors, with their limited availability; in order to infer practical network conditions. Sensor data are utilized to achieve resource forecasting, phase identification, and distribution system state estimation. The technology that was developed and demonstrated in this project could be transformational to utilities, including the smaller municipal utilities such as in Riverside, which may not have the resources to deploy advanced distribution system and DERMS solutions in order to support high penetration of solar power integration. This project created a real-world prototype to provide utilities with an assessment of smart grid monitoring and control technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Welding of Haynes 282 to Steels to Enable Modular Rotors for Advanced Ultra Super-Critical Steam Turbines

Steam turbines for an Advanced Ultra Super Critical (AUSC) fossil fired power plant will operate at temperatures well above those of current commercial steam cycles with inlet temperatures more than 760 C , which is beyond the capabilities of alloy steels presently used for steam turbine applications and requires advanced materials. Large components such as steam turbine rotors may be made of nickel based super alloys such as Haynes 282 (H282). However, monolithic forgings of superalloys in the sizes required for large steam turbines rotors can be prohibitively expensive besides many technical challenges. To minimize cost and alleviate the related technical challenges, superalloy use needs to be limited to locations on the steam turbine rotor where strength and temperature requirements cannot be met by conventional steels. This is possible if nickel-based superalloys can be successfully welded to steels and the related technical challenges - machining parts made of dissimilar welded materials, non-destructive examination of such welds for flaw detection; and, material properties of such hybrid components – are sufficiently addressed. In this technology development project, we successfully welded H282 to plates up to ~ 75 mm (~ 3 inches) to a 3.5NiCrMoV steel of similar thickness. Advanced ultrasonic inspection technique called Phased Array Ultrasonic Testing (PAUT) was employed to examine the dissimilar H282-Steel welds, into which flat bottomed side drilled holes (SDH) of various diameters were introduced, to determine the minimum detectable feature sizes; it was shown that with PAUT SDH of dia. down to 0.5 mm could be detected in the base alloys and SDH with dia. down to 2.4 mm could be detected in the weld metal under multiple orientations successfully. An autonomous machining process monitoring system was developed and demonstrated whereby the forces acting on the cutting tool could be actively monitored as the cutting tool transitioned from H282 to Steel across the weld using which the machining parameters can be potentially altered without interruption to extend tool life. This project successfully achieved its objectives of - i. Developing a welding methodology and viable welding geometries, to successfully join H282 to steel 3.5CrMoNiV steel to enable manufacture of modular steam turbine rotors for AUSC applications (conditions of at least 760 °Celsius and 3,100 psia (pounds per square inch absolute pressure) and evaluate the material properties of the welded specimen. ii. Employ the advanced ultrasonic inspection technique to the dissimilar weld metal joint and determine the minimum detectable feature sizes. iii. Develop effective machining techniques to machine such hybrid structures with online tool force monitoring and effect machine state metrics for optimal results.

20 FOSSIL-FUELED POWER PLANTS↗

Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel

Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0–100 μg mL –1 ), europium (0–150 μg mL –1 ), samarium (0–250 μg mL –1 ), praseodymium (0–350 μg mL –1 ), neodymium (0–1000 μg mL –1 ), and HNO 3 (2–4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet–visible–near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9–5.7% for the seven factors. Further, the optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Summary of Gas Generation Behavior Observed in 3013 Surveillance and Monitoring Program Shelf-Life Experiments

Gas generation experiments have been conducted in small- and full-scale test containers at Los Alamos National Laboratory on samples of plutonium oxide material collected from plutonium processes across the DOE complex and tested at the bounding conditions for the Department of Energy 3013 Standard. The gas composition and pressures in the sealed experimental containers were measured over periods of months to years. These experiments have provided results for the formation and consumption of hydrogen and other gases. The conditions supporting the formation of flammable gas mixtures of hydrogen and oxygen in flammable gas mixtures were also determined. Different behaviors were observed between the materials tested based on their compositions, the stabilization performed on the material, and the post stabilization handling of the material. Many of the experiments are still ongoing. This report summarizes the results obtained for the gas generation behavior for high-purity plutonium oxides and salt-bearing impure plutonium oxides in sealed containers.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Coupled Thermo-Hydrological-Mechanical-Chemical Behavior of Anisotropic Granite for Geologic Disposal of High-Level Radioactive Waste: A Core-Scale Laboratory Investigation

The coupled thermo-hydrological-mechanical-chemical (THMC) behavior of rock within an Excavation Damaged Zone (EDZ) is critical for the safety and long-term performance of a geological repository for high-level radioactive wastes. While many laboratory experiments have been conducted to investigate EDZ rocks, the flow and deformation characteristics resulting from anisotropic rock textures and microcrack distribution under triaxial loading and elevated temperatures remain poorly understood. Particularly, cracks at various scales serve as fast paths for fluid flow and solute transport and present as focal points of mechanical weakness, which complicate the coupled THMC processes in anisotropic EDZ rocks and challenge modeling predictions. Here, in this study, a series of core-scale experiments was conducted on three granite samples under repository-relevant conditions. These rock samples were obtained from the Grimsel Underground Research Laboratory (URL), featured by anisotropic minerals (represented by bedding layers) and microcrack distributions and coarse cm-scale grain sizes. During the experiments, samples were subjected to an elevated temperature at 90 °C and different triaxial loading conditions either by radial (normal to bedding layers) or axial (parallel to bedding layers) compaction. Water was injected into the samples, and the rock permeability evolutions and effluent water chemistry were monitored closely. For intact samples, thermal expansion of minerals at 90 °C resulted in a large, 75% irreversible permeability reduction and rock strengthening under radial compaction, while thermal impact was limited to a 15% permeability reduction under axial compaction. In contrast, for a sample containing open cracks, the growth of fractures during the experiment resulted in an abrupt permeability increase and fast failure at 90 °C. The effluent water chemistry indicates much more considerable mineral dissolution from large shear sliding than that in rocks dominated by mechanical compaction. These results helped better understand the coupled THMC processes in anisotropic rocks containing cracks, evaluate the behaviors of EDZ rocks, and predict the long-term evolution of EDZ for the performance of the repositories.

Coupled THMC processes↗

Advanced Energy Scale Correction Techniques for the X-ray Transition Edge Sensors of the Athena mission

The X-ray Integral Field Unit (X-IFU) onboard the future European X-ray telescope Athena will be the first space instrument carrying an array of more than a thousand transition edge sensors. One of the key challenges of the X-IFU is the measurement of narrow X-ray atomic lines to determine velocity shifts at an unprecedented level of accuracy. For this reason, the energy scale of the instrument needs to be known with extreme accuracy, of 0.4 eV (1σ) up to 7 keV. The energy scale will be measured on the ground through a dedicated calibration campaign using fiducial X-ray sources. Though calibrated, the energy scale is extremely sensitive to the environmental conditions around the TES array, and drifts in the readout chain electronics. Uncorrected, the energy scale can naturally drift up to hundreds of eVs. Changes of the TES gain will be monitored via onboard X-ray calibration sources, and the energy scale will be corrected either per pixel, or within a small groups of pixels. Although simulations show that a 0.4 eV level can be achieved, the very high accuracy required by the X-IFU calls for experimental validation. A dedicated measurement campaign has been performed by NASA Goddard Space Flight Center to characterize the energy scale of a prototype kilo-pixel array of X-IFU-representative TESs. The analysis of the data demonstrated the ability to correct for various drifts using two fiducial lines to track the temporal gain variation. In this paper, we propose to extend this study on the same data set by investigating multi-parameter correction techniques based on both the pulse-height of the fiducial line and the prepulse baseline level, using the knowledge of the TES energy scale at reference temperature/magnetic field set points acquired on the ground. Investigations on the co-adding of pixels to perform a joint correction over pools of pixels is also explored.

79 ASTRONOMY AND ASTROPHYSICS↗

Hurricanes and turbulent floods threaten arsenic-contaminated coastal soils and vulnerable communities

Coastal environments, particularly those adjacent to Superfund sites, are at increased risk of contaminant release during natural disasters, posing serious threats to nearby communities. To investigate this issue, we employed an advanced laboratory flood simulator to impose arsenic-contaminated sediments to turbulent flooding events. We further monitored changes in arsenic collocation and solid-phase speciation using advanced synchrotron radiation-based techniques to understand the impacts of flooding on arsenic mobility. Our results demonstrate that turbulent conditions significantly enhance the resuspension of arsenic-rich sediments, resulting in increased arsenic release into the water. This mobilization is driven by the erosion of the reduced sediments and the redox-mediated transformation and dissolution of Fe and Mn (oxyhydr) oxides, which promote the release of As(III). We found that arsenic speciation on resuspended particles is closely tied to shear stress, with As(V) prevailing at low stress and the more toxic As(III) dominating at higher stress levels. In the post-erosion phase, solid-phase As(III) decreased while dissolved As(III) increased, indicating ongoing desorption. The persistence of multiple arsenic species on resuspended particles marks them as potential long-range transport vectors. Thus, the environmental impact of flooding and sediment resuspension extends beyond the event itself, raising longer-term concerns for arsenic mobility. Our comprehensive geospatial analysis revealed substantial overlap between arsenic-contaminated soils and regions at high risk of flooding and hurricanes across the conterminous United States. This overlap disproportionately impacts economically disadvantaged and marginalized communities. Approximately 40 million Americans reside within 10 kilometers of these high-risk contaminated zones, with nearly 28 million exposed to hurricane threats and around 18 million vulnerable to flooding risks. Alarmingly, over 40% of those affected by hurricanes and 33% of those impacted by flooding belong to underrepresented minority and low-income populations. These findings highlight the urgent need for targeted mitigation strategies to protect public health and address environmental justice concerns.

54 ENVIRONMENTAL SCIENCES↗

In-situ and in-operando analysis of voltage losses using sense wires for proton exchange membrane water electrolyzers

Proton exchange membrane water electrolyzer development requires understanding processes at the materials and interface levels to reach the required performance and lifetime targets for increasing market penetration. To achieve the required progress, it is critical to develop advanced in-situ diagnostics that allow observation of the changes that come with the reduction of catalyst loading combined with long-term intermittent operation. This work presents an internal voltage sensing method that enables observing internal voltage drops in an operating electrolyzer cell. It allows the total cell resistance to be separated into anode, CCM, and cathode resistance. The method is demonstrated by operating cells with different anode porous transport layers (PTLs). The changing properties of the PTLs result in significant variations of the resistances. Enhancing the PTL properties with a protective coating reduces the anode resistance significantly. In addition to these observations, the method enables a real-time monitoring of resistance related values that impact performance in water electrolyzers. Long-term experiments with the method allow us to gain insights into processes that occur during operation such as conditioning or degradation. The internal voltage sensing method presented in this study is a novel technique for diagnosing, analyzing, and optimizing water electrolyzers and other energy conversion devices.

42 ENGINEERING↗

Toward a classification of PT-symmetric quantum systems: From dissipative dynamics to topology and wormholes

Studies of many-body non-Hermitian parity-time (PT)-symmetric quantum systems are attracting a lot of interest due to their relevance in research areas ranging from quantum optics and continuously monitored dynamics to Euclidean wormholes in quantum gravity and dissipative quantum chaos. While a symmetry classification of non-Hermitian systems leads to 38 universality classes, we show that, under certain conditions, PT-symmetric systems are grouped into 24 universality classes. We identify 14 of them in a coupled two-site Sachdev-Ye-Kitaev (SYK) model and confirm the classification by spectral analysis using exact diagonalization techniques. Intriguingly, in 4 of these 14 universality classes, AIII ν , BDI ν † , BDI + + ν , and CI − − ν , we identify a basis in which the SYK Hamiltonian has a block structure in which some blocks are rectangular, with ν ∈ N the difference between the number of rows and columns. We show analytically that this feature leads to the existence of ν robust purely eigenvalues, whose level statistics follow the predictions of Hermitian random matrix theory for classes A, AI, BDI, and CI, respectively. We have recently found that this ν is a topological invariant, so these classes are topological. By contrast, nontopological real eigenvalues display a crossover between Hermitian and non-Hermitian level statistics. Similarly to the case of Lindbladian dynamics, the reduction of universality classes leads to unexpected results, such as the absence of Kramers degeneracy in a given sector of the theory. Another novel feature of the classification scheme is that different sectors of the PT-symmetric Hamiltonian may have different symmetries. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

Carbon Tetrachloride Degradation Results for 200-ZP-1 Operable Unit

Carbon tetrachloride (CT) contamination in the 200-ZP-1 Operable Unit (OU) at the Hanford Site originated from large-volume discharges to the subsurface during plutonium production operations between 1955 and 1973. Contamination migrated through more than 70 meters of unsaturated sediment to reach the underlying unconfined aquifer, where it persists as a large and complex groundwater plume. The 200-ZP-1 OU Record of Decision (ROD) requires that groundwater CT concentrations be reduced to 3.4 µg/L within 125 years. Current groundwater modeling projections estimate that the existing pump-and-treat, even when combined with monitored natural attenuation (specifically hydrolysis), will not achieve this target within the designated timeframe. A fundamental contributor to this shortfall is the extremely slow rate of CT hydrolysis under Hanford aquifer conditions, which has been estimated to have a half-life of 630 years. If faster-acting biotic and abiotic degradation processes are operating within the aquifer, their contribution to CT mass reduction could have a meaningful impact. However, site-specific measurements of these processes and their rates have not previously been performed. This report documents the results of a two-phase laboratory investigation designed to characterize and quantify the capacity of site-specific 200-ZP-1 OU sediments and groundwater to support natural attenuation of CT through biotic and abiotic pathways. In this context, degradation capacity is defined as the intrinsic potential of the subsurface matrix to transform CT under optimized, controlled conditions. System capacity is evaluated in two ways: (1) as rate-limited capacity, which establishes the maximum kinetic velocity of CT transformation and is measured using half-lives and first order rate constants; and (2) as mass limited capacity, which defines the total contaminant mass the batch experimental system can degrade before reactants are exhausted, representing the maximum amount of contaminant the microbial community and reactive mineral phases can transform under the experimental conditions.

abiotic degradation↗

Modernization of the Radiation Measurements Laboratory at the Advanced Test Reactor Complex

The Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) is a unique, water-cooled, high-flux test reactor capable of performing tests prototypical of PWR operating conditions. The Radiation Measurements Laboratory (RML) was founded in the 1960s to support reactor operations and to conduct independent scientific research. For nearly six decades RML has performed four primary functions: monitoring of radioactivity by gamma-ray spectroscopy of routine reactor samples, fluence rate determinations for irradiation cycles, fission-rate measurements for the ATR-Critical (ATR-C) Facility, and independent research and development of radiation detection systems and applications. Through the decades, RML has seen technological advancements that have been integrated into each of the critical functions of the laboratory. However, many of the measurement and analysis systems employed to this day can be improved by modernization. Recent progress at RML is improving reliability and accuracy of the radiation measurements performed in support of nuclear energy research for the U.S.A. Department of Energy. The control and data collection systems supporting ATR-C have been upgraded. New High-Purity Germanium (HPGe) spectrometers have been procured with liquid nitrogen recycling capabilities to improve up-time and reduce measurement uncertainties. Fluence-rate measurement techniques are also being improved to ensure accuracy, avoid systemic errors, and identify biases. In a parallel effort, new scientific research avenues are being explored which will provide an opportunity to further enhance the utilization of ATR and ensure the sustainability of the RML as nuclear research continues to evolve. The RML is improving the effectiveness of irradiation services provided by ATR while ensuring a sustainable future for nuclear energy research.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

How Dynamic Time Warping Can Assist Conventional Cross-correlation

Waveform cross-correlation is a sensitive phase-matched filtering technique that can detect seismic events for nuclear explosion monitoring. However, there are outstanding challenges with correlation detectors, most notably a direct dependence on the completeness of the waveform template library. To ameliorate these challenges, we investigate how dynamic time warping (DTW) may make waveform correlation more robust. DTW analyzes the differences between two time series and attempts to “warp” one time series relative to another in a recursive manner. We apply DTW to synthetic earthquake and recorded explosion templates to expand the capability of correlation detectors. We explore what conditions (e.g., source, station distance, frequency bands) and/or DTW algorithms generate stronger correlation scores. We show that DTW performs well on noisy signals and can dramatically improve the cross-correlation coefficient between a template and data-stream waveform. We conclude with recommendations on how to utilize DTW in nuclear monitoring detection.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Use of Convolutional Neural Network Image Classification and High-Speed Ion Probe Data Toward Real-Time Detonation Characterization in a Water-Cooled Rotating Detonation Engine

As rotating detonation engines (RDEs) progress in maturity, the importance of monitoring advancements toward development of active control becomes more critical. Experimental RDE data processing at time scales which satisfy real-time diagnostics will likely require the use of machine learning. This study aims to develop and deploy a novel real-time monitoring technique capable of determining detonation wave number, direction, frequency, and individual wave speeds throughout experimental RDE operational windows. To do so, the diagnostic integrates image classification by a convolutional neural network (CNN) and ionization current signal analysis. Wave mode identification through single-image CNN classification bypasses the need to evaluate sequential images and offers instantaneous identification of the wave mode present in the RDE annulus. Here, real-time processing speeds are achieved due to low data volumes required by the methodology, namely one short-exposure image and a short window of sensor data to generate each diagnostic output. The diagnostic acquires live data using a modified experimental setup alongside Pylon and PyDAQmx libraries within a python data acquisition environment. Lab-deployed diagnostic results are presented across varying wave modes, operating conditions, and data quality, currently executed at 3–4 Hz with a variety of iteration speed optimization options to be considered as future work. These speeds exceed that of conventional techniques and offer a proven structure for real-time RDE monitoring. The demonstrated ability to analyze detonation wave presence and behavior during RDE operation will certainly play a vital role in the development of RDE active control, necessary for RDE technology maturation toward industrial integration.

42 ENGINEERING↗

A network of soil moisture, soil temperature, air temperature, net radiation, ground heat flux and ground water for Chicago, Illinois

This dataset contains environmental monitoring data collected using solar-powered Multi-Function Research (MFR) Long Range Wide Area (LoRaWAN)-enabled nodes at 11 sites in Chicago, Illinois, as part of the DOE Urban Integrated Field Lab CROCUS project. The MFR node system consists of an Input/Output Digital Input Module (IB8) interface box (ICT International) providing wired connections for environmental sensors and an MFR-Node-L data logger that manages power, data processing, and LoRaWAN communication. The wireless data are ingested via Sage network (https://sagecontinuum.org/) nodes that contain LoRaWAN antennae. Measurements were collected from 11 MFR nodes deployed across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), West Woodlawn "Blacks in Green" (BIG), and Indian Boundary Prairies (IBP). Each MFR node supports a consistent suite of sensors measuring atmospheric, soil, and hydrological variables. Atmospheric measurements include 2m air temperature (°C), 2m vapor pressure deficit (kPa), and 2m shortwave/longwave radiation (incoming and outgoing, W/m²) measured using ATH-VPD and Apogee SN500 sensors. Soil measurements include volumetric water content (VWC, %) and temperature (°C) at four depths (15, 30, 45, and 60 cm below surface) using Meter Teros54 sensors, and heat flux (W/m²) at 10 cm depth using Huske HFP01-05 sensors. At selected locations, Meter Hydros21 sensors measure groundwater depth (mm), specific conductivity (dS/m), and temperature (°C). The dataset includes timestamps, site identifiers with location names, device IDs, Global Positioning System (GPS) coordinates, variable names with units, measurement depths, values, sensor names, and Sage node identifiers. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 6-bit binary system indicating physical range violations, step spikes, 24-hour flat-line conditions, 6-hour jitter, 7-day ultra-low variance, and persistent high offset. Data is provided in CSV and CF-compliant NetCDF formats. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Sap Flow Meter (SFM1x) sensors.

Chicago↗

Unpacking the drivers of diurnal dynamics of sun-induced chlorophyll fluorescence (SIF): Canopy structure, plant physiology, instrument configuration and retrieval methods

Sun-induced chlorophyll fluorescence (SIF) from spaceborne sensors is a promising tool for global carbon cycle monitoring, but its application is constrained by insufficient understanding of the drivers underlying diurnal SIF dynamics. SIF measurements from ground-based towers can reveal diurnal SIF dynamics across biomes and environmental conditions; however, meaningful interpretation of diurnal variations requires disentangling impacts from canopy structure, plant physiology, instrument configuration and retrieval methods, which often interact with and confound each other. This study aims to unpack these drivers using 1) concurrent ground and airborne canopy-scale and leaf-scale measurements at a corn field, 2) a mechanistic SIF model that explicitly considers the dynamics of photochemistry (via the fraction of open photosystem II reaction centers, qL) and photoprotection (via nonphotochemical quenching, NPQ) as well as their interactive dependence on the sub-canopy light environment, and 3) cross-comparison of SIF instrument configurations and retrieval methods. We found that crop row orientations and sun angles can introduce a distinctive midday dip in SIF in absence of stress, due to a midday drop of absorbed photosynthetically active radiation (APAR) when crop rows are north-south oriented. Canopy structure caused distinctive responses in both qL and NPQ at different positions within the vertical canopy that collectively influenced fluorescence quantum yield (Φ F ) at the leaf scale. Once integrated at the canopy scale, diurnal dynamics of both APAR and canopy escape probability (ε) are critical for accurately shaping diurnal SIF variations. While leaf-level qL and NPQ exhibited strong diurnal dynamics, their influence was attenuated at the canopy scale due to opposing effects on SIF at different canopy layers. Furthermore, different system configurations (i.e., bi-hemispherical vs. hemispherical-conical) and retrieval methods can bias the SIF magnitude and distort its diurnal shapes, therefore confounding the interpretation of inherent strength and dynamics of SIF emission. Our findings demonstrate the importance of crop row structures, interactive variations in canopy structure and plant physiology, instrument configuration, and retrieval method in shaping the measured dynamics of diurnal SIF. This study highlights the necessity to account for these factors to accurately interpret satellite SIF, and informs future synthesis work with different SIF instrumentation and retrieval methods across sites.

59 BASIC BIOLOGICAL SCIENCES↗