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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Lorentz and CPT violation in partons

A framework is presented for the factorization of high-energy hadronic processes in the presence of Lorentz and CPT violation. The comprehensive effective field theory describing Lorentz and CPT violation, the Standard-Model Extension, is used to demonstrate factorization of the hadronic tensor at leading order in electroweak interactions for deep inelastic scattering and for the Drell-Yan process. Effects controlled by both minimal and nonminimal coefficients for Lorentz violation are explored, and the equivalent parton-model description is derived. The methodology is illustrated by determining cross sections and studying estimated attainable sensitivities to Lorentz violation using real data collected at the Hadronen-Elektronen Ring Anlage and the Large Hadron Collider and simulated data for the future US-based electron-ion collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Measurement of the inclusive cross sections for W and Z boson production in proton-proton collisions at $\sqrt{\textrm{s}}$ = 5.02 and 13 TeV

Measurements of fiducial and total inclusive cross sections for W and Z boson production are presented in proton-proton collisions at $ \sqrt{s} $ = 5.02 and 13 TeV. Electron and muon decay modes (ℓ = e or μ) are studied in the data collected with the CMS detector in 2017, in dedicated runs with reduced instantaneous luminosity. The data sets correspond to integrated luminosities of 298 ± 6 pb$^{−1}$ at 5.02 TeV and 206 ± 5 pb$^{−1}$ at 13 TeV. Measured values of the products of the total inclusive cross sections and the branching fractions at 5.02 TeV are σ(pp → W + X)$ \mathcal{B} $(W → ℓν) = 7300 ± 10 (stat) ± 60 (syst) ± 140 (lumi) pb, and σ(pp → Z+X)$ \mathcal{B} $(Z → ℓ$^{+}$ℓ$^{−}$) = 669 ± 2 (stat) ± 6 (syst) ± 13 (lumi) pb for the dilepton invariant mass in the range of 60–120 GeV. The corresponding results at 13 TeV are 20480 ± 10 (stat) ± 170 (syst) ± 470 (lumi) pb and 1952 ± 4 (stat) ± 18 (syst) ± 45 (lumi) pb. The measured values agree with cross section calculations at next-to-next-to-leading-order in perturbative quantum chromodynamics. Fiducial and total inclusive cross sections, ratios of cross sections of W$^{+}$ and W$^{−}$ production as well as inclusive W and Z boson production, and ratios of these measurements at 5.02 and 13 TeV are reported.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Search for decays of the 125 GeV Higgs boson into a Z boson and a $\rho$ or $\phi$ meson

Decays of the 125 GeV Higgs boson into a Z boson and a ρ$^{0}$(770) or ϕ(1020) meson are searched for using proton-proton collision data collected by the CMS experiment at the LHC at $ \sqrt{s} $ = 13 TeV. The analysed data set corresponds to an integrated luminosity of 137 fb$^{−1}$. Events are selected in which the Z boson decays into a pair of electrons or a pair of muons, and the ρ and ϕ mesons decay into pairs of pions and kaons, respectively. No significant excess above the background model is observed. As different polarization states are possible for the decay products of the Z boson and ρ or ϕ mesons, affecting the signal acceptance, scenarios in which the decays are longitudinally or transversely polarized are considered. Upper limits at the 95% confidence level on the Higgs boson branching fractions into Zρ and Zϕ are determined to be 1.04–1.31% and 0.31–0.40%, respectively, where the ranges reflect the considered polarization scenarios; these values are 740–940 and 730–950 times larger than the respective standard model expectations. These results constitute the first experimental limits on the two decay channels.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Search for resonances in events with photon and jet final states in proton-proton collisions at $ \sqrt{s} $ = 13 TeV

A search for resonances in events with the γ+jet final state has been performed using proton-proton collision data collected at $\sqrt s$ = 13 TeV by the CMS experiment at the LHC. The total data analyzed correspond to an integrated luminosity of 138 fb –1 . Models of excited quarks and quantum black holes are considered. Using a wide-jet reconstruction for the candidate jet, the γ+jet invariant mass spectrum measured in data is examined for the presence of resonances over the standard model continuum background. The background is estimated by fitting this mass distribution with a functional form. The data exhibit no statistically significant deviations from the expected standard model background. Exclusion limits at 95% confidence level on the resonance mass and other parameters are set. Excited light-flavor quarks (excited bottom quarks) are excluded up to a mass of 6.0 (3.8) TeV. Quantum black hole production is excluded for masses up to 7.5 (5.2) TeV in the Arkani-Hamed-Dimopoulos-Dvali (Randall-Sundrum) model. These lower mass bounds are the most stringent to date among those obtained in the γ+jet final state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Search for $t$-channel scalar and vector leptoquark exchange in the high-mass dimuon and dielectron spectra in proton-proton collisions at $\sqrt{s}=13$ TeV

A search for t-channel exchange of leptoquarks (LQs) is performed in dimuon and dielectron spectra using proton-proton collision data collected at $\sqrt{s}=13$ TeV with the CMS detector at the CERN LHC. The data correspond to an integrated luminosity of 138 fb −1 . Eight scenarios are considered, in which up or down quarks couple to muons or electrons via a scalar or vector LQ exchange, for dilepton invariant masses above 500 GeV. The LQ masses are probed up to 5 TeV, beyond a regime probed by previous pair-production and single-production searches. The differential distributions of dilepton events are fit to templates that model the nonresonant LQ exchange and various standard model background processes. Limits are set on LQ-fermion coupling strengths for scalar and vector LQ masses in the 1–5 TeV range at 95% confidence level, establishing stringent limits on first- and second-generation LQs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Hydrological Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (Findable, Accessible, Interoperable, and Reusable: (go-fair.org)). Easy availability of FAIR data has become possible because the hydrology-oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provide more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICON) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.

(ICON) principles to address↗
DOE OSTI2025

A Standardized Analysis Process Using Digital Image Correlation to Calculate In Situ Cladding Strain from Modified Burst Tests for Fuel Performance Code Validation

Historical data collection on nuclear fuel cladding materials has focused on generating a statistically significant amount of data to assess the material and its failure behavior. Furthermore, data generated to support material model and failure criteria development were previously posttest evaluations, so a large number of tests was required to gain new understanding. A way to expedite this process is to develop techniques capable of generating large, high-fidelity data sets from a single test with lower uncertainty or quantified uncertainty. One such example of this approach is Oak Ridge National Laboratory’s use of modified burst tests (MBTs) to analyze the mechanical behavior and failure conditions of cladding during a simulated reactivity-initiated accident (RIA). Each test incorporates digital image correlation (DIC) analysis techniques that are used to assess the accumulated strain in situ, as well as eventual cladding failure. This work has been fruitful in defining strain-to-failure conditions for materials like silicon carbide (SiC) fiber–reinforced/SiC matrix composite tubes (SiC/SiC), iron-chromium-aluminum (FeCrAl) alloy tubes, and chromium-coated Zircaloy-4 tubes. However, there are numerous DIC software available, including open-source and proprietary software. The different DIC software use various algorithms to process images and calculate displacement values. Using these different software and algorithms can lead to varying results, and perhaps larger-than-expected uncertainties. In the present study, previously published MBT data encompassing a variety of test conditions were reanalyzed with two different DIC software to assess the variance in the calculated strain results. The data consisted of SiC/SiC, FeCrAl, and chromium-coated Zircaloy-4 tubes. Plots of the calculated strains during the transient revealed good agreement between the two DIC software. The average root-mean-square errors between the two software was 0.20% strain, which is slightly larger than a previously reported error value for these tests. In conclusion, this variance in results is low enough that this analysis method can be used for code validation.

Reactivity-initiated accident↗
DOE OSTI2023

Data transfer for STAR grid jobs

The Solenoidal Tracker at RHIC (STAR) is a multipurpose experiment at the Relativistic Heavy Ion Collider (RHIC) with the primary goal to study the formation and properties of the quark-gluon plasma. STAR is an international collaboration of member institutions and laboratories from around the world. Yearly data-taking period produces PBytes of raw data collected by the experiment. STAR primarily uses its dedicated facility at BNL to process this data, but has routinely leveraged distributed systems, both high throughput (HTC) and high performance (HPC) computing clusters, to significantly augment the processing capacity available to the experiment. The ability to automate the efficient transfer of large data sets on reliable, scalable, and secure infrastructure is critical for any large-scale distributed processing campaign. For more than a decade, STAR computing has relied upon GridFTP with its x509-based authentication to build such data transfer systems and integrate them into its larger production workflow. The end of support by the community for both GridFTP and the x509 standard requires STAR to investigate other approaches to meet its distributed processing needs. In this study we investigate two multi-purpose data distribution systems, Globus.org and XRootD, as alternatives to GridFTP. We compare both their performance and the ease by which each service is integrated into the type of secure and automated data transfer systems STAR has previously built using GridFTP. The presented approach and study may be applicable to other distributed data processing use cases beyond STAR.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Robust framework and software implementation for fast speciation mapping

One of the greatest benefits of synchrotron radiation is the ability to perform chemical speciation analysis through X-ray absorption spectroscopies (XAS). XAS imaging of large sample areas can be performed with either full-field or raster-scanning modalities. A common practice to reduce acquisition time while decreasing dose and/or increasing spatial resolution is to compare X-ray fluorescence images collected at a few diagnostic energies. In this work, several authors have used different multivariate data processing strategies to establish speciation maps. Furthermore, the theoretical aspects and assumptions that are often made in the analysis of these datasets are focused on. A robust framework is developed to perform speciation mapping in large bulk samples at high spatial resolution by comparison with known references. Two fully operational software implementations are provided: a user-friendly implementation within the MicroAnalysis Toolkit software, and a dedicated script developed under the R environment. The procedure is exemplified through the study of a cross section of a typical fossil specimen. Additionally, the algorithm provides accurate speciation and concentration mapping while decreasing the data collection time by typically two or three orders of magnitude compared with the collection of whole spectra at each pixel. Whereas acquisition of spectral datacubes on large areas leads to very high irradiation times and doses, which can considerably lengthen experiments and generate significant alteration of radiation-sensitive materials, this sparse excitation energy procedure brings the total irradiation dose greatly below radiation damage thresholds identified in previous studies. This approach is particularly adapted to the chemical study of heterogeneous radiation-sensitive samples encountered in environmental, material, and life sciences.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

An Autonomous Critical Data Extrapolator for the AGN-201m

Nuclear nonproliferation serves as a key goal, being undertaken by the International Atomic Energy Agency (IAEA). To recognize proliferation there are two pathways that states, who intend to use nuclear material for malicious purposes can take, diversion can misuse. Diversion is when fissile nuclear material is declared to the IAEA for non-weapon purposes, but then covertly removed. If the source of nuclear material, that is not declared and not fissionable, is placed inside the reactor core to create fissile material used to create weapons then the state is using the second pathway of proliferation, misuse. With the emerging development in areas of simulation and machine learning the creation of virtual models of reactor systems, digital twins, serve as a potential method to identify proliferation through detecting anomalous behavior in the reactor. A digital twin for a physical nuclear reactor has never been developed, as digital twins serve as an emerging technology. To investigate the process for development and use of a digital twin for a nuclear reactor Idaho State University’s AGN-201m serves as the nuclear reactor used for development of this digital twin. A data acquisition system has been installed to the reactor system allowing for the transfer of collected data from a reactor operation to Idaho National Laboratory’s Deeplynx data warehouse. When utilizing data to train reactor physics and machine learning models, a significant challenge encountered is the initial state of the data. Nuclear proliferation will have the capacity to be detected when the reactor immediately starts up, nor will it occur after the reactor shuts down. Generally, it will be detected when the reactor is operating at some desired power over a sufficient period for that specific reactor design. For the AGN-201m this will be when the reactor is critical (generally 1 mW or above) for a timespan that is within or less than the range of a regular business day. Datasets sent to Deeplynx have had to be manually cut to when the reactor is critical based on plots of power levels. This method is inefficient and laborious, especially when using multiple datasets at once to train a model. To provide a more streamlined approach an automated critical data extrapolator is developed, with capabilities of recognizing when the reactor operation first reaches criticality, and when the reactor undergoes a SCRAM and is shutdown.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast

Abstract. The California Pacific coast is characterized by considerable wind resource and areas of dense population, propelling interest in offshore wind energy as the United States moves toward a sustainable and decarbonized energy future. Reanalysis models continue to serve the wind energy community in a multitude of ways, and the need for validation in locations where observations have been historically limited, such as offshore environments, is strong. The U.S. Department of Energy (DOE) owns two lidar buoys that collect wind speed observations across the wind turbine rotor layer along with meteorological and oceanographic data near the surface to characterize the wind resource. Lidar buoy data collected from recent deployments off the northern California coast near Humboldt County and the central California coast near Morro Bay allow for validation of commonly used reanalysis products. In this article, wind speeds from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP) are validated at heights within the wind turbine rotor layer ranging from 50 to 100 m. The validation results offer guidance on the performance and uncertainty associated with utilizing reanalyses for offshore wind resource characterization, providing the offshore wind energy community with information on the conditions that lead to reanalysis error. At both California coast locations, the reanalyses tend to underestimate the observed rotor-level wind resource. Occasions of large reanalysis error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production (> 10 m s−1), and mischaracterization of the diurnal wind speed cycle in summer months.

17 WIND ENERGY↗
DOE OSTI2022

A Real-Time Data Dashboard for Monitoring Travel Behavior

When running a smartphone app-based travel behavior study, it is important to keep participants engaged in contributing data. Long study durations and/or large samples make it difficult to engage participants in reporting their travel behavior. We developed a dashboard web application called 'emdash' to facilitate and ease the data collection maintenance task. It allows deployers to view trip trajectories, track which participants are uploading data, and generate report ready plots. Emdash can also be useful for deployers to identify ways to best encourage a particular transportation mode shift and to provide feedback and reporting to participants. Using the dashboard, a single admin was able to support a classic travel study with 80 participants. The dashboard is currently deployed in a two-year electric bike (e-bike) pilot study called CanBikeCO, conducted in six locations across Colorado, USA. CanBikeCO targets carbon emissions reduction and equity improvement by evaluating changes to participants' travel behavior when they are offered free e-bikes. Each CanBikeCO location has access to their own version of emdash to help them monitor and support participants. After testing and gathering feedback, we made improvements to the dashboard's usability and scalability, and proposed a method to make detecting data issues easier. Improvements include configurable options and limiting the volume of data sent to the dashboard as study size and duration increase.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2026

Rolling Root Mean Square Based Multimodal Anomaly Detection for Real Time Monitoring of Smart Grid

Reliable real-time monitoring is valuable for maintaining the operational integrity of modern electrical smart grids. Deployment of heterogeneous sensing technologies in substations has enabled high-resolution, multichannel waveform monitoring, but also introduces challenges for anomaly detection due to noise, baseline drift, and modality-dependent signal characteristics. In this work, we present a computationally efficient unsupervised method for multimodal event detection based on Rolling Root Mean Square based Event Detection (RRMSED). The method is developed using in-house, field deployed sensors collecting data at a utility substation. The sensing system comprises voltage and current sensors, triaxial accelerometers, and magnetometers, collectively capturing electrical, vibrational, and magnetic waveform measurements at high temporal resolution. RRMSED operates by extracting rolling RMS energy features and their first-order temporal differences from consecutive waveform segments for each channel and then applying channel-specific statistical thresholds learned from historical data. A persistence-based exceedance logic is employed to robustly identify transient events while suppressing impulsive noise, and to provide precise temporal localization with high resolution. The framework is designed for continuous server-side operation and can be deployed in real time without requiring complex models. Experiments on simulated waveform data with known ground truth demonstrate low false positive (FP) and false negative (FN) rates. Application to real substation data shows RRMSED to identify events that are not captured by conventional monitoring indicators including fast transient detection algorithm currently deployed in the system. These results indicate that rolling RMS based features provide an effective and practical basis for real-time multimodal event detection in smart-grid substations.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2022

Magnetic pair distribution function data using polarized neutrons and ad hoc corrections

Here, we report the first example of magnetic pair distribution function (mPDF) data obtained through the use of neutron polarization analysis. Using the antiferromagnetic semiconductor MnTe as a test case, we present high-quality mPDF data collected on the HYSPEC instrument at the Spallation Neutron Source using longitudinal polarization analysis to isolate the magnetic scattering cross section. Clean mPDF patterns are obtained for MnTe in both the magnetically ordered state and the correlated paramagnet state, where only short-range magnetic order is present. We also demonstrate significant improvement in the quality of high-resolution mPDF data through the application of ad hoc corrections that require only minimal human input, minimizing potential sources of error in the data processing procedure. We briefly discuss the current limitations and future outlook of mPDF analysis using polarized neutrons. Overall, this work provides a useful benchmark for mPDF analysis using polarized neutrons and provides an encouraging picture of the potential for routine collection of high-quality mPDF data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Keyhole-mode Microscopy Dataset for Laser Powder-bed Fusion Modeling

Laser powder-bed fusion (LPBF) is an additive manufacturing (AM) technology that uses high-power sintering lasers to precisely construct metal designs. Material is accumulated by selectively sintering regions of a metal powder layer to a growing structure underneath forming a 3D geometry. Certain conditions make fusion in this process to operate in "keyhole-mode," characterized by targeted materials evaporating as plasma. While keyhole-mode operations can produce deeper molten pools than that observed in "conduction-mode", this mode of operation is often undesirable, as its molten regions can collapse on themselves, encapsulating metal vapors and forming cavities. The formation of such cavities negatively affects strength and consistency of the fused materials. To identify and avoid these detrimental effects of keyhole-mode operation, considerable data collection and analysis regarding this mode of operation is needed. Therefore, under the support of the "Open Data Initiative," Lawrence Livermore National Lab is releasing a dataset for analysis of this keyhole effect, and the conditions which transition operation from conduction-mode to keyhole-mode melting. This dataset consists of 600+ micrographs from laser powder-bed single track runs, with differing cross-section angles and input parameters, operating under both normal and keyhole mode operations. This journal documents the collection, organization, and usage of this dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Finally, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools

We report the overarching hypothesis of this study was that temporal microbial potentiometric sensor (MPS) signal patterns could be used to predict changes in commonly monitored water quality parameters by using artificial intelligence/machine learning tools. To test this hypothesis, the study first examines a proof of concept by correlating between MPS's signals and high algae concentrations in an algal cultivation pond. Then, the study expanded upon these findings and examined if multiple water quality parameters could be predicted in real surface waters, like irrigation canals. Signals generated between the MPS sensors and other water quality sensors maintained by an Arizona utility company, including algae and chlorophyll, were collected in real time at time intervals of 30 min over a period of 9 months. Data from the MPS system and data collected by the utility company were used to train the ML/AI algorithms and compare the predicted with actual water quality parameters and algae concentrations. Based on the composite signal obtained from the MPS, the ML/AI was used to predict the canal surface water's turbidity, conductivity, chlorophyll, and blue-green algae (BGA), dissolved oxygen (DO), and pH, and predicted values were compared to the measured values. Initial testing in the algal cultivation pond revealed a strong linear correlation (R 2 = 0.87) between mixed liquor suspended solids (MLSS) and the MPSs' composite signals. The Normalized Root Mean Square Error (NRMSE) between the predicted values and measured values were <6.5%, except for the DO, which was 10.45%. The results demonstrate the usefulness of MPSs to predict key surface water quality parameters through a single composite signal, when the ML/AI tools are used conjunctively to disaggregate these signal components. The maintenance-free MPS offers a novel and cost-effective approach to monitor numerous water quality parameters at once with relatively high accuracy.

54 ENVIRONMENTAL SCIENCES↗