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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Flexible User-Defined Domain Decomposition in Kilometer-Scale E3SM Land Model Simulation

The Energy Exascale Earth System Model (E3SM) Land Model (ELM) has been extended to kilometer-scale (km-ELM) resolutions, enabling high-fidelity simulations of terrestrial processes at 1 km x 1 km grid spacing. In ELM, domain decomposition partitions the computational domain across processors, ensuring efficient parallel execution. Currently, round-robin decomposition is applied, providing a straightforward way to distribute computational workload. As ELM continues evolving at the kilometer-scale (km-scale), particularly with integrating lateral flow modeling, decomposition strategies must also account for the increased workload and data movement. This paper introduces a flexible user-defined domain decomposition framework, allowing users to customize domain partitioning based on application requirements. The impact of different decomposition strategies is evaluated across various applications concerning computation, communication, and I/O. Results demonstrate that while 1D partitioning yields superior I/O performance, k-nearest neighbors (KNN) clustering effectively reduces inter-process communication overhead. This study lays the groundwork for scalable partitioning in large-scale land surface simulations, enhancing next-generation Earth system modeling.

Wang, Dali [ORNL] (ORCID:0000000168065108)↗
DOE OSTI2024

Cybersecurity Considerations for Emerging Energy Technologies

AI, cloud computing, post-quantum cryptography, zero-trust architectures, microgrids, and virtual power plants. What do these things have in common? They are all emerging technologies in the clean energy space that will be a critical part of grid modernization efforts. As we work towards clean energy and decarbonization targets, these technologies, developed to solve real-world problems, will help us reach goals and achieve new efficiencies as the paradigm of grid operation shifts. However, there are growing concerns about the cybersecurity risks associated with these trending topics as they are used in critical infrastructure applications. This talk will cover gaps, challenges, and opportunities for the secure implementation of grid modernization solutions and novel energy applications of state-of-the-art networking and communications. Proactive risk mitigation strategies, including the application of cyber-informed engineering, will be discussed. Practical applications of these techniques will help provide countermeasures to the impact of cyberattacks on critical infrastructure technologies in a new, digitized grid landscape.

14 SOLAR ENERGY↗
DOE OSTI2025

Design and Construction of the CMS Outer Tracker for the Phase-2 Upgrade

The High Luminosity LHC (HL-LHC) is expected to deliver an integrated luminosity of 3000-4000~fb$^{-1}$ after 10 years of operation with peak instantaneous luminosity reaching about 5-7.5$\times10^{34}$cm$^{-2}$s$^{-1}$. During Long Shutdown 3, several components of the CMS detector will undergo major changes, called Phase-2 upgrades, to be able to operate in the challenging environment of the HL-LHC. The current CMS tracker will be replaced. The Phase-2 Outer Tracker (OT) will have high radiation tolerance, higher granularity, and the capability to handle higher data rates. Moreover, the OT will provide tracking information to the Level-1 trigger, for the first time at hadron colliders, allowing trigger rates to be kept at a sustainable level without sacrificing physics potential. For this, the OT will be made of modules with two closely spaced silicon sensors read out by front-end ASICs, which can correlate hits in the two sensors creating short track segments (stubs), used for tracking in the L1 track finder. The modules come in two flavors: strip-strip (2S) and pixel-strip (PS), containing different sensor configurations and multiple ASICs. This contribution will present the design of the Phase-2 OT, the first results with pre-production devices, and the quality assurance procedures used to ensure the functionality of the modules: from fulfilling the precision specification of the module assembly procedure to ensuring the proper communication among the module's ASICs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

A Panoramic View of MXenes via an Atomic Coordination‐Based Design Strategy

Two‐dimensional (2D) transition metal carbides and nitrides, known as MXenes, possess unique physical and chemical properties, enabling diverse applications in fields ranging from energy storage to communication, catalysis, sensing, healthcare, and beyond. Despite extensive research and notable advancements, a fundamental understanding of MXenes’ phase diversity and its connection to their hierarchical precursors, including the intermediate MAX phases and the ancestral bulk phases, remains limited. Here, in this study, it is hypothesized that the atomic coordination environments adopted by transition metal and nonmetallic atoms in their three‐dimensional (3D) bulk precursors may persist in 2D MXenes to govern their phase diversity. Using high‐throughput modeling based on first‐principles density functional theory, a wide range of MXene phases is unveiled and comprehensively evaluate their relative stabilities across a large chemical space. The key to the approach lies in considering various atomic coordination environments drawn from four types of ancestral bulk phases. Through this comprehensive structural library of MXenes, general guiding principles are uncovered, such as a close alignment between the phase stability of MXenes and that of their 3D precursors. These findings introduce a new design strategy in which the atomic coordination environments in bulk phases can serve as reliable predictors for accessing the diverse structural landscape of MXenes.

MXenes↗
DOE OSTI2025

Enhancing Automotive Intrusion Detection Through Multi-Modal Fusion: A CAN FD-LiDAR Approach

As vehicles become smarter and more autonomous, they increasingly depend on advanced sensors and communication technologies to operate securely. However, such growing dependence on technology—whether it’s CAN (Controller Area Network) for internal communication or LiDAR (Light Detection and Ranging) for sensing the world around them—also expands the attack surface for the types of cyber attacks. Traditional intrusion detection systems (IDS) typically monitor these systems in isolation, limiting their ability to detect sophisticated, crosssystem attacks. To address this, we propose a multi-modal fusion approach that combines real-world CAN FD signals (from the HCRL dataset) with LiDAR features (from the nuScenes dataset) to enhance attack detection. Our method employs a twostage ensemble approach. Calibrated XGBoost and LightGBM models initially process CAN FD (Fuzzing Data) and LiDAR data independently, detecting timing anomalies and space abnormalities. They are subsequently logarithmically combined with a logistic regression meta-model along with 17 engineered features capturing cross-modal behavior, prediction conflicts, and nonlinear interactions. This approach achieves an AUC of 0.87 and an F1-score of 0.82, surpassing single-modality baselines and early fusion methods, at merely 2 ms inference latency. Compared with deep learning competitors, it is 3 times more efficient, providing a lightweight, interpretable, and real time solution to automotive cybersecurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Maximum Entropy Principle in Deep Thermalization and in Hilbert-Space Ergodicity

We report universal statistical properties displayed by ensembles of pure states that naturally emerge in quantum many-body systems. Specifically, two classes of state ensembles are considered: those formed by (i) the temporal trajectory of a quantum state under unitary evolution or (ii) the quantum states of small subsystems obtained by partial, local projective measurements performed on their complements. These cases, respectively, exemplify the phenomena of “Hilbert-space ergodicity” and “deep thermalization.” In both cases, the resultant ensembles are defined by a simple principle: The distributions of pure states have maximum entropy, subject to constraints such as energy conservation, and effective constraints imposed by thermalization. We present and numerically verify quantifiable signatures of this principle by deriving explicit formulas for all statistical moments of the ensembles, proving the necessary and sufficient conditions for such universality under widely accepted assumptions, and describing their measurable consequences in experiments. We further discuss information-theoretic implications of the universality: Our ensembles have maximal information content while being maximally difficult to interrogate, establishing that generic quantum state ensembles that occur in nature hide (scramble) information as strongly as possible. Our results generalize the notions of Hilbert-space ergodicity to time-independent Hamiltonian dynamics and deep thermalization from infinite to finite effective temperature. Our work presents new perspectives to characterize and understand universal behaviors of quantum dynamics using statistical and information-theoretic tools.

Eigenstate thermalization↗
DOE OSTI2024

Proceedings for the Workshop on Applied Nuclear Data Activities 2024

The Workshop for Applied Nuclear Data Activities (WANDA) is designed to increase communication among nuclear data (ND) users in multidisciplinary federal programs, ND producers, ND funders, and other ND experts. It also presents an opportunity to cross-pollinate ideas as well as introduce ND gaps identified by federal programs to ND experts and ND capabilities to the various federal ND users. WANDA 2024 included five technical sessions, three of which focused on Fusion Energy Sciences (FES)—FES Fusion Neutronics, FES Tritium Production, and FES Material Damage—and two stand-alone sessions—Isotopes and Targetry for Nuclear Data and Uncertainty Quantification. The FES sessions successfully brought new voices to the WANDA discussions, expanding the application space in which nuclear data are critical. FES programs need accurate nuclear data with realistic uncertainty quantification to properly estimate, for example, shielding, activation, tritium production, helium production, structural material integrity, and superconducting magnet operation. This includes a variety of projectile (neutrons, photons, charged particles) and target atoms. One of the action items common to all the FES sessions was a need to perform sensitivity studies to identify the prioritization of nuclear data needs. The Isotopes and Targetry session highlighted the many capabilities available to produce high-quality targets for nuclear data measurements, including 3D printing with spherical powders, combustion synthesis coupled with spin coating & electrospraying, inkjet printing, and isotopic doping. These new methods open doors for more accurate measurement, but it was also stressed that sample characterization following any method of fabrication is of the highest importance to accurately interpret nuclear data measurement results that used that sample. The Uncertainty Quantification (UQ) session was broken into two categories: nuclear data uncertainty quantification and the use of that uncertainty quantification. Thematic to the UQ session was the loss of information when going from nuclear data measurement, to evaluation, to evaluated file, and finally to neutron transport calculations. Current evaluated ND libraries typically only contain covariances, which assume that the probability distributions are Gaussian. Beyond being a simplified assumption for many evaluations, this can lead to negative values on many observables when attempting to sample the covariance. The covariance format, however, is very efficient in that a simple set of linear equations can transform uncertainty from parameters or cross sections to the application of interest. Focused collaboration is needed between nuclear data evaluators and nuclear data users to ensure that needs are being met.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Parallel Runtime Interface for Fortran (PRIF) Specification (Rev. 0.6)

This document specifies an interface to support the multi-image parallelism features of Fortran, named the Parallel Runtime Interface for Fortran (PRIF). PRIF is a solution in which a runtime library is primarily responsible for implementing coarray allocation, deallocation and accesses, image synchronization, atomic operations, events, teams and collective subroutines. The Fortran compiler is responsible for transforming the invocation of Fortran-level multi-image parallelism features into procedure calls to the necessary PRIF subroutines. The interface is designed for portability across shared- and distributed-memory machines, different operating systems, and multiple architectures. Implementations of this interface are intended as an augmentation for the compiler's own runtime library. With an implementation-agnostic interface, alternative parallel runtime libraries may be developed that support the same interface. One benefit of this approach is the ability to vary the communication substrate. A central aim of this document is to define a parallel runtime interface in standard Fortran syntax, which enables us to leverage Fortran to succinctly express various properties of the procedure interfaces, including argument attributes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Molten Salt Loop Operational Experience and Test Campaigns in FY24

The Facility to Alleviate Salt Technology Risks (FASTR) at the US Department of Energy (DOE) Oak Ridge National Laboratory (ORNL) was developed to demonstrate technology for high-temperature chloride salt systems (Figure 1). FASTR is primarily constructed using alloy C-276 and is designed to operate at temperatures of up to 725°C. The facility is loaded with 250 kg of NaCl-KCl-MgCl 2 salt. This salt provides a relevant test environment for de-risking technology while avoiding the costs and hazards associated with beryllium-based or uranium-bearing salts. The facility’s major components include a centrifugal pump for salt circulation, an air-based heat exchanger to reject heat, a suite of instrumentation, and trace heating to prevent salt freezing. The salt was purified in 2020 and 2022, and the pumped loop first operated in December 2022. FASTR is a unique US capability for high-temperature molten halide salt testing. FASTR’s scale, co located purification system, and relatively large power (465 kW) differentiates it from other testing systems. Furthermore, access to the DOE-supported facility and efficient communication of results— which are generally disseminated publicly—distinguish FASTR as being broadly significant throughout the molten salt reactor community. FASTR is similar to ORNL’s Liquid Salt Test Loop (LSTL), although FASTR contains chloride-based salt instead of the fluoride-based salt (LiF-NaF-KF) found in LSTL. Furthermore, FASTR is approximately 2× larger than LSTL in terms of pipe size and length, power, salt volume, flow rate, and number of thermocouples. The LSTL first operated in 2016. At the end of FY23, there was a suspected gas leak in the LSTL that halted operation. At the start of FY24, a leak in the LSTL pump’s tank gas space was confirmed. Because the gas-space leak prevented operation of LSTL, FY24 efforts were focused on operation of FASTR. This report summarizes the progress made during FY24 in support of the DOE Office of Nuclear Energy (DOE-NE) work package, AT-24OR070202 Salt Loop and Capability for Testing Sensors and Off Gas Components.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Surf-Deformer: Mitigating Dynamic Defects on Surface Code via Adaptive Deformation

In this paper, we introduce Surf-Deformer, a code deformation framework that seamlessly integrates adaptive defect mitigation functionality into the current surface code workflow. It crafts several basic deformation instructions based on fundamental gauge transformations, which can be combined to explore a larger design space than previous methods. This enables more optimized deformation processes tailored to specific defect situations, restoring the QEC capability of deformed codes more efficiently with minimal qubit resources. Additionally, we design an adaptive code layout that accommodates our defect mitigation strategy while ensuring efficient execution of logical operations. Our evaluation shows that Surf-Deformer outperforms previous methods by significantly reducing the end-to-end failure rate of various quantum programs by 35× to 70×, while requiring only about 50% of the qubit resources compared to the previous method to achieve the same level of failure rate. Ablation studies show that Surf-Deformer surpasses previous defect removal methods in preserving QEC capability and facilitates surface code communication by achieving nearly optimal throughput.

Yin, Keyi↗
DOE OSTI2026

Energy Resilience Options for the Koolauloa Community Resilience Hub – Energy Technology Innovation Partnership Project, Cohort 2: Summary of Findings and Assessment

Hui o Hau‘ula (HoH) is a community organization dedicated to the well-being of the population of the Ko‘olauloa district on the Hawaiian island of Oahu. In response to growing concerns about challenges related to extreme weather events or natural disasters, HoH formulated the concept of the Ko‘olauloa Community Resilience Hub, or KCRH. The KCRH facility would serve as a focal point for the community during normal conditions, while also providing essential services and acting as a safe space during emergencies, natural or man-made. The concept of the KCRH was initially developed in partnership with the Hawaii Natural Energy Institute, +Lab Architects, and the City and County of Honolulu. In the fall of 2023, the U.S. Department of Energy, under the Energy Technology Innovation Partnership Project Program (ETIPP), provided support to the KCRH project, in the form of technical assistance (TA) to be provided by its National Laboratory complex. In this case, the core TA was provided by Sandia National Laboratories, and it was directed to providing options for designing an energy system based at the KCRH that could support critical loads in the event of a 30- day grid outage. The National Renewable Energy Laboratory (NREL) provided communications and logistics support in the project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Design and construction of the CMS Outer Tracker for the Phase-2 Upgrade

he High Luminosity LHC (HL-LHC) is expected to deliver an integrated luminosity of $3000-4000$~fb$^{-1}$ after 10 years of operation with peak instantaneous luminosity reaching about $5-7.5\times10^{34}$cm$^{-2}$s$^{-1}$. During Long Shutdown 3, several components of the CMS detector will undergo major changes, called Phase-2 upgrade, to be able to operate in the challenging environment of the HL-LHC. The current CMS silicon strip tracker has to be replaced with a new detector. The Phase-2 Outer Tracker (OT) will have higher radiation tolerance, higher granularity, and the capability to handle higher data rates compared to the current system. Another key feature of the OT will be to provide tracking information to the Level-1 (L1) trigger, allowing trigger rates to be kept at a sustainable level without sacrificing physics potential. For this, the OT will be made out of modules with two closely spaced sensors read out by front-end ASICs, which can correlate hits in the two sensors creating short track segments called stubs. The stubs will be used for tracking in the L1 track finder. The modules come in two flavors: strip-strip (2S) and pixel-strip (PS), which contain different sensor configurations and multiple ASICs. In this contribution, the design of the CMS Phase-2 OT, the technological choices, and the quality assurance (QA) procedures used to ensure the functionality of the modules will be reported. The contribution will cover the first results with pre-production devices and the different aspects taken into account during the QA: from fulfilling the precision specification of the module assembly procedure to ensuring the proper communication between the different ASICs on the module. The module noise performance is also checked and the full module functionality is verified at different temperatures.

Zoi, Irene↗
DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

S-QGPU: Shared quantum gate processing unit for distributed quantum computing

We propose a distributed quantum computing (DQC) architecture in which individual small-sized quantum computers are connected to a shared quantum gate processing unit (S-QGPU). The S-QGPU comprises a collection of hybrid two-qubit gate modules for remote gate operations. In contrast to conventional DQC systems, where each quantum computer is equipped with dedicated communication qubits, S-QGPU effectively pools the resources (e.g., the communication qubits) together for remote gate operations, and, thus, significantly reduces the cost of not only the local quantum computers but also the overall distributed system. Our preliminary analysis and simulation show that S-QGPU's shared resources for remote gate operations enable efficient resource utilization. When not all computing qubits (also called data qubits) in the system require simultaneous remote gate operations, S-QGPU-based DQC architecture demands fewer communication qubits, further decreasing the overall cost. Alternatively, with the same number of communication qubits, it can support a larger number of simultaneous remote gate operations more efficiently, especially when these operations occur in a burst mode.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Supramolecular Metal‐Organic Framework Electrocatalysts With Hydration‐Adaptive Gallery Expansion and Linker‐Mediated Distal Ni‐Co Cooperation Enable Framework‐Retentive Oxygen Evolution

Atomic‑level tailoring of electronic communication between spatially separated metal sites offers a route to accelerate multielectron electrocatalysis. We report Ni-TPTC, Co-TPTC, and heterobimetallic Ni-Co-TPTC supramolecular metal-organic frameworks (SMOFs) for the oxygen evolution reaction (OER) built from a terphenyl‑tetracarboxylate (TPTC) linker. Single-crystal X-ray diffraction of heterobimetallic Ni–Co–TPTC reveals trans-bis-aqua octahedral chains assembled into a π-stacked, hydrogen-bonded lattice and confirms retention of the parent architecture, while powder X-ray diffraction supports an isostructural Co analog. In 1.0 M KOH, Ni-Co-TPTC reaches 20 mA cm −2 at 350 mV and maintains operation at ∼60 mA cm −2 for 45 h with modest decay of the initial current. Time‑dependent ex situ diffraction shows dominant framework reflections with low‑angle shifts consistent with gallery dilation (d‑spacing ∼7.7 → ∼9.2 Å) during early operation, while Co K‑edge X‑ray absorption and microscopy reveal Co‑rich oxide/oxyhydroxide‑like domains at longer times that correlate with decay. X‑ray photoelectron spectroscopy, quantified by the Remote Binding‑Energy Differential, tracks composition‑dependent Ni-Co polarization, and DFT free‑energy analysis suggests that a model Ni-Co environment lowers the Co‑centered potential‑limiting step by ∼0.24 eV relative to the Co‑only framework. Furthermore, these results suggest that framework‑retentive expansion and distal Ni-Co coupling can drive OER activity before gradual deligation and oxide formation mark longer‑time degradation.

Supramolecular metal-organic frameworks (SMOFs)↗
DOE OSTI2024

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations

Abstract Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.

Siddiquee, Masudur R.↗
DOE OSTI2024

MODAQ-BB (Modular Offshore Data Acquisition System - Blackbox) [SWR-24-72]

MODAQ-BB is a compact, rapid deployment data acquisition system that can withstand water depths up to 400m. Codenamed "BlackBox" (or simply BB), since its initial purpose was to track marine assets and record vital data streams that could later be recovered in the event of a mishap - much like a traditional black box used in aviation and shipping, the name has stuck. MODAQ BlackBox is a battery-operable microcontroller platform with internal inertial sensing, GPS, satellite communications, and additional I/O (input/output) support in a depth-rated pressure enclosure. Since BB is self-contained and relatively compact, it can be quickly deployed with minimal effort. The enclosure can be clamped to a tube (such as part of a railing) or mast in a location with unobstructed view of the sky using the available clamp accessory or a common hose clamp. While BB is designed to operate unattended, users can configure what data are uploaded and the frequency of satellite transmissions. Once data are uploaded, they can be relayed to an email distribution list, the MODAQ:Web operational dashboard, or a custom destination. BB has found utility in the National Laboratory of the Rockies' (NLR's) Waterpower projects beyond its original vision and has been configured and successfully deployed in more traditional data-gathering applications where a simple, battery-operated solution was indicated. As part of the MODAQ family, BB fills a space in the spectrum of missions that can be supported that were previously impractical using the traditional MODAQ hardware architecture due to factors such as weight, size, and cost.

Raye, Robert↗