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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Perovskite- and Dye-Sensitized Solar-Cell Device Databases Auto-generated Using ChemDataExtractor

The number of scientific publications reporting cutting-edge third-generation photovoltaic devices is increasing rapidly, owing to the pressing need to develop renewable-energy technologies that address the climate-change crisis. Consequently, the field could benefit from a central repository where photovoltaic-performance metrics, such as the power-conversion efficiency (η) are recorded. We present two automatically generated databases that contain photovoltaic properties and device material data for dye-sensitized solar cells (DSCs) and perovskite solar cells (PSCs), totalling 660,881 data entries representing 57,678 photovoltaic devices. The databases were generated by applying the text-mining toolkit ChemDataExtractor on a corpus of 25,720 articles. A multi-faceted evaluation, incorporating manual and automatic methods, was applied to ensure that the data contained therein were of the highest quality, with precision metrics ranging from 73.1% to 95.8%. The DSC database contains 475,045 entries representing 41,680 devices, and the PSC database contains 185,836 entries representing 15,818 devices. The databases are available in MongoDB and JSON formats, which can be queried in Python, R, Java and MATLAB for data-driven photovoltaic materials discovery.

14 SOLAR ENERGY↗
DOE OSTI2023

Degree-preserving graph dynamics: a versatile process to construct random networks

Real-world networks evolve over time via the addition or removal of vertices and edges. In current network evolution models, vertex degree varies or grows arbitrarily. A recently introduced degree-preserving network growth (DPG) family of models preserves vertex degree, resulting in structures significantly different from and more diverse than previous models. Despite its degree preserving property, the DPG model is able to replicate the output of several well-known real-world network growth models. Simulations showed that many real-world networks can also be constructed from small seed graphs via the DPG process. Here, we start the development of a rigorous mathematical theory underlying the DPG family of network growth models. We prove that the degree sequence of the output of some of the well-known, real-world network growth models can be reconstructed via the DPG process, using proper parametrization. We also show that the general problem of deciding whether a simple graph can be obtained via the DPG process from a small seed (DPG feasibility) is, however, NP-complete. In conclusion, it is an intriguing open problem to uncover whether there is a structural reason behind the DPG-constructability of real-world networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Modeling of the Advanced Test Reactor Using OpenMC, Cubit and Griffin

In the pursuit of the ability to perform multiphysics simulations of the Advanced Test Reactor, it is crucial to have a fast and highly accurate deterministic model. To achieve this, a contemporary two-step method is employed. The first step involves generating homogenized cross sections using OpenMC, a cutting-edge Monte Carlo neutron transport code. OpenMC offers excellent modular capabilities, allowing for easy component integration and flexibility in incorporating new designs into the model. The second step involves deterministic transport calculations, which are performed using Griffin, a reactor multiphysics application based on the Multiphysics Object-Oriented Simulation Environment. To ensure the accurate spatial resolution and assignment of material cross sections, a Cubit-generated mesh for the Advanced Test Reactor is utilized as an intermediate step between the OpenMC and Griffin models; Griffin utilizes the mesh for its finite element solution, while OpenMC material IDs are written to the mesh file to be used in Griffin material assignments. Additionally, a Python-based script converts the cross sections generated by OpenMC into the ISOXML format required by Griffin. Preliminary comparisons indicate good agreement between the neutron multiplication factors obtained from the standalone OpenMC model and the Griffin model, with differences of less than 50 pcm in the two-dimensional geometry configuration. However, in three-dimensional calculations, an unacceptably large error is found in the Griffin solution. Future work is planned to resolve this discrepancy.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Theory of edge localized mode suppression by static resonant magnetic perturbations in the DIII-D tokamak

According to a recent paper [Hu et al ., Phys. Plasmas 26, 120702 (2019)], mode penetration at the top of the pedestal is a necessary and sufficient condition for the suppression of edge localized modes (ELMs) by a resonant magnetic perturbation (RMPs) in an H-mode tokamak discharge. This paper employs asymptotic matching theory to model a particular DIII-D discharge in which ELMs were suppressed by an externally generated, static, n ¼ 2, RMP whose amplitude was modulated at a frequency of 1 Hz. It is demonstrated that the response of the plasma to the applied RMP, in the immediate vicinities of the rational (i.e., resonant) surfaces, is governed by nonlinear, rather than by linear, physics. This is the case because the magnetic island widths associated with driven reconnection exceed the linear layer widths, even in cases where driven reconnection is strongly suppressed by plasma rotation. The natural frequency at a given rational surface (i.e., the helical frequency at which the locally resonant component of the RMP would need to propagate in order to maximize driven reconnection) is found to be offset from the local E B frame in the ion diamagnetic direction. The size of the offset is mostly determined by neoclassical poloidal rotation. Finally, the predictions of a fully nonlinear plasma response model are found to be broadly consistent with the DIII-D experimental data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Module-OT: A Turnkey Solution for Securing Energy Systems

The Modular Security Apparatus for Managing Distributed Cryptography for Command-and-Control Messages on Operational Technology Networks (Module-OT) is a flexible and lightweight solution for grid-edge devices focusing on end-to-end security. It is a bump- in- the-wire solution acting as a secure conduit for data between devices or systems across a network. It improves the cybersecurity posture of DER systems by providing authentication, authorization, and data integrity to secure DER communications. Additionally, it performs key management, provides data security through whitelisting Internet Protocol addresses and ports, blocks unauthorized connections, controls user access, and allows serial or Ethernet connections for added flexibility. The core software is portable to various Linux-based operating systems and is developed to be customized by the developer and researcher communities. Module-OT has been validated in the lab, has been demonstrated at a 500-KW PV-plus-storage site, and has been proven ready to secure operational technology devices. Its core functionality meets current standards, including validation procedures of the NIST Cryptographic Algorithm Validation Program (CAVP) and the Federal Information Processing Standard (FIPS 140-2). Because of its capability to provide an accessible and affordable option for stepping up security across modern energy systems, Module-OT can serve as an effective technological option to standardize cybersecurity moving forward.

cryptography↗
DOE OSTI2024

Nanoscale Three-Dimensional Imaging of Integrated Circuits Using a Scanning Electron Microscope and Transition-Edge Sensor Spectrometer

X-ray nanotomography is a powerful tool for the characterization of nanoscale materials and structures, but it is difficult to implement due to the competing requirements of X-ray flux and spot size. Due to this constraint, state-of-the-art nanotomography is predominantly performed at large synchrotron facilities. We present a laboratory-scale nanotomography instrument that achieves nanoscale spatial resolution while addressing the limitations of conventional tomography tools. The instrument combines the electron beam of a scanning electron microscope (SEM) with the precise, broadband X-ray detection of a superconducting transition-edge sensor (TES) microcalorimeter. The electron beam generates a highly focused X-ray spot on a metal target held micrometers away from the sample of interest, while the TES spectrometer isolates target photons with a high signal-to-noise ratio. This combination of a focused X-ray spot, energy-resolved X-ray detection, and unique system geometry enables nanoscale, element-specific X-ray imaging in a compact footprint. The proof of concept for this approach to X-ray nanotomography is demonstrated by imaging 160 nm features in three dimensions in six layers of a Cu-SiO 2 integrated circuit, and a path toward finer resolution and enhanced imaging capabilities is discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Cybersecurity Certification Recommendations for Interconnected Grid Edge Devices and Inverter Based Resources

Escalating deployment of PV and grid-edge devices on the distribution grid has increased the sustainability and efficiency of the electric grid. However, the increasing number of distributed energy resources (DERs) deployed creates a heightened cyber-physical interdependency on the distribution grid and thus creates more vectors for cyber-attacks to exploit through information and communication technology (ICT) systems and networks. For example, control signal packets can be modified, intercepted, or corrupted due to vulnerabilities in communication protocols used by microgrid controllers and grid edge devices for power control. Therefore, to mitigate and prevent cyber-attacks on grid edge devices and the inverter-based resources connected to the distribution grid, the U.S. Department of Solar Energy Technologies Office (SETO) awarded funding to the National Renewable Energy Laboratory and Sandia National Laboratory (SNL) to research, develop, and harmonize cybersecurity standards for Photovoltaic (PV) systems and for other kinds of DERs. To help develop a standard for DER cybersecurity, NREL established certification recommendations and test cases, in consensus with the solar industry and UL, for ensuring intrinsic design security for DERs. These recommendations were developed to bolster the cybersecure functionalities such as TLS, MAC, CRL, session resumption/renegotiation, and password, system, and service security management within the DER devices. The proposed test cases verify authentication, authorization, confidentiality, and data integrity for data and communications of DERs that use Transmission Control Protocol/Internet Protocol (TCP/IP). They were also developed to protect DER communications from eavesdropping, replay, man-in-the-middle, denial of service (DoS), spoofing through security certificates, least-privilege violation, and brute-force credentials. This report, which has been validated and reviewed by UL, expands upon those test cases to provide DER cybersecurity certification recommendations which increase DER resiliency and help to mitigate cyber-attacks. UL's collaboration with NREL and approval of this document will accelerate the adoption of a UL standard for DER cybersecurity.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2024

Numerical implications of including drifts in SOLPS-ITER simulations of EAST

The inclusion of drifts in plasma edge codes like SOLPS-ITER is required to match simulation data with experimental profiles. However, this remains numerically challenging. In this paper, the effect of some numerical factors on the final plasma solution is investigated. This study is performed on three EAST simulations in the upper single null configuration: an attached purely deuterium case, an attached case with limited Ne-seeding, and a detached Ne-seeded case. The effects of the anomalous conductivity and anomalous thermo-electric coefficient on the plasma potential are investigated. Next, the effect of the employed grids is shown. In order to investigate these effects, accurate drift simulations are needed. Therefore, the employed time step and numerical parameters are discussed for the three studied simulations. For all presented simulations, it is verified that the restriction of the grid to the first flux surface tangent to the main chamber wall is sufficient within the context of non-extended simulations. This means that the main power dissipation takes place inside the simulated domain, and only a small fraction of the power is leaving the B2.5 grid through the grid boundary closest to the first wall. Finally, the effect of drifts on the asymmetry between the inner and outer target for EAST simulations is demonstrated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

funcX: Federated Function as a Service for Science

Here, funcX is a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. Unlike centralized FaaS systems, funcX decouples the cloud-hosted management functionality from the edge-hosted execution functionality. funcX's endpoint software can be deployed, by users or administrators, on arbitrary laptops, clouds, clusters, and supercomputers, in effect turning them into function serving systems. funcX's cloud-hosted service provides a single location for registering, sharing, and managing both functions and endpoints. It allows for transparent, secure, and reliable function execution across the federated ecosystem of endpoints-enabling users to route functions to endpoints based on specific needs. funcX uses containers (e.g., Docker, Singularity, and Shifter) to provide common execution environments across endpoints. funcX implements various container management strategies to execute functions with high performance and efficiency on diverse funcX endpoints. funcX also integrates with an in-memory data store and Globus for managing data that may span endpoints. We motivate the need for funcX, present our prototype design and implementation, and demonstrate, via experiments on two supercomputers, that funcX can scale to more than 130000 concurrent workers. We show that funcX's container warming-aware routing algorithm can reduce the completion time for 3,000 functions by up to 61% compared to a randomized algorithm and the in-memory data store can speed up data transfers by up to 3x compared to a shared file system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Added value of site load measurements in probabilistic lifetime extension: a Lillgrund case study

Site-specific fatigue estimation is an essential part of wind turbine lifetime extension, with various methods depending on data availability. The present study compares probabilistic lifetime extension assessment results for rotor blades with and without load measurements. It also addresses two key questions in such assessments: the applicability of the Frandsen model for estimating waked turbulence under complex and mixed wake conditions and the extrapolation of mid-term data over longer time periods. The case study wind turbine is SWT-2.3-93, located at the edge of the Lillgrund wind farm, situated in the Øresund Strait between Denmark and Sweden. The turbine is extensively instrumented, with 5 years of data available from its supervisory control and data acquisition (SCADA) system. Although the Frandsen turbulence estimates deviate in a different manner from measurements at below- and above-rated mean wind speeds, the model remains a conservative approach for fatigue load prediction and reliability. In the current case study, the site-specific assessment using strain gauge measurements yields a 33 % higher annual fatigue reliability index after 35 years compared to a scenario based on the Frandsen estimation combined with ambient environmental data and a generic aeroelastic model. The results also demonstrate that the sensitivity of fatigue reliability to load uncertainty is negligible when load measurements are used directly but relatively high when relying on the Frandsen model in combination with a generic aeroelastic model. Overall, the high variability of the lifetime extension in different scenarios of data availability and accuracy shows the importance and added value of high-quality measurements combined with wind-farm-level SCADA and a model updated in real time (digital twins).

17 WIND ENERGY↗
DOE OSTI2025

Diffusion Codes: Self-Correction from Small(er)-Set Expansion with Tunable Non-locality

Optimal constructions of classical LDPC codes can be obtained by choosing the Tanner graph uniformly at random among biregular graphs. We introduce a class of codes that we call ``diffusion codes'', defined by placing each edge connecting bits and checks on some graph, and acting on that graph with a random SWAP network. By tuning the depth of the SWAP network, we can tune a tradeoff between the amount of randomness -- and hence the optimality of code parameters -- and locality with respect to the underlying graph. For diffusion codes defined on the cycle graph, if the SWAP network has depth $\sim Tn$ with $T> n^{2β}$ for arbitrary $β>0$, then we prove that almost surely the Tanner graph is a lossless ``smaller set'' vertex expander for small sets up size $δ\sim \sqrt T \sim n^β$, with bounded bit and check degree. At the same time, the geometric size of the largest stabilizer is bounded by $\sqrt T$ in graph distance. We argue, based on physical intuition, that this result should hold more generally on arbitrary graphs. By taking hypergraph products of these classical codes we obtain quantum LDPC codes defined on the torus with smaller-set boundary and co-boundary expansion and the same expansion/locality tradeoffs as for the classical codes. These codes are self-correcting and admit single-shot decoding, while having the geometric size of the stabilizer growing as an arbitrarily small power law. Our proof technique establishes mixing of a random SWAP network on small subsystems at times scaling with only the subsystem size, which may be of independent interest.

Combinatorics (math.CO)↗
DOE OSTI2024

Liquid metal walls

Here, the plasma performance of fusion devices depends strongly on the chosen wall materials. Solid plasma-facing components (PFCs) are predominantly used in present devices, and are the most investigated candidates for fusion designs. High-Z materials such as tungsten (W) are the leading solid PFC material candidates. To date, a material choice that scales to steady-state reactor conditions has not been identified. Moreover, if plasma transient events such as edge-localized modes and disruptions cannot be altogether avoided or sufficiently mitigated, the projected peak heat and particle loads far exceed the power exhaust capabilities of solids. Liquid metal (LM) PFCs represent an intrinsic self-healing boundary that are both resilient to surface damage from transients, and that could handle high steady-state heat and particle fluxes. Flowing LM PFCs can be designed to remove “slag,” the buildup of material erosion due to plasma-material interactions, including charge exchange sputtering in the main chamber. Further, LM offer the prospect to manage hydrogenic species otherwise retained in the PFCs, which is important from a safety and inventory standpoint. The two most promising LMs are lithium (Li) and tin (Sn), although Sn–Li eutectics may be considered. While Sn offers a higher temperature window with low vapor pressure and low hydrogen retention, Li offers the prospect of enhanced energy confinement and higher acceptable core contamination limits, and this section focuses on Li PFCs. An LM PFC development research program developed LM PFC concepts for a nuclear fusion device via engineering design calculations, single-effect experiments, and staged prototypical experiments. A self-consistent design window was identified with liquid Li flow speeds ~5–10 m/s; plasma contamination was negligible for predicted Li evolution rates. While these preconceptual designs hold promise, there is substantial R&D needed to advance the technical readiness levels of LM PFCs for application to fusion power plants.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Explainable tokamak-agnostic forecasting of fusion plasma instability via megahertz turbulent fluctuations

Scientific applications of artificial intelligence (AI) often remain limited by device-specific training and unexplained “black-box” approaches, creating fundamental barriers to cross-system generalization. This challenge is critical for nuclear fusion, where future reactors will have limited operational data for AI training. Here, we demonstrate that our neural network, trained solely on megahertz-scale turbulence measurements from one machine (DIII-D), forecasts Type-I edge localized mode (ELM) onsets in a different tokamak (KSTAR) through zero-shot weight transfer following physics-consistent preprocessing without device-specific retraining. Through an explainable AI framework combining gradient-weighted class activation mapping with physics validation, we reveal that our network can internalize physics relationships governing the ELM instabilities rather than memorizing device-specific patterns. The network perceives spatiotemporal features that correlate consistently with independently calculated instability growth rates, magnetohydrodynamic stability limits, and pedestal structure dynamics. Statistical analyses of dimensionally-reduced saliency features reveal the identical triangular features between the saliency representations, instability growth rates, and prediction probability across tokamaks, providing evidence that our forecasting system can show tokamak-agnostic generalization. This work contributes to a foundation for explainable scientific AI systems, where cross-system developments are essential for transcending traditional domain-specific constraints.

AI↗
DOE OSTI2021

First coupled GENE–XGC microturbulence simulations

Covering the core and the edge region of a tokamak, respectively, the two gyrokinetic turbulence codes Gyrokinetic Electromagnetic Numerical Experiment (GENE) and X-point Gyrokinetic Code (XGC) have been successfully coupled by exchanging three-dimensional charge density data needed to solve the gyrokinetic Poisson equation over the entire spatial domain. Certain challenges for the coupling procedure arise from the fact that the two codes employ completely different numerical methods. This includes, in particular, the necessity to introduce mapping procedures for the transfer of data between the unstructured triangular mesh of XGC and the logically rectangular grid (in a combination of real and Fourier space) used by GENE. Constraints on the coupling scheme are also imposed by the use of different time integrators. First, coupled simulations are presented. We have considered collisionless ion temperature gradient turbulence, in both circular and fully shaped plasmas. Coupled simulations successfully reproduce both GENE and XGC reference results, confirming the validity of the code coupling approach toward a whole device model. Here, many lessons learned in the present context, in particular, the need for a coupling procedure as flexible as possible, should be valuable to our and other efforts to couple different kinds of codes in pursuit of a more comprehensive description of complex real-world systems and will drive our further developments of a whole device model for fusion plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2022

EDGE COVID-19: a web platform to generate submission-ready genomes from SARS-CoV-2 sequencing efforts

Abstract Summary Genomics has become an essential technology for surveilling emerging infectious disease outbreaks. A range of technologies and strategies for pathogen genome enrichment and sequencing are being used by laboratories worldwide, together with different and sometimes ad hoc, analytical procedures for generating genome sequences. A fully integrated analytical process for raw sequence to consensus genome determination, suited to outbreaks such as the ongoing COVID-19 pandemic, is critical to provide a solid genomic basis for epidemiological analyses and well-informed decision making. We have developed a web-based platform and integrated bioinformatic workflows that help to provide consistent high-quality analysis of SARS-CoV-2 sequencing data generated with either the Illumina or Oxford Nanopore Technologies (ONT). Using an intuitive web-based interface, this workflow automates data quality control, SARS-CoV-2 reference-based genome variant and consensus calling, lineage determination and provides the ability to submit the consensus sequence and necessary metadata to GenBank, GISAID and INSDC raw data repositories. We tested workflow usability using real world data and validated the accuracy of variant and lineage analysis using several test datasets, and further performed detailed comparisons with results from the COVID-19 Galaxy Project workflow. Our analyses indicate that EC-19 workflows generate high-quality SARS-CoV-2 genomes. Finally, we share a perspective on patterns and impact observed with Illumina versus ONT technologies on workflow congruence and differences. Availability and implementation https://edge-covid19.edgebioinformatics.org, and https://github.com/LANL-Bioinformatics/EDGE/tree/SARS-CoV2. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗