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At least 1,135 records · Page 63

Control Design for a Marine Hydrokinetic Cycloturbine Vehicle

Abstract Marine Hydrokinetic (MHK) cycloturbines generate sustainable power by exploiting tidal currents. By powering the turbines and using pitching foils for control, a vehicle comprised of MHK cycloturbines also has the ability to station keep and maneuver. The vehicle consists of four counter-rotating cycloturbines, with hydrofoils oriented perpendicular to the flow in a paddlewheel configuration. Lift and drag generated from these foils sum together to produce thrust. An experimentally tuned simulation model that solves the six-degrees-of-freedom rigid body equations of motion for the MHK vehicle subject to hydrodynamic, hydrostatic, and propulsive forces is used to aid the design of vehicle controllers. Global feedback controllers are initially designed by applying classical control methods to an approximate linear model of the system dynamics. A higher performing nonlinear controller is designed using the nonlinear dynamic inversion (NDI) method. NDI accounts for the nonlinearities of the MHK system and therefore is suitable for a wide range of operating conditions. The response of the classical and NDI controllers to speed, depth, roll, pitch, and yaw commands are evaluated and compared in simulation. The classical controller outperforms the NDI controller for small amplitude maneuvers, although the degradation with NDI is minor. However, in the nonlinear operating regime the NDI controller outperforms the classical controller and the classical controller exhibits instability.

Automation & Control Systems↗

A global study of hygroscopicity-driven light-scattering enhancement in the context of other in situ aerosol optical properties

The scattering and backscattering enhancement factors (ƒ(RH) and ƒ b (RH)) describe how aerosol particle light scattering and backscattering, respectively, change with relative humidity (RH). They are important parameters in estimating direct aerosol radiative forcing (DARF). In this study we use the dataset presented in Burgos et al. (2019) that compiles ƒ(RH) and ƒ b (RH) measurements at three wavelengths (i.e., 450, 550 and 700 nm) performed with tandem nephelometer systems at multiple sites around the world. We present an overview of ƒ(RH) and ƒ b (RH) based on both long-term and campaign observations from 23 sites representing a range of aerosol types. The scattering enhancement shows a strong variability from site to site, with no clear pattern with respect to the total scattering coefficient. In general, higher ƒ(RH) is observed at Arctic and marine sites, while lower values are found at urban and desert sites, although a consistent pattern as a function of site type is not observed. The backscattering enhancement ƒ b (RH) is consistently lower than ƒ(RH) at all sites, with the difference between ƒ(RH) and ƒ b (RH) increasing for aerosol with higher ƒ(RH). This is consistent with Mie theory, which predicts higher enhancement of the light scattering in the forward than in the backward direction as the particle takes up water. Our results show that the scattering enhancement is higher for PM 1 than PM 10 at most sites, which is also supported by theory due to the change in scattering efficiency with the size parameter that relates particle size and the wavelength of incident light. At marine-influenced sites this difference is enhanced when coarse particles (likely sea salt) predominate. For most sites, ƒ(RH) is observed to increase with increasing wavelength, except at sites with a known dust influence where the spectral dependence of ƒ(RH) is found to be low or even exhibit the opposite pattern. The impact of RH on aerosol properties used to calculate radiative forcing (e.g., single-scattering albedo, ω 0 , and backscattered fraction, b) is evaluated. The single-scattering albedo generally increases with RH, while b decreases. The net effect of aerosol hygroscopicity on radiative forcing efficiency (RFE) is an increase in the absolute forcing effect (negative sign) by a factor of up to 4 at RH = 90 % compared to dry conditions (RH < 40 %). Because of the scarcity of scattering enhancement measurements, an attempt was made to use other more commonly available aerosol parameters (i.e., ω 0 and scattering Ångström exponent, α sp ) to parameterize ƒ(RH). The majority of sites (75 %) showed a consistent trend with ω 0 (higher ƒ(RH = 85 %) for higher ω 0 ), while no clear pattern was observed between ƒ(RH = 85 %) and αsp. This suggests that aerosol ω 0 is more promising than α sp as a surrogate for the scattering enhancement factor, although neither parameter is ideal. Nonetheless, the qualitative relationship observed between ω 0 and ƒ(RH) could serve as a constraint on global model simulations.

54 ENVIRONMENTAL SCIENCES↗

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data

As electric vehicle (EV) adoption rises globally, concerns about the impact on aging electrical grids grow, particularly regarding the charging behavior of EV drivers. This study analyzes real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019 to develop interpretable models that characterize charging behavior and quantify influencing factors. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into how various elements, such as next trip distance and state of charge (SOC), influence charging decisions. The dataset comprises over three million park-trip pairs from 1,997 vehicles, revealing that features related to driving behavior significantly dictate charging behavior, while infrastructure and regional factors have lesser impacts. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This work utilizes real-world EV driving and charging data to train interpretable models that describe charging behavior and quantify the factors most associated with how drivers use charging infrastructure. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

Acquire and Test IDAES Framework Components

The cooperation under the Grid Modernization Initiative and the Applied Energy Tri-Laboratory umbrellas requires mutual understanding of capabilities and a common effort to identify gaps existing in the available computational modeling and analysis tools. In this work, we report acquisition and testing of the National Energy Technology Laboratory’s Institute for the Design of Advanced Energy Systems (IDAES) framework, particularly including both electric power systems production cost model simulator Prescient as well as system optimization IDAES itself. Further, we consider the methods in which developments in Prescient and IDAES for grid-energy simulation and optimization may be included in the Framework for Optimization of ResourCes and Economics (FORCE) ecosystem that has been under collaborative development by Idaho National Laboratory, Argonne National Laboratory, and Oak Ridge National Laboratory. We propose two integration methods to introduce Prescient and IDAES into existing FORCE automated workflows. One integration involves using Prescient as a market reduced-order model and IDAES models similar to how existing Modelica models are used. The other integration involves wrapping the Prescient-IDAES dispatch optimization process with the stochastic capacity optimization workflow in FORCE.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments

Abstract. Optimizing radar observation strategies is one of the most important considerations in pre-field campaign periods. This is especially true for isolated convective clouds that typically evolve faster than the observations captured by operational radar networks. This study investigates uncertainties in radar observations of the evolution of the microphysical and dynamical properties of isolated deep convective clouds developing in clean and polluted environments. It aims to optimize the radar observation strategy for deep convection through the use of high-spatiotemporal cloud-resolving model simulations, which resolve the evolution of individual convective cells every 1 min, coupled with a radar simulator and a cell tracking algorithm. The radar simulation settings are based on the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field campaigns held in the Houston, TX, area but are generalizable to other field campaigns focusing on isolated deep convection. Our analysis produces the following four outcomes. First, a 5–7 m s−1 median difference in maximum updrafts of tracked cells is shown between the clean and polluted simulations in the early stages of the cloud lifetimes. This demonstrates the importance of obtaining accurate estimates of vertical velocity from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells and using vertical cross section scanning every minute capture the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves multi-Doppler radar updraft retrievals above 5 km above ground level for regions with updraft velocities greater than 10 m s−1. Third, we propose an optimized strategy composed of cell tracking by quick (1–2 min) vertical cross section scans from more than one radar in addition to the operational volume scans. We also propose the use of a single-RHI (range height indicator) updraft retrieval technique for cells close to the radars, for which multi-Doppler radar retrievals are still challenging. Finally, increasing the number of deep convective cells sampled by such observations better represents the median maximum updraft evolution with sample sizes of more than 10 deep cells, which decreases the error associated with sampling the true population to less than 3 m s−1.

54 ENVIRONMENTAL SCIENCES↗

Radial–axial transport coordination enhances sugar translocation in the phloem vasculature of plants

Abstract Understanding mass transport of photosynthates in the phloem of plants is necessary for predicting plant carbon allocation, productivity, and responses to water and thermal stress. Several hypotheses about optimization of phloem structure and function and limitations of phloem transport under drought have been proposed and tested with models and anatomical data. However, the true impact of radial water exchange of phloem conduits with their surroundings on mass transport of photosynthates has not been addressed. Here, the physics of the Munch mechanism of sugar transport is re-evaluated to include local variations in viscosity resulting from the radial water exchange in two dimensions (axial and radial) using transient flow simulations. Model results show an increase in radial water exchange due to a decrease in sap viscosity leading to increased sugar front speed and axial mass transport across a wide range of phloem conduit lengths. This increase is around 40% for active loaders (e.g. crops) and around 20% for passive loaders (e.g. trees). Thus, sugar transport operates more efficiently than predicted by previous models that ignore these two effects. A faster front speed leads to higher phloem resiliency under drought because more sugar can be transported with a smaller pressure gradient.

58 GEOSCIENCES↗

Optimizing Price-Informed Operation of a Battery Storage System in an Office Building

New prescriptive efficiency requirements amended to US model energy codes historically have been evaluated using an average, blended electricity rate, which obscures demand charges. Post 2019, new measures can be evaluated using a representative time-of-use (TOU) tariff yet more sophisticated analysis methods are needed to consider a variety of TOU tariffs and assess price-informed control. To address these needs, this study couples building prototype simulation model with varying-in-sophistication battery storage operating strategies for different electricity TOU tariffs. The analysis compares the impact of operating a battery storage system following simpler rule-of-thumb methods versus a semi-optimized priced-informed heuristic approach. The investigation demonstrates that the heuristic approach results in greater electricity cost savings and is practical to implement.

Lei, Xuechen↗

Scenario Generation for Built Environment Decision Support under Uncertainty: Case Studies of Airflow Modeling and Climate-Resilient Infrastructure System Design

When confronted with unforeseen challenges, practicing informed decision making is crucial for enhancing resilience in the built environment. While scan-to-building information modeling (BIM) is a well-established approach for creating detailed digital representations of physical assets, its application in assessing and improving infrastructure resilience remains underexplored. This study addresses this gap by proposing a novel application of scan-to-BIM, namely, scan-to-BIM-to-digital twin (S-BIM-DT) workflow. By integrating reality capture and digital twin technologies, this workflow creates continuously updated and accurate digital representations of physical assets, enabling the generation of various scenarios. Unlike traditional methods, the S BIM-DT workflow facilitates continuous model refinement, supporting informed resilience strategies. By combining these technologies into a cohesive process, the workflow facilitates decision making under uncertainty, enabling stakeholders to evaluate and respond to various scenarios effectively. We demonstrate the implementation of the S-BIM-DT workflow through two use cases that highlight its capability to enhance resilience at different scales. The first use case involves the Combined Transportation, Emergency, and Communications Center (CTECC) in Austin, Texas. BIM-enriched computational fluid dynamics (CFD) modeling simulates airflow and develops alternative scenarios for optimizing the heating, ventilation, and air conditioning (HVAC) systems. This approach enhances resilience against airborne health threats in a postCOVID context. The second use case focuses on designated areas within Beaumont, Texas, as part of the Southeast Texas Urban Integrated Field Laboratory (SETx-UIFL) research. By developing inundation maps to assess extreme weather events, this modeling aids in preparedness efforts and informs the development of climate-resilient infrastructure in vulnerable neighborhoods. Results indicate that the S-BIM-DT workflow effectively generates scenarios that enhance resilience in the built environment by facilitating informed decision making. Furthermore, this study serves as a bridge between advanced scan-to-BIM methodologies and the practical strategies needed to improve built infrastructure resilience.

Built environment↗

From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world

Soils represent the largest terrestrial reservoir of organic carbon, and the balance between soil organic carbon (SOC) formation and loss will drive powerful carbon-climate feedbacks over the coming century. To date, efforts to predict SOC dynamics have rested on pool-based models, which assume classes of SOC with internally homogenous physicochemical properties. However, emerging evidence suggests that soil carbon turnover is not dominantly controlled by the chemistry of carbon inputs, but rather by restrictions on microbial access to organic matter in the spatially heterogeneous soil environment. The dynamic processes that control the physicochemical protection of carbon translate poorly to pool-based SOC models; as a result, we are challenged to mechanistically predict how environmental change will impact movement of carbon between soils and the atmosphere. In this study, we propose a novel conceptual framework to explore controls on belowground carbon cycling: Probabilistic Representation of Organic Matter Interactions within the Soil Environment (PROMISE). In contrast to traditional model frameworks, PROMISE does not attempt to define carbon pools united by common thermodynamic or functional attributes. Rather, the PROMISE concept considers how SOC cycling rates are governed by the stochastic processes that influence the proximity between microbial decomposers and organic matter, with emphasis on their physical location in the soil matrix. We illustrate the applications of this framework with a new biogeochemical simulation model that traces the fate of individual carbon atoms as they interact with their environment, undergoing biochemical transformations and moving through the soil pore space. We also discuss how the PROMISE framework reshapes dialogue around issues related to SOC management in a changing world. We intend the PROMISE framework to spur the development of new hypotheses, analytical tools, and model structures across disciplines that will illuminate mechanistic controls on the flow of carbon between plant, soil, and atmospheric pools.

54 ENVIRONMENTAL SCIENCES↗

Solar Thermochemical Carbon Dioxide Splitting Using Ceria and Iron Aluminate Foam Devices and Simulation of a Plant System for Demonstration

An international research project has been undertaken to integrate a unique solar thermal processing reactor system with ceria and iron aluminate as active redox materials for CO2 splitting. Experimental investigations for CO2 splitting were conducted using a solar simulator and tube furnace at Niigata University, followed by demonstrations using a high-flux solar furnace (HFSF) at the National Renewable Energy Laboratory (NREL) in Golden, CO. Each experimental setup consisted of foam devices composed of reticulated porous ceramic (RPC). The RPC has a full ceria or iron aluminate body. It fabricated using the replica method and subjected to a two-step redox reaction, which iteratively separated a stream of CO2 into O2 and CO. Reactivity was evaluated using CO production per mass of the reactive material. The tubular furnace yielded a CO production of 6.41 mL/g at a reduction temperature of 1600degrees C, showcasing a higher CO production rate and total amount than those obtained from experiments conducted with solar simulators and solar furnace setups. For iron aluminate RPC, the productivity was measured as 3.57 mL/g using HFSF at a reduction temperature of 1450degrees C. These results are somewhat higher than those of the previous experiment at lower reduction temperatures of 1400degrees C-1500degrees C. Additionally, the production of CO in the case of ceria RPC was compared with the steady flow model simulation, which assumed chemical equilibrium at various levels of oxygen partial pressure during the reduction process. On the basis of these results, this study proposes a solar fuel system with an open receiver that uses a high-temperature heat transfer fluid.

carbon dioxide thermochemical splitting↗

An Atmospheric Aerosol Short Course for Early Career Scientists

The first Department of Energy (DOE) Environmental and Molecular Science Laboratory (EMSL)/Atmospheric Radiation Measurement (ARM) aerosol summer school was held July 15-19, 2019 at the EMSL user facility located at Pacific Northwest National Laboratory. The school set out to provide graduate students and early-career scientists an overview of atmospheric aerosol science and an introduction to the measurement techniques available through the EMSL and ARM user facilities to address current aerosol science issues. Instructors for the course were drawn from scientists from the EMSL (Laskin et al., 2016; China et al., 2018) and ARM facilities (McComiskey and Ferrare, 2016) as well as from the DOE Atmospheric System Research (ASR) program. Student support for the summer school was also jointly provided by EMSL, ARM, and ASR. The school spanned five days with each day covering a particular theme. 25 students from 21 institutions attended morning lectures on aerosol composition, properties, theory, measurements, and modeling. During afternoons, the students explored instruments that collect and analyze aerosol samples at EMSL and PNNL’s Atmospheric Measurements Laboratory. They also tested relevant computer modeling simulations and learned about NWChem, EMSL’s open-source computational chemistry software suite.

54 ENVIRONMENTAL SCIENCES↗

Hybrid simulations of sub-cyclotron compressional and global Alfven Eigenmode stability in spherical tokamaks

A comprehensive numerical study has been conducted in order to investigate the stability of beam-driven, sub-cyclotron frequency compressional (CAE) and global (GAE) Alfven Eigenmodes in low aspect ratio plasmas for a wide range of beam parameters. The presence of CAEs and GAEs has previously been linked to anomalous electron temperature profile flattening at high beam power in NSTX experiments, prompting further examination of the conditions for their excitation. Linear simulations are performed with the hybrid MHD-kinetic initial value code HYM in order to capture the general Doppler-shifted cyclotron resonance that drives the modes. Three distinct types of modes are found in simulations -- co-CAEs, cntr-GAEs, and co-GAEs -- with differing spectral and stability properties. The simulations reveal that unstable GAEs are more ubiquitous than unstable CAEs, consistent with experimental observations, as they are excited at lower beam energies and generally have larger growth rates. Local analytic theory is used to explain key features of the simulation results, including the preferential excitation of different modes based on beam injection geometry and the growth rate dependence on the beam injection velocity, critical velocity, and degree of velocity space anisotropy. The background damping rate is inferred from simulations and estimated analytically for relevant sources not present in the simulation model, indicating that co-CAEs are closer to marginal stability than modes driven by the cyclotron resonances.

Compressional Alfven Eigenmodes↗

A deep neural network to search for new long-lived particles decaying to jets

A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which are predicted by several theoretical extensions of the standard model. The tagger is a multiclass classifier based on a deep neural network, which is parameterised according to the proper decay length $\mathrm{c}\tau_0$ of the LLP. A novel scheme is defined to reliably label jets from LLP decays for supervised learning. Samples of pp collision data, recorded by the CMS detector at a centre-of-mass energy of 13 TeV, and simulated events are used to train the neural network. Domain adaptation by backward propagation is performed to improve the simulation modelling of the jet class probability distributions observed in pp collision data. The potential performance of the tagger is demonstrated with a search for long-lived gluinos, a manifestation of split supersymmetric models. The tagger provides a rejection factor of 10 000 for jets from standard model processes, while maintaining an LLP jet tagging efficiency of 30-80% for gluinos with 1 mm $\leq$ $c\tau_0$ $\leq$ 10 m. The expected coverage of the parameter space for split supersymmetry is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Bridging New Observational Capabilities and Process-Level Simulation: Insights into Aerosol Roles in the Earth System

The spatial distribution of ambient aerosol particles significantly impacts aerosol–radiation–cloud interactions, which contribute to the largest uncertainty in global anthropogenic radiative forcing estimations. However, the atmospheric boundary layer and lower free troposphere have not been adequately sampled in terms of spatiotemporal resolution, hindering a comprehensive characterization of various atmospheric processes and impeding our understanding of the Earth system. To address this research data gap, we have leveraged the development of uncrewed aerial systems (UAS) and advanced measurement techniques to obtain mesoscale spatial data on aerosol microphysical and optical properties around the U.S. Southern Great Plains (SGP) atmospheric observatory. Our study also benefits from state-of-the-art laboratory facilities that include three-dimensional molecular imaging techniques enabled by secondary ion mass spectrometry and nanogram-level chemical composition analysis via micronebulization aerosol mass spectrometry. Through our study, we have developed a framework for observation–modeling integration, enabling an examination of how various assumptions about the organic–inorganic components mixing state, inferred from chemical analysis, affect clouds and radiation in observation-constrained model simulations. By integrating observational constraints (derived from offline chemical analysis of the aerosol surface using collected samples) with in situ UAS observations, we have identified a prominent role of organic-enriched nanometer layers located at the surface of aerosol particles in determining profiles of aerosol optical and hygroscopic properties over the SGP observatory. Furthermore, we have improved the agreement between predicted clouds and ground-based cloud lidar measurements. This UAS–model–laboratory integration exemplifies how these new advanced capabilities can significantly enhance our understanding of aerosol–radiation–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗

Misuse Detection for a Generalized SFR Test Reactor

Sodium-cooled Fast Reactors (SFRs) present unique challenges for international safeguards. SFRs possess neutron physics characteristics that if configured appropriately could produce more fissile material than consumed. An adversary state may choose to build an SFR, justified by a lack of domestic natural uranium and limited access or interest to procuring uranium from international markets. Once constructed, the state may choose to misuse the SFR for the purpose of diverting fissile plutonium from declared operation. This work shows that a demonstration SFR does not need to be configured as a plutonium breeder to create one Significant Quantity (SQ) of plutonium in a short amount of time (e.g., one to few years). However, such an extreme case of misuse would change the core reactivity in such a way as to be easily indicated by deviations of control rod position compared to declared operation. In this work a contrived SFR demonstration reactor was modeled for the purpose of exploring proliferation scenarios and how such misuse could be detected using the SFR's Reactor Data Acquisition System (RDAS). Typically, the International Atomic Energy Agency (IAEA) does not have access to the control rod position, power, thermal, pressure sensing and indicating systems of nuclear power plants. However, this work shows that such data streams can be compared against a parallel detailed simulation model (a Digital Twin) to detect possible misuse.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Assessment of integral models for non-Boussinesq lazy plumes using numerical simulations

Integral modelling of turbulent buoyant plumes is crucial for rapid predictions of plume characteristics. While the governing equations are typically derived using self-similarity and a Boussinesq approximation, these assumptions may not hold for plumes originating from finite-area sources with large density ratios. Here, this work evaluates the accuracy of integral-scale models for non-Boussinesq lazy plumes using high-fidelity numerical simulations of turbulent helium plumes. We analyse the plume kinematics by computing vertical fluxes, plume radius and radial profiles, establishing some disparities between common practice and physical accuracy. We identify how the definition of the plume radius changes the perception of the plume structure when the flow is not self-similar and derive a relationship between the flux-based and threshold-based definitions without requiring self-similarity. We then examine the plume dynamics by evaluating the source terms from the governing plume equations. Our results support neglecting diffusive and viscous effects but emphasise the importance of the mean pressure gradient, even in the self-similar regime. Two coefficients need to be modelled: the well-known entrainment coefficient and the lesser-known momentum correction coefficient, which is a correction required for the momentum equation to account for self-similar and slender approximations. The momentum correction coefficient is found to be approximately constant and slightly greater than the assumed value of 1. The standard entrainment coefficient models perform well up to a local Richardson number three times the asymptotic value but overpredict entrainment for larger Richardson numbers. We propose a correction using the known finite limit of entrainment at infinite Richardson number.

Meehan, Michael Alexander [Sandia National Laborat↗

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic-boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. Here, to further reduce the remaining bias in GPP after LAI bias correction, we re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

58 GEOSCIENCES↗

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗