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DOE OSTI2017

Field Test of Wake Steering at an Offshore Wind Farm

In this paper, a field test of wake steering control is presented. The field test is the result of a collaboration between the National Renewable Energy Laboratory (NREL) and Envision Energy, a smart energy management company and turbine manufacturer. In the campaign, an array of turbines within an operating commercial offshore wind farm in China have the normal yaw controller modified to implement wake steering according to a yaw control strategy. The strategy was designed using NREL wind farm models, including a computational fluid dynamics model, SOWFA, for understanding wake dynamics and an engineering model, FLORIS, for yaw control optimization. Results indicate that, within the certainty afforded by the data, the wake-steering controller was successful in increasing power capture, by amounts similar to those predicted from the models.

17 WIND ENERGY↗

DOE OSTI2021

Early age volume changes in metakaolin geopolymers: Insights from molecular simulations and experiments

The early age volume changes occurring during the geopolymerisation reaction are not sufficiently understood yet, due to shortage of experimental data and theoretical models. This work presents new results on chemical and autogenous deformation of sodium-activated geopolymers from metakaolin, focussing on the first 72 h of reaction. The results show that the geopolymers undergo early-age chemical expansion, not shrinkage. A model is proposed to explain the experimental result, leveraging recent advances from molecular simulations. The model predicts how the extent of chemical expansion is controlled by confined water in the molecular structure of the geopolymer. However, despite this underlying chemical expansion, geopolymer samples undergo autogenous shrinkage at the macroscale, which excludes self-desiccation as the origin of autogenous deformation. A better insight is gained by monitoring the kinetics of geopolymerisation using isothermal and differential calorimetry. Two kinetic regimes are identified, with apparent activation energies of approximately 90 kJ/mol and 70 kJ/mol. This suggests that two microscopic mechanisms concur to determine the early-age volume changes of geopolymer pastes.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Data-driven modeling of power generation for a coal power plant under cycling

Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

How Does Water Dissociation Work in Bipolar Membranes?

Bipolar membranes (BPMs) create counteracting spatial gradients of pH and electrostatic potential in electrochemical systems, enabling applications in pH regulation, electrocatalysis, and separations. At the polarized junction of a BPM the water dissociation (WD, 2H2O ⇌ H3O+ + OH-) reaction can be driven, but it remains poorly understood. In this Perspective, we integrate molecular insights from bulk-water autoionization and the associated field effects with continuum descriptions of BPM electrostatics and experimental WD kinetic analyses to describe possible mechanisms of voltage-driven WD. Pristine BPM junctions highlight both the limits of primarily electric-field-driven WD and the practical challenges of junction stability at extreme reverse bias. Introducing heterogeneous catalyst layers, commonly metal oxides and graphene oxides, accelerates WD by orders of magnitude through hypothesized coupled effects in which surface acid-base functionality and high-density hydroxyl sites mediate proton-transfer steps, and catalyst mobile electronic/ionic charges redistribute the junction electric potential drop to shape the local electric fields and reactive microenvironments. Kinetic analyses suggest two regimes of heterogeneous WD mechanism, including field-driven ordering of interfacial water and a Second-Wien-Effect dissociation-barrier lowering. We conclude by defining the key unknown variables (local pH, electrostatic potential, catalyst charge state and relationships among mechanisms) and outlining experimental and multiscale modeling strategies needed for predictive WD catalysis and for controlling related ion-transfer reactions.

Wu, Yifan↗

DOE OSTI2019

A Survey on Social Media to Enhance the Cyber-PhysicalSocial Resilience of Smart Grid

Smart sensor that enables power grid resiliency at distribution network is a very expensive solution and its implementation has been very sluggish. Social media has great potential to become an alternate abstract information channel that can replace/reinforce smart sensor capabilities towards a more resilient power grid. In this paper, we present a survey on the progress of utilizing social media as a platform to extract and analyse abstract data to produce intelligent information. This information can be utilized to plan and implement resources towards power grid resiliency from micro level (consumers) to macro level (cities/states). This paper discusses the reliability of social sensors, various concepts relating to social media and their implementation to solve problems, and how we can develop a framework based on the social media towards a resilient power grid. The realization of social media framework for information will benefit as robust communication and control channels that can be used together with prediction models to improve the accuracy and effectiveness of system operators' decision-making.

DISASTER RESPONSE, HASHTAGS, POWER GRID RESILIENCE↗

DOE OSTI2017

Mechanistic insights into electrochemical reduction of CO 2 over Ag using density functional theory and transport models

Significance Chemical storage of solar energy can be achieved by electrochemical reduction of CO 2 to CO and H 2 , and subsequent conversion of this mixture to fuels. Identifying optimal conditions for electrochemical cell operation requires knowledge of the CO 2 reduction mechanism and the influence of all factors controlling cell performance. We report a multiscale model for predicting the current densities for H 2 and CO formation from first principles. Our approach brings together a quantum-chemical analysis of the reaction pathway, a microkinetic model of the reaction dynamics, and a continuum model for mass transport of all species through the electrolyte. This model is essential for identifying a physically correct representation of product current densities dependence on the cell voltage and CO 2 partial pressure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Sci—Fri PM: Topics — 04: What if bystander effects influence cell kill within a target volume? Potential consequences of dose heterogeneity on TCP and EUD on intermediate risk prostate patients

In vitro evidence has suggested that radiation induced bystander effects may enhance non-local cell killing which may influence radiotherapy treatment planning paradigms. This work applies a bystander effect model, which has been derived from published in vitro data, to calculate equivalent uniform dose (EUD) and tumour control probability (TCP) and compare them with predictions from standard linear quadratic (LQ) models that assume a response due only to local absorbed dose. Comparisons between the models were made under increasing dose heterogeneity scenarios. Dose throughout the CTV was modeled with normal distributions, where the degree of heterogeneity was then dictated by changing the standard deviation (SD). The broad assumptions applied in the bystander effect model are intended to place an upper limit on the extent of the results in a clinical context. The bystander model suggests a moderate degree of dose heterogeneity yields as good or better outcome compared to a uniform dose in terms of EUD and TCP. Intermediate risk prostate prescriptions of 78 Gy over 39 fractions had maximum EUD and TCP values at SD of around 5Gy. The plots only dropped below the uniform dose values for SD ∼ 10 Gy, almost 13% of the prescribed dose. The bystander model demonstrates the potential to deviate from the common local LQ model predictions as dose heterogeneity through a prostate CTV is varies. The results suggest the potential for allowing some degree of dose heterogeneity within a CTV, although further investigations of the assumptions of the bystander model are warranted.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Landslides caused by the Mw7.8 Kaikōura earthquake and the immediate response

Tens of thousands of landslides were generated over 10,000 km 2 of North Canterbury and Marlborough as a consequence of the 14 November 2016, M w 7.8 Kaikōura Earthquake. The most intense landslide damage was concentrated in 3500 km 2 around the areas of fault rupture. Given the sparsely populated area affected by landslides, only a few homes were impacted and there were no recorded deaths due to landslides. Landslides caused major disruption with all road and rail links with Kaikōura being severed. The landslides affecting State Highway 1 (the main road link in the South Island of New Zealand) and the South Island main trunk railway extended from Ward in Marlborough all the way to the south of Oaro in North Canterbury. The majority of landslides occurred in two geological and geotechnically distinct materials reflective of the dominant rock types in the affected area. In the Neogene sedimentary rocks (sandstones, limestones and siltstones) of the Hurunui District, North Canterbury and around Cape Campbell in Marlborough, first-time and reactivated rock-slides and rock-block slides were the dominant landslide type. These rocks also tend to have rock material strength values in the range of 5-20 MPa. In the Torlesse ‘basement’ rocks (greywacke sandstones and argillite) of the Kaikōura Ranges, first-time rock and debris avalanches were the dominant landslide type. These rocks tend to have material strength values in the range of 20-50 MPa. A feature of this earthquake is the large number (more than 200) of valley blocking landslides it generated. This was partly due to the steep and confined slopes in the area and the widely distributed strong ground shaking. The largest landslide dam has an approximate volume of 12(±2) M m 3 and the debris from this travelled about 2.7 km 2 downslope where it formed a dam blocking the Hapuku River. The long-term stability of cracked slopes and landslide dams from future strong earthquakes and large rainstorms are an ongoing concern to central and local government agencies responsible for rebuilding homes and infrastructure. A particular concern is the potential for debris floods to affect downstream assets and infrastructure should some of the landslide dams breach catastrophically. At least twenty-one faults ruptured to the ground surface or sea floor, with these surface ruptures extending from the Emu Plain in North Canterbury to offshore of Cape Campbell in Marlborough. The mapped landslide distribution reflects the complexity of the earthquake rupture. Landslides are distributed across a broad area of intense ground shaking reflective of the elongate area affected by fault rupture, and are not clustered around the earthquake epicentre. The largest landslides triggered by the earthquake are located either on or adjacent to faults that ruptured to the ground surface. Surface faults may provide a plane of weakness or hydrological discontinuity and adversely oriented surface faults may be indicative of the location of future large landslides. Their location appears to have a strong structural geological control. Initial results from our landslide investigations suggest predictive models relying only on ground-shaking estimates underestimate the number and size of the largest landslides that occurred.

58 GEOSCIENCES↗

DOE OSTI2017

LOWERING THE LEVELIZED COST OF ELECTRICITY OF A CONCENTRATING SOLAR POWER TOWER WITH A SUPERCRITICAL CARBON DIOXIDE POWER CYCLE

In order to maintain viability as a future power-generating technology, concentrating solar power (CSP) must reduce its levelized cost of electricity (LCOE). The cost of CSP is assessed with the System Advisor Model (SAM) from the National Renewable Energy Laboratory (NREL). The performance of an integrally geared compressor-expander recuperated recompression cycle with supercritical carbon dioxide (sCO2) as the working fluid is modeled. A comparison of the cycle model to the integrated SAM cycle performance is made. The cycle model incorporates innovative cycle control methods to improve the range of efficiency, including inventory control. The SAM model is modified to accommodate the predicted cycle performance. The ultimate goal of minimizing the LCOE is targeted through multiple approaches, including the cost of the power block, the impact of system scale, the sizing of the thermal system relative to the power block system, the operating approach for changes in ambient temperature and availability of sunlight. Through reduced power block cost and a detailed cycle model, the LCOE is modeled to be 5.98 ¢/kWh, achieving targeted techno-economic performance. The LCOE of the CSP system is compared to the cost of hybrid solar and fossil-fired systems. An analysis is made on the efficacy of a fossil backup system with CSP and how that relates to potential future costs of carbon dioxide emissions

schmitt, josh↗

DOE OSTI2025

Design, Optimization, and Control of Floating Offshore Wind Farms for Optimal Energy Production (Final report)

The uncertainty and irregularity of ocean waves and the ocean environment is a major factor in the development of commercial scale floating wind turbines as the operation of floating structures in such an environment can lead to irregular and unpredictable loading, fatigue, and ultimately a reduction in the operational life of the turbine system which affects energy production over the lifetime of the turbine. Control solutions that can limit float motions and mitigate stressful events on the structure become essential for extending lifetime and limiting the operational uncertainty of a floating wind turbine. Digital twins are computational replicas of physical systems that operate in parallel with the operation of the physical system. Given advanced knowledge of a systems input, digital twins have the ability to predict the behavior of a system in advance, which can be valuable in the control of that system. In this project, we developed and assessed potential digital twin models developed in house and openly available (OpenFast) for use in the real time control of the six degree of freedom response motions of a floating wind turbine in ocean waves. Coupling these models with near-field real time irregular sea surface (wve) measurement/sensings and prediction models, we used the digital twin to predict how the floating turbine will respond to the incoming waves. Applying this information to a motion control system of the float, one can limit and control float motions to prevent undesirable loading events/large angular motions, thus increasing system life and ultimately contributing to optimizing energy production. Due to the computational intensity of operating a digital twin in real time, we investigated the use of artificial intelligence techniques to speed-up the processes of the digital twin, as well as the wave reconstruction/prediction models. Model tank testing at the University of Rhode Island and University of Maine both validated and demonstrated the developed techniques on simple float geometries and a scale model of the NREL 15 MW reference turbine.

17 WIND ENERGY↗

DOE OSTI2026

Effects of plasma squareness on RMP response in MAST-U

The effect of the squareness of the plasma boundary shape on the plasma response to the applied n = 2 (n is the toroidal mode number) resonant magnetic perturbation, and consequently on controlling the edge-localized mode (ELM) in MAST-U in the double-null configuration, is numerically systematically investigated. The modeling is performed utilizing three different plasma response models: the single-fluid model, the two-fluid model and the magnetohydrodynamic-kinetic hybrid model. The effect of the plasma squareness on ELM control is found to depend on the equilbrium kinetic profiles. Fixing the equilibrium radial profiles to that from a recent MAST-U discharge, the experimental plasma shape is found to be close to optimal for ELM control in terms of squareness. The optimal coil phasing does not significantly vary with varying plasma squareness. These findings robustly hold with (i) two figures of merit (FoMs) defined for predicting the ELM control feasibility, and (ii) three plasma response models employed for evaluating these FoMs.

MAST-U↗

DOE OSTI2017

Transient Simulation of the Multi-SERTTA Experiment with MAMMOTH

This work details the MAMMOTH reactor physics simulations of the Static Environment Rodlet Transient Test Apparatus (SERTTA) conducted at Idaho National Laboratory in FY-2017. TREAT static-environment experiment vehicles are being developed to enable transient testing of Pressurized Water Reactor (PWR) type fuel specimens, including fuel concepts with enhanced accident tolerance (Accident Tolerant Fuels, ATF). The MAMMOTH simulations include point reactor kinetics as well as spatial dynamics for a temperature-limited transient. The strongly coupled multi-physics solutions of the neutron flux and temperature fields are second order accurate both in the spatial and temporal domains. MAMMOTH produces pellet stack powers that are within 1.5% of the Monte Carlo reference solutions. Some discrepancies between the MCNP model used in the design of the flux collars and the Serpent/MAMMOTH models lead to higher power and energy deposition values in Multi-SERTTA unit 1. The TREAT core results compare well with the safety case computed with point reactor kinetics in RELAP5-3D. The reactor period is 44 msec, which corresponds to a reactivity insertion of 2.685% delta k/k$. The peak core power in the spatial dynamics simulation is 431 MW, which the point kinetics model over-predicts by 12%. The pulse width at half the maximum power is 0.177 sec. Subtle transient effects are apparent at the beginning insertion in the experimental samples due to the control rod removal. Additional difference due to transient effects are observed in the sample powers and enthalpy. The time dependence of the power coupling factor (PCF) is calculated for the various fuel stacks of the Multi-SERTTA vehicle. Sample temperatures in excess of 3100 K, the melting point UO$_2$, are computed with the adiabatic heat transfer model. The planned shaped-transient might introduce additional effects that cannot be predicted with PRK models. Future modeling will be focused on the shaped-transient by improving the control rod models in MAMMOTH and adding the BISON thermo-elastic models and thermal-fluids heat transfer.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2011

IMPACTS OF SMALL COLUMN ION EXCHANGE STREAMS ON DWPF GLASS FORMULATION KT07-SERIES GLASS COMPOSITIONS

This report is the third in a series of studies of the impacts of the addition of Crystalline Silicotitanate (CST) and Monosodium Titanate (MST) from the Small Column Ion Exchange (SCIX) process on the Defense Waste Processing Facility (DWPF) glass waste form and the applicability of the DWPF process control models. MST from the Salt Waste Processing Facility is also considered in the study. The KT07-series glasses were selected to evaluate any potential impacts of noble metals on their properties and performance. The glasses characterized thus far for the SCIX study have not included noble metals since they are not typically tracked in sludge batch composition projections. However, noble metals can act as nucleation sites in glass melts, leading to enhanced crystallization. This crystallization can potentially influence the properties and performance of the glass, such as chemical durability, viscosity, and liquidus temperature. The noble metals Ag, Pd, Rh, and Ru were added to the KT07-series glasses in concentrations based on recent measurements of Sludge Batch 6, which was considered to contain a high concentration of noble metals. The KT04-series glasses were used as the baseline compositions. After fabrication, the glasses were characterized to determine their homogeneity, chemical composition, durability, and viscosity. Liquidus temperature measurements are also underway but were not complete at the time of this report. The liquidus temperature results for the KT07-series glasses, along with several of the earlier glasses in the SCIX study, will be documented separately. All of the KT07-series glasses, both quenched and slowly cooled, were found to be amorphous by X-ray diffraction. Chemical composition measurements showed that all of the glasses met their targeted compositions. The Product Consistency Test (PCT) results showed that all of the glasses had chemical durabilities that were far better than that of the Environmental Assessment benchmark glass. The measured PCT responses were well predicted by the current DWPF Product Composition Control System (PCCS) durability models. The measured viscosity values for each KT07-series glass were acceptable for DWPF processing and were well predicted by the current PCCS model. Overall, the results show that the inclusion of relatively high concentrations of noble metals (in terms of expected values for a DWPF sludge batch) had no significant impact on the properties and performance of these glass compositions. There were no significant differences in the measured properties when compared to those of the KT04-series glasses, which did not contain noble metals. Liquidus temperature measurements are still underway and there may be an impact of the noble metals on those measurements. However, no adverse effects were noted in terms of crystallization after slow cooling. At the completion of these studies, all of the data generated will be reviewed with regard to the applicability of the DWPF PCCS models and recommendations will be made as to whether the validation ranges of the current models can be extended, or whether some or all of the models need to be refit to allow for the incorporation of the SCIX streams. As changes are made to the projected sludge compositions and the volume of the SCIX material, additional evaluations should be performed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2014

TU-F-17A-03: An Analytical Respiratory Perturbation Model for Lung Motion Prediction

Purpose: Breathing irregularity is common, causing unreliable prediction in tumor motion for correlation-based surrogates. Both tidal volume (TV) and breathing pattern (BP=ΔVthorax/TV, where TV=ΔVthorax+ΔVabdomen) affect lung motion in anterior-posterior and superior-inferior directions. We developed a novel respiratory motion perturbation (RMP) model in analytical form to account for changes in TV and BP in motion prediction from simulation to treatment. Methods: The RMP model is an analytical function of patient-specific anatomic and physiologic parameters. It contains a base-motion trajectory d(x,y,z) derived from a 4-dimensional computed tomography (4DCT) at simulation and a perturbation term Δd(ΔTV,ΔBP) accounting for deviation at treatment from simulation. The perturbation is dependent on tumor-specific location and patient-specific anatomy. Eleven patients with simulation and treatment 4DCT images were used to assess the RMP method in motion prediction from 4DCT1 to 4DCT2, and vice versa. For each patient, ten motion trajectories of corresponding points in the lower lobes were measured in both 4DCTs: one served as the base-motion trajectory and the other as the ground truth for comparison. In total, 220 motion trajectory predictions were assessed. The motion discrepancy between two 4DCTs for each patient served as a control. An established 5D motion model was used for comparison. Results: The average absolute error of RMP model prediction in superior-inferior direction is 1.6±1.8 mm, similar to 1.7±1.6 mm from the 5D model (p=0.98). Some uncertainty is associated with limited spatial resolution (2.5mm slice thickness) and temporal resolution (10-phases). Non-corrected motion discrepancy between two 4DCTs is 2.6±2.7mm, with the maximum of ±20mm, and correction is necessary (p=0.01). Conclusion: The analytical motion model predicts lung motion with accuracy similar to the 5D model. The analytical model is based on physical relationships, requires no training, and therefore is potentially more resilient to breathing irregularities. On-going investigation introduces airflow into the RMP model for improvement. This research is in part supported by NIH (U54CA137788/132378). AY would like to thank MSKCC summer medical student research program supported by National Cancer Institute and hosted by Department of Medical Physics at MSKCC.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2012

Impact of resonant magnetic perturbations on nonlinearly driven modes in drift-wave turbulence

In this work, we study the effects of resonant magnetic perturbations (RMPs) on turbulence, flows, and confinement in the framework of resistive drift wave turbulence. We extend the Hasegawa-Wakatani model to include RMP fields. The effect of the RMPs is to induce a linear coupling between the zonal electric field and the zonal density gradient, which drives the system to a state of electron radial force balance for large ({delta}B{sub r}/B{sub 0}). Both the vorticity flux (Reynolds stress) and particle flux are modulated. We derive an extended predator prey model which couples zonal potential and density dynamics to the evolution of turbulence intensity. This model has both turbulence drive and RMP amplitude as control parameters and predicts a novel type of transport bifurcation in the presence of RMPs. We find states that are similar to the ZF-dominated state of the standard predator-prey model, but for which the power threshold is now a function of the RMP strength. For small RMP amplitude, the energy of zonal flows decreases and the turbulence energy increases with ({delta}B{sub r}/B{sub 0}), corresponding to a damping of zonal flows.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process–guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Comparison of structurally diverse simulation models for prediction of epidemic outcomes caused by a long-distance dispersed pathogen

Long-distance dispersal (LDD) pathogens pose substantial challenges for epidemic control due to their ability to generate new infection foci at great distances. While various modeling approaches have been developed to understand and manage such outbreaks, little work has compared how models of different structures behave under shared conditions. Here, in this study, we compare four structurally distinct epidemiological models — EPIMUL, GEMF, PoPS, and Warwick — each adapted to simulate the spread of wheat stripe rust (WSR), a wind-dispersed LDD pathogen, under identical epidemiological parameters and dispersal kernel. Using data from a controlled field experiment, we evaluate the ability of each model to replicate disease prevalence under nine intervention scenarios that vary in timing and culling area. While the models differ substantially in design — ranging from spatial grid-based to network-based and raster-based frameworks — the shared dispersal kernel allowed for close alignment in their predictions. All models accurately captured general epidemic trends, particularly the strong effect of early intervention on disease suppression. We qualitatively compared their behavioral responses across scenarios and also evaluated an ensemble prediction by averaging across model outputs. Our findings highlight how integrating shared epidemiological components into distinct modeling frameworks can improve consistency and accuracy, while reinforcing the importance of early culling in managing LDD pathogen outbreaks.

Dispersal kernel↗