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DOE OSTI2024

Evaluating crown scorch predictions from a computational fluid dynamics wildland fire simulator

Abstract Background Crown scorch—the heating of live leaves, needles, and buds in the vegetative canopy to lethal temperatures without widespread combustion—is one of the most common fire effects shaping post-fire canopies. Despite the ability of computational fluid dynamic models to finely resolve fire activity and buoyant plume dynamics including heterogenous 3D distributions of forest canopy heating, these models have had only limited use in simulating fire effects and have not been used to evaluate crown scorch. Here, we demonstrate a method of evaluating crown scorch using a computational fluid dynamics model, FIRETEC, and validate this approach by simulating the experiments that were used to develop Van Wagner’s 1973 crown scorch model. Results The average scorch height prediction from FIRETEC compares well with the empirical model derived by Van Wagner, which is the most widely used empirical model for crown scorch. We further find that the 3D buoyant plume dynamics from a steady and homogeneous idealized heat source on the ground results in a spatially heterogenous crown scorch pattern reflecting complex heating dynamics that are best represented by percent scorch rather than height of scorch. Conclusions The ability of the computational fluid dynamics model to capture variation in crown scorch due to 3D buoyant plume dynamics provides direct links between forest structure, fire behavior, and fire effects that can be used by forest managers and researchers to better understand how fires result in crown damage under various environmental and management scenarios.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A thermally-limited bubble growth model for the relaxation time of superheated fuels

Here, we propose a novel approach to evaluate the relaxation time of vapor bubble growth in the context of the flash boiling of a superheated liquid. In alternative to the empirical correlation derived from superheated water experiments almost fifty years ago, the new model describes the thermally-dominated growth of vapor bubbles in terms that are dependent on the local Jakob number (the ratio of sensible heat to latent heat during phase change) and the number density of vapor bubbles. The model is tested by plugging the resulting relaxation time into the Homogenous Relaxation Model (HRM). Flash-boiling simulations carried out with HRM are compared with n-pentane (C 5 H 12 ) injection and boil-off experiments conducted with a real-size, axial-hole, transparent gasoline injector discharging into a constant-pressure vessel. The long-distance microscopy images from the experiments, processed to derive the projected liquid volume (PLV) of the spray, provide a unique set of time-resolved validation data for direct fuel injection simulations. At conditions ranging from flaring to mild and minimal flash boiling, we show that switching to the new relaxation time improves the agreement with the measured PLV profiles with respect to the standard empirical model. Particularly at flaring conditions, the predicted increase in gas cooling caused by rapid vapor production is shown to be more consistent with the observed boil-off.

42 ENGINEERING↗

DOE OSTI2021

An FEM-Based Peridynamic Model for Failure Analysis of Unidirectional Fiber-Reinforced Laminates

To predict the mixed damage modes of unidirectional fiber-reinforced polymer (FRP) laminates under dynamic loading, an FEM-based peridynamic model is introduced in this paper. Based on its geometric structure and material composition, a long fiber lamina is considered a transversely isotropic medium as a result of homogenization at the meso-scale. The laminated structure is modeled by stacking surface mesh layers with arbitrary fiber angles along the thickness direction. The peridynamic bonds between Gauss points connect the separated elements. These bonds are classified as inner-layer bonds and inter-layer bonds. To represent the anisotropy of a laminate, the micro-elastic modulus of the inner-layer and inter-layer bonds is calculated from the anisotropic engineering material constants separately. To capture complex failure behaviors of laminate structures, an empirical damage model is proposed for the tension/ compression breakage of peridynamic bonds. This damage model can control the in-plane and delamination failure process. Finally, benchmark tests are conducted to validate the elastic response of laminates under dynamic loading. In terms of damage analysis, the proposed model can capture the complex damage modes and resistive force of laminate structures.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Comprehensive framework for data-driven model form discovery of the closure laws in thermal-hydraulics codes

The two-phase two-fluid model is a basis of many thermal-hydraulics codes used in design, licensing, and safety considerations of nuclear power plants. Thermal-hydraulics codes rely on the closure laws to close the system of conservation equations and describe the interactions between phases. These laws, derived from years of experimental investigations, are semi-empirical correlations that lack generality and have a limited range of applicability. Increase of computational power, availability of new experiments, and development of high-fidelity simulations has increased the number of validation data. The discrepancies between the code predictions and the validation data are a great source of knowledge. Missing physics that are not included in the model but are important for the considered phenomena can be discovered by propagating the information from the experimental results through the model. Furthermore, physics-discovered data-driven model form (P3DM) methodology integrates available integral effect tests and separate effects tests to determine the necessary corrections to the model form of the closure laws. In contrast to existing calibration techniques, the methodology modifies the functional form of the closure laws. Based on the functional form of the correction, the missing physics that were not included in the original model can be discovered. The methodology provides the alternative to the machine learning approach, in which the model is discovered in the form of the intractable black-box relation. In this work, the methodology was applied to the CTF subchannel code to improve the prediction of the two-phase flow phenomena.

42 ENGINEERING↗

DOE OSTI2021

An experimentally validated evaporative phase change heat transfer model for low mass flux applications using R134a in plate heat exchangers

Plate frame heat exchangers are common in liquid-coupled vapor compression systems due to their compactness and ease of maintenance. In some of these systems, the refrigerant within the evaporator can enter the heat exchanger as a subcooled liquid. As a result, the refrigerant passes through three different phases: single-phase liquid, two-phase fluid, and a superheated vapor. Unfortunately, there have been limited prior studies that have identified the best method to predict performance under these circumstances. Furthermore, previous investigations have not evaluated refrigerant evaporation in large industrial sized plate frame heat exchangers under conditions experienced in the present study experimental test facility, especially at mass fluxes below 7 kg m –2 s –1 . In the present investigation, a model was developed to predict the performance of plate and frame heat exchangers when the fluid enters as a subcooled liquid and exits as a superheated vapor. The model used full-sized plate heat exchanger geometry that was discretized into 20 sections to accurately capture local heat transfer and pressure drop effects. The model was validated using an R134a counter flow heat exchanger used in a turbo-compression cooling system test facility that had subcooled liquid entering the evaporator at low mass fluxes. A variety of empirical correlations were evaluated to determine what combination yielded the best predictive capability over the following range of conditions: 5.8 < G < 6.8 kg m –2 s –1 , 2 < q” < 2.8 kW m –2 , and 10 °C < T sat < 15 °C. When the correlations used a fixed control volume length in the heat flux calculation, the most accurate combination resulted in a mean absolute error of 5.5%. Finally, future studies can use the approach described here to optimize heat exchanger size and performance.

42 ENGINEERING↗

DOE OSTI2026

Scaling dynamics in low-salt-rejection reverse osmosis for high-salinity produced water desalination: Mechanistic modeling and membrane autopsy

Membrane scaling remains a critical barrier to the reliable operation of desalination systems, particularly for hypersaline produced water (PW) treatment. This study fills the knowledge gap of autopsy-based model validation for PW desalination by elucidating scaling mechanisms in a Low-Salt-Rejection Reverse Osmosis (LSRRO) system through the integration of pilot-scale experimentation and complementary modeling approaches. A semi-empirical modeling framework was developed and applied to a multistage pilot LSRRO system equipped with nanofiltration and RO membranes treating high-salinity PW from the Permian Basin. Water quality analysis showed that total dissolved solids decreased from ~130,000 mg/L to ~1900 mg/L in the permeate, then further reduced to ~300 mg/L by a second-pass RO. Two different thermodynamic modeling approaches were evaluated: the first extends the LSRRO framework by incorporating system complexity and scaling phenomena, whereas the second method explicitly captures concentration polarization in localized supersaturation. Both methods illustrate the tendency for carbonate and sulfate scaling throughout the stages. Membrane autopsies revealed a silica-dominated deposit matrix, localized CaSO 4 at Stage 2, and minor barite/celestite despite their prominence in model predictions. Quantum-chemical calculations indicated silica scaling can be rationalized by favorable adsorption of H 4 SiO 4 on Fe-oxide surfaces (ΔG ≈ −44 kJ/mol), providing a kinetic pathway for interfacial inorganic polymerization even when bulk equilibrium predictions are conservative. Overall, the thermodynamic scaling modeling and membrane autopsy revealed heterogeneous, localized deposits with limited impact on LSRRO performance, while quantum analysis rationalized the thermodynamically unfavorable precipitation formation under bulk equilibrium, reconciling model–autopsy discrepancies. These insights support targeted pretreatment and silica-specific antiscalants to extend membrane lifetime and increase recovery, providing a transferable framework for hypersaline water desalination systems. The combined experimental–computational approach provides new mechanistic insight into scaling in hypersaline membrane systems and establishes a transferable framework for predicting and mitigating scaling in next-generation desalination technologies.

Low-salt-rejection reverse osmosis↗

DOE OSTI2024

Deployment of BISON models of fuel restructuring at high burnup and related fission gas behavior in UO 2

This milestone report details the advancements made in fiscal year 2024 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to improve the modeling of fission gas behavior in high burnup UO 2 nuclear fuel in the BISON fuel performance code. As nuclear fuel is pushed to higher burnups, significant microstructural changes occur within the fuel, including the formation of a high burnup structure (HBS) on the pellet rim and a dark zone deeper within the pellet. These regions, characterized by subgrain formation and increased pore densities, have critical implications for fission gas behavior and release, which are not well understood. The modeling capabilities in BISON did not adequately predict these phenomena, leading to an underestimation of fuel restructuring and - potentially - of fission gas release. To address these gaps, this milestone focused on three key objectives: (1) reviewing and assessing Sifgrs's capabilities for low burnup fuel, on which high burnup capabilities rely, (2) validating and expanding HBS fission gas modeling capabilities, including investigating mechanisms for fission gas release from HBS, and (3) expanding Sifgrs to enable modeling of dark zone formation and its effects on fission gas behavior. These objectives were achieved and are described herein. The achievements of this NEAMS milestone are significant for the industry's goal of burnup extension. The improved predictive modeling capabilities for both low- and high-burnup conditions enhance our understanding of fuel performance under both normal operations and transient scenarios. Although goals were reached, future work is necessary to validate these models against experimental data and quantify their accuracy in different conditions. In parallel, mechanistic modeling efforts should continue to extend and refine these capabilities to increase accuracy while reducing reliance on empirical models. This will ensure robust performance across a broader range of conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Forecasting Commercial Building Electricity Consumption, Zone Airflow and Zone Temperature: Update - Development of a Generalized Machine Learning Approach

The U.S. power grid is being transformed to make it smarter, more efficient, and cleaner. This transformation is leading to the addition of a significant of energy generated by distributed, variable, and renewable resources. Because of the variable nature of renewable generation, the short- and long-term supply and demand imbalances are less predictable, and conventional approaches to mitigating the imbalances will be less efficient or cost effective. To address this challenge and to support the mission and the vision of the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) Building Technologies Office has developed a Grid-Interactive Efficient Building Strategy. The strategy focuses on simultaneously improving building energy efficiency and supporting reliability and resilience of the electric grid more efficiently and at a lower cost. In addition, EERE and DOE’s Office of Electricity created an initiative led by DOE and supported by the national laboratories under the Grid Modernization Lab Consortium structure to enhance grid modernization. The work reported in this document is part of the first set of projects funded under the initiative to design, develop, and validate scalable transactive control technologies for the commercial buildings sector. Transactive controls requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical grey- and black-box models have been widely used to express flexibility. Although this approach is generally easy to construct and simple to use, it does not capture non-linear behavior that some end-use loads represent. Therefore, Pacific Northwest National Laboratory (PNNL) with support from Western Washington University conducted this research to explore the use of deep machine learning (ML) techniques. The work reported in this document is limited to forecasting whole building electricity consumption, the zone airflow and the zone temperature predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model

This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.

biofuel sustainability↗

DOE OSTI2022

A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection

To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Data-driven discovery of a formation prediction rule on high-entropy ceramics

The interest in high entropy ceramics (HECs) has increased steadily due to their superior properties. However, the prediction of their formation still poses challenges for the discovery of new systems. Here, we discover a rational rule for designing single-phase high entropy metal diborides (HEBs) using data-driven approach. The machine learning (ML) model is trained on data collected via high-throughput experiments (HTEs). K nearest neighbor (KNN) model shows an experimental validation accuracy of 93.75%. By implementing interpretable ML method, we demonstrate that a mismatch of the bonds between boron and transition metals (δ B-TM ) dominates the formation of HEBs. We propose an empirical rule that HEBs favor forming a single phase when δ B-TM < 3.66; otherwise, multiphase. The rule has a high accuracy of 93.33% for new HEBs predictions. In addition, we contribute 165 high quality HEBs data in total, which can promote the development of materials informatics in HEBs. Furthermore, this data-driven strategy can be expanded to accelerate the search for new HECs, paving a pathway to design novel HECs with superior properties rapidly.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Low- n stability and plasma response to RMP in various STEP scenarios

The low-n (n is the toroidal mode number) magnetohydrodynamic (MHD) stability and plasma response are numerically investigated for various scenarios designed for STEP, that are relevant for the H-mode pedestal analysis. Control of the edge-localized modes (ELMs) with externally applied resonant magnetic perturbations (RMPs) is considered. Optimization of the ELM control coil current configuration, based on the computed plasma MHD response and well-established figures of merit validated on present-day experiments, finds reasonable robustness of a fixed coil phasing (for a given n-number) to control ELMs in all five STEP plasmas considered. Based on certain semi-empirical criteria, the required coil current to achieve ELM suppression is estimated to be about 10–20 kAt with the n = 1 or 2 RMP configuration and about 100–200 kAt for the n = 4 RMP. Systematic linear stability calculations are used to map out stability windows for the low-n kink-peeling modes, in terms of the ideal-wall location and variation of the edge safety factor q 95 with respect to the target design. The kink-peeling stability boundary is found to be generally sensitive to the q 95 variation, which has implications for achieving the quiescent H-mode regime in STEP. Full toroidal quasilinear initial-value simulations for these STEP plasmas find that generation of the edge-harmonic oscillations (EHOs) depends sensitively on the plasma scenario, the initial linear stability of the kink-peeling modes, the initial plasma toroidal flow and q 95 . In general, it is easier (more robust) to access the EHO-regime for two of the cases considered with smaller plasma volume and higher on-axis safety factor. Finally, quasilinear simulations find robust density pumpout due to applied RMPs in these STEP plasmas, but the effect on the plasma toroidal flow varies among different cases.

EHO↗

DOE OSTI2025

Predictive numerical modeling of plasma-induced surface roughness and wettability evolution in LM-PAEK/CF tape

Plasma surface modification effectively enhances adhesion in thermoplastic composites, yet its impacts on high-performance polymers like low-melting polyaryletherketone (LM-PAEK) remain inadequately quantified. Here, this study integrates experimental analysis and numerical modeling to characterize surface roughness and wettability changes in LM-PAEK/carbon fiber composites treated with atmospheric plasma. Atomic Force Microscopy quantified surface topography (n = 10 per condition for contact angles), while static contact-angle assessments measured wettability. Roughness rapidly increased from ∼0.2 nm to 1.6 nm, and contact angle reduced from ∼90° to 24°, both stabilizing after 25–30 s of exposure. A semi-empirical, physics-informed framework was calibrated to these data, coupling surface chemistry via the Owens–Wendt decomposition with topography via the Wenzel roughness factor, and evaluated using out-of-sample (cross-validated) tests, while static contact angle assessments measured wettability. Numerical predictions matched experimental results closely (RMSE <5%, R 2 > 0.95). Incorporating material-specific parameters, the calibrated model supports plasma-treatment optimization and provides quantitative guidance for improving interfacial adhesion in thermoplastic composite manufacturing.

Atomic Force Microscopy↗

DOE OSTI2023

Modelling and Simulation of a Thermoelectric Heat Pump with Micro-Channel Heat Transfer

Thermoelectric heat pumps (TEHPs) have advantages of modularity and simple design for heating and cooling in some applications. Models of thermoelectric (TE) heat pumps are widely studied. Nevertheless, most existing modeling work focuses on the TE material or air-sourced TE modules. TEHP modeling, especially the relationship between modules and heat exchangers, has not been comprehensively conducted. This work presents a water-to-water TEHP modeling framework that combines Goldsmid’s approach for TE material performance, Gnielinski’s correlation for convective heat transfer, and thermal balance theory for heat exchange networks. This combined framework provides an accurate theoretical analysis of the water-to-water TEHP system. Subsequently, the framework was used to empirically determine TE material properties (electric resistivity, thermal conductivity, and Seebeck coefficient) that minimize modeling errors versus experimentally observed values from the literature. Finally, an additional set of experimental TEHP data was used to validate the model, all with relative absolute deviations of approximately 10% when predicting heating capacity and 10%–20% when forecasting cooling capacity at a 30 K temperature lift. For future work, people can further develop models of TEHPs with an air-based heat sink on one side and water channels on the other side.

Wan, Hanlong↗

DOE OSTI2025

Enhanced Feedstock Characterization and Modeling to Facilitate Optimal Preprocessing and Deconstruction of Corn Stover (Final Report)

This project addresses the challenge of processing corn stover by fractionating this biomass feedstock to both streamline processing and generate new potential co-products. Additionally, the project developed new field-deployable analytical tools that can be coupled with empirical models that were used to predict feedstock properties and processing performance. The overall scope of this project was: (1) identify conditions for optimal corn stover fractionation using a two- stage physical fractionation, (2) assess how physical fractionation impacts properties, partitioning of biomass, and response to processing, (3) further adapt, develop, and validate several advanced characterization tools for assessing biomass properties that can be linked to processing behavior, and (4) develop and validate predictive models based on measurements that can be performed “in the field” or “at the biorefinery gate” to predict feedstock processing behavior (preprocessing and deconstruction). The first objective employed pre-separation processing (size reduction) which was next subjected to enhanced separations to yield fractions enriched or depleted in select compositional components or properties. For the second objective, fractions were screened for their response to post-separation processing (pretreatment and enzymatic hydrolysis). Detailed characterization profiles were developed and dynamic image analysis to assess distribution of particle size and morphology. For the final objective, we utilized these tools to develop empirical models to assess the relative abundance of tissue type in order to assess fractionation efficacy and to predict fraction performance during pretreatment and enzymatic hydrolysis.

09 BIOMASS FUELS↗

DOE OSTI2026

Preparing Fermions via Classical Sampling and Linear Combinations of Unitaries

We present an extension of the Evolving density matrices on Qubits (E$ρ$OQ) framework that enables efficient fault-tolerant preparation of fermionic quantum states. The original method circumvents state preparation by stochastic sampling, but faces a sign problem in fermionic systems leading to a large number of circuits necessary. We resolve this by combining classical stochastic sampling with a linear combination of unitaries method that avoids the exponential circuit scaling that plagued naïve implementations. The resulting algorithm requires $\mathcal{O}(M^2)$$R_Z$ rotations for circuit preparation, where $M$ is the number of retained basis states. We validate the method for ground and excited states in the Thirring model, including by computing two-point correlation functions relevant to scattering. In this model for fixed accuracy $\varepsilon$, $M$ is found to scale empirically as $M \propto \frac{1}{mg}\log(1/g)\log(1/m)$.

Gustafson, Erik J. [RIACS, Mtn. View] (ORCID:00000↗

DOE OSTI2022

Equilibrium reconstruction of DIII-D plasmas using predictive modeling of the pressure profile

New workflows have been developed for predictive modeling of magnetohydrodynamic (MHD) equilibrium in tokamak plasmas. The goal of this work is to predict the MHD equilibrium in tokamak discharges without having measurements of the kinetic profiles. The workflows include a cold start tool, which constructs all the profiles and power flows needed by transport codes; a Grad–Shafranov equilibrium solver; and various codes for the sources and sinks. For validation purposes, a database of DIII-D tokamak discharges has been constructed that is comprised of scans in the plasma current, toroidal magnetic field, and triangularity. Initial efforts focused on developing a workflow utilizing an empirically derived pressure model tuned to DIII-D discharges with monotonic safety factor profiles. This workflow shows good agreement with experimental kinetic equilibrium calculations, but is limited in that it is a single fluid (equal ion and electron temperatures) model and lacks H-mode pedestal predictions. The best agreement with the H-mode database is obtained using a theory-based workflow utilizing pressure profile predictions from a coupled TGLF turbulent transport and EPED pedestal models together with external magnetics and Motional Stark Effect (MSE) data to construct the equilibrium. Here, we obtain an average root mean square error of 5.1% in the safety factor profile when comparing the predicted and experimental kinetic equilibrium. We also find good agreement with the plasma stored energy, internal inductance, and pressure profiles. Including MSE data in the theory-based workflow results in noticeably improved agreement with the q-profiles in high triangularity discharges in comparison with the results obtained with magnetic data only. The predictive equilibrium workflow is expected to have wide applications in experimental planning, between-shot analysis, and reactor studies.

Kinsey, J. E. (ORCID:0000000193347473)↗

DOE OSTI2025

Simulation of Physics-Based 0-10Hz Strong Motion Using High Performance Computing Supporting Refinements to Regional Ground Motion Models for the Central Eastern US

In collaboration with the U.S. Nuclear Regulatory Commission (NRC) the LLNL has developed a computationally efficient simulation platform designed to perform physics-based ground motion simulations for crustal earthquakes in the Stable Continental Regions of Central and Eastern US (CEUS), using high-performance computing. The main objective of the earthquake simulations was to use synthetic ground motion to provide constrains to refinements of existing ergodic Ground Motion Models (GMMs), for large magnitude earthquakes and near-fault distances, for which these models are less reliable. Physics-based broadband (0-10Hz) ground motion simulations were used to estimate the near-fault ground motion amplitudes and within event and between-event variabilities associated with fault rupture characteristics. In our simulations we used a 3D regional velocity model that was based on Saikia’s 1D velocity model (1994). In simulations performed during the first stage of this project the Saikia’s velocity model demonstrated better performance in modelling high frequency regional wave propagation for the CEUS region recorded during the Mw5.0 November 7, 2016, Cushing Oklahoma (Taylor et al., 2017), and Mw5.8 September 3, 2016, Pawnee Oklahoma earthquakes. The proposed regional 3D model includes random perturbations to the 1D background model using the stochastic scheme of Pitarka and Mellors (2021). In addition, validation analysis of the rupture generator and regional wave propagation models, using comparisons with different GMMs for Mw6.5 and Mw7.0 scenario earthquakes in the CEUS region resulted in a very good match between the simulated and empirical ground motion models. For the purposes of seismic hazard assessment at the existing and planned nuclear power plants, NRC is interested in studies aimed at improving the current ground motion models (GMM) for both Stable Continental Regions (SCR) in the Central and Eastern US and Active Crustal Regions (ACR) in the Western US. Due to lack of recorded data, these improvements require synthetic data for short fault distances and large magnitude earthquakes for which the existing recorded data is not enough to uniquely constrain the GMMs. The need for simulations and strong motion data is especially critical for the CEUS region where we do not have recorded data from potentially large damaging earthquakes with moment magnitudes 6.0 and higher. In this the project, we focused on 10Hz simulations of Mw7.0 scenario earthquakes with strike slip and thrust faulting mechanisms. We used more than 50 Mw7.0 earthquake rupture scenarios to investigate the ground motion uncertainty due to unknown earthquake rupture parameters, in particular, the slip distribution, rupture velocity, and faulting mechanism, and their implication on ground motion amplification due to forward rupture directivity effects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗