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Influence of Numerical Modeling Approaches on Damped Behavior of Flexible Beams: Preprint

Composites structures are widely used in aerospace and wind energy applications for their excellent stiffness and strength-to-weight properties. In these structures, structural damping is critical to predict vibration amplitudes, performance, and reliability. Structural damping is of particular interest for slender wings, rotorcraft blades, and wind turbine blades that can exhibit complex vibration phenomena and are frequently modeled with geometrically exact beam theory (GEBT). Standard approaches of stiffness proportional or modal damping merely assign user defined values and cannot predict damping behavior. This work compares stiffness proportional damping to two more advanced damping approaches: modal strain energy and Prony series. The modal strain energy approach uses a sectional analysis tool to calculate the beam stiffness and postprocess internal stresses from GEBT simulations. The internal stresses are then used to calculate modal damping factors. The Prony series is implemented within GEBT to directly model viscoelastic behavior of the composites. These approaches are compared by modeling the evolution of the damping factors of a realistic flexible wind turbine blade with varying rotational speed. Discrepancies between the approaches suggest areas for future modeling development, but differences in nonlinear damping values are less than current uncertainties about the magnitude of structural damping.

17 WIND ENERGY

Improving High Resolution Offshore Wind Resource Assessments and Forecasts using Observations in the MA/RI Lease Areas

The third Wind Forecast Improvement Project (WFIP3) sought to improve understanding of the physical phenomena in the atmosphere and ocean that dictate the structure and variability of wind and thermodynamic fields within the Marine Atmospheric Boundary Layer (MABL). WFIP3 conducted a comprehensive 18-month observational study over the Northeast U.S. outer continental shelf, a high use coastal zone, using a 3-D multiscale sensor array to highly resolve the temporal, vertical, and horizontal structure of the coupled atmospheric and oceanic boundary layers. Multiple land-based study sites adjacent to the coastal ocean observed surface meteorology and vertical profiles of atmospheric properties via passive infrared and microwave radiometers, active lidars and radars, and radiosondes. At sea, an array of surface flux buoys and two vertical profiling lidar buoys observed both atmospheric and oceanic properties, augmented by land-based oceanographic radar systems and routine ship-based surveys. Intensive observations of the MABL over the ocean was done from an air-sea interaction flux tower and extended deployments of a large barge platform. WFIP3 focused on mesoscale and sub-mesoscale flows -- including sea breezes, low-level jets, low-level clouds, and coastal storms -- and the ability of advanced numerical model parameterizations to represent them within fully coupled oceanic and atmospheric modeling systems and foundational weather forecast models. Numerous critical forecasting phenomena were observed, however the project was terminated prior to the completion of the field observational period and the analysis period.

54 ENVIRONMENTAL SCIENCES

Code-to-code comparison between FLASH and HYDRA in gas-puff Z-pinch modeling

The numerical modeling of gas-puff Z pinches involves the nonlinear coupling of a broad range of complex, multi-physics phenomena that makes such simulations challenging. The challenge is further compounded by nonlinear instabilities that can impact the dynamics of imploding gas-puff Z pinches, such as the magneto Rayleigh–Taylor instability (MRTI). If the growth rate and amplitude of the latter is comparable to the relevant timescales and properties of the imploding plasma, the MRTI can dramatically alter implosion dynamics, dictate pinch stability, and govern the plasma properties achievable in pulsed-power-driven laboratory experiments. National Laboratories and academic teams have developed numerical tools that can accurately model Z-pinch configurations and provide reliable design capabilities that can guide experimental choices and assist in interpreting experimental results. Most such tools, however, are not broadly available. Here, we present newly developed Z-pinch simulation capabilities of the publicly available FLASH code, applied in the study of MRTI growth and dynamical effects in gas-puff implosions. To verify the new implementations, we perform a comparison of FLASH gas-puff implosion simulations with previously published calculations with the HYDRA code from Lawrence Livermore National Laboratory, which have been validated with experimental data from the CESZAR pulsed-power driver at the University of California, San Diego. The experiments involved double- and triple-nozzle configurations, in an experimental attempt to stabilize the pinch to the MRTI. The code-to-code comparison shows similar results between the FLASH and HYDRA simulations, supporting the use of FLASH in the modeling of future gas-puff Z-pinch experiments at CESZAR.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY

System and method for wave prediction

A method and system for prediction of wave properties include collecting time-series data streams from one or more wave measurement devices and processing the data to identify data parameters to establish boundary conditions of a numerical model. The numerical model may be used to compute a predicted wave field of time-series data for a variety of wave properties at a target location.

Previsic, Mirko

A numerical study of process complexity in permafrost dominated regions

Numerical modeling of permafrost dynamics requires adequate representation of atmospheric and surface processes, a reasonable parameter estimation strategy, and site-specific model development. The three main research objectives of the study are: (i) to propose a novel methodology that determines the required level of surface process complexity of permafrost models by conducting parameter sensitivity and calibration, (ii) to design and compare three numerical models of increasing surface process complexity, and (iii) to calibrate and validate the numerical models at the Yakou catchment on the Qinghai-Tibet Plateau as an exemplary study site. The calibration was carried out by coupling the Advanced Terrestrial Simulator (numerical model) and PEST (calibration tool). Simulation results showed that (i) A simple numerical model that considers only subsurface processes can simulate active layer development with the same accuracy as other more complex models that include surface processes. (ii) Peat and mineral soil layer permeability, Van Genuchten alpha, and porosity are highly sensitive. (iii) Liquid precipitation aids in increasing the rate of permafrost degradation. (iv) Deposition of snow insulated the subsurface during the thaw initiation period. We have developed and released an integrated code that couples the numerical software ATS to the calibration software PEST. The numerical model can be further used to determine the impacts of climate change on permafrost degradation.

Calibration

Techno-economic feasibility of borehole thermal energy storage system connected to geothermal heat pumps for seasonal heating load of two buildings in Fairbanks, Alaska

Borehole thermal energy storage (BTES) is an effective solution for managing imbalanced heating and cooling loads in cold regions. This study evaluated the long-term feasibility of a BTES system in the Fairbanks area, Alaska, through building energy modeling, resource characterization, and numerical modeling. The system was designed to store waste heat from a nearby coal power plant during summer and provide thermal energy during winter to geothermal heat pumps supplying heating loads in two buildings. Heating load profiles were modeled for the buildings using EnergyPlus, and the results indicated the annual heating load was 5.6 times greater than the cooling load. 40 borehole heat exchangers were pre-designed approximately 100 m away from the two buildings in terms of land availability and regulatorily optimized depth. The 20-year performance of the designed BTES system under two operational scenarios—one with a 5-year preheating period and one without—was numerically modeled using subsurface temperature and properties characterized through the literature review and thermal response tests. Both scenarios demonstrated that the BTES has the capacity to fully cover the heating loads in the two buildings throughout the 20-year lifetime. Production temperatures at central wells were 33 % higher on average than at outer wells in both scenarios. The 5-year preheating period increased subsurface and extraction temperatures, and correspondingly annual average and total thermal energy production was higher for 8 years than in the scenario without the preheating period. These results highlight the long-term reliability and sustainability of the BTES system in meeting heating demands over its lifetime, with the preheating period offering potential performance improvements. Implementing the BTES system in cold regions with high heating demand, such as Fairbanks, Alaska, could provide a long-term, sustainable energy solution for managing imbalanced heating and cooling loads.

15 GEOTHERMAL ENERGY

Scalable foundation models for numerical simulations on HPC platforms

In recent years, foundation models (FMs) have begun to reshape numerical simulations on high-performance computing (HPC) platforms. These large, pre-trained AI models enable rapid predictions across a broad range of physical domains, including Earth system modeling, fluid dynamics, materials science, as well as complex multi-modal simulations in aerospace engineering and fusion research. By training on diverse datasets, FMs learn intricate relationships and underlying physical behavior while also enabling the quantification of uncertainty in their predictions. This capability allows simulations that once required days of numerical calculation to be completed in minutes (FM inference), supporting real-time design optimization, uncertainty-aware decision making, and more comprehensive exploration of complex scenarios.

AI

A two-fluid solar-wind model with intermittent Alfvénic turbulence

In one of the leading theories for the origin of the solar wind, photospheric motions launch Alfvén waves (AWs) that propagate along open magnetic-field lines through the solar atmosphere and into the solar wind. The radial variation in the Alfvén speed causes some of the AWs to reflect, and counter-propagating AWs subsequently interact to produce Alfveńic turbulence, in which AW energy cascades from long wavelengths to short wavelengths and dissipates, heating the plasma. In this paper we develop a one-dimensional two-fluid solar-wind model that includes Alfvénic turbulence, proton temperature anisotropy and a novel method for apportioning the turbulent heating rate between parallel proton heating, perpendicular proton heating and electron heating. We employ a turbulence model that accounts for recent observations from NASA’s Parker Solar Probe, which find that AW fluctuations in the near-Sun solar wind are intermittent and less anisotropic than in previous models of anisotropic magnetohydrodynamic turbulence. Our solar-wind model reproduces a wide range of remote observations of the corona and in-situ measurements of the solar wind, and our turbulent heating model consists of analytic equations that could be usefully incorporated into other solar-wind models and numerical models of more distant astrophysical plasmas.

astrophysical plasmas

Assessing the Impact of a Forest Canopy on Near-Surface Wind Statistics

Representing the forest canopy in atmospheric numerical models should improve simulated winds within and above the canopy up to a few hundred meters above the ground. Here, in this study, we implement a forest canopy parameterization into the Weather Research and Forecasting (WRF) Model in a large-eddy simulation (LES) mode by applying drag forces across multiple layers within the canopy height. We use unique observations from the Lidar Experiments for Assessing Flow over Forests (LEAFF) field campaign at the Wind River Experimental Forest (WREF) in the U.S. Pacific Northwest to evaluate model performance. In a 2-day case study, the canopy parameterization improved wind predictions both within and above the canopy, particularly during the daytime and at finer grid resolution. Without it, winds were frequently overpredicted above the canopy. Similarly, derived quantities such as the wind shear index also yielded estimates closer to observations with the canopy parameterization implemented. These findings suggest that representing the canopy using drag forces alone can improve simulated mean winds up to 200 m above the surface. Furthermore, second-order statistical moments of wind were more sensitive to canopy density than first-order moments, especially during the daytime. This increased sensitivity and the improved daytime performance in wind speed—evidenced by the lowest bias from observations (3% compared to 20% over diurnal cycle)—imply that winds above the canopy layer are strongly influenced by how well turbulence above the canopy is modeled. The results of this study can serve as a foundation for parameterizing forest canopy effects in coarser weather forecast models.

Energy - Wind

Recent Progress on Surface Water Quality Models Utilizing Machine Learning Techniques

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

machine learning (ML)

Development, Monitoring, and Control of Fracture Thermal Energy Storage (FTES) in Crystalline Rock Formations (DEMO-FTES) (CRADA Final Report)

The DEMO-FTES project sought to demonstrate the thermal efficiency of fracture thermal energy storage (FTES) through numerical simulations, laboratory and meso-scale field tests. A detailed dimensional and scaling analysis was performed to identify key parameters and how they can be most effectively scaled to the laboratory and decameter scale. Numerical models were developed and used for three purposes: 1. Before field experiments, numerical modelling can be used to estimate fracture properties based on previous data from the EGS Collab experiment and then predict thermal hydrological behaviors of the fracture system with hot water injection/withdrawal, therefore, help to design the experiments (e.g., to decide the duration of the cycles based on the flow rate the pump can provide, and the estimated fracture properties); 2. After the field experiment, to estimate the system properties during the experiment (as the size and shape of a fracture could change over time), and understand why system performance is different than what has been predicted, i.e., to help understand the meso-scale test; and, 3. To model the lab experiments and estimate fracture properties and storage efficiency. Ultimately, the experiment and numerical models could shed light on the processes and uncertainty happening during fracture activation and help understand the scaling between lab and field test, and finally, the design and optimization of potential fracture thermal energy storage systems.

25 ENERGY STORAGE

Effect of toolpath in large-format additive manufacturing with bio-derived composites

There has been growing interest in integrating bio-derived composites into Large-format additive manufacturing (LFAM) feedstocks to reduce the use of petroleum-derived materials and reduce the overall carbon footprint of LFAM. However, these materials present unique challenges during manufacturing due to their variability, which can lead to unintended deformations and failures attributable to suboptimal process conditions. While numerical modelling has been extensively employed to simulate numerous manufacturing processes, its application in LFAM with bio-derived composites remains limited. This study addresses this gap by systematically developing a numerical model to simulate the LFAM process using bio-based materials, specifically wood fiber-reinforced polylactic acid (PLA/WF). Experimental investigations were conducted to characterise the thermal and mechanical properties of additively manufactured PLA/WF specimens. Numerical simulations were performed to predict temperature profiles and deformations during LFAM. The effect of varying infill patterns, internal structures, and tool paths on the temperature distribution and deformation of printed parts was explored using the developed model. This article aims to advance the utilisation of bio-derived composites in LFAM systems and provide a comprehensive understanding of the LFAM process. The findings offer valuable insights for optimising process parameters and enhancing the performance of LFAM with bio-based composites.

Large-format additive manufacturing

Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration

Abstract Beavers ( Castor canadensis ) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model‐data integration workflow to address uncertainty in groundwater response to beaver‐induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning‐based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down‐valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down‐valley underflow, from 2 during dry pond periods to 20 during wet periods, serving as an analogy for conditions without and with beaver ponds. The study highlights the influence of floodplain structure on groundwater storage, water balance, and water quality impacted by beaver ponds. A thick gravel bed layer, with a large down‐valley underflow, minimizes the effect of beaver‐induced inundation on water quality. We emphasize the need for field‐scale measurements of floodplain structure and improved characterization of evapotranspiration changes to reduce uncertainty in groundwater response. Plain Language Summary Beavers change the flow of water in river corridors by creating ponds, expanding wetlands, and flooding floodplains. This increases surface water area, promotes plant growth, and enhances biodiversity. However, the impact of this flooding on groundwater flow is not well understood, especially in mountainous areas with gravel layers where water moves easily beneath soil. In this study, we used numerical modeling to investigate how beaver ponds influence groundwater in a mountainous floodplain of the Upper Colorado River Basin. We adapted a machine learning method to validate our numerical models using multiple field data sets. Our findings show that beaver ponds significantly increase vertical water flow from the soil to the gravel during wet periods, compared to when the ponds are fully drained. The study also highlights the importance of floodplain structure in controlling both water flow in gravel layers along the river direction and vertical flow from the soil to the gravel with the presence of beavers. To reduce uncertainty in groundwater response, we emphasize the need for more field‐scale measurements of floodplain structure, hydraulic properties, and evapotranspiration changes. Key Points Floodplain structures and hydraulic conductivities are important for groundwater response with beaver ponds in mountainous floodplains Large down‐valley underflow in permeability‐stratified floodplains reduces beaver‐induced impacts on groundwater storage and water quality Machine learning‐based model calibration methods are effective for estimating posterior distributions of groundwater model parameters

Wang, Lijing

High-Fidelity Numerical Wave Tank Verification & Validation Study: Wave Generation Through Paddle Motion: Preprint

This paper presents a numerical benchmark study of wave propagation due to a paddle motion using different high-fidelity numerical models, which are capable of replicating the nearly actual physical wave tank testing. A full time series of the measured wave generation paddle motion which was used to generate wave propagation in the physical wave tank will be utilized in each of the models contributed by IEA OES Task 10's participants, which includes both computational fluid dynamics (CFD) and smooth hydrodynamic particle (SPH). The high-fidelity simulations of the physical wave testcase will allow for the evaluation of the initial transient effects from wave ramp-up and its evolution in the wave tank over time for two representative regular waves with varying levels of nonlinearity. A couple of interesting metrics like the predicted wave surface elevation at select wave probes, wave period, and phase-shift in time will be assessed to evaluate the relative accuracy of numerical models versus experimental data within specified time intervals. These models will serve as a guide for modelers in the wave energy community and provide a base case to allow further and more detailed numerical modeling of the fixed Kramer Sphere Cases under wave excitation force wave tank testing.

HYDRO ENERGY,TIDAL AND WAVE POWER

Computational capacity in hydrodynamic real-time hybrid simulation applied to simulate the dynamic response of floating offshore wind turbines

Real-time hybrid simulation (RTHS) mitigates similitude distortions in model-scale tests of floating offshore wind turbines (FOWTs) by coupling physical experiments with numerical models in real time. The coupling requires faster-than-real-time numerical computations to satisfy temporal similitude with the physical experiment, presenting a bottleneck for using more complex numerical models in RTHS. This paper presents a hydrodynamic-RTHS (hydro-RTHS) framework for FOWTs that simulates the hydrodynamics physically and the aerodynamics numerically with sensor feedback from the physical testing. The framework adapts the three-loop hardware architecture to leverage greater computational resources and mitigate strict temporal requirements, enabling more computationally demanding numerical analyses in hydro-RTHS. The three-loop hardware architecture integrates multiple machines, each dedicated to either numerical analysis or RTHS controls, with a rate-transition algorithm to synchronize the tasks executed across the different machine processors. Virtual and physical tests verified and validated the hydro-RTHS framework, respectively. The ”virtual” tests, which approximates the physical domain numerically, verified the RTHS framework with respect to a numerical full-scale complete FOWT model simulated in the open-source software, OpenFAST. The virtual tests were able to maintain comparable control signals while enabling greater computational resources for the numerical calculations. Real-world physical tests demonstrated that the hydro-RTHS framework computes aerodynamic forces similar to the complete OpenFAST model, validating the hydro-RTHS framework using the three-loop hardware architecture. Findings show that the hydro-RTHS framework with the three-loop hardware architecture is computationally efficient, with reserve capacity to simulate more complex problems due to the customized software, hardware, and rate-transition algorithm.

17 WIND ENERGY

Toward integrating high-fidelity CFD approaches in the thermal-hydraulic analysis of turbulent dry cask systems

Nuclear power plants have been supplying resilient and reliable electricity for decades, contributing to energy independence of the U.S.. However, nuclear waste management remains one of the most significant challenges in the industry. The safety of dry cask storage systems relies heavily on their thermal-hydraulic performance. Computational Fluid Dynamics (CFD) simulations are often used to demonstrate this performance and ensure that the system design meets safety standards. This study presents reduced numerical models for various types of dry cask systems. These numerical models can produce efficient and fast results based on the employed modeling strategies. Additionally, the study uses a novel approach to high-fidelity simulations to evaluate modeling assumptions in dry cask modeling. Large Eddy Simulations (LES) are used for this purpose, particularly in regions where fluid velocity is relatively high and the turbulence characteristics become important. Furthermore, the results of these high-fidelity simulations will enhance the interpretation of outcomes produced from a lower-fidelity CFD model.

CFD

Coupled Aerodynamic and Hydrodynamic Hybrid Simulation of Floating Offshore Wind Turbines

The development and innovation of floating offshore wind energy in the U.S. requires detailed high-fidelity observations and measurements of turbine and platform loading due to wind, waves, and currents. However, full-scale and quasi-full-scale experiments require significant financial and temporal investments for construction, experimental testing, and long-term field campaigns. To support the commercial advancement of the offshore wind energy industry, specialized wind tunnel and wave basin experimental facilities are critical to be able to test FOWT designs at small scale under controlled conditions prior to full-scale deployment. Oregon State University (OSU) is internationally known as a leader in water and energy research, development, and testing. The O.H. Hinsdale Wave Research Laboratory (HWRL) and the Wallace Energy Systems and Renewables Facility (WESRF) at OSU have extensive experience building, modeling, monitoring, controlling, and actuating scaled systems. Experiments on wave-structure interaction have been performed at the HWRL since its establishment in 1972. Studies have included the interaction of waves with coastal structures (breakwaters, seawalls, buildings, cylinders, bridges, fixed foundations of offshore wind turbines, etc.) and with floating structures (e.g., wave energy converters, maneuvering of vessels, etc.). Hinsdale is actively used by marine energy technology developers, both for private testing and OSU-collaborative research projects. However, despite the availability of several large-scale facilities for hydrodynamic testing (at OSU and elsewhere in the U.S.), existing experimental laboratories are generally limited in their ability to accurately generate combined wind and wave conditions. The simulation of both wind and waves in experimental testing is complicated due to a number of constraints, including: [i] incompatible similitude laws governing the wind and waves for scaled experiments, [ii] producing accurate wind over a large enough control volume via fans, and [iii] generating wind that reasonably represents the atmospheric boundary layer in existing wave basins/flumes. Hence, physical test data providing insight into the simultaneous wave- and wind-structure response of floating offshore wind components can be difficult to generate. Given the aforementioned challenges in classic hydrodynamic experiments, the motivation of this project is to establish a real-time hybrid simulation (RTHS) approach that can apply aero- and hydro-dynamic loading by augmenting wave-only experimental facilities with virtual aerodynamic forces through numerical models representing the remaining dynamic forces. RTHS is a physical-numerical approach that partitions a prototype system into physical and numerical sub-assemblies that interact with each other through actuators and sensors in real time. In coupling physical and numerical models, the hybrid simulation approach applied herein is ideal for problems with: (1) structures subjected to different scaling laws, such as floating offshore wind turbines subjected to combined aero/hydro-dynamic loading, (2) structures that are too large or complex to be tested entirely in a laboratory setting, such as deep-water mooring applications, and (3) component testing, where the behavior of a portion of the assembly is uncertain but still interacts with other portions of the structure, such as testing the fatigue life of turbine blades. Few U.S. experimental facilities are able to test simultaneous aero- and hydro-dynamic loading and none can accurately produce aero/hydro-dynamic response on scaled FOWT models due to conflicting similitude laws between the wind (commonly Reynolds) and the waves (commonly Froude). To aid in accelerating the development of the U.S. floating offshore industry, there is a significant need to develop a flexible, modular framework that can expand the capacities of existing wave-only laboratories. The project goal is to demonstrate a hydrodynamic real-time hybrid simulation (hydro-RTHS) framework that couples numerical wind and physical waves acting on a FOWT, thus representing simultaneous aero/hydro-dynamic loading. The FOWT is partitioned into a full-scale numerical sub-assembly associated with the aerodynamics and a model-scale physical sub-assembly associated with the hydrodynamics. The numerical-physical partition associated with hydro-RTHS mitigates scaling constraints by supplying different scaling laws to the physical and numerical sub-assemblies. Herein, length, force, and time are scaled and exchanged between the sub-assemblies using Froude scaling to represent the open-channel flow in the physical sub-assembly. Other similitude laws could also be utilized depending on the problem definition. It is envisioned that the ability to model FOWTs under waves and wind, with mitigation of similitude distortions, would result in reduced development costs (currently, FOWT concept development is performed with full-size pro- totypes at enormous expense and risk) and increase the reliability of the FOWT industry (since extreme wave and wind conditions and contingency events can be tested safely in a controlled environment).

16 TIDAL AND WAVE POWER

Modeling betatron radiation using particle-in-cell codes for plasma wakefield accelerator diagnostics

The analysis of plasma wakefield acceleration experimental measurements, particularly in the characterization of photons emitted through the betatron radiation mechanism, requires the development of accurate numerical models. These computational models are crucial for supporting modern instrumentation designed to measure the single-shot, double-differential angular-energy radiation spectra resulting from interactions between beams and plasmas. Motivated by the needs of such applications, this paper presents detailed numerical models of betatron radiation generated in beam-plasma acceleration experiments. These models are based on the integration of the Liénard-Wiechert (LW) potentials, applied to computed particle trajectories. The particle trajectories are generated using three distinct methods: first, by tracking particles through idealized fields in the blowout regime of PWFA; second, by obtaining trajectories using the fast quasistatic particle-in-cell (PIC) code quickpic; and third, obtaining trajectories from the fully self-consistent PIC code osiris. To ensure the accuracy and reliability of these models, the paper includes various benchmark tests using analytical expressions, as well as employing the PIC code epoch, which takes an alternative approach by using a Monte Carlo quantum electrodynamics (QED)-based radiation model. Additionally, the paper presents simulations of the expected experimental betatron radiation spectra, taking into account parameters relevant to PWFA and plasma photocathode experiments at the SLAC FACET-II facility.

Yadav, M. [University of California, Los Angeles,