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NSTXU Diagnostic Disruption Dynamic Loading Represented by Response Spectra

This article presents the results of transient dynamic simulations of loads due to disruption eddy currents on the NSTXU vacuum vessel. Dynamic loading at diagnostic mounting locations is expressed as response spectra derived from the time history results of the dynamic structural simulations of a variety of disruption scenarios. The disruption simulations draw on a history of the project assessments of worst case disruptions for specific components. Major efforts to assess disruption loading have included the vacuum vessel which is the major structural support for the machine, as well as the passive plates (PPs), high harmonic fast wave (HHFW) antenna, and centerstack casing. Each one of these efforts included transient electromagnetic simulations producing time-dependent eddy current Lorentz loads (and in some cases halo loads) which then were applied to time-dependent structural dynamic analyses intended to obtain the proper dynamic amplification factors. In some instances, the EM model and structural model were identical allowing direct transfer of EM forces to the structural model. In other cases, the EM and structural model were not identical and the vector potential (VP) transfer method was used. The results files from these analyses were available (or re-run) to post process in ANSYS Classic time history postprocessor. In conclusion, the ANSYS command is used to create response spectra from time history data at desired points on the vessel.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY

EM Physics

Geant4 provides a comprehensive set of electromagnetic (EM) processes and models for electron/positron, gamma and long-lived charged particles, spanning energies from 100 eV to 100 TeV. Covering diverse energy regions often requires multiple models, which can be constructed using pre-packaged or user-defined EM physics constructors. Geant4 also supports detailed low-energy EM physics through models like Livermore, Penelope, and ICRU73, offering extensive data for elements across a wide energy range (250 eV–100 GeV). Application domains include space, medical, and radiobiology simulations. Additionally, Geant4 provides robust options for simulating complex optical photon production and transportation processes, enhancing its versatility in physics research and applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND

Statistical methods for astronomical data with upper limits. II - Correlation and regression

Statistical methods for calculating correlations and regressions in bivariate censored data where the dependent variable can have upper or lower limits are presented. Cox's regression and the generalization of Kendall's rank correlation coefficient provide significant levels of correlations, and the EM algorithm, under the assumption of normally distributed errors, and its nonparametric analog using the Kaplan-Meier estimator, give estimates for the slope of a regression line. Monte Carlo simulations demonstrate that survival analysis is reliable in determining correlations between luminosities at different bands. Survival analysis is applied to CO emission in infrared galaxies, X-ray emission in radio galaxies, H-alpha emission in cooling cluster cores, and radio emission in Seyfert galaxies.

Isobe, T.

Lithium Circuit Test Section Design and Fabrication

The Early Flight Fission - Test Facilities (EFF-TF) team has designed and built an actively pumped lithium flow circuit. Modifications were made to a circuit originally designed for NaK to enable the use of lithium that included application specific instrumentation and hardware. Component scale freeze/thaw tests were conducted to both gain experience with handling and behavior of lithium in solid and liquid form and to supply anchor data for a Generalized Fluid System Simulation Program (GFSSP) model that was modified to include the physics for freeze/thaw transitions. Void formation was investigated. The basic circuit components include: reactor segment, lithium to gas heat exchanger, electromagnetic (EM) liquid metal pump, load/drain reservoir, expansion reservoir, instrumentation, and trace heaters. This paper will discuss the overall system design and build and the component testing findings.

Godfroy, Thomas

The Impact of Mood and Affect on Operationally Relevant Performance Among Astronaut-Like Individuals in Space Analog Settings

Crewmembers of future space exploration missions will face many challenges associated with living and working in extreme conditions for prolonged periods of time. Thus, it is imperative to understand how spaceflight hazards impact mood and affect, cognitive functioning, and ultimately, operational performance. The Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite is a set of standardized measures used to assess behavioral health and performance related to future exploration class space missions. HFBP-EM includes the Profile of Mood States (POMS) as a self-report measure of psychological distress, and the Robotic On-Board Trainer (ROBoT) as an operationally relevant task that simulates the capture and grappling maneuvers required to operate the Canadarm2 on the International Space Station. Although previous space analog studies have demonstrated fluctuations in mood and affect ratings assessed by the POMS (Basner, 2014), no studies to date have examined impacts on operationally relevant tasks. Here we investigate the relationship between POMS ratings and performance on the ROBoT task in space analog settings.

S I Dev

Flow Components in a NaK Test Loop Designed to Simulate Conditions in a Nuclear Surface Power Reactor

A test loop using NaK as the working fluid is presently in use to study material compatibility effects on various components that comprise a possible nuclear reactor design for use on the lunar surface. A DC electromagnetic (EM) pump has been designed and implemented as a means of actively controlling the NaK flow rate through the system and an EM flow sensor is employed to monitor the developed flow rate. These components allow for the matching of the flow rate conditions in test loops with those that would be found in a full-scale surface-power reactor. The design and operating characteristics of the EM pump and flow sensor are presented. In the EM pump, current is applied to a set of electrodes to produce a Lorentz body force in the fluid. A measurement of the induced voltage (back-EMF) in the flow sensor provides the means of monitoring flow rate. Both components are compact, employing high magnetic field strength neodymium magnets thermally coupled to a water-cooled housing. A vacuum gap limits the heat transferred from the high temperature NaK tube to the magnets and a magnetically-permeable material completes the magnetic circuit. The pump is designed to produce a pressure rise of 5 psi, and the flow sensor's predicted output is roughly 20 mV at the loop's nominal flow rate of 0.5 GPM.

Polzin, Kurt A.

Spacecraft Block Scheduling for NASA’s Deep Space Network

Currently, NASA’s Deep Space Network (DSN) is responsible for uplink to, downlink from, and/or tracking of dozens of missions for space agencies across the world. The DSN scheduling process starts about four months prior to the start of the schedule week, a process in which requirements are defined and then the schedule is created, de-conflicted, and negotiated over the next 2–3 weeks with a team of mission representatives. Now scheduled for late 2019, Exploration Mission1 (EM-1) will deploy upwards of 12 SmallSat missions that will be served by the DSN. This will increase the DSN’s actively serviced spacecraft by up to 30%, further increasing the difficulty of meeting all mission needs via the over subscribed network. To mitigate their impact on DSN scheduling, a block scheduling process is proposed for scheduling the SmallSats. Block scheduling consists of aggregating spacecraft together into larger “pseudo-spacecraft” based on geometric alignment that then follow the same process as any other DSN mission to receive segments of track time. These tracks are then decomposed into tracks for individual users based on their specific requirements. This paper describes a full novel scheduling tool set for building candidate blocks, evaluating the efficacy of these blocks, and optimal and suboptimal de-blocking schemes. To demonstrate these developed tools, results from three simulations are presented: a blocking example with lunar SmallSats, blocking potential in the greater DSN spacecraft catalog, and opportunistic multiple spacecraft per aperture potential for the DSN spacecraft catalog. Block scheduling has the potential to reduce overhead and scheduling resources for the EM-1 SmallSats while also providing them with a better means to meet their mission requirements.

Bilén, Sven G.

Status of NASA's Space Launch System

NASA's Space Launch System (SLS) continued to make significant progress in 2015 and 2016, completing hardware and testing that brings NASA closer to a new era of deep space exploration. Programmatically, SLS completed Critical Design Review (CDR) in 2015. A team of independent reviewers concluded that the vehicle design is technically and programmatically ready to move to Design Certification Review (DCR) and launch readiness in 2018. Just five years after program start, every major element has amassed development and flight hardware and completed key tests that will lead to an accelerated pace of manufacturing and testing in 2016 and 2017. Key to SLS' rapid progress has been the use of existing technologies adapted to the new launch vehicle. The existing fleet of RS-25 engines is undergoing adaptation tests to prove it can meet SLS requirements and environments with minimal change. The four-segment shuttle-era booster has been modified and updated with a fifth propellant segment, new insulation, and new avionics. The Interim Cryogenic Upper Stage is a modified version of an existing upper stage. The first Block I SLS configuration will launch a minimum of 70 metric tons (t) of payload to low Earth orbit (LEO). The vehicle architecture has a clear evolutionary path to more than 100t and, ultimately, to 130t. Among the program's major 2015-2016 accomplishments were two booster qualification hotfire tests, a series of RS-25 adaptation hotfire tests, manufacturing of most of the major components for both core stage test articles and first flight tank, delivery of the Pegasus core stage barge, and the upper stage simulator. Renovations to the B-2 test stand for stage green run testing was completed at NASA Stennis Space Center. This year will see the completion of welding for all qualification and flight EM-1 core stage components and testing of flight avionics, completion of core stage structural test stands, casting of the EM-1 solid rocket motors, additional testing of RS-25 engines and flight engine controllers This paper will discuss these and other technical and programmatic successes and challenges over the past year and provide a preview of work ahead before the first flight of this new capability.

Honeycutt, John

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Abstract Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.

36 MATERIALS SCIENCE

Antenna analysis using neural networks

Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern shaping. The interesting thing about D-C synthesis is that the side lobes have the same amplitude. Five-element arrays were used. Again, 41 pattern samples were used for the input. Nine actual D-C patterns ranging from -10 dB to -30 dB side lobe levels were used to train the network. A comparison between simulated and actual D-C techniques for a pattern with -22 dB side lobe level is shown. The goal for this research was to evaluate the performance of neural network computing with antennas. Future applications will employ the backpropagation training algorithm to drastically reduce the computational complexity involved in performing EM compensation for surface errors in large space reflector antennas.

Smith, William T.

Dynamics and activation of membrane-bound B cell receptor assembly

B-cell receptor (BCR) complexes are expressed on the surface of a B-cell and are critical in antigen recognition and modulating the adaptive immune response. Even though the relevance of antibodies has been known for almost a hundred years, the antigen-dependent activation mechanism of B-cells has remained elusive. Several models have been proposed for BCR activation, including cross-linking, conformation-induced oligomerization, and dissociation activation models. Recently, the first cryo-EM structures of the human B-cell antigen receptor of the IgM and IgG isotypes have been published that validates the asymmetric organization of the BCR complex. Here, we carry out extensive molecular dynamics simulations to probe the conformational changes upon antigen binding and the influence of the membrane lipids. We identify two critical dynamical events that could be associated with antigen-dependent activation of BCR. First, antigen binding causes increased flexibility in regions distal to the antigen binding site. Second, antigen binding alters the rearrangement of IgM transmembrane helices, including the relative interaction of Igα/Igβ that mediates intracellular signaling. Furthermore, these transmembrane rearrangements lead to changes in localized lipid composition. Our work indirectly supports the conformational-change induced models of BCR activation and contributes to the understanding of the antigen-dependent activation mechanism of BCRs.

59 BASIC BIOLOGICAL SCIENCES

Determination of Uncertainties for Analytically Derived Material Properties to Be Used in Monte Carlo Based Orion Heatshield Sizing

Ablative materials are often used for spacecraft heatshields to protect underlying structures from the extreme environments associated with atmospheric reentry. NASA's Orion EM-1 capsule has been designed to use a molded Avcoat material system. In order to determine the required heatshield thickness, a Monte Carlo approach to the sizing process was proposed. To perform the Monte Carlo simulation, statistical uncertainties on all material property input parameters were required. Obtaining these values for measured properties is straightforward, however input parameters that are derived analytically have historically used uncertainties based on engineering judgment. A MATLAB program was created to use laboratory generated thermogravimetric analysis (TGA) data to calculate uncertainties on the Arrhenius parameters for molded Avcoat. Uncertainties associated with the normalized ablation rate and pyrolysis gas enthalpy were also generated using a wrapper script and the ACE code. These uncertainties could then be tied directly to measured values of individual elemental constituents. The resulting uncertainty values will allow for a probabilistic sizing approach on molded Avcoat with a higher level of confidence in the input parameters.

material property uncertainties

Rough surface scattering from moving ocean surfaces as an indicator of scattering mechanisms

Ever since Crombie used the Doppler spectrum of RF signals scattered from the ocean surface to provide conclusive evidence for Bragg resonant scattering, scattering from the ocean surface has served as a testing ground for the identification of governing scattering mechanisms in rough surface scattering. The moving ocean surface has the advantage for rough surface scattering that, being a dispersive medium, features with different spatial scales the tendency to travel at different speeds. Thus, by looking at the temporal signature of the scattered field, one can try to isolate the scattering mechanism. This feature has been exploited by various authors. To give a recent example, we cite the work of Plant, who examined the Doppler spectrum of the return signal to infer the presence of two-scale scattering in scattering at medium incidence angles. The problem with restricting oneself to the examination of the Doppler spectrum, as has been done traditionally, is that one restricts the phenomena under investigation to temporally homogeneous phenomena: transient phenomena, which are localized in time, are not localized in frequency space. There is increasing evidence that this type of phenomena, due to specular scattering or breaking waves, may also play an important role in determining the scattering mechanism in ocean-like surfaces. To overcome this problem, we introduce the use of the Wavelet Transform to study the frequency-temporal signatures of the scattered field from moving ocean waves. We calculate this signature using various analytic scattering theories and show that the Wavelet Transform provides a useful tool for separating the different scattering mechanisms operating in scattering in ocean-like surfaces. Next we simulate realistic nonlinear moving ocean waves and calculate the temporal scattered field signature by using the method of moments and the stop-start approximation: the surface is assumed stationary during a scattering event, but moves between incident EM pulses. We compute and compare the Doppler frequency spectrum and the Wavelet Transform of the resulting signal for various types of surfaces and show how different scattering mechanisms may be isolated by this technique. Finally, we compare these numerical results against the analytical predictions in order to obtain the domain of applicability of various scattering theories for moving ocean surfaces which may include wave breaking and specular glints.

Rodriguez, Ernesto

TEM Analyses of Itokawa Regolith Grains and Lunar Soil Grains to Directly Determine Space Weathering Rates on Airless Bodies

Samples returned from the moon and Asteroid Itokawa by NASA's Apollo Missions and JAXA's Hayabusa Mission, respectively, provide a unique record of their interaction with the space environment. Space weathering effects result from micrometeorite impact activity and interactions with the solar wind. While the effects of solar wind interactions, ion implantation and solar flare particle track accumulation, have been studied extensively, the rate at which these effects accumulate in samples on airless bodies has not been conclusively determined. Results of numerical modeling and experimental simulations do not converge with observations from natural samples. We measured track densities and rim thicknesses of three olivine grains from Itokawa and multiple olivine and anorthite grains from lunar soils of varying exposure ages. Samples were prepared for analysis using a Leica EM UC6 ultramicrotome and an FEI Quanta 3D dual beam focused ion beam scanning electron microscope (FIB-SEM). Transmission electron microscope (TEM) analyses were performed on the JEOL 2500SE 200kV field emission STEM. The solar wind damaged rims on lunar anorthite grains are amorphous, lack inclusions, and are compositionally similar to the host grain. The rim width increases as a smooth function of exposure age until it levels off at approximately 180 nm after approximately 20 My (Fig. 1). While solar wind ion damage can only accumulate while the grain is in a direct line of sight to the Sun, solar flare particles can penetrate to mm-depths. To assess whether the track density accurately predicts surface exposure, we measured the rim width and track density in olivine and anorthite from the surface of rock 64455, which was never buried and has a surface exposure age of 2 My based on isotopic measurements. The rim width from 64455 (60-70nm) plots within error of the well-defined trend for solar wind amorphized rims in Fig. 1. Measured solar flare track densities are accurately reflecting the surface exposure of the grains. Track densities correlate with the amorphous rim thicknesses. While the space-weathered rims of anorthite grains are amorphous, the space-weathered rims on both Itokawa and lunar olivine grains show solar wind damaged rims that are not amorphous. Instead, the rims are nanocrystalline with high dislocation densities and sparse inclusions of nanophase Fe metal. The rim thicknesses on the olivine grains also correlate with track density. The Itokawa olivine grains have track densities that indicate surface exposures of approximately 10(exp 5) years. Longer exposures (up to approximately 10(exp 7) years) do not amorphize the rims, as evidenced by lunar soil olivines with high track densities (approximately 10(exp 11) cm(exp -2)). From the combined data, shown in Fig. 1, it is clear that olivine is damaged (but not amorphized) more rapidly by the solar wind compared to anorthite. The olivine damaged rim forms quickly (in approximately 10(exp 6) y) and saturates at approximately 120nm with longer exposure time. The anorthite damaged rims form more slowly, amorphize, and grow thicker than the olivine rims. This is in agreement with numerical modeling data which predicts that solar wind damaged rims on anorthite will be thicker than olivine. However, the models predict that both olivine and anorthite rims will amorphize and reach equilibrium widths in less than 10(exp 3) y, in contrast to what is observed for natural samples. Laboratory irradiation experiments, which show rapid formation of fully amorphous and blistered surfaces from simulated solar wind exposures are also in contrast to observations of natural samples. These results suggest that there is a flux dependence on the type and extent of irradiation damage that develops in olivine. This flux dependence suggests that great caution be used in extrapolating between high-flux laboratory experiments and the natural case, as demonstrated by. We constrain the space weathering rate through analysis of returned samples. Provided that the track densities and the solar wind damaged rim widths exhibited by the Itokawa grains are typical of the fine-grained regions of Itokawa, then the space weathering rate is on the order of 10(exp 5) y. Space weathering effects in lunar soils saturate within a few My of exposure while those in Itokawa regolith grains formed in approximately 10(exp 5) y. Olivine and anorthite respond differently to solar wind irradiation. The space weathering effects in olivine are particularly difficult to reconcile with laboratory irradiation studies and numerical models. Additional measurements, experiments, and modeling are required to resolve the discrepancies among the observations and calculations involving solar wind amorphization of different minerals on airless bodies.

Berger, Eve L.

NASA SPoRT Modeling and Data Assimilation Research and Transition Activities Using WRF, LIS and GSI

weather research and forecasting ===== The NASA Short‐term Prediction Research and Transition (SPoRT) program has numerous modeling and data assimilation (DA) activities in which the WRF model is a key component. SPoRT generates realtime, research satellite products from the MODIS and VIIRS instruments, making the data available to NOAA/NWS partners running the WRF/EMS, including: (1) 2‐km northwestern‐hemispheric SST composite, (2) daily, MODIS green vegetation fraction (GVF) over CONUS, and (3) NASA Land Information System (LIS) runs of the Noah LSM over the southeastern CONUS. Each of these datasets have been utilized by specific SPoRT partners in local EMS model runs, with select offices evaluating the impacts using a set of automated scripts developed by SPoRT that manage data acquisition and run the NCAR Model Evaluation Tools verification package. SPoRT is engaged in DA research with the Gridpoint Statistical Interpolation (GSI) and Ensemble Kalman Filter in LIS for soil moisture DA. Ongoing DA projects using GSI include comparing the impacts of assimilating Atmospheric Infrared Sounder (AIRS) radiances versus retrieved profiles, and an analysis of extra‐tropical cyclones with intense non‐convective winds. As part of its Early Adopter activities for the NASA Soil Moisture Active Passive (SMAP) mission, SPoRT is conducting bias correction and soil moisture DA within LIS to improve simulations using the NASA Unified‐WRF (NU‐WRF) for both the European Space Agency's Soil Moisture Ocean Salinity and upcoming SMAP mission data. SPoRT has also incorporated real‐time global GVF data into LIS and WRF from the VIIRS product being developed by NOAA/NESDIS. This poster will highlight the research and transition activities SPoRT conducts using WRF, NU‐WRF, EMS, LIS, and GSI.

Case, Jonathan L.

Multiphysics modeling of accelerators through code integration

This work aims to improve the ability of particle accelerator researchers to develop high-performance accelerator cavity designs by creating an overall multiphysics framework that integrates and couples existing application codes. This framework will allow accelerator researchers to build multiphysics models that will optimize cavity design, improve understanding of whole-device performance, and reduce the development and fabrication costs of accelerator research. We utilize the open-source VizSchema data standard as an intermediate data structure interface layer to standardize interfaces between individual application codes. VizScema is extensively documented online, and plugins for VizSchema are available for popular visualization packages, including VisIt and ParaView. Currently, the work focuses on coupling the EM field solver COMSOL and the electron gun code MICHELLE to allow COMSOL field-solve results to be seamlessly used by MICHELLE for particle-solve. Later work will extend this integration to include other fields, particles, and thermodynamics simulation codes.

43 PARTICLE ACCELERATORS

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements

Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to their coupled nature. While experimental data on certain state variables may be available, integrating these data with numerical solvers remains a significant challenge. Physics-informed neural networks (PINNs) have shown promising results in various engineering disciplines, particularly in handling noisy data and solving inverse problems in partial differential equations (PDEs). However, their effectiveness in forecasting nonlinear phenomena in multiphysics regimes, particularly involving turbulence, is yet to be fully established. Here, this study introduces NeuroSEM, a hybrid framework integrating PINNs with the highfidelity Spectral Element Method (SEM) solver, Nektar++. NeuroSEM leverages the strengths of both PINNs and SEM, providing robust solutions for multiphysics problems. PINNs are trained to assimilate data and model physical phenomena in specific subdomains, which are then integrated into the Nektar++ solver. We demonstrate the efficiency and accuracy of NeuroSEM for thermal convection in cavity flow and flow past a cylinder. The framework effectively handles data assimilation by addressing those subdomains and state variables where the data is available. We applied NeuroSEM to the Rayleigh-B´enard convection system, including cases with missing thermal boundary conditions and noisy datasets. Finally, we applied the proposed NeuroSEM framework to real particle image velocimetry (PIV) data to capture flow patterns characterized by horseshoe vortical structures. Our results indicate that NeuroSEM accurately models the physical phenomena and assimilates the data within the specified subdomains. The framework’s plug-and-play nature facilitates its extension to other multiphysics or multiscale problems. Furthermore, NeuroSEM is optimized for efficient execution on emerging integrated GPU-CPU architectures. This hybrid approach enhances the accuracy and efficiency of simulations, making it a powerful tool for tackling complex engineering challenges in various scientific domains.

42 ENGINEERING

Development and Evaluation of a General Drag Model for Gas-Solid Flows via Deep Learning

This project presents the development and evaluation of a general drag model for gas–solid multiphase flows using deep learning techniques. A comprehensive database of more than 4,000 experimental and numerical data points for spherical and non spherical particles was compiled, incorporating geometric features such as sphericity, aspect ratio, and orientation. Several predictive approaches—including traditional em pirical correlations, machine learning, and deep neural networks—were benchmarked, with the proposed Drag Coefficient Correlation-aided Deep Neural Network (DCC DNN) demonstrating superior accuracy. To account for particle–particle interactions, additional drag data were generated using CFD-based simulations of packed and flu idized beds, leading to the development of a retrained model capable of incorporat ing volume fraction effects. Integration of the trained model with the MFiX CFD solver was achieved using FTorch, enabling drag predictions during discrete element method (DEM) simulations. Validation against experimental data for single particles and fluidized beds confirmed the model’s improved predictive ability, particularly for non-spherical geometries. While the model performed strongly under fluidized con ditions, limitations remained in unfluidized regimes, suggesting a need for expanded datasets. Overall, this study demonstrates the feasibility of combining deep learning with physics-informed CFD to improve drag modeling for gas–solid flows, with promis ing implications for scaling multiphase simulations in industrial applications.

42 ENGINEERING