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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Analysis of New Data of Electro-static Assisted Air Dehumidification Processes

The removal of water vapor from the air to reduce relative humidity is a well-known indoor environmental comfort requirement. Common dehumidification approaches require a substantial amount of energy and usually involve the cooling of atmospheric humid air below its dew point or the use of absorbent/adsorbent materials to extract water vapor out of the air. More recently, researchers investigated the effect of electrostatic forces for enhancing water vapor condensation. However, the studies are limited, and there is a lack of correlations that can predict the dehumidification rate. Also, the findings from the literature focused mainly on small flow rates on the order of one to two cfm. Electrically-enhanced condensation consists of the use of highly charged particles, preferably highly charged water droplets, that attract polar water vapor molecules to their surfaces and promote condensation, a phenomenon known as dielectrophoresis. An effect of the electric charge is the reduction of the vapor pressure on the droplets' surface with respect to the saturated pressure predicted by the Kelvin equation. Consequently, the equilibrium between evaporation and condensation is shifted towards condensation. Following the application of the modified Kelvin-Thomson theory, we developed a preliminary physics-based model to predict an effective size range of the charged droplets for optimal dehumidification. The range resulted in about 2 to 4 m in diameter, under few simplifying assumptions. The effect of the size and the charge of the electrosprayed droplets on the overall dehumidification rate was briefly discussed. The use of electrosprays to produce small but highly charged droplets was broadly discussed in this paper. The cone-jet mode was identified as the most suitable electrospray operational mode, and it generated droplets of small size and high electrical charge. The cone-jet stability was also analyzed in detail. The preliminary data of the present work and the model results indicated that several electrospray heads were required to achieve a 5% dehumidification rate for airflow rates of about 5 cfm.

Morcelli, Stefano↗
DOE OSTI2024

Multi-phenomenology Yield Characterization

This report serves as the first delivery of a four-year applied science effort to transform and advance the error bounds for the yield estimate of an explosion. Each year’s delivery will be in this form, culminating in the submission of this work for peer review to a scientific journal. Importantly, the yearly progress reports can then also be viewed as expanding drafts working towards a formal journal article submission. For the first tranche of funding, we collaborated with Air Force Technical Applications Center (AFTAC) scientists to identify unclassified real-world data that demonstrate and validate our advanced error propagation methods. Collaboration includes visits to AFTAC and telecons. For this development, we illustrate the fusion of seismic, acoustic, optical, and surface effect signatures from an explosion. The mathematics and code being adapted to this specific application (Williams et al., 2021) involves physics models of multiple sensor signatures. We have also identified related physics models and have integrated them into code. Current methods of underground explosion yield estimation for the Threshold Test Ban Treaty (TTBT) have served the US treaty monitoring mission well for decades. A research objective of the Defense Nuclear Nonproliferation Research and Development (DNN R&D) office of the National Nuclear Security Administration (NNSA) has always been to provide new technical capabilities for monitoring lower thresholds. The general model and error propagation code to be developed in this project is based on significant advances in error modeling and propagation needed to analyze data at lower detection thresholds. The second tranche of funding for this project began on May 1, 2022, and planned work for the second tranche includes: i) completing the integration of physical model code into the general error model framework; this code accommodates a wide range of linear/nonlinear source models, fixed/ random effects, and frequentist/Bayesian analyses (the purpose of which is not to dictate to users how to analyze data, but instead to allow users the maximum flexibility in their work); ii) illustrative application of code to identified data, and; iii) initial planning with AFTAC researchers on delivery of code to the Common Development Environment at AFTAC, and continued writing of the planned final journal article submission (year two of this progress report), with particular emphasis on descriptions of data identified for this effort.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML-based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically interpretable Mass-Conserving-Perceptron (MCP) as a way to bridge the gap between PC-based and ML-based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass-conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off-the-shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall-runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML-based physical-conceptual representation of the coupled nature of mass-energy-information flows through geoscientific systems.

58 GEOSCIENCES↗
DOE OSTI2020

Kepler Data Analysis: Non-Gaussian Noise and Fourier Gaussian Process Analysis of Stellar Variability

We develop a statistical analysis model of Kepler stellar flux data in the presence of planet transits, non-Gaussian noise, and stellar variability. We first develop a model for the Kepler noise probability distribution in the presence of outliers, which make the noise probability distribution non-Gaussian. We develop a signal likelihood analysis based on this probability distribution, in which we model the signal as a sum of the star variability and planetary transits. We argue that these components need to be modeled together if optimal signal is to be extracted from the data. For the stellar variability model we develop an optimal Gaussian process analysis using a Fourier-based Wiener filter approach, where the power spectrum is non-parametric and learned from the data. We develop high dimensional optimization of the objective function, where we jointly optimize all the model parameters, including thousands of star variability modes, and planet transit parameters. We apply the method to Kepler-90 data and show that it gives a better match to the stellar variability than the existing methods, and robustly handles noise outliers. As a consequence, the planet radii have a higher value than what the existing methods give, including splines and celerite.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Alternative modeling methods for plasma-based Rf ion sources

Rf-driven ion sources for accelerators and many industrial applications benefit from detailed numerical modeling and simulation of plasma characteristics. For instance, modeling of the Spallation Neutron Source (SNS) internal antenna H{sup −} source has indicated that a large plasma velocity is induced near bends in the antenna where structural failures are often observed. This could lead to improved designs and ion source performance based on simulation and modeling. However, there are significant separations of time and spatial scales inherent to Rf-driven plasma ion sources, which makes it difficult to model ion sources with explicit, kinetic Particle-In-Cell (PIC) simulation codes. In particular, if both electron and ion motions are to be explicitly modeled, then the simulation time step must be very small, and total simulation times must be large enough to capture the evolution of the plasma ions, as well as extending over many Rf periods. Additional physics processes such as plasma chemistry and surface effects such as secondary electron emission increase the computational requirements in such a way that even fully parallel explicit PIC models cannot be used. One alternative method is to develop fluid-based codes coupled with electromagnetics in order to model ion sources. Time-domain fluid models can simulate plasma evolution, plasma chemistry, and surface physics models with reasonable computational resources by not explicitly resolving electron motions, which thereby leads to an increase in the time step. This is achieved by solving fluid motions coupled with electromagnetics using reduced-physics models, such as single-temperature magnetohydrodynamics (MHD), extended, gas dynamic, and Hall MHD, and two-fluid MHD models. We show recent results on modeling the internal antenna H{sup −} ion source for the SNS at Oak Ridge National Laboratory using the fluid plasma modeling code USim. We compare demonstrate plasma temperature equilibration in two-temperature MHD models for the SNS source and present simulation results demonstrating plasma evolution over many Rf periods for different plasma temperatures. We perform the calculations in parallel, on unstructured meshes, using finite-volume solvers in order to obtain results in reasonable time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Thermal fluctuations and carrier localization induced by dynamic disorder in MAPbI 3 described by first-principles based tight-binding model

Halide perovskites are strongly influenced by large amplitude anharmonic lattice fluctuations at room temperature. We develop a tight binding model for dynamically disordered MAPbI$_3$ based on density functional theory (DFT) calculations to calculate electronic structure for finite temperature crystal structures at the length scale of thermal disorder and carrier localization. The model predicts individual Hamiltonian matrix elements and band structures with high accuracy, owing to the inclusion of additional matrix elements and descriptors for non-Coulombic interactions. We apply this model to electronic structure at length and time scales inaccessible to first principles methods, finding an increase in band gap, carrier mass, and the sub-picosecond fluctuations in these quantities with increasing temperature as well as the onset of carrier localization in large supercells induced by thermal disorder at 300 K. Finally, we identify the length scale $L^*= 5$ nm as the onset of localization in the electronic structure, associated with associated with decreasing band edge fluctuations, increasing carrier mass, and Rashba splitting approaching zero.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Pseudospectral Maxwell solvers for an accurate modeling of Doppler harmonic generation on plasma mirrors with particle-in-cell codes

With the advent of petawatt class lasers, the very large laser intensities attainable on target should enable the production of intense high-order Doppler harmonics from relativistic laser-plasma mirror interactions. At present, the modeling of these harmonics with particle-in-cell (PIC) codes is extremely challenging as it implies an accurate description of tens to hundreds of harmonic orders on a broad range of angles. In particular, we show here that due to the numerical dispersion of waves they induce in vacuum, standard finite difference time domain (FDTD) Maxwell solvers employed in most PIC codes can induce a spurious angular deviation of harmonic beams potentially degrading simulation results. This effect was extensively studied and a simple toy model based on the Snell-Descartes law was developed that allows us to finely predict the angular deviation of harmonics depending on the spatiotemporal resolution and the Maxwell solver used in the simulations. Our model demonstrates that the mitigation of this numerical artifact with FDTD solvers mandates very high spatiotemporal resolution preventing realistic three-dimensional (3D) simulations even on the largest computers available at the time of writing. Finally, we finally show that nondispersive pseudospectral analytical time domain solvers can considerably reduce the spatiotemporal resolution required to mitigate this spurious deviation and should enable in the near future 3D accurate modeling on supercomputers in a realistic time to solution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Framework for Modeling High-Impact, Low-Frequency Power Grid Events to Support Risk-Informed Decisions

Natural and man-made hazardous events resulting in loss of grid infrastructure assets challenge the electric power grid’s security and resilience. However, the planning and allocation of appropriate contingency resources for such events requires an understanding of their likelihood and the extent of their potential impact. Where these events are of low likelihood, a risk-informed perspective on planning can be problematic as there exists an insufficient statistical basis to directly estimate the probabilities and consequences of their occurrence. Since risk-informed decisions rely on such knowledge, a basis for modeling the risk associated with high-impact low frequency events (HILFs) is essential. Insights from such a model can inform where resources are most rationally and effectively expended. The present effort is focused on development of a HILF risk assessment framework. Such a framework is intended to provide the conceptual and overarching technical basis for the development of HILF risk models that can inform decision makers across numerous stakeholder sectors. The North American Electric Reliability Corporation (NERC) 2014 Standard TPL-001-4 considers severe events for transmission reliability planning, but does not address events of such severity that they have the potential to fail a substantial fraction of grid assets over a region, such as geomagnetic disturbances (GMD), extreme seismic events, and coordinated cyber-physical attacks. These are beyond current planning guidelines. As noted, the risks associated with such events cannot be statistically estimated based on historic experience; however, there does exist a stable of risk modeling techniques for rare events that have proven of value across a wide range of engineering application domains. There is an active and growing interest in evaluating the value of risk management techniques in the State transmission planning and emergency response communities, some of this interest in the context of grid modernization activities. The availability of a grid HILF risk model, integrated across multi-hazard domains which, when interrogated, can support transparent, defensible and effective decisions, is an attractive prospect among these communities. In this report, we document an integrated HILF risk framework intended to inform the development of risk models. These models would be based on the systematic and comprehensive (to within scope) characterization of hazards to the level of detail required for modeling risk, identification of the stressors associated with the hazards (i.e., the means of impacting grid and supporting infrastructure), characterization of the vulnerability of assets to these stressors and the probabilities of asset compromise, the grid’s dynamic response to the asset failures, and assessment of subsequent severities of consequence with respect to selected impact metrics, such as power outage duration and geographic reach. Specifically, the current framework is being developed to;1. Provide the conceptual and overarching technical paradigms for the development of risk models; 2. Identify the classes of models required to implement the framework - providing examples of existing models, and also identifying where modeling gaps exist; 3. Identify the types of data required, addressing circumstances under which data are sparse and the formal elicitation of informed judgment might be required; and 4. Identify means by which the resultant risk models might be interrogated to form the necessary basis for risk management.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

DEVELOPMENT OF 20 IEER ROOFTOP UNITS A SIMULATION STUDY

Based on detailed steady-state system and component modeling, we developed a rooftop unit system design, which is able to achieve IEER (Integrated Energy Efficiency Ratio) higher than 20. We modeled fin-&-tube and micro-channel heat exchangers using segment-to-segment approach, and use ARI 10-coefficient compressor map to simulate compressor performance. The system modeling is based on a component-based modeling approach, which facilitates flexible simulation of complicated system configurations. Starting with a baseline system having IEER of 16.6, we extensively investigated numerous technical options, i.e. varying compressor sizes, heat exchanger fin densities, fin-&-tube or micro-channel heat exchanger, suction line heat exchanger, desiccant wheel, tandem compressor, variable-speed compressor, and condenser evaporative pre-cooling; and developed an innovative system configuration combining a tandem compression system with a variable-speed compression system. The combined system can achieve high IEER as well as process the outdoor ventilation air over an extensive range. We successfully evaluated the design concept for a 20-ton unit as well as a 10-ton unit. All the selected components are readily accessible on the market, and we validated the performance predictions against existing RTU products at the rating condition. This paper illustrates a potentially cost-effective high IEER RTU design.

Modeling↗
DOE OSTI2014

Breaking Computational Barriers: Real-time Analysis and Optimization with Large-scale Nonlinear Models via Model Reduction

Model reduction for dynamical systems is a promising approach for reducing the computational cost of large-scale physics-based simulations to enable high-fidelity models to be used in many- query (e.g., Bayesian inference) and near-real-time (e.g., fast-turnaround simulation) contexts. While model reduction works well for specialized problems such as linear time-invariant systems, it is much more difficult to obtain accurate, stable, and efficient reduced-order models (ROMs) for systems with general nonlinearities. This report describes several advances that enable nonlinear reduced-order models (ROMs) to be deployed in a variety of time-critical settings. First, we present an error bound for the Gauss-Newton with Approximated Tensors (GNAT) nonlinear model reduction technique. This bound allows the state-space error for the GNAT method to be quantified when applied with the backward Euler time-integration scheme. Second, we present a methodology for preserving classical Lagrangian structure in nonlinear model reduction. This technique guarantees that important properties--such as energy conservation and symplectic time-evolution maps--are preserved when performing model reduction for models described by a Lagrangian formalism (e.g., molecular dynamics, structural dynamics). Third, we present a novel technique for decreasing the temporal complexity --defined as the number of Newton-like iterations performed over the course of the simulation--by exploiting time-domain data. Fourth, we describe a novel method for refining projection-based reduced-order models a posteriori using a goal-oriented framework similar to mesh-adaptive h -refinement in finite elements. The technique allows the ROM to generate arbitrarily accurate solutions, thereby providing the ROM with a 'failsafe' mechanism in the event of insufficient training data. Finally, we present the reduced-order model error surrogate (ROMES) method for statistically quantifying reduced- order-model errors. This enables ROMs to be rigorously incorporated in uncertainty-quantification settings, as the error model can be treated as a source of epistemic uncertainty. This work was completed as part of a Truman Fellowship appointment. We note that much additional work was performed as part of the Fellowship. One salient project is the development of the Trilinos-based model-reduction software module Razor , which is currently bundled with the Albany PDE code and currently allows nonlinear reduced-order models to be constructed for any application supported in Albany. Other important projects include the following: 1. ROMES-equipped ROMs for Bayesian inference: K. Carlberg, M. Drohmann, F. Lu (Lawrence Berkeley National Laboratory), M. Morzfeld (Lawrence Berkeley National Laboratory). 2. ROM-enabled Krylov-subspace recycling: K. Carlberg, V. Forstall (University of Maryland), P. Tsuji, R. Tuminaro. 3. A pseudo balanced POD method using only dual snapshots: K. Carlberg, M. Sarovar. 4. An analysis of discrete v. continuous optimality in nonlinear model reduction: K. Carlberg, M. Barone, H. Antil (George Mason University). Journal articles for these projects are in progress at the time of this writing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

A comprehensive model for the ultrashort visible light irradiation of semiconductors

We present a new model describing the irradiation of semiconductors with ultrashort laser pulses. Based on the earlier developed multiple rate equation [Rethfeld, Phys. Rev. Lett. 92, 187401 (2004)], the model additionally includes the interaction of electrons with the phononic subsystem of the lattice and allows for the direct determination of the conditions for crystal damage. In contrast to commonly used approaches based on the thermodynamic description of the material, our model is applicable to nonequilibrium electronic conditions. Accounting for the dynamic changes in the optical properties of the target (i.e., reflectivity, photoabsorption coefficients), the developed model allows for a comprehensive evaluation of the damage by tracing the changes in the optical parameters, lattice heating and subsequent melting. Our model effectively describes the dynamics of the electronic subsystem and lattice heating and the results are in very good agreement with experimental measurements on the transient reflectivity and the fluence damage threshold of silicon irradiated with a femtosecond laser pulse.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Microscopic core-quasiparticle coupling model for spectroscopy of odd-mass nuclei

Predictions of the spectroscopic properties of low-lying states are critical for nuclear structure studies but are problematic for nuclei with an odd nucleon due to the interplay of the unpaired single particle with nuclear collective degrees of freedom. To predict the spectroscopic properties of odd-mass medium-heavy and heavy nuclei with a model that treats single-particle and collective degrees of freedom within the same microscopic framework. A microscopic core-quasiparticle coupling (CQC) model based on the covariant density functional theory is developed that contains the collective excitations of even-mass cores and spherical single-particle states of the odd nucleon as calculated from a quadrupole collective Hamiltonian combined with a constrained triaxial relativistic Hartree-Bogoliubov model. Additionally, predictions of the new model for excitation energies, kinematic and dynamic moments of inertia, and transition rates are shown to be in good agreement with results of low-lying spectroscopy measurements of the axially deformed odd-proton nucleus 159 Tb and the odd-neutron nucleus 157 Gd. A microscopic CQC model based on covariant density functional theory is developed for odd-mass nuclei and shown to give predictions that agree with measurements of two medium-heavy nuclei. Future studies with additional nuclei are planned.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Response of a Memristive Biomembrane and Demonstration of Potential Use in Online Learning

The pervasive von Neumann architecture uses complex processor cores and sequential computation. In contrast, the brain is massively parallel and highly efficient, owing to the ability of the neurons and synapses to store and process information simultaneously and to adapt according to incoming information. These features have motivated researchers to develop a host of brain-inspired computers, devices, and models, collectively referred to as neuromorphic computing systems. The quest for synaptic materials capable of closely mimicking biological synapses has led to an alamethicin-doped, synthetic biomembrane with volatile memristive properties which can emulate key synaptic functions to facilitate learning and computation. In contrast to its solid-state counterparts, this two-terminal, biomolecular memristor features similar structure, switching mechanisms, and ionic transport modality as biological synapses while consuming considerably lower power. To use the device as a circuit element, it is important to understand its response to different kinds of input signals. Here we develop a simplified closed form analytical solution based on the underlying state equations for pulse and sine wave inputs. A Verilog-A model based on Runge-Kutta method was developed to incorporate the device in a circuit simulator. Finally, the paper demonstrates possible applications for short- and long-term learning using its unique volatile memristive properties.

Hasan, Md Sakib↗
DOE OSTI2017

Comparative Study of Clustering Techniques for Real-Time Dynamic Model Reduction

Dynamic model reduction in power systems is necessary for improving computational efficiency. Traditional model reduction using linearized models or offline analysis would not be adequate to capture power system dynamic behaviors, especially the new mix of intermittent generation and intelligent consumption makes the power system more dynamic and non-linear. Real time dynamic model reduction emerges as an important need. This paper explores the use of clustering techniques to analyze real-time phasor measurements to determine generator groups and representative generators for dynamic model reduction. Two clustering techniques – graph clustering and evolutionary clustering – are studied in this paper. Various implementations of these techniques are compared and also compared with a previously developed Singular Value Decomposition (SVD)-based dynamic model reduction approach. Various methods exhibit different levels of accuracy when comparing the reduced model simulation against the original model. But some of them are consistently accurate. From this comparative perspective, this paper provides a good reference point for practical implementations.

model reduction, graph clustering, svd, evolutiona↗
DOE OSTI2015

Assessing the nonlinear response of fine particles to precursor emissions: Development and application of an extended response surface modeling technique v1.0

An innovative extended response surface modeling technique (ERSM v1.0) is developed to characterize the nonlinear response of fine particles (PM₂̣₅) to large and simultaneous changes of multiple precursor emissions from multiple regions and sectors. The ERSM technique is developed based on the conventional response surface modeling (RSM) technique; it first quantifies the relationship between PM₂̣₅ concentrations and the emissions of gaseous precursors from each single region using the conventional RSM technique, and then assesses the effects of inter-regional transport of PM₂̣₅ and its gaseous precursors on PM₂̣₅ concentrations in the target region. We apply this novel technique with a widely used regional chemical transport model (CTM) over the Yangtze River delta (YRD) region of China, and evaluate the response of PM₂̣₅ and its inorganic components to the emissions of 36 pollutant–region–sector combinations. The predicted PM₂̣₅ concentrations agree well with independent CTM simulations; the correlation coefficients are larger than 0.98 and 0.99, and the mean normalized errors (MNEs) are less than 1 and 2% for January and August, respectively. It is also demonstrated that the ERSM technique could reproduce fairly well the response of PM₂̣₅ to continuous changes of precursor emission levels between zero and 150%. Employing this new technique, we identify the major sources contributing to PM₂̣₅ and its inorganic components in the YRD region. The nonlinearity in the response of PM₂̣₅ to emission changes is characterized and the underlying chemical processes are illustrated.

58 GEOSCIENCES↗
DOE OSTI2024

Data-driven Quasi-static Surrogate Model for Nuclear-powered Integrated Energy Systems

The integration of nuclear power into energy systems presents a promising avenue to address the growing global energy demands while minimizing greenhouse gas emissions. In this paper, we introduce a data-driven quasi-static surrogate model for nuclear-powered Integrated Energy Systems (IES) that comprises various components, including a small modular reactor (SMR), energy manifold (EM), balance of plant (BOP), high-temperature steam electrolysis (HTSE), and district heating (DH) system. Traditional physics-based models for these components often entail significant computational overhead and time consumption, necessitating the development of efficient surrogate models. The development of a complete surrogate model for the IES involves the creation of individual surrogate models for each component, leveraging machine learning techniques and simulated data. These isolated surrogate models are subsequently integrated, enabling a holistic view of the IES and reducing the computational burden associated with detailed physics-based simulations. This paper outlines the development process, validation, and the performance evaluation of the surrogate models. The findings shed light on the accuracy and applicability of the surrogate models in practical scenarios, demonstrating their potential to expedite the analysis of nuclear-powered IES and inform future research and development efforts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Data-driven Quasi-static Surrogate Models for Nuclear-powered Integrated Energy Systems

The integration of nuclear power into energy systems presents a promising avenue to address the growing global energy demands while minimizing greenhouse gas emissions. In this paper, we introduce a data-driven quasi-static surrogate model for nuclear-powered Integrated Energy Systems (IES) that comprises various components, including a small modular reactor (SMR), steam manifold, balance of plant (BOP), high-temperature steam electrolysis (HTSE), and district heating (DH) system. Traditional physics-based models for these components often entail significant computational resource and time consumption, necessitating the development of efficient surrogate models. The development of a complete surrogate model for the IES involves the creation of individual surrogate models for each component, leveraging machine learning techniques and simulated data. These isolated surrogate models are subsequently integrated, enabling a holistic view of the IES and reducing the computational burden associated with detailed physics-based simulations. This paper outlines the development process, validation, and the performance evaluation of the surrogate models. The exceptional performance, with low root-mean-squared errors and R-squared scores of at least 99.8% across all individual surrogate models, underscores their accuracy and practical applicability. These results demonstrate the potential of these models to expedite the analysis of nuclear-powered IES, offering insights that can shape future research and development efforts.

08 HYDROGEN↗
DOE OSTI2016

Three-dimensional dendritic needle network model for alloy solidification

Here, we present a three-dimensional (3D) formulation of the multiscale Dendritic Needle Network (DNN) model for dendritic microstructure growth. This approach is aimed at simulating quantitatively the solidification dynamics of complex hierarchical networks in spatially extended dendritic arrays, hence bridging the scale gap between phase-field simulations at the scale of a few dendrites and coarse-grained simulations on the larger scale of entire polycrystalline structures. In the DNN model, the dendritic network is represented by a network of branches that interact through the solutal diffusion field. The tip velocity V(t) and tip radius ρ(t) of each needle is determined by combining a standard solvability condition that fixes the product ρ 2 V and a solute flux conservation condition that fixes the product ρV 2 in 2D and ρV in 3D as a function of a solutal flux intensity factor ℱ(t). The latter measures the intensity of the solute flux in the dendrite tip region and can be calculated by contour (2D) or surface (3D) integration around the tip of each needle. We first present an extended formulation of the 2D DNN model where needles have a finite thickness and parabolic tips. This formulation remains valid for a larger range of tip Péclet number than the original thin needle formulation and is readily extended to 3D needles with paraboloidal tips. The 3D DNN model based on this thick-needle formulation is developed for both isothermal and directional solidification. Model predictions are validated by comparisons with known analytical solutions that describe the early transient and steady-state growth regimes. We exploit the power of the DNN model to characterize the competitive growth of well-developed secondary branches in 3D on the scale of the diffusion length. The results show that the length of active secondary branches increases as a power law of distance behind the tip with an exponent in good quantitative agreement with experimental measurements. Finally, we apply the model to simulate the three-dimensional directional solidification of an Al-7wt% Si alloy, which we directly compare to observed microstructures from microgravity experiments onboard the International Space Station. The predictions of selected microstructural features, such as dendrite arm spacings, show a good agreement with experiments. The computationally-efficient DNN model opens new avenues for investigating the dynamics of large dendritic arrays at length and time scales relevant to solidification experiments and processes.

36 MATERIALS SCIENCE↗