3D printed structures for modeling the Young’s modulus of bamboo parenchyma
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Dixon, P. G.↗
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Results for “Structural Modeling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Graph-Based Similarity Metrics for Comparing Simulation Model Causal Structures
The causal structure of a simulation is a major determinant of both its character and behavior, yet most methods we use to compare simulations focus only on simulation outputs. We introduce a method that combines graphical representation with information theoretic metrics to quantitatively compare the causal structures of models. The method applies to agent-based simulations as well as system dynamics models and facilitates comparison within and between types. Comparing models based on their causal structures can illuminate differences in assumptions made by the models, allowing modelers to (1) better situate their models in the context of existing work, including highlighting novelty, (2) explicitly compare conceptual theory and assumptions to simulated theory and assumptions, and (3) investigate potential causal drivers of divergent behavior between models. We demonstrate the method by comparing two epidemiology models at different levels of aggregation.
97 MATHEMATICS AND COMPUTING↗
Feature Extraction for Structural Dynamics Model Validation
As structural dynamics becomes increasingly non-modal, stochastic and nonlinear, finite element model-updating technology must adopt the broader notions of model validation and uncertainty quantification. For example, particular re-sampling procedures must be implemented to propagate uncertainty through a forward calculation, and non-modal features must be defined to analyze nonlinear data sets. The latter topic is the focus of this report, but first, some more general comments regarding the concept of model validation will be discussed.
97 MATHEMATICS AND COMPUTING↗
Criteria for folding in structure-based models of proteins
In structure-based models of proteins, one often assumes that folding is accomplished when all contacts are established. This assumption may frequently lead to a conceptual problem that folding takes place in a temperature region of very low thermodynamic stability, especially when the contact map used is too sparse. We consider six different structure-based models and show that allowing for a small, but model-dependent, percentage of the native contacts not being established boosts the folding temperature substantially while affecting the time scales of folding only in a minor way. We also compare other properties of the six models. We show that the choice of the description of the backbone stiffness has a substantial effect on the values of characteristic temperatures that relate both to equilibrium and kinetic properties. Models without any backbone stiffness (like the self-organized polymer) are found to perform similar to those with the stiffness, including in the studies of stretching.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Use of a Dual-Structure Constitutive Model for Predicting the Long-Term Behavior of an Expansive Clay Buffer in a Nuclear Waste Repository
Expansive soils are suitable as backfill and buffer materials in engineered barrier systems to isolate heat-generating nuclear waste in deep geological formations. The canisters containing nuclear waste would be placed in tunnels excavated at a depth of several hundred meters. The expansive soil should provide enough swelling capacity to support the tunnel walls, thereby reducing the impact of the excavation-damaged zone on the long-term mechanical and flow-barrier performance. In addition to their swelling capacity, expansive soils are characterized by accumulating irreversible strain on suction cycles and by effects of microstructural swelling on water permeability that for backfill or buffer materials can significantly delay the time it takes to reach full saturation. In order to simulate these characteristics of expansive soils, a dual-structure constitutive model that includes two porosity levels is necessary. The authors present the formulation of a dual-structure model and describe its implementation into a coupled fluid flow and geomechanical numerical simulator. The authors use the Barcelona Basic Model (BBM), which is an elastoplastic constitutive model for unsaturated soils, to model the macrostructure, and it is assumed that the strains of the microstructure, which are volumetric and elastic, induce plastic strain to the macrostructure. The authors tested and demonstrated the capabilities of the implemented dual-structure model by modeling and reproducing observed behavior in two laboratory tests of expansive clay. As observed in the experiments, the simulations yielded nonreversible strain accumulation with suction cycles and a decreasing swelling capacity with increasing confining stress. Finally, the authors modeled, for the first time using a dual-structure model, the long-term (100,000 years) performance of a generic heat-generating nuclear waste repository with waste emplacement in horizontal tunnels backfilled with expansive clay and hosted in a clay rock formation. The thermo-hydro-mechanical results of the dual-structure model were compared with those of the standard single-structure BBM. The main difference between the simulation results from the two models is that the dual-structure model predicted a time to fully saturate the expansive clay barrier on the order of thousands of years, whereas the standard single-structure BBM yielded a time on the order of tens of years. These examples show that a dual-structure model, such as the one presented here, is necessary to properly model the thermo-hydro-mechanical behavior of expansive soils.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry
Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy.
Wang, Tianle [Brookhaven National Laboratory (BNL)↗
3D-equivariant graph neural networks for protein model quality assessment
Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. We develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method—AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method—AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA.
59 BASIC BIOLOGICAL SCIENCES↗
Specification Document for OC6 Phase II: Verification of an Advanced Soil-Structure Interaction Model for Offshore Wind Turbines
The Norwegian Geotechnical Institute (NGI) has developed a new macro-element model that accounts for the soil-structure interaction in offshore wind turbines. This development was done as part of the REDWIN (REDucing cost of offshore WINd by integrated structural and geotechnical design) project. The focus of Phase II of the OC6 project was to integrate REDWIN’s new soil-structure modeling capability into the coupled modeling tools that are used to design fixed-bottom offshore wind systems. The integration was then verified through a series of load cases using an example monopile-based offshore wind system examined within the WAS-XL project. This document provides the details needed to model the system examined in OC6 Phase II, using the new REDWIN modeling approach, as well as other standard modeling approaches, including apparent fixity, distributed springs, and coupled springs.
17 WIND ENERGY↗
Development of Algebraic and Topological-Based Structured Packing Model
Poster being presented at the 2024 annual AICHE meeting held from October 27-31, 2024. The poster focuses on developing an algebraic and topological model for designing structured packing for a CO2 absorption tower. The model can be optimized to determine an optimal packing structure.
Summits, Stephen↗
Process-Structure-Property Modeling for Severe Plastic Deformation Processes Using Orientation Imaging Microscopy and Data-Driven Techniques
Machining is a severe plastic deformation process, wherein the workpiece material is subjected to high deformation rates and temperatures. During metal machining, the dynamic recrystallization mechanism causes grain refinement into the sub-micron range. In this study, we investigate the microstructure evolution of oxygen-free high conductivity copper (OFHC Cu) subject to a machining process where the cutting speed and rake angle are controlled to manipulate the process strain, strain rate, and temperatures. Microstructures of the deformed chips are quantified using orientation imaging microscopy and novel statistical descriptors that capture the morphology and local lattice misorientations generated during the several mechanistic stages of the dynamic recrystallization process. Mechanical properties of the resulting chips are quantified using spherical nanoindentation protocols. A multiple output Gaussian process regression model is used to simultaneously model the structure-property evolution, which differs from more common approaches that establish such relationships sequentially. This modeling strategy is particularly attractive since it can flexibly provide both structure and property uncertainty estimates. In addition, the statistical modeling framework allows for the inclusion of multi-fidelity data. The statistical metrics utilized serve as efficient microstructure descriptors, which retain the physics of the observed structures without having to introduce ad hoc microstructure feature definitions.
36 MATERIALS SCIENCE↗
A critical examination of compound stability predictions from machine-learned formation energies
Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.
36 MATERIALS SCIENCE↗
LES FOR SIMULATING THE GAS EXCHANGE PROCESS IN A SPARK IGNITION ENGINE
The gas exchange process is known to be a significant source of cyclic variability in Internal Combustion Engines (ICE). Traditionally, Large Eddy Simulations (LES) are expected to capture these cycle-to-cycle variations. This paper reports a numerical effort to establish best practices for capturing cyclic variability with LES tools in a Transparent Combustion Chamber (TCC) spark ignition engine. The main intention is to examine the sensitivity of cycle averaged mean and Root Mean Square (RMS) flow fields and Proper Orthogonal Decomposition (POD) modes to different computational hardware, adaptive mesh refinement (AMR) and LES sub-grid scale (SGS) models, since these aspects have received little attention in the past couple of decades. This study also examines the effect of near-wall resolution on the predicted wall shear stresses. LES is pursued with commercially available CONVERGE code. Two different SGS models are tested, a one-equation eddy viscosity model and dynamic structure model. The results seem to indicate that both mean and RMS fields without any SGS model are not much different than those with LES models, either one-equation eddy viscosity or dynamic structure model. Computational hardware results in subtle quantitative differences, especially in RMS distributions. The influence of AMR on both mean and RMS fields is negligible. The predicted shear stresses near the liner walls is also found to be relatively insensitive to near-wall resolution except in the valve curtain region.
Ameen, Muhsin M↗
Microscopic model for intersubband gain from electrically pumped quantum-dot structures
We study theoretically the performance of electrically pumped self-organized quantum dots as a gain material in the mid-infrared range at room temperature. We analyze an AlGaAs/InGaAs based structure composed of dots-in-a-well sandwiched between two quantum wells. We numerically analyze a comprehensive model by combining a many-particle approach for electronic dynamics with a realistic modeling of the electronic states in the whole structure. We investigate the gain both for quasi-equilibrium conditions and current injection. We find, comparing different structures, that steady-state gain can only be realized by an efficient extraction process, which prevents an accumulation of electrons in continuum states, that make the available scattering pathways through the quantum-dot active region too fast to sustain inversion.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Local structure graph models with higher-order dependence
Local structure graph models (LSGMs) describe random graphs and networks as a Markov random field (MRF)—each graph edge has a specified conditional distribution dependent on explicit neighbourhoods of other graph edges. Centered parameterizations of LSGMs allow for direct control and interpretation of parameters for large- and small-scale structures (e.g., marginal means vs. dependence). Here, we extend this parameterization to account for triples of dependent edges and illustrate the importance of centered parameterizations for incorporating covariates and interpreting parameters. Using a MRF framework, common exponential random graph models are also shown to induce conditional distributions without centered parameterizations and thereby have undesirable features. This work attempts to advance graph models through conditional model specifications with modern parameterizations, covariates and higher-order dependencies.
97 MATHEMATICS AND COMPUTING↗
OC6 Phase II: Integration and verification of a new soil–structure interaction model for offshore wind design
Abstract This paper provides a summary of the work done within the OC6 Phase II project, which was focused on the implementation and verification of an advanced soil–structure interaction model for offshore wind system design and analysis. The soil–structure interaction model comes from the REDWIN project and uses an elastoplastic, macroelement model with kinematic hardening, which captures the stiffness and damping characteristics of offshore wind foundations more accurately than more traditional and simplified soil–structure interaction modeling approaches. Participants in the OC6 project integrated this macroelement capability to coupled aero‐hydro‐servo‐elastic offshore wind turbine modeling tools and verified the implementation by comparing simulation results across the modeling tools for an example monopile design. The simulation results were also compared to more traditional soil–structure interaction modeling approaches like apparent fixity, coupled springs, and distributed springs models. The macroelement approach resulted in smaller overall loading in the system due to both shifts in the system frequencies and increased energy dissipation. No validation work was performed, but the macroelement approach has shown increased accuracy within the REDWIN project, resulting in decreased uncertainty in the design. For the monopile design investigated here, that implies a less conservative and thus more cost‐effective offshore wind design.
17 WIND ENERGY↗
Hydrodynamic and Structural Response Modeling of a Prototype Floating Membrane Reservoir System for Pumped Storage Hydropower
Hydrodynamic, computational fluid dynamics, and finite-element modeling were performed for a novel floating membrane reservoir system design for closed-loop pumped storage hydropower application. The conceptual design, which is now protected under an invention disclosure with a patent pending, offers a potential low-cost, low-impact solution to address the high costs, long investment return periods, and environmental disruptions encountered with traditional pumped storage development while offering modularity to enable replication at many locations. Prior to physical tests on a prototype, this paper documents the response analysis and modeling completed to simulate hydraulic and structural performance under different reservoir deployment and alignment arrangements, evaluate system stability, and refine the conceptual design. The results indicate that the excitation frequency from vortex shedding is at least an order of magnitude lower than the water sloshing frequencies in the reservoir, the structural natural frequency of the entire reservoir, and the vibrational frequencies of the side membranes, although excitation frequencies from other sources could cause mechanical resonance under certain conditions. To control destabilizing effects and prevent rocking motion and vibration, the authors conclude that the design could be improved by including a support structure around the floating reservoir, which could also provide floating-membrane containment, improve safety, and facilitate vertical reservoir movement. These conclusions, based on hydrodynamic and structural response modeling, help define specifications for upcoming full-scale prototype construction, deployment, and testing. This study demonstrates standard modeling technique application to an innovative design for which similar applications have not been previously evaluated. As a result, the refined design is capable of improving the scalability and feasibility of pumped storage hydropower in the United States and will be considered for commercialization following prototype testing.
13 HYDRO ENERGY↗
How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive
This dataset contains model output and input data, as well as source code examples for the Terrestrial Ecosystem Model with the Dynamic Vegetation Model and Dynamic Organic Soil (DVM-DOS-TEM) for the field sites Imnavait creek and the Bonanza creek Long Term Ecological Research Network (LTER). The data covers simulations from the last glacial maximum (LGM) until 2100 for a selection of paleo scenarios, setting the mean temperature of the LGM up to 10°C lower than pre-industrial conditions. The model structure was modulated to represent various model versions, and this dataset contains the relevant changes in the source code. The raw output data, the processed statistical data, the setup and processing scripts as well as parameter value distribution files from a parameter sensitivity analysis are included as well. Model outputs include active layer depth, organic soil carbon, soil layer depths, gross primary productivity (GPP) with and without nitrogen limitation, net primary productivity (NPP), soil liquid water content, heterotrophic, maintenance, and growth respiration, soil temperature, and vegetation carbon (*.nc files). The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research.Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.
54 ENVIRONMENTAL SCIENCES↗