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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Prediction of stress-strain response of SCS-6/Timetal-21S subjected to a hypersonic flight profile

Thermomechanical response of a cross-ply SCS-6/Timetal-21S composite subjected to a generic hypersonic flight profile with the temperature ranging from -130 C to 816 C was evaluated experimentally and analytically. A two dimensional micromechanical anlaysis, VISCOPLY, was used to predict the stress-strain response of the laminate and of the constituents in each ply during thermomechanical loading conditions. In the analysis, the fiber was modeled as elastic with transverse orthotropic and temperature dependent properties and the matrix was modeled using a thermoviscoplastic constitutive relation. The fiber transverse modulus was reduced in the analysis to simulate fiber-matrix interface failure. Reasonable agreement was found between measured and predicted laminate stress-strain response when fiber-matrix debonding was modeled.

Mirdamadi, Massoud↗
DOE OSTI2022

MULTISTEP AND CONTINUOUS PHYSICS-INFORMED NEURAL NETWORK METHODS FOR LEARNING GOVERNING EQUATIONS AND CONSTITUTIVE RELATIONS

In this work, we investigate the applicability and relative merit of discrete and continuous versions of physics-informed neural network (PINN) methods for learning unknown governing equations or constitutive relations in a nonlinear dynamical system. In the case of unknown dynamics, entire right-hand-side (RHS) equations of the ordinary differential equations are unknown. In the case of unknown constitutive relations, however, the RHS equations are known up to the specification of constitutive relations (that may depend on the state of the system). We use a deep neural network to model unknown governing equations or constitutive relations. The discrete PINN approach combines classical multistep discretization methods for dynamical systems with neural-network-based machine learning methods. On the other hand, the continuous versions utilize deep neural networks to minimize the residual function for the continuous governing equations. We use the case of a fedbatch bioreactor system to study the effectiveness of these approaches and discuss conditions for their applicability. Our results indicate that the accuracy of the trained neural network models is much higher for the cases where we only have to learn a constitutive relation instead of all dynamics. This finding corroborates the well-known fact from scientific computing that building as much structural information as is available into an algorithm can enhance its efficiency and/or accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An experimental, theoretical, and kinetic modeling study of post-flame oxidation of ammonia

The post -flame oxidation rate of ammonia was investigated in a novel atmospheric pressure flow reactor at temperatures of 1280 ± 16 K and as a function of residence time and mixture composition (1-10% O 2 , dry and moist). The experimental results, as well as selected data from literature, were analyzed using an updated detailed chemical kinetic model. The medium temperature, very lean conditions enhance the importance of reactions of the nitroxyl (HNO) intermediate. High-level theory was used to calculate the rate constant for HNO + NH 2 , indicating that this step is significantly faster than values used in literature. Furthermore, a trajectory based approach was used to determine collision efficiencies for selected bath gases for HNO + M. The experimental results show that the NH 3 oxidation rate increases with temperature and O 2 concentration, while the presence of water vapor slightly inhibits reaction. Formation of NO and N 2 O was strongly promoted at higher levels of O 2 . Modeling results agreed well with the measurements, except at the lowest level of O 2 . The predicted oxidation rate of NH 3 was shown to result from a delicate balance between chain branching and terminating steps involving NH 2 , H 2 NO, and HNO. Recent theoretical work on reactions of these species by Klippenstein and coworkers and Stagni et al. was instrumental in improving modeling predictions. After initiation, NO reached a pseudo -steady-state level, where the pathways to NO were largely balanced by the NH 2 + NO reaction. Nitric oxide was partly oxidized to NO 2 , with the NH 2 + NO 2 reaction responsible for most of the N 2 O formation. Novelty and significance statement: This study provides the first detailed kinetic analysis of the lean postflame oxidation of ammonia, based on time -resolved flow reactor data in a novel reactor. In addition to the post -flame oxidation rate of ammonia, data for formation of NO and N 2 O were compared with modeling predictions. The medium temperature, very lean conditions enhance the importance of reactions of the HNO and H 2 NO intermediates. Inclusion in the model of results from recent high-level theoretical work, including present calculations for HNO + NH 2 and HNO + M, was crucial for capturing the observed behavior. It is argued that the post -initiation steady-state NH 3 oxidation rates constitute important data for model validation, along with ignition delays and laminar flame speeds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Prediction of thermal and mechanical stress-strain responses of TMC's subjected to complex TMF histories

This paper presents an experimental and analytical evaluation of cross-plied laminates of Ti-15V-3Cr-3Al-3Sn (Ti-15-3) matrix reinforced with continuous silicon-carbide fibers (SCS-6) subjected to a complex TMF loading profile. Thermomechanical fatigue test techniques were developed to conduct a simulation of a generic hypersonic flight profile. A micromechanical analysis was used. The analysis predicts the stress-strain response of the laminate and of the constituents in each ply during thermal and mechanical cycling by using only constituent properties as input. The fiber was modeled as elastic with transverse orthotropic and temperature-dependent properties. The matrix was modeled using a thermoviscoplastic constitutive relation. The fiber transverse modulus was reduced in the analysis to simulate the fiber-matrix interface failures. Excellent correlation was found between measured and predicted laminate stress-strain response due to generic hypersonic flight profile when fiber debonding was modeled.

Johnson, W. S.↗
DOE OSTI2020

Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model

Surface flow and subsurface flow constitute a naturally linked hydrologic continuum that has not traditionally been simulated in an integrated fashion. Recognizing the interactions between these systems has encouraged the development of integrated hydrologic models (IHMs) capable of treating surface and subsurface systems as a single integrated resource. IHMs are dynamically evolving with improvements in technology, and the extent of their current capabilities are often only known to the developers and not general users. This article provides an overview of the core functionality, capability, applications, and ongoing development of one open-source IHM, ParFlow. ParFlow is a parallel, integrated, hydrologic model that simulates surface and subsurface flows. ParFlow solves the Richards equation for three-dimensional variably saturated groundwater flow and the two-dimensional kinematic wave approximation of the shallow water equations for overland flow. The model employs a conservative centered finite-difference scheme and a conservative finite-volume method for subsurface flow and transport, respectively. ParFlow uses multigrid-preconditioned Krylov and Newton–Krylov methods to solve the linear and nonlinear systems within each time step of the flow simulations. The code has demonstrated very efficient parallel solution capabilities. ParFlow has been coupled to geochemical reaction, land surface (e.g., the Common Land Model), and atmospheric models to study the interactions among the subsurface, land surface, and atmosphere systems across different spatial scales. This overview focuses on the current capabilities of the code, the core simulation engine, and the primary couplings of the subsurface model to other codes, taking a high-level perspective.

58 GEOSCIENCES↗
DOE OSTI2020

Influence of crystal structure on constitutive anisotropy of silica sand at particle-scale

In spite of recent advanced geotechnical research regarding the influence of particle-scale morphology and fabric on the constitutive behavior of silica sands, there is still no published literature that investigated the influence of crystal structure of sand particles on their constitutive behavior. The main constituent of silica sand particles is quartz mineral, which has a trigonal crystal system and hexagonal lattice. This paper investigates the influence of quartz crystal structure on the constitutive response of silica sand particles using 3D X-ray diffraction (3DXRD), synchrotron micro-computed tomography (SMT), and 3D finite element (FE) analysis. Here, unconfined uniaxial compression experiments on single particles of synthetic silica cubes exposed the notion of constitutive anisotropy caused by the crystal structure of quartz. Since the silica cubes have simple regular geometry (1 mm) 3 , they were loaded in different directions with respect to their crystal local planes. The crystal structure was also identified for a natural silica sand specimen composed of 2705 particles and loaded in confined uniaxial compression. Physical limitations of the experimental measurements made a strong case for the use of 3D FE analysis to study how the change in crystal local orientation of individual sand particles can fundamentally produce anisotropy in the constitutive response of natural sand specimens. The 3D FE analysis was validated to accurately model the crystal-based constitutive anisotropy in silica particles using the results of the silica cube experiments. The analysis was then executed on 3D FE meshes that closely matched the physical shape and fabric of the 2705 particles in the natural sand specimen. Both the experiments and 3D FE analysis showed that the crystal structure of quartz essentially causes a directional anisotropy in the constitutive behavior of silica particles.

58 GEOSCIENCES↗
NASA NTRS2012

The Analysis of Adhesively Bonded Advanced Composite Joints Using Joint Finite Elements

The design and sizing of adhesively bonded joints has always been a major bottleneck in the design of composite vehicles. Dense finite element (FE) meshes are required to capture the full behavior of a joint numerically, but these dense meshes are impractical in vehicle-scale models where a course mesh is more desirable to make quick assessments and comparisons of different joint geometries. Analytical models are often helpful in sizing, but difficulties arise in coupling these models with full-vehicle FE models. Therefore, a joint FE was created which can be used within structural FE models to make quick assessments of bonded composite joints. The shape functions of the joint FE were found by solving the governing equations for a structural model for a joint. By analytically determining the shape functions of the joint FE, the complex joint behavior can be captured with very few elements. This joint FE was modified and used to consider adhesives with functionally graded material properties to reduce the peel stress concentrations located near adherend discontinuities. Several practical concerns impede the actual use of such adhesives. These include increased manufacturing complications, alterations to the grading due to adhesive flow during manufacturing, and whether changing the loading conditions significantly impact the effectiveness of the grading. An analytical study is conducted to address these three concerns. Furthermore, proof-of-concept testing is conducted to show the potential advantages of functionally graded adhesives. In this study, grading is achieved by strategically placing glass beads within the adhesive layer at different densities along the joint. Furthermore, the capability to model non-linear adhesive constitutive behavior with large rotations was developed, and progressive failure of the adhesive was modeled by re-meshing the joint as the adhesive fails. Results predicted using the joint FE was compared with experimental results for various joint configurations, including double cantilever beam and single lap joints.

Stapleton, Scott E.↗
NASA NTRS2020

Analysis of Sample Acquisition Dynamics Using Discrete Element Method

The analysis presented in this paper is conducted in the framework of the Ocean Worlds Autonomy Testbed for Exploration Research and Simulation (OceanWATERS) project, currently under development at NASA Ames Research Center. OceanWATERS aims at designing a simulation environment which allows for testing autonomy of scientific lander missions to the icy moons of our solar system. Mainly focused on reproducing the end effector interaction with the inherent terrain, this paper introduces a novel discrete element method (DEM)-based approach to determine forces and torques acting on the lander’s scoop during the sample acquisition process. An accurate force feedback from the terrain on the scoop is required by fault-detection and autonomous decision-making algorithms to identify when the requested torque on the robotic arm’s joints exceeds the maximum available torque. Knowledge of the terrain force feedback significantly helps evaluating the arm’s links structural properties and properly selecting actuators for the joints. Models available in literature constitute a partial representation of the dynamics of the interaction. As an example, Balovnev derived an analytical expression of the vertical and horizontal force acting on a bucket while collecting a sample as a function of its geometry and velocity, soil parameters and reached depth. Although the model represents an adequate approximation of the two force components, it ignores the direction orthogonal to the scoop motion and neglects the torque. This work relies on DEM analysis to compensate for analytical models’ deficiencies and inaccuracies, i. e. provide force and torque 3D vectors, defined in the moving reference (body) frame attached to the scoop, at each instant of the sample collection process. Results from the first presented analysis relate to the specific OceanWATERS sampling strategy, which consists of collecting the sample through five consecutive passes with increasing depth, each pass following the same circularlinear- circular trajectory. Data is collected given a specific scoop design interacting with two types of bulk materials, which may characterize the surface of icy planetary bodies: snow and ice. Although specifically concerned with the OceanWATERS design, this first analysis provides the expected force trends for similar sampling strategies and allows to deduce phenomenological information about the general scooping process. In order to further instruct the community on the use of DEM tools as a solution to the sampling collection problem, two more analyses have been carried out, mainly focused on reducing the DEM computation time, which increases with a decrease in particle size. After running a set of identical simulations, where the only changing parameter is the size of the spherical particle, it is observed that the resulting force trajectories, starting from a given particle size, converge to the true trend. It is deducible that a further decrease in size yields negligible improvements in the accuracy, while it sensibly increases computation time. A final analysis aims at discussing limitations of approximating bulk material particles having a complex shape, e. g. ice fragments, with spheres, by comparing force trends resulting in the two cases for the same simulation scenario.

Catanoso, Damiana↗
NASA NTRS1989

Notched fatigue of single crystal PWA 1480 at turbine attachment temperatures

The focus is on the lower temperature, uncoated and notched features of gas turbine blades. Constitutive and fatigue life prediction models applicable to these regions are being developed. Fatigue results are presented which were obtained thus far. Fatigue tests are being conducted on PWA 1480 single crystal material using smooth strain controlled specimens and three different notched specimens. Isothermal fatigue tests were conducted at 1200, 1400, and 1600 F. The bulk of the tests were conducted at 1200 F. The strain controlled tests were conducted at 0.4 percent per second strain rate and the notched tests were cycled at 1.0 cycle per second. A clear orientation dependence is observed in the smooth strain controlled fatigue results. The fatigue lifes of the thin, mild notched specimens agree fairly well with this smooth data when elastic stress range is used as a correlating parameter. Finite element analyses were used to calculate notch stresses. Fatigue testing will continue to further explore the trends observed thus far. Constitutive and life prediction models are being developed.

Meyer, T. G.↗
DOE OSTI2023

Bentonite buffer under high temperature: laboratory experiments and coupled process modeling

Abstract. Bentonite buffer in a geological repository will be simultaneously heated from decaying radioactive waste and hydrated from the surrounding host rock, triggering complex and coupled THMC (thermal–hydrological–mechanical–chemical) processes. Understanding the THMC behavior of bentonite-based engineered barrier system (EBS) is key to the evaluation and prediction of its long-term performance. Studies on the THMC process have been focused on conditions under 100 ∘C, as most design concepts impose a thermal limit of 100 ∘C in bentonite. Recently, studies under high-temperature conditions have been conducted to evaluate the possibility of raising the thermal limit and expanding the data/knowledge base to increase the confidence level. In this abstract, we present a series of bench-scale laboratory experiments at high temperatures (up to 200 ∘C) and the corresponding modeling work. Two sets of column tests were conducted, and each set consisted of two test columns: a control column undergoing only hydration (non-heated) and an experiment column experiencing both heating and hydration (heated). During the experiment, frequent X-ray computed tomography (CT) images were collected to provide a 3D visualization of the density distribution and present the spatiotemporal evolution of (1) hydration/dehydration, (2) clay swelling/shrinkage, (3) displacement, and (4) mineral precipitation. The two sets of tests differ with respect to several experimental conditions, such as bentonite type, compacted density and water content, water chemistry, and hydration pressure, but the important difference is that the first set used bentonite powder with a dry density of 1.28 g cm−3, whereas the second set used granulated bentonite (mixture of pellets and powder) with a dry density of 1.45–1.5 g cm−3. In both sets of experiments, a comprehensive post-dismantling characterization of bentonite samples was carried out after the column tests had been running for 1.5 years. Comparing non-heated and heated columns, the temperature gradient led to lower degree of homogenization of bentonite after bentonite became fully saturated; comparing the first and second sets, granulated and powdered bentonite exhibited drastically different hydration behavior. A THM model with a 2D axisymmetric grid system was used to interpret the data from the first set of tests. The model considers the combined impact of saturation, fluid pressure, and porosity change due to swelling/compression on the spatiotemporal distribution of bulk density and movement of the thermocouple modules. Observations from the tests help us understand the early perturbation of bentonite buffer under high temperature, and data from these tests improve the calibration of key constitutive hydrological and mechanical models and, therefore, enhance the modeling capability with respect to calculating the long-term evolution of bentonite buffer.

Zheng, Liange↗
DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗
NASA NTRS1995

Multiobjective optimization of hybrid regenerative life support technologies. Topic D: Technology Assessment

We developed simple mathematical models for many of the technologies constituting the water reclamation system in a space station. These models were employed for subsystem optimization and for the evaluation of the performance of individual water reclamation technologies, by quantifying their operational 'cost' as a linear function of weight, volume, and power consumption. Then we performed preliminary investigations on the performance improvements attainable by simple hybrid systems involving parallel combinations of technologies. We are developing a software tool for synthesizing a hybrid water recovery system (WRS) for long term space missions. As conceptual framework, we are employing the state space approach. Given a number of available technologies and the mission specifications, the state space approach would help design flowsheets featuring optimal process configurations, including those that feature stream connections in parallel, series, or recycles. We visualize this software tool to function as follows: given the mission duration, the crew size, water quality specifications, and the cost coefficients, the software will synthesize a water recovery system for the space station. It should require minimal user intervention. The following tasks need to be solved for achieving this goal: (1) formulate a problem statement that will be used to evaluate the advantages of a hybrid WRS over a single technology WBS; (2) model several WRS technologies that can be employed in the space station; (3) propose a recycling network design methodology (since the WRS synthesis task is a recycling network design problem, it is essential to employ a systematic method in synthesizing this network); (4) develop a software implementation for this design methodology, design a hybrid system using this software, and compare the resulting WRS with a base-case WRS; and (5) create a user-friendly interface for this software tool.

Manousiouthakis, Vasilios↗
NASA NTRS2024

Computational Analysis of the X-57 Maxwell Airplane, the Landing Configuration with High-Lift Blowing and Aileron Deflections (Preliminary Fuselage)

The X-57 Maxwell is an all-electric airplane with a distributed electric propulsion system used for a high-lift system at takeoff and landing conditions. The Kestrel and USM3D flow solvers were used at NASA Langley to investigate the performance of the X-57 Maxwell in the development of an aerodynamic database. The configuration investigated in this paper had a 30° flap deflection, and in addition the pilot’s right aileron were deflected and undeflected in different cases. The solutions were computed at an airspeed of 58 KEAS, for an altitude of 6000 feet, and a flight Reynolds number of 0.588e+06 per foot. To evaluate the high-lift distributed electric propulsion system without aileron deflections, the solutions were computed for an angle-of-attack sweep from −2° to 20°, with the high-lift propellers blowing and the cruise propellers excluded from the simulation. To investigate the aileron effectiveness, the solutions were computed at angles of attack of −2° and 14°, for aileron deflections from −25° to 18° with the high-lift propellers blowing and the cruise propellers windmilling at idle-power. The high-lift propellers and the idle-power cruise propellers were modeled with an actuator disk. Results show negligible differences in lift, drag and pitching moment whether the idle-powered cruise propellers were included or excluded from the simulation. In general, the Kestrel and USM3D codes compared well for lift, drag and pitching moment for the landing configuration with no aileron control. The Kestrel code, using the Spalart-Allmaras turbulence model with the rotation corrections terms, predicted an increasing lift coefficient up to maximum lift coefficient of 4.65 at a 15° angle of attack. The USM3D code, using the Spalart-Allmaras turbulence model turbulence model with the Quadratic Constitutive Relation predicted an increasing lift coefficient up to maximum lift coefficient of 4.5 at a 12° angle of attack, with the lift remaining constant through a 14◦ angle of attack. A possible difference in lift coefficient between the codes for high angles of attack may result from the different available options used with the standard Spalart-Allmaras turbulence model. The Kestrel code predicted more drag across the range of angle of attack than the USM3D code. The codes compared well for pitching moment coefficient across the range of angle of attack. The Kestrel and USM3D codes compared well for aileron effectiveness at a 2° angle of attack. The Kestrel code predicts better aileron effectiveness than USM3D at a 14° angle of attack. There is more flow separation in the region outboard of the last high-lift nacelle for the USM3D solutions at a 14° angle of attack, which diminishes the ability of the aileron to be effective.

X-57 Maxwell Airplane↗
NASA NTRS1976

A complete model of turbulence

A set of constitutive equations suitable for a priori computation of turbulent shear flows has been developed. Since no properties of a given turbulent flow need be known in advance in order to obtain a solution, the equations comprise a complete model of turbulence. Perturbation analysis shows that the model predicts a composite five-layer structure for an incompressible turbulent boundary layer, viz, a defect layer, a law-of-the-wall layer, a viscous sublayer, a near-surface roughness layer, and a viscous superlayer at the boundary-layer edge. Analysis of the defect layer demonstrates the key improvement of the model over its predecessor, the Saffman-Wilcox two-equation model of turbulence. Examination of model-predicted sublayer structure yields model-parameter boundary conditions appropriate for surfaces with roughness and mass injection. Results of numerical computations of compressible and incompressible equilibrium boundary layers show that, for such flows, the model is as accurate as mixing-length theory. Applications to transitional boundary layers and to nonequilibrium relaxation of a boundary layer passing from a rough to a smooth surface indicate that the model's applicability extends far beyond that of mixing-length theory's.

Wilcox, D. C.↗
DOE OSTI2023

A Physics-Based Data-Driven Approach for Modeling of Environmental Degradation in Elastomers

Abstract Elastomers are now commonly used in a number of industries, including aerospace, structure, transportation, shipbuilding, and automotive, due to their excellent workability, formability, and flexibility. During their activity, elastomers are subjected to harsh environmental conditions, which decreases their resilience. False predictions made early in their lives can have major financial and environmental implications. Elastomers’ performance and properties, such as strength, durability, and density, are influenced by chemical changes in these materials, known as degradation, which occurs over time. This process can alter the morphology of a polymer matrix as well as cause chain scission and cross-linking, resulting in different behaviors than that of the unaged material. To demonstrate the effect of thermaloxidative aging on the mechanical behavior of elastomers, several experimental and theoretical models have been proposed. In view of the large volume of experimental data available on micro-structural evolution in the course of aging, we propose a physics-based data-driven approach to overcome the shortcomings of both phenomenological and micro-mechanical models. This work presents a novel thermodynamically consistent, multiagent machine-learned model for predicting the constitutive behavior of cross-linked elastomers during environmental aging, such as thermo-oxidative and hydrolytic aging for various states of deformation. Single mechanism degradation changes the polymer matrix over time where it is causing chain scission, reduction of cross-links, and morphology change. To capture the idealized Mullins effect and permanent set due to the effect of single aging mechanisms on nonlinear mechanical responses of elastomers, we propose a data-driven model for simulating inelastic elements in a polymer matrix. By using a sequential order reduction, we were able to reduce the 3D stress-strain tensor mapping problem to a small number of super-constrained 1D mapping problems. To systematically classify such mapping problems into a few categories, an assembly of multiple replicated conditional neural network learning agents (L-agents) is used based on our recent work. Each category is represented by a different type of agent. The effect of deformation history, aging time, and aging temperature is captured by this model. The model is validated using a broad collection of data, ranging from our experimental results to data from the literature. In addition, thermodynamic consistency and frame independence are investigated. The most significant achievements of this model are its precision, simplicity, and prediction of inelasticity under various states of deformation. The model’s accuracy and simplicity make it a good option for commercial and industrial applications. Conveniently, due to the model modular nature, it can be expanded in the future to include viscoelasticity and non-isotropic formation for better precision.

Ghaderi, Aref↗
NASA NTRS2010

Modeling Progressive Failure of Bonded Joints Using a Single Joint Finite Element

Enhanced finite elements are elements with an embedded analytical solution which can capture detailed local fields, enabling more efficient, mesh-independent finite element analysis. In the present study, an enhanced finite element is applied to generate a general framework capable of modeling an array of joint types. The joint field equations are derived using the principle of minimum potential energy, and the resulting solutions for the displacement fields are used to generate shape functions and a stiffness matrix for a single joint finite element. This single finite element thus captures the detailed stress and strain fields within the bonded joint, but it can function within a broader structural finite element model. The costs associated with a fine mesh of the joint can thus be avoided while still obtaining a detailed solution for the joint. Additionally, the capability to model non-linear adhesive constitutive behavior has been included within the method, and progressive failure of the adhesive can be modeled by using a strain-based failure criteria and re-sizing the joint as the adhesive fails. Results of the model compare favorably with experimental and finite element results.

Stapleton, Scott E.↗
DOE OSTI2020

Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

In this paper, we present a physics informed deep neural network (DNN) method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) models. We use PDEs in addition to measurements to train DNNs to approximate unknown parameters and constitutive relationships as well as states. The proposed approach increases the accuracy of DNN approximations of partially known functions when a limited number of measurements is available and allows for training DNNs when no direct measurements of the functions of interest are available. We employ physics informed DNNs to estimate the unknown space-dependent diffusion coefficient in a linear diffusion equation and an unknown constitutive relationship in a non-linear diffusion equation. For the parameter estimation problem, we assume that partial measurements of the coefficient and states are available and demonstrate that under these conditions, the proposed method is more accurate than state-of-the-art methods. For the non-linear diffusion PDE model with a fully unknown constitutive relationship (i.e., no measurements of constitutive relationship are available), the physics informed DNN method can accurately estimate the non-linear constitutive relationship based on state measurements only. Finally, we demonstrate that the proposed method remains accurate in the presence of measurement noise.

42 ENGINEERING↗
DOE OSTI2024

Improved biomass feedstock materials handling and feeding engineering data sets, design methods, and modeling/simulation tools

Forest Concepts led a project to develop a set of tools which enable feedstock handing equipment designers to better understand and model the flowability of bulk particulate biomass materials. The work products included improved flowability mathematical models, data sets used to populate the models, and two new laboratory devices – a biomass-scale true cubical triaxial tester and a biomass-scale gas pycnometer. This work was funded in part by the US Department of Energy under contact DE-EE0008254.

09 BIOMASS FUELS↗