SEARCH · Engineering Papers

Results for “parametric model dependence”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Computational Response Theory for Dynamics

Quantifying the sensitivity - how a quantity of interest (QoI) varies with respect to a parameter – and response – the representation of a QoI as a function of a parameter - of a computer model of a parametric dynamical system is an important and challenging problem. Traditional methods fail in this context since sensitive dependence on initial conditions implies that the sensitivity and response of a QoI may be ill-conditioned or not well-defined. If a chaotic model has an ergodic attractor, then ergodic averages of QoIs are well-defined quantities and their sensitivity can be used to characterize model sensitivity. The response theorem gives sufficient conditions such that the local forward sensitivity – the derivative with respect to a given parameter - of an ergodic average of a QoI is well-defined. We describe a method based on ergodic and response theory for computing the sensitivity and response of a given QoI with respect to a given parameter in a chaotic model with an ergodic and hyperbolic attractor. This method does not require computation of ensembles of the model with perturbed parameter values. The method is demonstrated and some of the computations are validated on the Lorenz 63 and Lorenz 96 models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Overview of Base Model in Parametric Studies Specific to Performance of U-Mo Plates

This paper provides an overview of the base model specifically developed to perform parametric sensitivity studies on the U-10Mo monolithic fuel system. U-Mo monolithic fuels are being considered for the conversion of test reactors into high-performance research reactors that operate using proliferation-resistant, low-enriched uranium (LEU) fuels. These plate-type fuels contain a high-density, low-enrichment fuel sandwiched between zirconium diffusion barriers and encapsulated in aluminum claddings. All U.S. high-performance research reactors have released the designs of their LEU monolithic fuel reactor cores. These designs include nearly 50 distinct fuel plate geometries with different operational parameters. Consequently, a single generic plate geometry representing all the extreme points in this design matrix is unrealistic. To evaluate the performance for various parameters, a set of sensitivity studies was performed. These studies considered various input parameters (i.e., geometric, operational, and material property-related). The results revealed valuable information about plate performance and the sensitivity of this performance to various modeling inputs. To establish a reference state for comparing these result, base model featuring representative irradiation conditions was developed. To capture in-reactor behavior accurately, incorporation of representative constitutive models capable of evolving properties with respect to temperature, irradiation time, and burnup was needed. The behavioral models considered burnup-dependent properties, swelling, creep, and degradation. This paper introduces the base model created for the parametric sensitivity studies. The detailed description of the procedure includes the model geometry, model discretization, thermo-mechanical coupling, material properties and behavioral models. This paper also provides selected results and assesses the performance of the base model.

42 ENGINEERING↗

DOE OSTI2025

Micropolar deep material network

This study extends the Deep Material Network (DMN), a physics-informed machine learning framework, to predict the homogenized mechanical response of composite materials with micropolar (Cosserat-type) constitutive behavior. This extension incorporates microstructure-dependent size effects, enabling accurate, efficient, and size-aware predictions for composites with complex internal architectures. While traditional, direct numerical simulation micropolar models effectively capture size effects by introducing extra local degrees of freedom, they bring significant computational challenges, particularly for multiscale analyses relevant to engineering applications. The micropolar DMN developed in this paper achieves high accuracy while significantly reducing computation time compared to micropolar direct numerical simulations. This advancement enables multiscale analyses and parameter studies that were previously impractical, such as high-cycle fatigue simulations and comprehensive investigations of internal length scale effects notably in size-dependent plastic response and the optimization of lattice structures. By uniting microstructure-sensitive modeling, physics-driven learning, and scalable surrogate modeling, the micropolar DMN paves the way for accelerated material design, large-scale parametric studies, and the reliable incorporation of size-dependent effects across a wide range of engineering applications, including optimization and next-generation composite design.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Revealing the unusual rate-dependent mechanical behaviors of nematic liquid crystal elastomers

Liquid crystal elastomers (LCEs) exhibit unique mechanical properties of soft elasticity and enhanced energy dissipation with rate dependency. They are potentially transformative materials for applications in mechanical impact mitigation and vibration isolation. However, previous studies have primarily focused on the mechanics of LCEs under equilibrium and quasistatic loading conditions. Critical knowledge gaps exist in understanding their rate-dependent behaviors, which are a complex mixture of traditional network viscoelasticity and the soft elastic behaviors with changes in the mesogen orientation and order parameter. Together, these inelastic mechanisms lead to unusual rate-dependent energy absorption responses of LCEs. In this work, we developed a viscoelastic constitutive theory for monodomain nematic LCEs to investigate how multiple underlying sources of inelasticity manifest in the rate-dependent and dissipative behaviors of monodomain LCEs. Here, the theoretical modeling framework combines the neo-classical network theory with evolution rules for the mesogen orientation and order parameter with conventional viscoelasticity. The model is calibrated with uniaxial tension and compression data spanning six decades of strain rates. The established 3D constitutive model enables general loading predictions taking the initial mesogen orientation and order parameter as inputs. Additionally, parametric studies were performed to further understand the rate dependence of monodomain LCEs in relation to their energy absorption characteristics. Based on the parametric studies, particularly loading scenarios are identified as conditions where LCEs outperform conventional elastomers regarding energy absorption.

42 ENGINEERING↗

DOE OSTI2023

A helical actuator driven by biased SMA: design, model, and experiment

A helical actuator driven by biased shape memory alloy (SMA) patterns embedded into a soft composite ribbon base is presented in this work. Instead of common U-shape SMA wires, a single SMA wire is woven into planar patterns, which enable helical deformation of the composite ribbon from an initially flat geometry. An analytical static model is established for accurate and rapid prediction of the helical reconfiguration arising from the shape memory effect of woven SMA patterns, followed by validation of the static model using the finite element method (FEM). The finite element results are compared with the analytical solutions given by this static model, which show a high agreement. Parametric study of the influences of eight independent design variables on the dependent helical parameters, such as combined curvatures, pitches, and helical angles, is completed. It is found that the helically deformed geometry is mainly dominated by diameters, biased positions, inclined angles, and numbers of skewed segments of the SMA wire. Fabrication and in-situ experimental test of a prototype of such helical actuators qualitatively demonstrate its dramatic three-dimensional (3D) spiral reconfiguration from a two-dimensional (2D) flat ribbon. In conclusion, such SMA patterns will allow more diverse designs of soft actuators for a wider range of robotic applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Perturbative Diagonalization for Time-Dependent Strong Interactions

We present a systematic method to implement a perturbative Hamiltonian diagonalization based on the time-dependent Schrieffer-Wolff transformation. Applying our method to strong parametric interactions we show how, even in the dispersive regime, full Rabi model physics is essential to describe the dressed spectrum. Our results unveil several qualitatively different results, including realization of large energy-level shifts, tunable in magnitude and sign with the frequency and amplitude of the pump mediating the parametric interaction. Crucially, Bloch-Siegert shifts, typically thought to be important only in the ultrastrong or deep-strong coupling regimes, can be rendered large even for weak dispersive interactions to realize points of exact cancelation of dressed shifts (“blind spots”) at specific pump frequencies. The framework developed here highlights the rich physics accessible with time-dependent interactions and serves to significantly expand the functionalities for control and readout of strongly interacting quantum systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Sensitivity of Au + Au collisions to the symmetric nuclear matter equation of state at 2–5 nuclear saturation densities

We demonstrate that proton and pion flow measurements in heavy-ion collisions at incident energies ranging from 1 to 20 GeV per nucleon in the fixed target frame can be used for an accurate determination of the symmetric nuclear matter equation of state at baryon densities equal 2–4 times nuclear saturation density n 0 . We simulate Au + Au collisions at these energies using a hadronic transport model with an adjustable vector mean-field potential dependent on baryon density n B . Here, we show that the mean field can be parametrized to reproduce a given density dependence of the speed of sound at zero temperature $c$$^{2}_{s}$ (n B , T = 0), which we vary independently in multiple density intervals to probe the differential sensitivity of heavy-ion observables to the equation of state at these specific densities. Recent flow data from the STAR experiment at the center-of-mass energies √ s NN = {3.0, 4.5} GeV can be described by our model, and a Bayesian analysis of these data indicates a hard equation of state at n B ϵ (2, 3)n 0 and a possible phase transition at n B ϵ (3, 4)n 0 . More data at √ s NN = 2–5 GeV, as well as a more thorough analysis of the model systematic uncertainties will be necessary for a more precise conclusion.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Data-Driven Modeling and Correction of Vehicle Dynamics

We develop a data-driven framework for learning and correcting nonautonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, nonautonomous dynamics are governed by time-dependent control inputs, which pose challenges in learning predictive models directly from temporal snapshot data. To address these, we reformulate the vehicle dynamics via a local parameterization of the time-dependent inputs, yielding a modified system composed ofa sequence of local parametric dynamical systems. Here, we approximate these parametric systems using two complementary approaches. First, we employ the dimension reduction and interpolation in parameter space (DRIPS) methodology to construct efficient linear surrogate models, equipped with lifted observable spaces and manifold-based operator interpolation. This enables data-efficient learning of vehicle models whose dynamics admit accurate linear representations in the lifted spaces. Second, for more strongly nonlinear systems, we employ flow map learning (FML), a deep neural network (DNN) approach that approximates the parametric evolution map without requiring special treatment of nonlinearities. We further extend FML with a transfer-learning-based model correction procedure, enabling the correction of misspecified prior models using only a sparse set of high-fidelity or experimental measurements, without assuming a prescribed form for the correction term. Through a suite of numerical experiments on unicycle, simplified bicycle, and slip-based bicycle models, we demonstrate that DRIPS offers robust and highly data-efficient learning of nonautonomous vehicle dynamics, while FML provides expressive nonlinear modeling and effective correction of model-form errors under severe data scarcity.

data-driven modeling↗

DOE OSTI2025

Understanding the Effect of Sample Geometry on Temperature Distribution during Optical Floating Zone Crystal Growth in Vacuum Environment through Heat Transfer Modeling

Optical floating zone furnaces (OFZ) have had a transformative impact on fundamental science due to their ability to rapidly produce large single crystals of a wide variety of complex materials. However, a quantitative understanding of the OFZ growth environment is generally lacking due to the difficulty of measuring the local sample temperatures during OFZ growth, as well as to the general lack of information about the temperature-dependent physical parameters needed to model heat transfer. To overcome these challenges, we apply a physics-based heat transfer model, parametrized by measurements from synchrotron experiments and a machine-learning (ML) algorithm, to simulate the temperature distributions of samples heated in an OFZ furnace in a vacuum environment. This model is used to quantitatively understand how the sample maximum temperature and temperature gradient (key parameters that influence the success of crystal growth) are affected by the rod size, rod shape, and heat-zone position on the rod. The results of this study can be applied to make informed decisions on how crystal growth parameters can be tuned to modify temperature profiles and to optimize crystal growth outcomes even when data on internal sample temperature profiles (e.g., those obtained through in situ synchrotron experiments) are not accessible.

36 MATERIALS SCIENCE↗

DOE OSTI2025

COCOMO2: A Coarse-Grained Model for Interacting Folded and Disordered Proteins

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parametrized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multiscale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Correction to “COCOMO2: A Coarse-Grained Model for Interacting Folded and Disordered Proteins”

Molecular interactions↗

DOE OSTI2022

Multicomponent gas mixture parametric CFD study of condensation heat transfer in small modular reactor system safety

Safety is always the primary concern for designing and analyzing nuclear reactor systems. The requirements for the safety margin for advanced small modular reactor (SMR) systems are targeted even higher than the conventional commercial large-scale nuclear reactors incorporating the passive and inherent safety systems. The SMR systems are designed with the condensation passive containment cooling system (PCCS), which plays a critical role in removing reactor heat during a steam release accident case. However, the presence of non-condensable gas (NCG), like air, reduces the heat transfer performance. This physics phenomenon becomes multifactorial for nuclear reactor containment during a fuel failure accident case that releases hydrogen gas. Besides, the mixture component of steam-air-hydrogen varies in reactor accident cases, which need simulation and validation keeping parameters of importance. Reviews showed that previous studies for SMR’s PCCCS did not cover the condensation heat transfer (CHT) in the presence of multicomponent NCG mixture parametric computational fluid dynamics (CFD) simulation and validation, making a research gap in the SMR design safety. A comprehensive CHT parametric CFD study was performed for SMR PCCS to fill this research gap. This study used experimental data as simulation 3D physics domain inlet and outlet boundary conditions. However, the wall boundary conditions were constant temperature, curve-fit, and annular coolant for verifying the turbulence models. Parametric simulations were performed, verified, and optimized for steam-NCGs mixtures. The multicomponent gases, multiphase mixtures, and fluid film condensation models were applied with associated turbulence models. The results of the parametric study were evaluated for realistic reactor conditions. Results showed that parametric study provided critical insight about the dependency of multicomponent gas mixture parameters that supports reactor safety design, analysis, and licensing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Reduced Order Modeling conditioned on monitored features for response and error bounds estimation in engineered systems

Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to provide numerical error bounds associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.

Conditional VAEs↗

DOE OSTI2024

Temperature-dependent mechanical properties and crystal plasticity parameters for additively manufactured Haynes-214 alloy: Experiments and numerical modeling

Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. Crystal plasticity (CP) simulations provide an effective approach to gaining deeper insights into microstructure-property linkages under thermomechanical loading. This method can reduce the need for costly high-temperature mechanical testing while accounting for the effects of crystallographic texture and grain morphology on the mechanical behavior of AM materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning-differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R 2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Fluctuations in Hill’s equation parameters and application to cosmic reheating

Cosmic inflation provides a compelling framework for explaining several observed features of our Universe, but its viability depends on an efficient reheating phase that converts the inflaton’s energy into Standard Model particles. This conversion often proceeds through nonperturbative mechanisms such as parametric resonance, which is described by Hill’s equation. In this work, we investigate how stochastic fluctuations in the parameters of Hill’s equation can influence particle production during reheating. We show that such fluctuations can arise from couplings to light scalar fields and can significantly alter the stability bands in the resonance structure, thereby enhancing the growth of fluctuations and broadening the region of efficient energy transfer. Using random matrix theory and stochastic differential equations, we decompose the particle growth rate into deterministic and noise-induced components and demonstrate analytically and numerically that even modest noise leads to substantial particle production in otherwise stable regimes. Furthermore, these results suggest that stochastic effects can robustly enhance the efficacy of reheating across a wide swath of parameter space, with implications for early Universe cosmology, UV completions involving multiple scalar fields, and the resolution of the cosmological moduli problem.

Cosmology↗

DOE OSTI2025

Detecting gravitational signatures of dark matter with atom gradiometers

We study the purely gravitational signatures of dark matter from the ultralight to the ultraheavy mass range in proposed long-baseline atom gradiometers, focusing on terrestrial designs, such as AION-km and MAGIS-km, as well as space-based concepts, such as MAGIS-space, AEDGE and AEDGE+. Due to its exceptional acceleration sensitivity and depending on astrophysical backgrounds, a detector similar to AEDGE+ could detect a dark matter subcomponent which constitutes 𝒪⁡(10%) of the local dark matter energy density and is populated by compact clumps of mass between 10 6 kg and 10 10 kg (10 −25 ⁢𝑀⊙ ≲ 𝑀 ≲10 −21⁢ 𝑀⊙) in an otherwise unexplored region of dark matter model space. Furthermore, because the gravitational observable depends on the relative gravitational time delay measured by spatially separated atomic clouds, we find that atom gradiometers are parametrically more sensitive than laser interferometers, such as LIGO and LISA, to fast-oscillating spacetime perturbations sourced by energy density and pressure fluctuations of ultralight dark matter. Depending on astrophysical backgrounds, a detector akin to AEDGE+ could probe a DM overdensity of 𝒪⁡(10) times the local dark matter energy density for masses 𝑚 ≲10 −17 eV.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

An Efficient Reactive Force Field without Explicit Coordination Dependence for Studying Caustic Aluminum Chemistry

Reactive force fields (RFFs) are an expedient approach to sample chemical reaction paths in complex systems, relative to density functional theory. However, there is continued need to improve efficiencies, specifically in systems that have slow transverse degrees of freedom, as in highly viscous and superconcentrated solutions. Here, we present an RFF that is differentiated from current models (e.g., ReaxFF) by omitting explicit dependence on the atom coordination and employing a small parameter set based on Lennard-Jones, Gaussian, and Stillinger–Weber potentials. In this study, the model was parametrized from AIMD simulation data and is used to model aluminate reactivity in sodium hydroxide solutions with extensive validation against experimental radial distribution functions, computed free energy profiles for oligomerization, and formation energies. In conclusion, the model enables simulation of early stage Al(OH) 3 nucleation which has significant relevance to industrial processing of aluminum and has a computational cost that is reduced by 1 order of magnitude relative to ReaxFF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

MODELING AND PARAMETRIC STUDY OF END-GAS AUTOIGNITION TO ALLOW THE REALIZATION OF ULTRA-LOW EMISSIONS, HIGH-EFFICIENCY HEAVY-DUTY SPARK-IGNITED NATURAL GAS ENGINES

Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas (NG) engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by the low emission limits and the wide range of chemical reactivity in pipeline-quality natural gas. Depending on the region and the source of the natural gas, its reactivity, described by its Methane Number (MN), which is analogous to the Octane Number for liquid SI fuels, can span from 65 to 95. In order to realize diesel-like efficiencies, SI NG engines must be designed to operate at high Brake Mean Effective Pressures (BMEP), near or beyond knock limits, over a wide range of fuel reactivity. This requires a deep understanding of the combustion-engine interactions pertaining to flame propagation and End-Gas Autoignition (EGAI), i.e., the autoignition of the unburned gas (end gas) ahead of the flame front. However, EGAI, if controlled, provides an opportunity to increase SI NG engine efficiency by increasing the combustion rate and the total fraction of burned fuel, mitigating the effects of the slow flame speeds characteristic of natural gas fuels, which generally reduce BMEP and increase unburned hydrocarbon emissions. For this reason, to realize diesel-like efficiencies and ultra-low emissions on SI NG engines, this work proposes the study of the main parameters influencing the modeling and prediction of NG EGAI to allow for its control. In this work, a novel EGAI detection and onset determination method was developed to reliably quantify EGAI for data analysis and engine control. The new method allowed the prediction of EGAI on SI NG engines without the need to use engine- and operating-condition-dependent thresholds and reduced the error in quantifying the fraction of the total energy released by the EGAI event by up to 40%pts. One- and three-dimensional engine models were then developed to study the engine/fuel interactions that lead to NG EGAI and its performance benefits. These models, although having decent agreement with experimental data, showed the need to account for NOx chemistry when predicting NG EGAI due to a consistently later prediction of the EGAI onset (~1.65 crank-angle degrees) and thus, a new reduced chemical mechanism for real NG fuels was developed containing NOx chemistry. The new reduced mechanism improved the EGAI onset prediction agreement to within ±0.5 crank-angle degrees and decreased simulation time during combustion by nearly 50% when using the further reduced AREIS50NOx chemical mechanism. These models were then used to study the role of NG composition on EGAI, evaluate the engine/fuel interactions leading to NG EGAI, and perform engine optimization while leveraging EGAI to increase thermal efficiency. Piston design optimization combined with a Controlled EGAI (C-EGAI) combustion mode allowed a Heavy-Duty (HD) SI NG engine to operate at diesel-like efficiencies, i.e., Brake Thermal Efficiency (BTE) ≥44%. Experimental and modeling data analysis revealed that earlier and faster heat release increases combustion efficiency by an average of 1%pts, increases work transferred to the piston resulting in a decrease in exhaust losses by 50% depending on the engine operating condition while slightly increasing heat losses. Finally, the simulation results revealed an opportunity to further enhance the BTE (up to 50%) by enabling C-EGAI combustion at leaner conditions, λ=1.4-1.6.

Bestel, Diego Bernardi↗