Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “time domain 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.

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

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Time Domain Tool Validation Using ARES I-X Flight Data

The ARES I-X vehicle was launched from NASA's Kennedy Space Center (KSC) on October 28, 2009 at approximately 11:30 EDT. ARES I-X was the first test flight for NASA s ARES I launch vehicle, and it was the first non-Shuttle launch vehicle designed and flown by NASA since Saturn. The ARES I-X had a 4-segment solid rocket booster (SRB) first stage and a dummy upper stage (US) to emulate the properties of the ARES I US. During ARES I-X pre-flight modeling and analysis, six (6) independent time domain simulation tools were developed and cross validated. Each tool represents an independent implementation of a common set of models and parameters in a different simulation framework and architecture. Post flight data and reconstructed models provide the means to validate a subset of the simulations against actual flight data and to assess the accuracy of pre-flight dispersion analysis. Post flight data consists of telemetered Operational Flight Instrumentation (OFI) data primarily focused on flight computer outputs and sensor measurements as well as Best Estimated Trajectory (BET) data that estimates vehicle state information from all available measurement sources. While pre-flight models were found to provide a reasonable prediction of the vehicle flight, reconstructed models were generated to better represent and simulate the ARES I-X flight. Post flight reconstructed models include: SRB propulsion model, thrust vector bias models, mass properties, base aerodynamics, and Meteorological Estimated Trajectory (wind and atmospheric data). The result of the effort is a set of independently developed, high fidelity, time-domain simulation tools that have been cross validated and validated against flight data. This paper presents the process and results of high fidelity aerospace modeling, simulation, analysis and tool validation in the time domain.

Hough, Steven↗
DOE OSTI2022

A massively parallel time-domain coupled electrodynamics–micromagnetics solver

We present a high-performance coupled electrodynamics–micromagnetics solver for full physical modeling of signals in microelectronic circuitry. The overall strategy couples a finite-difference time-domain approach for Maxwell’s equations to a magnetization model described by the Landau–Lifshitz–Gilbert equation. The algorithm is implemented in the Exascale Computing Project software framework, AMReX, which provides effective scalability on manycore and GPU-based supercomputing architectures. Furthermore, the code leverages ongoing developments of the Exascale Application Code, WarpX, which is primarily being developed for plasma wakefield accelerator modeling. Our temporal coupling scheme provides second-order accuracy in space and time by combining the integration steps for the magnetic field and magnetization into an iterative sub-step that includes a trapezoidal temporal discretization for the magnetization. The performance of the algorithm is demonstrated by the excellent scaling results on NERSC multicore and GPU systems, with a significant (59×) speedup on the GPU using a node-by-node comparison. We demonstrate the utility of our code by performing simulations of an electromagnetic waveguide and a magnetically tunable filter.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

A time domain, weighted residual formulation of Maxwell's equations

A finite element model is developed and used to simulate two-dimensional electromagnetic wave propagation and scattering. The spatial discretization of the time-domain electrodynamic equations is accomplished by a Galerkin approach. The semi-discrete equations are solved explicitly using a second-order Runge-Kutta scheme. Both the electric and magnetic fields are discretized using a single grid, with the divergence-free conditions satisfied through a correction approach. Examples depicting the scattering of plane waves in 2D geometries are given to demonstrate the validity of the methodology.

Young, Jeffrey L.↗
NASA NTRS2018

WRF Hindcasts of Cold Front Passages over the ARM Eastern North Atlantic Site: A Sensitivity Study

The present study explores the ability of the Weather Research and Forecasting (WRF) Model to accurately reproduce the passage of extratropical cold fronts at the DOE-ARM Eastern North Atlantic (ENA) observation site on the Azores Islands. An analysis of three case studies is performed in which the impact of the WRF domain size, position of the model boundary relative to the ENA site, grid spacing, and spectral nudging conditions are explored. The results from these case studies indicate that model biases in the timing and duration of cold front passages change with the distance between the model domain boundary and the ENA site. For these three cases, if the western model boundary is further than 1500 km from the site, the front becomes too meridional and fails to reach the site, making 1000 km or 1500 km the optimal distances. In contrast, integrations with small distances (e.g., 500 km) between the site and domain boundaries have inadequate spatial spin-up, i.e. the domain is too small for the model to properly stabilize. For all 3 cases, regardless of domain size, the model has biases in its upper-level circulation that impact the position and timing of the front. However, this issue is most serious for 4000 km2 domains and larger. For these domains, prolonged spectral nudging can correct cold front biases. As such, this analysis provides a framework to optimize the WRF model configuration necessary for a realistic hindcast of a cold front passage at a fixed location centered in a domain as large as computationally possible.

Faycal Lamraoui↗
NASA NTRS1988

Time domain identification of element matrices

Accurate mathematical models are clearly an important part of the design, modification, control, and damage assessment of large space structures. A critical part of model determination of any type of structure is the use of system identification (SI), a process for using measured excitation and response data to improve the form of a mathematical model or the values of the parameters in it. The reasons for using SI are manifold, but most of them involve uncertainties in either the form of or the parameters in the mathematical model. Some of the more common uncertainties are the following: the nature of the damping, the characteristics of sloppy or sticky joints, inelastic material properties, and parameters in a reduced Degree-of-Freedom (DOF) model. This paper contains a description of the SI algorithm followed by two illustrations: one in which all parameters in a shear building model are determined using pull back and quick release data, and another in which an equivalent reduced DOF model is obtained for a two story frame.

Matzen, Vernon C.↗
DOE OSTI2020

Situation awareness and dynamic ensemble forecasting of abnormal behavior in cyber-physical system

A plurality of monitoring nodes may each generate a time-series of current monitoring node values representing current operation of a cyber-physical system. A feature-based forecasting framework may receive the time-series of and generate a set of current feature vectors using feature discovery techniques. The feature behavior for each monitoring node may be characterized in the form of decision boundaries that separate normal and abnormal space based on operating data of the system. A set of ensemble state-space models may be constructed to represent feature evolution in the time-domain, wherein the forecasted outputs from the set of ensemble state-space models comprise anticipated time evolution of features. The framework may then obtain an overall features forecast through dynamic ensemble averaging and compare the overall features forecast to a threshold to generate an estimate associated with at least one feature vector crossing an associated decision boundary.

Abbaszadeh, Masoud↗
NASA NTRS1992

Gigaflop (billion floating point operations per second) performance for computational electromagnetics

Accurate and rapid evaluation of radar signature for alternative aircraft/store configurations would be of substantial benefit in the evolution of integrated designs that meet radar cross-section (RCS) requirements across the threat spectrum. Finite-volume time domain methods offer the possibility of modeling the whole aircraft, including penetrable regions and stores, at longer wavelengths on today's gigaflop supercomputers and at typical airborne radar wavelengths on the teraflop computers of tomorrow. A structured-grid finite-volume time domain computational fluid dynamics (CFD)-based RCS code has been developed at the Rockwell Science Center, and this code incorporates modeling techniques for general radar absorbing materials and structures. Using this work as a base, the goal of the CFD-based CEM effort is to define, implement and evaluate various code development issues suitable for rapid prototype signature prediction.

Shankar, V.↗
NASA NTRS2011

Evaluation of Lightning Induced Effects in a Graphite Composite Fairing Structure

Defining the electromagnetic environment inside a graphite composite fairing due to lightning is of interest to spacecraft developers. This paper is the first in a two part series and studies the shielding effectiveness of a graphite composite model fairing using derived equivalent properties. A frequency domain Method of Moments (MoM) model is developed and comparisons are made with shielding test results obtained using a vehicle-like composite fairing. The comparison results show that the analytical models can adequately predict the test results. Both measured and model data indicate that graphite composite fairings provide significant attenuation to magnetic fields as frequency increases. Diffusion effects are also discussed. Part 2 examines the time domain based effects through the development of a loop based induced field testing and a Transmission-Line-Matrix (TLM) model is developed in the time domain to study how the composite fairing affects lightning induced magnetic fields. Comparisons are made with shielding test results obtained using a vehicle-like composite fairing in the time domain. The comparison results show that the analytical models can adequately predict the test and industry results.

Trout, Dawn H.↗
NASA NTRS1985

Dynamic Identification for Control of Large Space Structures

This is a compilation of reports by the one author on one subject. It consists of the following five journal articles: (1) A Parametric Study of the Ibrahim Time Domain Modal Identification Algorithm; (2) Large Modal Survey Testing Using the Ibrahim Time Domain Identification Technique; (3) Computation of Normal Modes from Identified Complex Modes; (4) Dynamic Modeling of Structural from Measured Complex Modes; and (5) Time Domain Quasi-Linear Identification of Nonlinear Dynamic Systems.

Ibrahim, S. R.↗
DOE OSTI2022

hPIC2: A hardware-accelerated, hybrid particle-in-cell code for dynamic plasma-material interactions

The exascale era of high performance computing promises to bring the field of computational plasma physics ever closer to the goal of accurate multiscale modeling. Such computers will rely on hardware acceleration to offload work to dedicated components, notably general-purpose graphics processing units (GPUs). However, devices from different manufacturers require software to be written with different parallel programming models, greatly increasing the code maintenance burden of applications designed to perform on more than one such device. hPIC2 is a hybrid plasma simulation code developed with the Kokkos performance portability framework to target the architectures that will drive exascale computing for the foreseeable future. As a hybrid simulation code, hPIC2 investigates the simultaneous use of various plasma models on the same domain, at the same time. hPIC2 also optionally couples to RustBCA, which accurately models ion-material interactions using the binary collision approximation (BCA) method. In conclusion, hPIC2 therefore achieves scalable performance on a variety of computing architectures when simulating complex and diverse plasmas, particularly near plasma-material interfaces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

A generalizable machine learning approach to predict land surface temperature

Monitoring of land surface and atmospheric states is highly reliant on satellite data. Traditionally, data products are generated using carefully tuned and validated algorithms for low-earth orbit (LEO) sensors. However, the emerging constellation of geostationary (GEO) sensors contributes global, high temporal resolution observations which can better capture the diurnal variability of key observables like land surface temperature (LST). Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from LEO and GEO satellites to develop a deep learning-based method for sensor-to-sensor algorithm emulation. Our model is trained on GOES-16 thermal bands to predict MODIS Terra LST and achieves a validation error <2K. Further, application of the model to unseen times of day and a second GEO sensor observing an unseen spatial domain demonstrate the generalization of the deep learning model across space, time and spectra. We anticipate that the synergies between a variety of active orbit configurations can be used to accelerate application of existing algorithms to new datasets.

Kate Marie Duffy↗
DOE OSTI2022

Testing and Expertise for Marine Energy (TEAMER) Program Support - Numerical Modeling Assistance for iProTech's "PIP" WEC Device: Cooperative Research and Development Final Report, CRADA Number CRD-20-17303

Develop a time-domain, 3 degree of freedom numerical model of IProTech's PIP device, including power take-off (PTO), that can be used to develop control algorithms and establish a baseline technology performance level (TPL). This is important to IProTech because having a good numerical model allows IProTech to optimize design and control algorithms on an ongoing basis. A baseline TPL enables the PIP technology to be benchmarked against other wave energy converter (WEC) concepts, providing (i) justification (or not) for investment in wave tank model testing, (ii) identifying optimum design parameters and control algorithms for the test model, (iii) specifying test conditions and (iv) interpreting wave tank test results. An accredited WEC-Sim model of the PIP device is an essential tool for further development of the technology. 1) The development of numerical models of the PIP concept will enable iProTech to evaluate the potential performance of this concept, and to investigate how sensitive the device is to different variables within the system (e.g. hydraulic components, geometry dimensions, etc.) 2) The numerical models were developed with WEC-Sim and PTO-Sim -- the de-facto industry standard WEC numerical modeling tools. A detailed model of the system's hydraulic circuit was developed with PTO-Sim. However, WEC-Sim is based on linear hydrodynamics and has several limitations; validation against high fidelity models/physical scale models would help to build confidence in the results.

16 TIDAL AND WAVE POWER↗
NASA NTRS1989

Review of FD-TD numerical modeling of electromagnetic wave scattering and radar cross section

Applications of the finite-difference time-domain (FD-TD) method for numerical modeling of electromagnetic wave interactions with structures are reviewed, concentrating on scattering and radar cross section (RCS). A number of two- and three-dimensional examples of FD-TD modeling of scattering and penetration are provided. The objects modeled range in nature from simple geometric shapes to extremely complex aerospace and biological systems. Rigorous analytical or experimental validatons are provided for the canonical shapes, and it is shown that FD-TD predictive data for near fields and RCS are in excellent agreement with the benchmark data. It is concluded that with continuing advances in FD-TD modeling theory for target features relevant to the RCS problems and in vector and concurrent supercomputer technology, it is likely that FD-TD numerical modeling will occupy an important place in RCS technology in the 1990s and beyond.

Taflove, Allen↗
DOE OSTI2021

Mixing Cell Model: A One-Dimensional Numerical Model for Assessment of Water Flow and Contaminant Transport in the Unsaturated Zone

This report describes the Mixing Cell Model code, a one dimensional model for water flow and solute transport in the unsaturated zone under steady state or transient flow conditions. The model is based on the principles and assumptions underlying mixing-cell model formulations. The unsaturated zone is discretized into a series of independent mixing cells. Each cell may have unique hydrologic, lithologic, and sorptive properties. Ordinary differential equations describe the material (water and solute) balance within each cell. Water-flow equations are derived from the continuity equation, assuming that unit gradient conditions exist at all times in each cell. Pressure gradients are considered implicitly through model discretization. Unsaturated hydraulic conductivity and moisture contents are determined by the material specific moisture-characteristic curves. Solute-transport processes include explicit treatment of advective processes, first order chain decay, and linear sorption reactions. Dispersion is addressed through implicit and explicit dispersion. Implicit dispersion is an inherent feature of all mixing-cell models and originates from the formulation of the problem in terms of mass balance around fully mixed volume elements. Expressions are provided that relate implicit dispersion to the physical dispersion of the system. Two FORTRAN codes were developed to solve the water flow and solute-transport equations: (1) the Mixing Cell Model for Flow (MCMF) solves transient water-flow problems and (2) the Mixing Cell Model for Transport (MCMT) solves the solute-transport problem. The transient water-flow problem is typically solved first by estimating the water flux through each cell in the model domain as a function of time using the MCMF code. These data are stored in either ASCII or binary files that are later read by the solute transport code (MCMT). Code output includes solute pore water concentrations, water and solute inventories in each cell and at each specified output time, and water and solute fluxes through each cell and specified output time. Computer run times for coupled transient water flow and solute transport were typically several seconds on a 2 GHz Intel Pentium IV desktop computer. The model was benchmarked against analytical solutions and finite element approximations to the partial differential equations (PDE) describing unsaturated flow and transport. Differences between the maximum solute flux estimated by the mixing cell model and the PDE models were typically less than two percent. This revision includes an option for a fixed concentration lower boundary condition for diffusive fluxes for versions 020321 and later.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

Model calculations of lightning electric fields

Calculated time-domain waveforms and frequency spectra are presented for three of the most important processes in a lightning discharge to ground: the return stroke, the stepped leader, and the preliminary breakdown. For each of these processes, the model calculations are given for 200 m and 50 km. The calculations are compared with available time and frequency domain measurements.

Master, M. J.↗
NASA NTRS2011

Estimation of Unsteady Aerodynamic Models from Dynamic Wind Tunnel Data

Demanding aerodynamic modelling requirements for military and civilian aircraft have motivated researchers to improve computational and experimental techniques and to pursue closer collaboration in these areas. Model identification and validation techniques are key components for this research. This paper presents mathematical model structures and identification techniques that have been used successfully to model more general aerodynamic behaviours in single-degree-of-freedom dynamic testing. Model parameters, characterizing aerodynamic properties, are estimated using linear and nonlinear regression methods in both time and frequency domains. Steps in identification including model structure determination, parameter estimation, and model validation, are addressed in this paper with examples using data from one-degree-of-freedom dynamic wind tunnel and water tunnel experiments. These techniques offer a methodology for expanding the utility of computational methods in application to flight dynamics, stability, and control problems. Since flight test is not always an option for early model validation, time history comparisons are commonly made between computational and experimental results and model adequacy is inferred by corroborating results. An extension is offered to this conventional approach where more general model parameter estimates and their standard errors are compared.

Murphy, Patrick↗
DOE OSTI2020

Computational modelling and experimental tank testing of the multi float WaveSub under regular wave forcing

A submerged wave device generates energy from the relative motion of floating bodies. In WaveSub, three floats are joined to a reactor; each connected to a spring and generator. Electricity generated damps the orbital movements of the floats. The forces are non-linear and each float interacts with the others. Tuning to the wave climate is achieved by changing the line lengths, so there is a need to understand the performance trade-offs for a large number of configurations. This requires an efficient, large displacement, multidirectional, multi-body numerical scheme. Results from a 1/25 scale wave basin experiment are described. Here, we show that a time domain linear potential flow formulation (Nemoh, WEC-Sim) can match the tank testing provided that suitably tuned drag coefficients are employed. Inviscid linear potential models can match some wave device experiments; however, additional viscous terms generally provide better accuracy. Scale experiments are also prone to mechanical friction, and we estimate friction terms to improve the correlation further. The resulting error in mean power between numerical and physical models is approximately 10%. Predicted device movement shows a good match. Overall, drag terms in time domain wave energy modelling will improve simulation accuracy in wave renewable energy device design. to understand the performance trade-offs for a large number of configurations. This requires an efficient, large displacement, multidirectional, multi-body numerical scheme. Results from a 1/25 scale wave basin experiment are described. Here, we show that a time domain linear potential flow formulation (Nemoh, WEC-Sim) can match the tank testing provided that suitably tuned drag coefficients are employed. Inviscid linear potential models can match some wave device experiments; however, additional viscous terms generally provide better accuracy. Scale experiments are also prone to mechanical friction, and we estimate friction terms to improve the correlation further. The resulting error in mean power between numerical and physical models is approximately 10%. Predicted device movement shows a good match. Overall, drag terms in time domain wave energy modelling will improve simulation accuracy in wave renewable energy device design.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Learning earthquake ground motions via conditional generative modeling

Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake locations, while physics-based methods are computationally intensive and require accurate representations of Earth structures and earthquake sources. We propose an artificial intelligence (AI) spectrogram generator, Conditional Generative Modeling for Ground Motion (CGM-GM). CGM-GM leverages earthquake magnitudes and geographic coordinates of earthquakes and sensors as inputs, when postprocessed with phase information, capturing spatially continuous Fourier amplitude spectra (FAS) as well as properties such as P and S arrivals, and waveform durations, without explicit physics constraints. This is achieved through a probabilistic autoencoder that extracts latent distributions in the time-frequency domain and variational sequential models for prior and posterior distributions. We evaluate the performance of CGM-GM using small-magnitude earthquake records from the San Francisco Bay Area, a region with high seismic risks. Here, we report that CGM-GM demonstrates potential for complementing physics-based simulations and non-ergodic empirical ground motion models, as well as shows promise in seismology and beyond.

geophysics↗