SEARCH · Engineering Papers

Results for “Nonlinear reduction”

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 415 records · Page 23

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Discrete Empirical Interpolation Method Based Dynamic Load Model Reduction

Dynamic load models add significant complexity to bulk power system time-domain simulations. The complexity is due to the large number of ordinary differential equations (ODEs) introduced by the dynamic load components such as induction motors. It is challenging to derive reduced-order models (ROMs) for dynamic loads due to the nonlinear functions in their governing equations. This paper applies the discrete empirical interpolation method enhanced proper orthogonal decomposition (DEIM-POD) to approximate the full dynamic load model with the ROM that minimizes the projection error of the nonlinear functions in dynamic load ODEs onto their dominant modes. This approach only requires evaluation of nonlinear functions at selected observation points. The observation points selected by DEIM also provide information for screening critical load buses where dynamic load model parameters contribute the most to the accuracy of ROM across multiple contingencies. The proposed approach is validated on IEEE 9-bus, WECC 179-bus and 2384-bus Polish systems.

bulk power system↗

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↗

NASA NTRS1972

The effects of various cure cycles upon the viability of Bacillus subtillis var. niger spores within solid propellant.

Saturethane solid propellant was used in all tests. The spore inoculum was evenly distributed in the propellant. Samples weighing approximately 5 g were aseptically removed, placed into curing ovens, and exposed to cure temperatures. Initial tests were conducted at 82 and 93 C. Analysis of the obtained data indicated that the survivor curves were not linear. Exposure of the inoculated propellant samples to 93 C reduced the initial population to less than 0.01% in about 20 hours. At 82 C, approximately 168 hours were required for a similar reduction. Tests involving curing temperatures of 105 and 115 C were also conducted. It is pointed out that changes in the mechanism of spore inactivation due to chemical and physical changes in the propellant could account for the nonlinear survivor curves obtained.

Brewer, W. A.↗

NASA NTRS1984

Microdensitometer errors: Their effect on photometric data reduction

The performance of densitometers used for photometric data reduction of high dynamic range electrographic plate material is analyzed. Densitometer repeatability is tested by comparing two scans of one plate. Internal densitometer errors are examined by constructing histograms of digitized densities and finding inoperative bits and differential nonlinearity in the analog to digital converter. Such problems appear common to the four densitometers used in this investigation and introduce systematic algorithm dependent errors in the results. Strategies to improve densitometer performance are suggested.

Bozyan, E. P.↗

NASA NTRS1990

The sonic boom of an oblique flying wing

An analysis of sonic boom characteristics of an oblique flying wing is presented. The wing, represented by a slewed lift and area-distribution as well as a panel geometry, promises a reduction of sonic boom signature. For every azimuth angle these distributions are represented by an equivalent body. The near-field pressure signature is determined by using the Whitham F-function with a correction to account for nonlinear wave propagation. The geometric asymmetry leads to an asymmetrical sonic boom beneath the flight track with bow shocks between 1.0 and 1.5 PSF. Due to favorable volume-lift interference the aft shock has only half the amplitude of the bow shock. A fast numerical method is described to calculate the perceived loudness.

Kroo, Ilan↗

NASA NTRS1989

Statistical retrieval of precipitation cell-top altitude using passive 118-GHz observations

A nonlinear statistical-retrieval operator for precipitation cell-top altitude using high-spatial-resolution passive 118-GHz O2 brightness spectra is demonstrated. The retrieval operator consists of a Karhunen-Loeve transformation followed by a rank reduction, a linearization, and a linear minimum mean-square-error estimator. Information from the 118-GHz data on the ambient atmospheric-temperature profile and the precipitation cell size is also incorporated into the linear stage of the retrieval operator. The rms retrieval error is 1.5 km for cumulus-stage cells with tops ranging from 1.5 to 16 km. The sensitivity of nadiral 118-GHz spectra to the cell-top altitude is predominantly due to the scattering and absorption of radiation originating from low, warm atmospheric levels by colder liquid and frozen precipitation This effect causes cold perturbations in the brightness spectrum, which typically become stronger with increasing cell-top altitude.

Gasiewski, A. J.↗

NASA NTRS1991

Concurrent tailoring of fabrication process and interphase layer to reduce residual stresses in metal matrix composites

A methodology is presented to reduce the residual matrix stresses in continuous fiber metal matrix composites (MMC) by optimizing the fabrication process and interphase layer characteristics. The response of the fabricated MMC was simulated based on nonlinear micromechanics. Application cases include fabrication tailoring, interphase tailoring, and concurrent fabrication-interphase optimization. Two composite systems, silicon carbide/titanium and graphite/copper, are considered. Results illustrate the merits of each approach, indicate that concurrent fabrication/interphase optimization produces significant reductions in the matrix residual stresses and demonstrate the strong coupling between fabrication and interphase tailoring.

Saravanos, D. A.↗

NASA NTRS2004

Performance Benefits for a Turboshaft Engine Using Nonlinear Engine Control Technology Investigated

The potential benefits of nonlinear engine control technology applied to a General Electric T700 helicopter engine were investigated. This technology is being developed by the U.S. Navy SPAWAR Systems Center for a variety of applications. When used as a means of active stability control, nonlinear engine control technology uses sensors and small amounts of injected air to allow compressors to operate with reduced stall margin, which can improve engine pressure ratio. The focus of this study was to determine the best achievable reduction in fuel consumption for the T700 turboshaft engine. A customer deck (computer code) was provided by General Electric to calculate the T700 engine performance, and the NASA Glenn Research Center used this code to perform the analysis. The results showed a 2- to 5-percent reduction in brake specific fuel consumption (BSFC) at the three Sikorsky H-60 helicopter operating points of cruise, loiter, and hover.

Jones, Scott M.↗

DOE OSTI2026

Measurement of the Alfvén Wave Parametric Decay Instability Growth Rate

Alfvén waves, a fundamental mode of magnetized plasmas, are ubiquitous in space and laboratory plasmas. The nonlinear behavior of these modes is thought to play a key role in important problems in space plasma, such as the heating of the solar corona and solar wind turbulence. In particular, theoretical predictions show that these Alfvén waves may be unstable to various parametric instabilities, but space observations of these processes are limited. We demonstrate the first measurement of the Alfvén wave parametric decay instability (PDI) growth rate. Experiments are conducted on the Large Plasma Device at UCLA in which a high amplitude 𝛿⁢𝐵/𝐵 0 ∼ 0.7% pump Alfvén wave is launched from one end of the device and a smaller seed Alfvén wave is launched from the other side. When the frequency of the seed wave is chosen to match the backward wave expected from PDI, damping of the seed wave is reduced. We compare this reduction in damping to the theoretically expected PDI growth rate while accounting for acoustic mode damping. Results show agreement between measurements and theoretical predictions. As a result, this not only provides critical validation for PDI theories and simulations that could help interpret future space observations but also suggests a new way of studying similar nonlinear wave phenomena.

Alfvén waves↗

NASA NTRS1990

Helicopter rotor dynamics and aeroelasticity - Some key ideas and insights

Four important current topics in helicopter rotor dynamics and aeroelasticity are discussed: (1) the role of geometric nonlinearities in rotary-wing aeroelasticity; (2) structural modeling, free vibration, and aeroelastic analysis of composite rotor blades; (3) modeling of coupled rotor/fuselage areomechanical problems and their active control; and (4) use of higher-harmonic control for vibration reduction in helicopter rotors in forward flight. The discussion attempts to provide an improved fundamental understanding of the current state of the art. In this way, future research can be focused on problems which remain to be solved instead of producing marginal improvements on problems which are already understood.

Friedmann, Peretz P.↗

NASA NTRS2002

Higher Order Time Integration Schemes for the Unsteady Navier-Stokes Equations on Unstructured Meshes

The rapid increase in available computational power over the last decade has enabled higher resolution flow simulations and more widespread use of unstructured grid methods for complex geometries. While much of this effort has been focused on steady-state calculations in the aerodynamics community, the need to accurately predict off-design conditions, which may involve substantial amounts of flow separation, points to the need to efficiently simulate unsteady flow fields. Accurate unsteady flow simulations can easily require several orders of magnitude more computational effort than a corresponding steady-state simulation. For this reason, techniques for improving the efficiency of unsteady flow simulations are required in order to make such calculations feasible in the foreseeable future. The purpose of this work is to investigate possible reductions in computer time due to the choice of an efficient time-integration scheme from a series of schemes differing in the order of time-accuracy, and by the use of more efficient techniques to solve the nonlinear equations which arise while using implicit time-integration schemes. This investigation is carried out in the context of a two-dimensional unstructured mesh laminar Navier-Stokes solver.

Jothiprasad, Giridhar↗

DOE OSTI2022

Physics-Informed Sparse Gaussian Process for Probabilistic Stability Analysis of Large-Scale Power System with Dynamic PVs and Loads

This work proposes a physics-informed sparse Gaussian process (SGP) for probabilistic stability assessment of large-scale power systems in the presence of uncertain dynamic PVs and loads. The differential and algebraic equations considering uncertainties from dynamic PVs and loads are reformulated to a nonlinear mapping relationship that allows the application of SGP. Thanks to the nonparametric characteristic of Gaussian process, the proposed framework does not require distributions of uncertain inputs and this distinguishes it from existing approaches. As the original Gaussian process is not scalable to large-scale systems with high dimensional uncertain inputs, this paper develops the SGP with a stochastic variational inference technique. It leads to approximately two orders of complex reduction. A data pre-processing step is also introduced to tackle the coexistence of stable and unstable cases by sample clustering and constructing separate SGPs. The probabilistic transient stability index is analyzed to assess system stability under different uncertain dynamics loads and PVs. Comparisons are performed with the sampling-based, the polynomial chaos expansion-based, and traditional Gaussian process-based methods on the modified IEEE 118-bus and Texas 2000-bus systems under various scenarios, including different levels of uncertainties and the existence of nonlinear correlations among dynamic PVs. The impacts of data quality and quantity issues are also investigated. It is shown that the proposed SGP achieves significantly improved computational efficiency while maintaining high accuracy with a limited number of data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Toward real-time optimization through model reduction and model discrepancy sensitivities

Optimization problems arise in a range of scenarios, from optimal control to model parameter estimation. In many applications, such as the development of digital twins, it is essential to solve these optimization problems within wall-clock-time limitations. However, this is often unattainable for complex systems, such as those modeled by nonlinear partial differential equations. One strategy for mitigating this issue is to construct a reduced-order model (ROM) that enables more rapid optimization. In particular, the use of nonintrusive ROMs—those that do not require access to the full-order model at evaluation time—is popular because they facilitate the computation of optimization solutions within the wall-clock time requirements. However, the optimization solution will be unreliable if the iterates move outside the ROM training data. This article proposes the use of hyper-differential sensitivity analysis with respect to model discrepancy (HDSA-MD) as a computationally efficient tool to augment ROM-constrained optimization and improve its reliability. The proposed approach consists of two phases: (i) an offline phase where several full-order model evaluations are computed to train the ROM, and (ii) an online phase where a ROM-constrained optimization problem is solved, a limited number of full-order model evaluations are computed, and HDSA-MD is used to enhance the optimization solution. Numerical results are demonstrated for two examples, atmospheric contaminant control and wildfire ignition location estimation, in which a ROM is trained offline using inaccurate atmospheric data. In conclusion, the HDSA-MD update yields a significant improvement in the ROM-constrained optimization solution using only one full-order model evaluation online with corrected atmospheric data.

PDE-constrained optimization↗

DOE OSTI2020

A 1D coupled transport/cold plasma wave model for parallel ponderomotive density modification near RF actuators

The plasma density profile close to RF actuators plays a vital role in the efficiency of power coupling from the antenna to the core plasma of fusion devices. Nonlinear interactions can be significant when considering the interaction of applied RF power and the tenuous SOL plasma, and the inclusion of this region in computational analysis is essential to providing robust predictive simulations of plasma response to RF application. Of particular interest is the observed density modification during RF operation. Here we present a self consistent description of the nonlinear ponderomotive force in the antenna near field region, via coupling a 1D field aligned transport model with a 1D cold plasma wave model. Results are presented, which show a parallel electric field of 300 Vm -1 drove a density reduction of ~ 20% over 25 RF periods.

Barnett, Rhea↗

DOE OSTI2024

CI-MOR Final Report: Analysis and Validation of Critical Infrastructure Models using Model Order Reduction

This report summarizes the research and capabilities developed as part of the project “Analysis and Validation of Critical Infrastructure Models using Model Order Reduction” (CI-MOR) LDRD project. CI-MOR research enables the solution of large, complex optimization models that naturally arise in national security challenges involving critical infrastructures. Specifically, CI-MOR researchers developed methods to (1) rigorously approximate complex, nonlinear optimization formulations, (2) identify alternative near-optimal solutions, (3) accelerate optimization workflows used for complex applications, and (4) rigorously integrate domain knowledge in stochastic-process models. This report provides an overview of the research done in CI-MOR, and we describe application exemplars used to illustrate CI-MOR capabilities. Furthermore, we describe the software developed by CI-MOR that researchers can leverage to analyze new applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Advanced Geomechanical Model to Predict the Impact of CO2-Induced Microstructural Alterations on the Cohesive-Frictional Behavior of Mt. Simon Sandstone

We investigated the influence of CO2-induced geochemical reactions on the cohesive-frictional properties of host rock within the context of CO2 storage in a saline aquifer and focused on the Mt. Simon sandstone. The research objective was to model geo-mechanical changes due to host rock exposure to CO2-saturated brine while accounting for heterogeneity, double-scale porosity, and granular structure. We formulated a three-level multi-scale model for host rocks. We conducted scanning electron microscopy analyses to probe the microstructure and grid nanoindentation to measure the mechanical response. We derived new nonlinear strength upscaling solutions to correlate the effective strength characteristics and the macroscopic yield surface to the micro-structure at the nano-, micro-, and meso-scales. Specifically, our theoretical model links CO2-induced microstructural alterations to a reduction in the size of the yield surface, and a drop in the value of the friction coefficient. In turn, regarding the Illinois Basin Decatur Project, the CO2-induced drop in friction coefficient is linked to an increase in the risk of fault slip and a higher probability of induced microseismicity during and after the end of CO2 underground injection operations. The theoretical model presented is essential for the geo-mechanical modeling of CO2 underground injection operations at multiple length-scales.

58 GEOSCIENCES↗

DOE OSTI2024

Visual Analytics of Multivariate Networks With Representation Learning and Composite Variable Construction

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This article presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Coherent homodyne detection for amplified cross-beam electric-field induced second harmonic

The electric-field induced second harmonic (E-FISH) signal sensing is commonly used as a nonlinear optical technique to probe electric fields inside a plasma region. Cross-beam E-FISH is being investigated to improve spatial resolution by defining the interaction volume via a controlled geometry of two overlapping noncolinear optical beams. This drastic reduction in interaction length of the electric field and laser region results in a significant signal reduction. Here, to overcome this signal reduction, we introduce coherent amplification of the cross-beam E-FISH signal by mixing the low E-FISH signal with a phase-locked bright local oscillator. We demonstrate enhancement of the signal. By introducing a local oscillator we can now derive the polarity of the measured electric field through the phase of the homodyne signal. To illustrate the technique, we, for the first time, to the best of our knowledge, measure the magnitude and the direction of the electric field in a cold atmospheric pressure plasma jet, which dynamically follows the profile of the applied bias current.

42 ENGINEERING↗