Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Nonlinear projection”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Validation of gyrokinetic simulations in NSTX and projections for high-k turbulence measurements in NSTX-U

An extensive validation effort performed for a modest-beta NSTX NBI-heated H-mode discharge predicts that electron thermal transport can be entirely explained by electron-scale turbulence fluctuations driven by the electron temperature gradient mode (ETG), both in conditions of strong and weak ETG turbulence drive. Thermal power-balance estimates computed by TRANSP as well as the shape of the high-k density fluctuation wavenumber spectrum and the fluctuation level ratio between strongly driven and weakly driven ETG-turbulence conditions can be matched by nonlinear gyrokinetic simulations and a synthetic diagnostic for high-k scattering. Linear gyrokinetic simulations suggest that the ion-scale instability in the weak ETG condition is close to the critical threshold for the kinetic ballooning mode instability, and nonlinear ion-scale gyrokinetic simulations show that turbulence might be in a state reminiscent of a Dimits' shift regime, opening speculation on the role that ion-scale turbulence might play for the weak ETG condition. A simulation that matched all experimental constraints is chosen to project high-k turbulence spectra in NSTX-U, revealing that the new high-k system [R. Barchfeld et al., Rev. Sci. Instrum. 89, 10C114 (2018)] should be sensitive to density fluctuations from radially elongated streamer structures. In this study, two schemes are designed to characterize the radial and poloidal wavenumber dependence of the density fluctuation wavenumber power spectrum around the streamer peak, suggesting future high-k fluctuation measurements could be sensitive to an asymmetry in the k r spectrum introduced due to the presence of strong background flow shear.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Grain2Mesh: Mesh Generation for Grain-Scale Nonlinear Elasticity Modeling

The nonlinear hysteretic behavior of rocks under cyclic loading is a crucial area of study in geomechanics. The macroscopic response of a variety of materials has been found to be contingent upon the behavior of the micro-scale structure. This project aims to develop a functional and maintainable software package for generating a multi-phase numerical mesh and accompanying simulation files for finite element modeling used in computational mechanics solvers. Meshes generated from images often lack key preprocessing that reduces noise and prevents mesh element distortion that can increase computational cost. By incorporating user feedback throughout, grain2mesh ensures a high-fidelity mesh that can be used to model grain-scale interactions such as shearing, crack propagation, and interfacial material contrast. Scientific applications of this software include material fracturing, stress-strain analysis for natural and engineered materials, and nonlinear meso-scale analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Does Regional Hydroclimate Change Scale Linearly With Global Warming?

Many aspects of climate change scale linearly with global warming. However, nonlinear changes are possible, especially in the context of hydroclimate, and under emissions scenarios with stabilized global temperature, as aspired to by current climate targets. In CMIP5 and 6, a progressively larger land area shows nonlinear changes as a function of global warming when considering precipitation, evaporation, and soil moisture, with the latter showing nonlinearity over ~50% of global land. Here, using ensemble simulations with the Community Earth System Model 1, in which individual forcing factors are held constant, we illustrate how nonadditive responses to anthropogenic greenhouse gases and industrial and fire-related aerosols can yield complex soil moisture changes in certain regions. This complexity contributes to uncertainty in regional soil moisture projections and suggests that the timing of, as well as model response uncertainty to, future aerosol reductions will have significant impacts on regional hydroclimate change as global temperatures stabilize.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Fork Experiments in the Hot Cell Using Spent Fuel Rods for International Nuclear Safeguards

This work leveraged the rare availability of 25 full-length pressurized water reactor spent fuel rods and 1 irradiated mixed-oxide rod at an Oak Ridge National Laboratory hot cell. This was done to collect measurement data with two Fork detectors to assess the detectors’ capability of verifying operator declaration data and detecting partial defects in spent fuel, which are the two primary goals of international safeguards on spent nuclear fuel. The data can also be used to benchmark the ORIGEN module, which has been adopted in the International Atomic Energy Agency’s (IAEA’s)/European Atomic Energy Community’s (Euratom’s) Integrated Review and Analysis Program to predict the Fork detector count rates in real time. In this project, the authors first calibrated two Fork detectors—a standard one and a modified one—by using known strong neutron and gamma sources. Then, the authors measured all 26 fuel rods at multiple locations along the length. The fuel rods were then assembled into three arrays—2 × 2, 3 × 3, and 5 × 5—by using specially designed support grids to mimic fuel assemblies and measure the arrays with both detectors. For the 5 × 5 array, 4 and 8 fuel rods of the array were replaced in two separate cases with short stainless-steel rods to mimic two partial defect scenarios, and the arrays were measured before and after the replacements. Polyethylene blocks were used in this experiment to mimic water. The results show that the Fork detectors were able to verify operator declarations and detect partial defects in spent fuel, and the authors were the first to demonstrate this through experiments. A discovery was also made that determined the root cause of the nonlinear response to gamma dose in the ion chambers used in IAEA and Euratom’s Fork detectors. After the experiments, both detectors were retrieved from the hot cell for future use. The data collected in this project will be used in a parallel International Nuclear Safeguards Engagement Program (INSEP) project to enhance the safeguards in the Finnish spent fuel encapsulation plant, and the data will be useful to other projects in the future given the increased safeguards needs due to spent fuel transfer and disposal activities worldwide.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Quantitative Non-Destructive Evaluation of Fatigue Damage Based on Multi-Sensor Fusion

Based on sensor fusion and machine learning, this project developed a novel non-destructive evaluation (NDE) methodology, which consists of a remaining useful life (RUL) prediction framework and regression models for predicting residual stress and full width at half maximum (FWHM). A series of fatigue testing experiments were conducted using 5052-H32 aluminum alloy specimens. All specimens were measured using linear ultrasonic (LU) and nonlinear ultrasonic (NLU) testing methods non-destructively. Machine learning models were developed to use LU and NLU measurements to predict loading condition, fatigue level, residual stress, and FWHM. It was demonstrated that the developed methodology could distinguish new and fatigue specimens with an accuracy of 97.53%. Also, the prediction errors for residual stress and FWHM were as low as 4.73% and 1.62%, respectively. An interactive database was created to publicly share the data generated from the project. It is envisioned that the developed NDE technology will equip manufacturers with a responsive screening system for incoming used metallic components, and potentially lead to a significant increase in using used metallic components for remanufacturing.

42 ENGINEERING↗
DOE OSTI2021

Microscopic Theory of Fluctuating Hydrodynamics in Nonlinear Lattices

The theory of fluctuating hydrodynamics has been an important tool for analyzing macroscopic behavior in nonlinear lattices. However, despite its practical success, its microscopic derivation is still incomplete. In this work, we provide the microscopic derivation of fluctuating hydrodynamics, using the coarse-graining and projection technique; the equivalence of ensembles turns out to be critical. The Green-Kubo (GK)-like formula for the bare transport coefficients are presented in a numerically computable form. Our numerical simulations show that the bare transport coefficients exist for a sufficiently large but finite coarse-graining length in the infinite lattice within the framework of the GK-like formula. Finally, this demonstrates that the bare transport coefficients uniquely exist for each physical system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

How Snow Aggregate Ellipsoid Shape and Orientation Variability Affects Fall Speed and Self-Aggregation Rates

Snow aggregate shapes and orientations have long been known to exhibit substantial variability. Despite this observed variability, most weather and climate prediction models use fixed power-law functions that deterministically map particle size to mass and fall speed. As such, integrated quantities like precipitation and self-aggregation rates currently ignore nonlinear effects resulting from variation in shape and orientation for aggregates of the same size. This study therefore develops an analytic framework that couples an empirically based bivariate distribution of ellipsoid shapes to classical hydrodynamic theory so as to capture an appropriate dispersion of masses, projected areas, and fall speeds for an assumed size distribution. For a fixed aggregate size, shape variations produce approximately ±0.13 m s -1 standard deviation of fall speed which increases the mass flux fall speed dispersion by more than 100% over traditional microphysics models. This increased fall speed dispersion results predominantly from shape-induced mass dispersion whereas orientation and drag dispersion play a lesser role. Shape variations can increase mass- and reflectivity-weighted fall speeds by up to 60% of traditional models whereas self-aggregation rates can increase by a factor of 100 for very small slope parameters. This implies that aggregate shape variations effectively forestall the theorized onset of fall speed distribution narrowing and subsequent quenching of the aggregation process. As a result, it is likely that secondary ice formation is necessary to prevent an ever decreasing slope parameter. The mathematical theory presented in this study is used to develop simple correction factors for snow forecast and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction

In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models. FSM is a data assimilation technique that blends model's predictions with noisy observations to correct initial state and/or model parameters. We apply this approach on a projection based reduced order model (ROM) of the one-dimensional viscous Burgers equation with a square wave defining a moving shock, and the two-dimensional vorticity transport equation formulating a decay of Kraichnan turbulence. We investigate the capability of the approach to approximate an optimal value for eddy viscosity with different measurement configurations. Specifically, we show that our approach can sufficiently assimilate information either through full field or sparse noisy measurements to estimate eddy viscosity closure to cure standard Galerkin reduced order model (GROM) predictions. Therefore, our approach provides a modular framework to correct forecasting error from a sparse observational network on a latent space. We highlight that the proposed GROM-FSM framework is promising for emerging digital twin applications, where real-time sensor measurements can be used to update and optimize surrogate model's parameters.

42 ENGINEERING↗
DOE OSTI2024

Learning Nonlinear Reduced Models from Data with Operator Inference

This review discusses Operator Inference, a nonintrusive reduced modeling approach that incorporates physical governing equations by defining a structured polynomial form for the reduced model, and then learns the corresponding reduced operators from simulated training data. The polynomial model form of Operator Inference is sufficiently expressive to cover a wide range of nonlinear dynamics found in fluid mechanics and other fields of science and engineering, while still providing efficient reduced model computations. The learning steps of Operator Inference are rooted in classical projection-based model reduction; thus, some of the rich theory of model reduction can be applied to models learned with Operator Inference. This connection to projection-based model reduction theory offers a pathway toward deriving error estimates and gaining insights to improve predictions. Furthermore, through formulations of Operator Inference that preserve Hamiltonian and other structures, important physical properties such as energy conservation can be guaranteed in the predictions of the reduced model beyond the training horizon. This review illustrates key computational steps of Operator Inference through a large-scale combustion example.

Mechanics↗
DOE OSTI2026

Gravity Well Commercial Economics Assessment: Potential Revenue and Cost: Cooperative Research and Development (Final Report)

In the Gravity Well Revenue Study, we evaluate the potential revenue from energy storage using historical energy-only electricity prices, forward-looking projections of hourly electricity prices, and actual reported revenue. This analysis examines the impact of storage duration and round-trip efficiency, as well as the location of the storage, on storage revenue within the current and projected U.S. power system. We also investigated the impact of round-trip efficiency on storage revenue. We found that the relationship between storage revenue and round-trip efficiency is nonlinear. The value of improved round-trip efficiency declines as round-trip efficiency increases. In the Gravity Well Future Cost Study, we applied learning curves to predict the future cost trajectory of Gravity Wells (GrWs). Two types of analysis were implemented. The first was a bottom-up analysis that used historical learning rates for cost components, such as motors and gearboxes, and cost categories (e.g., engineering and design, etc.) to determine the learning-by-doing based single-factor learning curve. The single factor learning curve expresses the relationship between the cost of GrW and the number of units deployed (or the cumulative capacity). In the second analysis, we predicted future GrW costs via a top-down approach. This approach accounts for historical cost trends in other renewable energy and storage technologies, which have similarities with GrWs. Using a multifactor learning curve that accounts for both intrinsic (cumulative capacity) and extrinsic (the elasticity in the price of steel) factors, we estimated the future cost of GrWs.

25 ENERGY STORAGE↗
DOE OSTI2025

The Observed and Projected Changes of Global Monsoons: Current Status and Future Perspectives

The global monsoon system, encompassing the Asian-Australian, African, and American monsoons, sustains two-thirds of the world’s population by regulating water resources and agriculture. Monsoon anomalies pose severe risks, including floods and droughts. Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms. Observational data reveal a 20th-century shift: increased rainfall pre-1950s, followed by aridification and partial recovery post-1980s, driven by both internal variability (e.g., Atlantic Multidecadal Oscillation) and external forcings (greenhouse gases, aerosols), while ENSO drives interannual variability through ocean-atmosphere interactions. Future projections under greenhouse forcing suggest long-term monsoon intensification, though regional disparities and model uncertainties persist. Models indicate robust trends but struggle to quantify extremes, where thermodynamic effects (warming-induced moisture rise) uniformly boost heavy rainfall, while dynamical shifts (circulation changes) create spatial heterogeneity. Volcanic eruptions and proposed solar radiation modification (SRM) further complicate predictions: tropical eruptions suppress monsoons, whereas high-latitude events alter cross-equatorial flows, highlighting unresolved feedbacks. The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons. Critical challenges remain. Model biases and sparse 20th-century observational data hinder accurate attribution. The interplay between natural variability and anthropogenic forcings, along with nonlinear extreme precipitation risks under warming, demands deeper mechanistic insights. Additionally, SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation. Addressing these gaps necessitates enhanced observational networks, refined climate models, and interdisciplinary efforts to disentangle multiscale drivers, ultimately improving resilience strategies for monsoon-dependent regions.

climate extreme events↗
DOE OSTI2023

Nonlinear, real-time optimization for actuator management in tokamaks

Experiments in DIII-D have been carried out to test a novel actuator management approach in tokamaks. Here, the actuator management scheme is posed as a nonlinear-optimization problem in which the actuator commands are calculated in real time according to the changing control priorities, plasma state, and actuator availability. Such optimization problem is solved using the augmented Lagrangian method, combined with a gradient projection method and a conjugate-gradient iteration algorithm. The algorithmic approach followed in this work does not depend on the particular control objectives or actuators considered, which facilitates its integration with other independently-designed control components within a plasma-control system. In addition, the actuator-management algorithm is able to handle the optimization problem in a computationally efficient manner, making it suitable for real-time implementations. Initial DIII-D results in the steady-state high-q min scenario have demonstrated the capabilities of the actuator manager to perform both simultaneous multiple mission and repurposing sharing, which will be required in ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Extending PETSc's Composable Hierarchical Solvers (Final Technical Report)

This report documents research activities conducted at CU Boulder as part of Extending PETSc’s Composable Hierarchical Solvers, which has been part of a collaboration with Argonne National Laboratory (separate award). Our work has focused on performance-portable end-to-end GPU solvers demonstrated via exemplary applications in nonlinear fluid and structural mechanics. We describe advances in algorithmic composition and analysis in the context of these applications, but the implementations are fully documented and decoupled, and in use by other projects. We believe the vertical integration achieved through collaboration with ECP’s CEED and the PSAAP center at CU was necessary to take risks with data structures and algorithms.

42 ENGINEERING↗
DOE OSTI2023

Scalable Reduced Order Model with Discontinuous Galerkin Domain Decomposition

scaleupROM is a scalable, physics-constrained reduced order model (ROM). It aims to provide robust, accelerated physics predictions at extrapolated scales, based on the small, component-level data. This is implemented by combining projection-based ROM with discontinuous Galerkin domain decomposition, in the framework of MFEM and libROM. It currently supports the Poisson equation and Stokes flow equation, and more work is in progress toward general, nonlinear physics systems.

Chung, Seung Whan↗
DOE OSTI2023

Single-shot observation of nonlinear pulse splitting in a Kerr medium

We report single-shot, time-resolved observation of self-steepening and temporal splitting of near-infrared, 50 fs, micro-joule pulses propagating nonlinearly in flint (SF11) glass. A coherent, smooth-profiled, 60-nm-bandwidth probe pulse that propagated obliquely to the main pulse through the Kerr medium recorded a time sequence of longitudinal projections of the main pulse’s induced refractive index profile in the form of a phase-shift “streak,” in which frequency–domain interferometry recovered with ∼10 fs temporal resolution. A three-dimensional simulation based on a unidirectional pulse propagation equation reproduced observed pulse profiles.

Chang, Yen-Yu↗
DOE OSTI2020

LaserNetUS at the Extreme Light Laboratory

This is the final report for LaserNetUS, Grant # DE-SC0019419. This project covered the first two annual cycles (2018-2020) of LaserNetUS experiments conducted at the Extreme Light Laboratory, University of Nebraska-Lincoln. The project provided students and scientists from four institutions (BYU, Stanford, UNR, and ARFL) with access to a world-class high-intensity laser facility. Experimental results were obtained on the topics of Nonlinear Thomson scattering, Relativistic vacuum acceleration, and electrons beams in relativistic high-energy-density plasma to study x-ray line emission and radio frequencies of ultrashort relativistic electron beam interactions. Another benefit was the training of 10 students (undergraduate or graduate) and 6 young scientists (postdocs or associate/research professors) in areas that are key to the future development of high energy density science and high-power laser technology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

LaserNetUS at the Extreme Light Laboratory. Final report

This is the final report for LaserNetUS, Grant # DE-SC0019419. This project covered the first two annual cycles (2018-2020) of LaserNetUS experiments conducted at the Extreme Light Laboratory, University of Nebraska-Lincoln. The project provided students and scientists from four institutions (BYU, Stanford, UNR, and ARFL) with access to a world-class high-intensity laser facility. Experimental results were obtained on the topics of Nonlinear Thomson scattering, Relativistic vacuum acceleration, and electron beams in relativistic high-energy-density plasma to study x-ray line emission and radio frequencies of ultrashort relativistic electron beam interactions. Another benefit was the training of 10 students (undergraduate or graduate) and 6 young scientists (postdoc or associate/research professors) in critical areas to the future development of high energy density science and high-power laser technology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗