Results for “parametric processes” — Page 41

NASA NTRSDate not supplied

Gradient-Enhanced Reliability Analysis of Transonic Aeroelastic Flutter

Reliability computation for transonic aeroelastic systems is a challenging task, given the complex physical mechanisms involved, and broader challenges associated with computing small probabilities of failure/flutter. This paper demonstrates the use of four different reliability estimation tools, with a particular focus on tools that can utilize gradient information: the derivative of flutter boundaries with respect to random parametric inputs. Results are shown for two test cases: the AGARD 445.6 case and the Common Research Model, and the strength and weaknesses of each UQ tool are compared in terms of cost and accuracy. Two of the UQ tools used here require gradients as part of a search process, but the other two are sample-based, and are able to leverage the existence of gradients for improved performance.

Bret K Stanford↗

NASA NTRS2023

EVA Task and 3D Pose Recognition from Video

Extravehicular Activity (EVA) has been known to involve potential risks of biomechanical stresses and injuries to crewmembers. Gathering of EVA motion patterns is necessary for risk analysis and mitigation. However, many existing techniques, such as motion capture systems, are not only cost-prohibitive but are impractical for retrospective analysis of past missions. In this work, a software tool was developed, which can estimate the 3D poses of a spacesuit from photographs or videos, without using special sensors or equipment. The tool is based on the state-of-the-art artificial intelligence and machine learning (AI/ML) system, which was trained by studying and capturing motion patterns of past and current spacesuit test data. The AI/ML tool was further enhanced using synthetically generated data, in which the suit postures, backgrounds, camera angles and illumination conditions were parametrically adjusted and rendered for training. The tool, incorporated the methodologies of Convolutional Neural Network (CNN), was trained, and tested in the cloud computing environment. The trained model was then applied on new imagery and video to extract estimated joint positions and suit outlines. The joint positions were further processed to capture activity (“digging”), pose labels (“bending”), and other useful downstream information. The model performance on new imagery and video was successfully assessed for accuracy and reliability. This AI/ML based posture recognition tool thus allows for the quantification of injury risk and task performance characterization for both current and past missions and training, which can immensely help to improve EVA task and suit design.

Kyung Han Kim↗

DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗

DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗

DOE OSTI2026

Direct cross section measurement of 102 Pd ⁢(𝛾,𝑝) and 102 Pd ⁢(𝛾,𝛼) for the astrophysical 𝑝 process

Background: A handful of neutron-deficient stable nuclei, known as the “p nuclei,” cannot be produced through astrophysical neutron capture processes. Instead, some of these nuclei are proposed to be produced by 𝛾-induced reactions on existing r- and s-process seeds. The specific astrophysical site or sites are not yet identified, however, with uncertainties in the cross sections of these 𝛾-induced reactions playing a role. Databases of reaction rates for astrophysical simulations often rely on theoretical statistical model calculations, such as Hauser-Feshbach, for rates where no experimental information is known. However, reasonable variations in the choice of parametrizations of various nuclear properties can create order-of-magnitude variations in the final predicted cross sections and reaction rates, which are then propagated through the models to the predicted final abundances. Purpose: To better constrain these statistical model calculations and ultimately reduce the uncertainties from the nuclear physics on our understanding of the p nuclei, a measurement of the cross sections of 𝛾-induced reactions on the p-nucleus 102 Pd was undertaken. This work represents the first measurement of its kind, using segmented silicon detectors to measure prompt charged particle emission from 𝛾-induced reactions. Methods: Quasimonoenergetic gamma beams from the High Intensity 𝛾 Source facility bombarded an enriched 102 Pd target. A segmented silicon array was arranged to detect the particles emitted from (𝛾,𝑝) and (𝛾,𝛼) reactions. Results: Reaction cross sections were deduced at multiple 𝛾-beam energies between 10 and 19 MeV, and compared to statistical model calculations using talys-1.96. The 102 Pd ⁢(𝛾,𝑝)⁢ 101 Rh reaction cross section was reasonably well reproduced by a subset of photon strength functions and level densities, though the strength to the ground state of 101 Rh was underestimated at higher incident gamma energies. The 102 Pd ⁢(𝛾,𝛼)⁢ 98 Ru was in general overpredicted by the various alpha-nucleus optical model potentials. Conclusions: While the theoretical cross sections used to model the (𝛾,𝑝) reactions for the p process may be reasonable, a more careful approach is needed in the case of (𝛾,𝛼). Further work to probe gamma-induced reaction cross sections at and near the p nuclei is warranted.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1992

A parametric analysis of radiative structure in aerobrake shock layers

A broad-spectrum version of the NEQAIR code was modified to account for self-absorption and applied to AFE flowfields calculated by the LAURA code with a variety of kinetic models. The resulting radiative fluxes were obtained in a decoupled fashion from the flowfield solver along the vehicle's stagnation streamline. The radiative flux obtained was broken down by causative process to study the radiative structure of the AFE's flowfield for the various kinetic models. In addition, the radiative fluxes for several points on a typical AFE trajectory were analyzed to examine how the radiative structure changes as the vehicle completes its aeropass. Only two radiative processes dominated the stagnation radiative flux, and the flow field conditions near the wal were found to exert considerable influence over the radiative flux to the wall.

Greendyke, Robert B.↗

DOE OSTI2024

Numerical investigation of equivalence ratio effects on Flex-Fuel mixing controlled combustion enabled by prechamber ignition

There is interest in adapting renewable and low-carbon intensity fuels to heavy-duty engines to help displace criteria and greenhouse gas emissions associated with diesel combustion. Low-carbon fuels have inherently low cetane numbers and thus pose a significant challenge when considering direct substitution in diesel combustion systems. Use of an actively fueled prechamber as an ignition source to initiate mixing-controlled combustion (PC-MCC) of low-cetane fuels is becoming an attractive combustion mode to alleviate the identified reactivity deficit. This work focuses on the utilization of an active prechamber to facilitate diesel-like combustion of bioethanol-gasoline fuel blends in a heavy-duty engine. Recent results in this emerging technology suggest that ignition quality is uniquely coupled to prechamber equivalence ratio. In this numerical study, the fundamental implications of prechamber equivalence ratio on the ignition performance of direct injected fuels ranging from E10 to E100 are investigated using CONVERGE. Parametric studies of the prechamber operating strategy were assessed at diesel-like conditions to characterize the performance trends relative to a diesel baseline at the same boundary conditions. Simulation results indicate that PC-MCC is flex-fuel capable and achieves diesel-like ignition qualities and combustion processes for all fuels considered under stoichiometric and rich prechamber conditions. To characterize the equivalence ratio trends observed, a novel inflow boundary modeling technique was utilized to prescribe turbulent jets in place of the prechamber where selective speciation of the jet composition was conducted to isolate the ignition contributions of radical and reacting species in lean, stoichiometric, and rich jets. The inflow boundary modeling suggests that excess fuel and combustion intermediates present in jets produced from stratified and fuel-rich prechamber operation promotes higher jet temperatures and as such are superior ignition sources. Relative to a lean prechamber jet, the peak temperature of a reacting jet from a rich prechamber was up to 600 K hotter at fixed distances from the orifice exit. Furthermore, radicals also demonstrated an influence on the ignition process, but the combustion mode was identified to be thermally dominant.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Learning thermodynamically constrained equations of state with uncertainty

Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material’s thermodynamic state variables—specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in EOS predictions. Model uncertainty is challenging for UQ studies since it requires exploring the space of all possible physically consistent functional forms. Thus, it is often neglected in favor of parametric uncertainty, which is easier to quantify without violating thermodynamic laws. This work presents a data-driven machine learning approach to constructing EOS models that naturally captures model uncertainty while satisfying the necessary thermodynamic consistency and stability constraints. We propose a novel framework based on physics-informed Gaussian process regression (GPR) that automatically captures total uncertainty in the EOS and can be jointly trained on both simulation and experimental data sources. A GPR model for the shock Hugoniot is derived, and its uncertainties are quantified using the proposed framework. We apply the proposed model to learn the EOS for the diamond solid state of carbon using both density functional theory data and experimental shock Hugoniot data to train the model and show that the prediction uncertainty is reduced by considering thermodynamic constraints.

Sharma, Himanshu (ORCID:000900050235718X)↗

NASA NTRS1990

A three-dimensional approach for analysis of sidewall injector mixing and combustion

A three-dimensional (3-D) analytical approach to calculate the mixing and combustion efficiency along engine combustors that use sidewall fuel injectors is described. The analysis consists of three parts: (1) an empirical correlation for fuel penetration, (2) application of integral conservation laws following jet turning and pressure equilibration, and (3) an analytical solution for the simplified three-dimensional partial differential equations to describe the downstream mixing and combustion process. Numerical results are compared to some empirical and experimental work, and favorable agreements are shown. The importance of including anisotropic turbulence and three-dimensional effects are illustrated by comparison with data and other less comprehensive analyses. The analytical solution is suitable for rapid estimates of engine performance and parametric studies of combustor designs containing transverse or angled injector ports arranged in rows and/or columns.

Quan, Victor↗

NASA NTRS1993

ISPAN (Interactive Stiffened Panel Analysis): A tool for quick concept evaluation and design trade studies

Interactive Stiffened Panel Analysis (ISPAN) modules, written in FORTRAN, were developed to provide an easy to use tool for creating finite element models of composite material stiffened panels. The modules allow the user to interactively construct, solve and post-process finite element models of four general types of structural panel configurations using only the panel dimensions and properties as input data. Linear, buckling and post-buckling solution capability is provided. This interactive input allows rapid model generation and solution by non finite element users. The results of a parametric study of a blade stiffened panel are presented to demonstrate the usefulness of the ISPAN modules. Also, a non-linear analysis of a test panel was conducted and the results compared to measured data and previous correlation analysis.

Hairr, John W.↗

NASA NTRS1991

Modeling Personnel Turnover in the Parametric Organization

A primary issue in organizing a new parametric cost analysis function is to determine the skill mix and number of personnel required. The skill mix can be obtained by a functional decomposition of the tasks required within the organization and a matrixed correlation with educational or experience backgrounds. The number of personnel is a function of the skills required to cover all tasks, personnel skill background and cross training, the intensity of the workload for each task, migration through various tasks by personnel along a career path, personnel hiring limitations imposed by management and the applicant marketplace, personnel training limitations imposed by management and personnel capability, and the rate at which personnel leave the organization for whatever reason. Faced with the task of relating all of these organizational facets in order to grow a parametric cost analysis (PCA) organization from scratch, it was decided that a dynamic model was required in order to account for the obvious dynamics of the forming organization. The challenge was to create such a simple model which would be credible during all phases of organizational development. The model development process was broken down into the activities of determining the tasks required for PCA, determining the skills required for each PCA task, determining the skills available in the applicant marketplace, determining the structure of the dynamic model, implementing the dynamic model, and testing the dynamic model.

Dean, Edwin B.↗

NASA NTRS1999

National Combustion Code, a Multidisciplinary Combustor Design System, Will Be Transferred to the Commercial Sector

The NASA Lewis Research Center and Flow Parametrics will enter into an agreement to commercialize the National Combustion Code (NCC). This multidisciplinary combustor design system utilizes computer-aided design (CAD) tools for geometry creation, advanced mesh generators for creating solid model representations, a common framework for fluid flow and structural analyses, modern postprocessing tools, and parallel processing. This integrated system can facilitate and enhance various phases of the design and analysis process.

Steele, Gynelle C.↗

DOE OSTI2021

Automated Parameterized CFD Simulations of Phase-Change Material Embedded Heat Exchangers

Thermal storage can be implemented using Phase-Change Materials (PCM), which absorb significant latent heat with a relatively small temperature change. PCM phase-change processes are transient and are driven by thermal diffusion and natural convection – the latter, especially for melting process. Modeling and simulation of PCM heat exchangers is typically computationally intensive due to the relative complex time-dependent underlying physics. This paper presents a study investigating a single straight tube with circular transverse fins in the vertical orientation, using PCM’s with 37oC nominal melting temperature, leveraging automated parametric CFD simulations. The computational domain is reduced to 2D axisymmetric for a segment of the HX comprising a region in between two fins with constant wall temperature as boundary condition. This approach allows for faster evaluations of the transient thermal characteristics for a given design space of interest. Correlations and reduced order models (ROM’s) are developed. Results indicate that the ROM’s reproduce the CFD simulations with high accuracy for designs from the training data set, however, designs from cross-validation and test sets do not always have good agreement. The limited number of designs in the training set is a possible cause for the lack of robustness of the ROM’s and thus additional training designs will be added in future work. The ROM’s solve the simulations on average 500,000 times faster than CFD.

25 ENERGY STORAGE↗

DOE OSTI2021

Optimizing Power Generation of a Bottom-Raised Oscillating Surge Wave Energy Converter Using a Theoretical Model: Preprint

Preliminary sizing of an oscillating surge wave energy converter (OSWEC) is an iterative process that relies on knowledge of the relevant hydrodynamic coefficients for a given geometry. Often, the complex definition of the device geometry requires coefficients to be obtained through experiments or numerical boundary element solvers such as WAMIT. These techniques demand significant user and computational effort, therefore inhibiting the fine scale parametrization of object dimensions. In this study, a theoretical model, originally presented in Michele et al. (2016), is developed and demonstrated to efficiently optimize the power production for an OSWEC device (subjected to certain environmental conditions) with variations in device widths, heights, and distances from the seabed. Assuming negligible device thickness, the OSWEC motions are modelled as a bottom-raised two-dimensional flap in regular waves using potential flow theory formulated in elliptical coordinates. The solutions to this diffraction-radiation problem are obtained using Mathieu functions with appropriate boundary conditions. The resulting potentials are then used to derive frequency-dependent expressions for the added mass and radiation damping coefficients, along with wave excitation magnitude in surge, pitch, and coupled surge-pitch motions. Good agreement in hydrodynamic coefficient curves is shown between the theoretical model and the numerical results obtained from the boundary element-based program WAMIT. The theoretical model is then employed to maximize the time-averaged output power while maintaining or reducing the hinge reaction force, with variations in device dimensions, wave frequency and amplitude.

ACE↗

DOE OSTI2021

Optimizing Power Generation of a Bottomraised Oscillating Surge Wave Energy Converter Using a Theoretical Model

Preliminary sizing of an oscillating surge wave energy converter (OSWEC) is an iterative process that relies on knowledge of the relevant hydrodynamic coefficients for a given geometry. Often, the complex definition of the device geometry requires coefficients to be obtained through experiments or numerical boundary element solvers such as WAMIT. These techniques demand significant user and computational effort, therefore inhibiting the fine scale parametrization of object dimensions. In this study, a theoretical model, originally presented in Michele et al. (2016), is developed and demonstrated to efficiently optimize the power production for an OSWEC device (subjected to certain environmental conditions) with variations in device widths, heights, and distances from the seabed. Assuming negligible device thickness, the OSWEC motions are modelled as a bottom-raised two-dimensional flap in regular waves using potential flow theory formulated in elliptical coordinates. The solutions to this diffraction-radiation problem are obtained using Mathieu functions with appropriate boundary conditions. The resulting potentials are then used to derive frequency-dependent expressions for the added mass and radiation damping coefficients, along with wave excitation magnitude in surge, pitch, and coupled surge-pitch motions. Good agreement in hydrodynamic coefficient curves is shown between the theoretical model and the numerical results obtained from the boundary element-based program WAMIT. The theoretical model is then employed to maximize the time-averaged output power while maintaining or reducing the hinge reaction force, with variations in device dimensions, wave frequency and amplitude.

ACE↗

NASA NTRS1999

Fundamental Study of Material Flow in Friction Stir Welds

The presented research project consists of two major parts. First, the material flow in solid-state, friction stir, butt-welds as been investigated using a marker insert technique. Changes in material flow due to welding parameter as well as tool geometry variations have been examined for different materials. The method provides a semi-quantitative, three-dimensional view of the material transport in the welded zone. Second, a FSW process model has been developed. The fully coupled model is based on fluid mechanics; the solid-state material transport during welding is treated as a laminar, viscous flow of a non-Newtonian fluid past a rotating circular cylinder. The heat necessary for the material softening is generated by deformation of the material. As a first step, a two-dimensional model, which contains only the pin of the FSW tool, has been created to test the suitability of the modeling approach and to perform parametric studies of the boundary conditions. The material flow visualization experiments agree very well with the predicted flow field. Accordingly, material within the pin diameter is transported only in the rotation direction around the pin. Due to the simplifying assumptions inherent in the 2-D model, other experimental data such as forces on the pin, torque, and weld energy cannot be directly used for validation. However, the 2-D model predicts the same trends as shown in the experiments. The model also predicts a deviation from the "normal" material flow at certain combinations of welding parameters, suggesting a possible mechanism for the occurrence of some typical FSW defects. The next step has been the development of a three-dimensional process model. The simplified FSW tool has been designed as a flat shoulder rotating on the top of the workpiece and a rotating, cylindrical pin, which extends throughout the total height of the flow domain. The thermal boundary conditions at the tool and at the contact area to the backing plate have been varied to fit experimental data such as temperature profiles, torque and tool forces. General aspects of the experimentally visualized material flow pattern are confirmed by the 3-D model.

Reynolds, Anthony P.↗

DOE OSTI2022

Machine Learning for Improving Surface-Layer-Flux Estimates

Abstract Flows in the atmospheric boundary layer are turbulent, characterized by a large Reynolds number, the existence of a roughness sublayer and the absence of a well-defined viscous layer. Exchanges with the surface are therefore dominated by turbulent fluxes. In numerical models for atmospheric flows, turbulent fluxes must be specified at the surface; however, surface fluxes are not known a priori and therefore must be parametrized. Atmospheric flow models, including global circulation, limited area models, and large-eddy simulation, employ Monin–Obukhov similarity theory (MOST) to parametrize surface fluxes. The MOST approach is a semi-empirical formulation that accounts for atmospheric stability effects through universal stability functions. The stability functions are determined based on limited observations using simple regression as a function of the non-dimensional stability parameter representing a ratio of distance from the surface and the Obukhov length scale (Obukhov in Trudy Inst Theor Geofiz AN SSSR 1:95–115, 1946), $$z/L$$ z / L . However, simple regression cannot capture the relationship between governing parameters and surface-layer structure under the wide range of conditions to which MOST is commonly applied. We therefore develop, train, and test two machine-learning models, an artificial neural network (ANN) and random forest (RF), to estimate surface fluxes of momentum, sensible heat, and moisture based on surface and near-surface observations. To train and test these machine-learning algorithms, we use several years of observations from the Cabauw mast in the Netherlands and from the National Oceanic and Atmospheric Administration’s Field Research Division tower in Idaho. The RF and ANN models outperform MOST. Even when we train the RF and ANN on one set of data and apply them to the second set, they provide more accurate estimates of all of the fluxes compared to MOST. Estimates of sensible heat and moisture fluxes are significantly improved, and model interpretability techniques highlight the logical physical relationships we expect in surface-layer processes.

Meteorology & Atmospheric Sciences↗

DOE OSTI2020

Automated detector simulation and reconstruction parametrization using machine learning

Rapidly applying the effects of detector response to physics objects (e.g. electrons, muons, showers of particles) is essential in high energy physics. Presently available tools for the transformation from truth-level physics objects to reconstructed detector-level physics objects involve manually defining resolution functions. These resolution functions are typically derived in bins of variables that are correlated with the resolution (e.g. pseudorapidity and transverse momentum). This process is time consuming, requires manual updates when detector conditions change, and can miss important correlations. Machine learning offers a way to automate the process of building these truth-to-reconstructed object transformations and can capture complex correlation for any given set of input variables. Such machine learning algorithms, with sufficient optimization, could have a wide range of applications: improving phenomenological studies by using a better detector representation, allowing for more efficient production of Geant4 simulation by only simulating events within an interesting part of phase space, and studies on future experimental sensitivity to new physics.

47 OTHER INSTRUMENTATION↗