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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.
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Hydrodynamics with parametric slowing down and fluctuations near the critical point
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Parametric Instability Driven by Weakly Trapped Particles in Nonlinear Plasma Waves
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Parametric Analysis of Vertically Oriented Metamaterials for Wideband Omnidirectional Absorption.
Abstract not provided.
Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network based Equivalent Model
The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.
Parametrization of textural patterns in {sup 123}I-ioflupane imaging for the automatic detection of Parkinsonism
Purpose: A novel approach to a computer aided diagnosis system for the Parkinson's disease is proposed. This tool is intended as a supporting tool for physicians, based on fully automated methods that lead to the classification of{sup 123}I-ioflupane SPECT images. Methods: {sup 123}I-ioflupane images from three different databases are used to train the system. The images are intensity and spatially normalized, then subimages are extracted and a 3D gray-level co-occurrence matrix is computed over these subimages, allowing the characterization of the texture using Haralick texture features. Finally, different discrimination estimation methods are used to select a feature vector that can be used to train and test the classifier. Results: Using the leave-one-out cross-validation technique over these three databases, the system achieves results up to a 97.4% of accuracy, and 99.1% of sensitivity, with positive likelihood ratios over 27. Conclusions: The system presents a robust feature extraction method that helps physicians in the diagnosis task by providing objective, operator-independent textural information about{sup 123}I-ioflupane images, commonly used in the diagnosis of the Parkinson's disease. Textural features computation has been optimized by using a subimage selection algorithm, and the discrimination estimation methods used here makes the system feature-independent, allowing us to extend it to other databases and diseases.
Parametric evaluation of ducted fuel injection in an optically accessible mixing-controlled compression-ignition engine with two- and four-duct assemblies
Ducted fuel injection (DFI) is a strategy to improve fuel/charge-gas mixing in direct-injection compression-ignition engines. DFI involves injecting fuel along the axis of a small tube in the combustion chamber, which promotes the formation of locally leaner mixtures in the autoignition zone relative to conventional diesel combustion. Previous work has demonstrated that DFI is effective at curtailing engine-out soot emissions across a wide range of operating conditions. This study extends previous investigations, presenting engine-out emissions and efficiency trends between ducted two-orifice and ducted four-orifice injector tip configurations. For each configuration, parameters investigated include injection pressure, injection duration, intake manifold pressure, intake manifold temperature, start of combustion timing, and intake-oxygen mole fraction. For both configurations and across all parameters, DFI reduced engine-out soot emissions compared to conventional diesel combustion, with little effect on other emissions and engine efficiency. Emissions trends for both configurations were qualitatively the same across the parameters investigated. The four-duct configuration had higher thermal efficiency and indicated-specific engine-out nitrogen oxide emissions but lower indicated-specific engine-out hydrocarbon and carbon monoxide emissions than the two-duct assembly. Both configurations achieved indicated-specific engine-out emissions for both soot and nitrogen oxides that comply with current on- and off-road heavy-duty regulations in the United States without exhaust-gas aftertreatment at an intake-oxygen mole fraction of 12%. High-speed in-cylinder imaging of natural soot luminosity shows that some conditions include a second soot-production phase late in the cycle. The probability of these late-cycle events is sensitive to both the number of ducted sprays and the operating conditions.
Simultaneous few-cycle pulse generation of the depleted pump and signal from an optical parametric amplifier
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Space shuttle: Stability and control effectiveness of the MDAC parametric delta canard booster at Mach 0.38. Volume 1: Canard parametric variations
A 0.015 scale model of a modified version of the MDAC space shuttle booster was tested in the Naval Ship Research and Development Center 7 x 10 foot transonic wind tunnel, to obtain force, static stability, and control effectiveness data. Data were obtained for a cruise Mach Number of 0.38, altitude of 10,000 ft, and Reynolds Number per foot of approximately 2 x one million. The model was tested through an angle of attack range of -4 deg to 15 deg at zero degree angle of sideslip, and at an angle of sideslip range of -6 deg to 6 deg at fixed angles of attack of 0 deg, 6 deg, and 15 deg. Other test variables were elevon deflections, canard deflections, aileron deflections, rudder deflections, wing dihedral angle, canard incidence angle, wing incidence angle, canard position, wing position, wing and canard control flap size and dorsal fin size.
Development of Ensemble Neural Network Convection Parametrizations for Climate Models Using ARM Data
The novel neural network (NN) approach has been formulated and used for development of a NN ensemble stochastic convection parametrization for climate models. This fast parametrization is built based on data from Cloud Resolving Model (CRM) simulations initialized with and forced by TOGA-COARE data. The SAM (System for Atmospheric Modeling), developed by D. Randall, M. Khairoutdinov, and their collaborators, has been used for CRM simulations. The observational data are also used for validation of model simulations. The SAM-simulated data have been averaged and projected onto the GCM space of atmospheric states to implicitly define a stochastic convection parametrization. This parametrization is emulated using an ensemble of NNs. An ensemble of NNs with different NN parameters has been trained and tested. The inherent uncertainty of the stochastic convection parametrization derived in such a way is estimated. Due to these inherent uncertainties, NN ensemble is used to constitute a stochastic NN convection parametrization. The developed NN convection parametrization have been validated in a diagnostic CAM (CAM-NN) run vs. the control CAM run. Actually, CAM inputs have been used, at every time step of the control/original CAM integration, for parallel calculations of the NN convection parametrization (CAM-NN) to produce its outputs as a diagnostic byproduct. Total precipitation (P) and cloudiness (CLD) time series, diurnal cycles, and P and CLD distributions for the large Tropical Pacific Ocean for the parallel CAM-NN and CAM runs show similarity and consistency with the NCEP reanalysis. The P and CLD distributions for the tropical area for the parallel runs have been analyzed first for the TOGA-COARE boreal winter season (November 1992 through February 1993) and then for the winter seasons of the follow-up parallel decadal simulations. The obtained results are encouraging and practically meaningful. They show the validity of the NN approach. This constitutes an important practical conclusion of the study: the obtained results on NN ensembles as a stochastic physics parametrization show a realistic possibility of development of NN convection parametrization for climate (and NWP) models based on learning cloud physics from CRM/SAM simulated data.
Parametric matrix models
We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neurons, parametric matrix models use matrix equations that emulate physical systems. Similar to how physics problems are usually solved, parametric matrix models learn the governing equations that lead to the desired outputs. Parametric matrix models can be efficiently trained from empirical data, and the equations may use algebraic, differential, or integral relations. While originally designed for scientific computing, we prove that parametric matrix models are universal function approximators that can be applied to general machine learning problems. After introducing the underlying theory, we apply parametric matrix models to a series of different challenges that show their performance for a wide range of problems. For all the challenges tested here, parametric matrix models produce accurate results within an efficient and interpretable computational framework that allows for input feature extrapolation.
Simulating neutron stars with a flexible enthalpy-based equation of state parametrization in sp ECTRE
Numerical simulations of neutron star mergers represent an essential step toward interpreting the full complexity of multimessenger observations and constraining the properties of supranuclear matter. Currently, simulations are limited by an array of factors, including computational performance and input physics uncertainties, such as the neutron star equation of state. In this work, we expand the range of nuclear phenomenology efficiently available to simulations by introducing a new analytic parametrization of cold, beta-equilibrated matter that is based on the relativistic enthalpy. Here we show that the new enthalpy parametrization can capture a range of nuclear behavior, including strong phase transitions. We implement the enthalpy parametrization in the spectre code, simulate isolated neutron stars, and compare performance to the commonly used spectral and polytropic parametrizations. We find comparable computational performance for nuclear models that are well represented by either parametrization, such as simple hadronic equations of state. We show that the enthalpy parametrization further allows us to simulate more complicated hadronic models or models with phase transitions that are inaccessible to current parametrizations.
TU-H-CAMPUS-IeP3-02: Neurovascular 4D Parametric Imaging Using Co-Registration of Biplane DSA Sequences with 3D Vascular Geometry Obtained From Cone Beam CT
Purpose: To create 4D parametric images using biplane Digital Subtraction Angiography (DSA) sequences co-registered with the 3D vascular geometry obtained from Cone Beam-CT (CBCT). Methods: We investigated a method to derive multiple 4D Parametric Imaging (PI) maps using only one CBCT acquisition. During this procedure a 3D-DSA geometry is stored and used subsequently for all 4D images. Each time a biplane DSA is acquired, we calculate 2D parametric maps of Bolus Arrival Time (BAT), Mean Transit Time (MTT) and Time to Peak (TTP). Arterial segments which are nearly parallel with one of the biplane imaging planes in the 2D parametric maps are co-registered with the 3D geometry. The values in the remaining vascular network are found using spline interpolation since the points chosen for co-registration on the vasculature are discrete and remaining regions need to be interpolated. To evaluate the method we used a patient CT volume data set for 3D printing a neurovascular phantom containing a complete Circle of Willis. We connected the phantom to a flow loop with a peristaltic pump, simulating physiological flow conditions. Contrast media was injected with an automatic injector at 10 ml/sec. Images were acquired with a Toshiba Infinix C-arm and 4D parametric image maps of the vasculature were calculated. Results: 4D BAT, MTT, and TTP parametric image maps of the Circle of Willis were derived. We generated color-coded 3D geometries which avoided artifacts due to vessel overlap or foreshortening in the projection direction. Conclusion: The software was tested successfully and multiple 4D parametric images were obtained from biplane DSA sequences without the need to acquire additional 3D-DSA runs. This can benefit the patient by reducing the contrast media and the radiation dose normally associated with these procedures. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.
Description and Use of SCALE Sampler Parametric Capability for Engineering Analysis and Optimization
The Sampler sequence was introduced into the SCALE nuclear modeling and simulation suite in SCALE 6.2 to perform uncertainty quantification via random sampling of nuclear data, material number densities, and dimensions. Sampler was expanded with the introduction of a parametric capability in SCALE 6.2.2. This paper discusses input for the Sampler parametric sequence and presents two case studies of analyses performed using the sequence. These case studies include preconceptual design of a package for transporting high assay low-enriched uranium (HALEU) oxide and scoping calculations to support subcritical limit development for a future update of the ANSI/ANS-8.1 (ANS-8.1) standard. The parametric capability within Sampler provides many benefits to analysts. For instance, parametric sweeps are frequently used to identify optimum parameter values as part of safety analysis or system design, but such sweeps can require substantial engineering time or may rely on custom-written scripts or scripts such as Write One, Run Many (or WORM) developed outside of any software quality assurance program. With the parametric capabilities in Sampler, however, a large number of inputs can be generated automatically without recourse to scripting by individual analysts. The parametric capability can also be used in lieu of the CSAS5S search sequence to identify optimum parameters more simply with straightforward inputs and outputs. Sampler can also be used to calculate input parameters from engineering specifications. For example, diameters can be converted to radii, or masses can be used to calculate number densities. Overall, the Sampler parametric capability provides a robust feature within SCALE, eliminating the need for user-developed scripting.
A multiresolution spatial parametrization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions.
The estimation of fossil-fuel CO2 emissions (ffCO2) from limited ground-based and satellite measurements of CO2 concentrations will form a key component of the monitoring of treaties aimed at the abatement of greenhouse gas emissions. To that end, we construct a multiresolution spatial parametrization for fossil-fuel CO2 emissions (ffCO2), to be used in atmospheric inversions. Such a parametrization does not currently exist. The parametrization uses wavelets to accurately capture the multiscale, nonstationary nature of ffCO2 emissions and employs proxies of human habitation, e.g., images of lights at night and maps of built-up areas to reduce the dimensionality of the multiresolution parametrization. The parametrization is used in a synthetic data inversion to test its suitability for use in atmospheric inverse problem. This linear inverse problem is predicated on observations of ffCO2 concentrations collected at measurement towers. We adapt a convex optimization technique, commonly used in the reconstruction of compressively sensed images, to perform sparse reconstruction of the time-variant ffCO2 emission field. We also borrow concepts from compressive sensing to impose boundary conditions i.e., to limit ffCO2 emissions within an irregularly shaped region (the United States, in our case). We find that the optimization algorithm performs a data-driven sparsification of the spatial parametrization and retains only of those wavelets whose weights could be estimated from the observations. Further, our method for the imposition of boundary conditions leads to a 10 computational saving over conventional means of doing so. We conclude with a discussion of the accuracy of the estimated emissions and the suitability of the spatial parametrization for use in inverse problems with a significant degree of regularization.