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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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At least 541 records · Page 30

Attitude angle effects on Nimbus-7 Scanning Multichannel Microwave Radiometer radiances and geophysical parameter retrievals

The attitude of the Nimbus-7 spacecraft has varied significantly over its lifetime. A summary of the orbital and long-term behavior of the attitude angles and the effects of attitude variations on Scanning Multichannel Microwave Radiometer (SMMR) brightness temperatures is presented. One of the principal effects of these variations is to change the incident angle at which the SMMR views the Earth's surface. The brightness temperatures depend upon the incident angle sensitivities of both the ocean surface emissivity and the atmospheric path length. Ocean surface emissivity is quite sensitive to incident angle variation near the SMMR incident angle, which is about 50 degrees. This sensitivity was estimated theoretically for a smooth ocean surface and no atmosphere. A 1-degree increase in the angle of incidence produces a 2.9 C increase in the retrieved sea surface temperature and a 5.7 m/sec decrease in retrieved sea surface wind speed. An incident angle correction is applied to the SMMR radiances before using them in the geophysical parameter retrieval algorithms. The corrected retrieval data is compared with data obtained without applying the correction.

Macmillan, Daniel S.↗

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of trained machine-learning models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a Windows app that has been created to deploy trained machine-learning models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of machine-learning application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). Current version of the app focuses on the performance prediction of conventional turbofans. The app gets user input for a turbofan design, preprocesses the input data, and deploys trained machine-learning models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The machine-learning predictive models were built by employing supervised deep-learning algorithm to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these machine-learning models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.

machine learning↗

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗

Three-dimensional phase field sintering simulations accounting for the rigid-body motion of individual grains

Sintering is a widely used powder processing technique in industrial applications. During sintering, atoms migrate to decrease the energy of the system via two main mechanisms: coarsening and densification, both of which lead to significant morphological variation of the sintered microstructure. When simulating sintering dynamics, the phase-field method has been broadly utilized because of its convenience in tracking morphology evolution. When a large number of grains is involved, it is common to use the same order parameter to describe multiple grains that are not in direct contact with one another (in order to reduce the computational memory demands). However, with this treatment it is difficult to handle the rigid-body motion of individual grains during densification. In this work, an implementation scheme is introduced to overcome the challenge of calculating individual particle motion based on existing equations. It uses a grouping algorithm and sets a cutoff radius on each grain for calculating the particle velocity during densification. This method allows for the incorporation of the densification mechanism, which has been commonly ignored in previous work, into phase-field sintering models in three-dimensional simulations with a large number of particles/grains. Moreover, through combination with the smoothed boundary method, material properties of sintered microstructures, such as the effective diffusivity and Young’s modulus, can be calculated during the sintering processes.

36 MATERIALS SCIENCE↗

Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as CUSZ and cuZFP) have been developed. However, they suffer from either low performance or low compression ratios. To this end, we propose CUSZ+ to target both high compression ratios and throughputs. We identify that data sparsity and data smoothness are key factors for high compression throughputs. Our key contributions in this work are fourfold: (1) We propose an efficient compression workflow to adaptively perform run-length encoding and/or variable-length encoding. (2) We derive Lorenzo reconstruction in decompression as multidimensional partial-sum computation and propose a fine-grained Lorenzo reconstruction algorithm for GPU architectures. (3) We carefully optimize each of CUSZ kernels by leveraging state-of-the-art CUDA parallel primitives. (4) We evaluate CUSZ+ using seven real-world HPC application datasets on V100 and A100 GPUs. Experiments show CUSZ+ improves the compression throughputs and ratios by up to 18.4x and 5.3x, respectively, over CUSZ on the tested datasets.

Tian, Jiannan↗

An analysis of gamma ray burst time histories

Gamma ray burst time histories, ranging in durations from milliseconds to thousands of seconds, are as varied as the number of bursts. They show a wide array of structures from those that are very smooth to those that contain a seemingly uncountable number of spikes riding on top of other spikes. These profiles have tantalized researchers for years - they obviously hold important information on the nature of GRB's, but to date no one has been successful in analyzing them. For the past year the author has been working on algorithms to analyze these data. Two approaches have been followed in this investigation. The first is an attempt to quantify the amount of structure, or spikiness, in a profile. The second involves applying the latest theorems on chaos and fractals with the aim of extracting useful information from what seems to be a random collection of shot noise.

Lestrade, John Patrick↗

Simultaenous Retrieval of Surface Roughness Parameters from Combined Active-Passive SMAP Observations

Soil roughness strongly influences processes like erosion, infiltration, moisture and evaporation of soils as well as growth of agricultural plants. An approach to soil roughness based on active-passive microwave covariation is proposed in order to simultaneously retrieve the vertical RMS height (s) and horizontal correlation length (l) of soil surfaces from simultaneously measured radar and radiometer microwave signatures. The approach is based on a retrieval algorithm for active-passive covariation including the improved Integral Equation Method (I2EM). The algorithm is tested with the global active-passive microwave observations of the SMAP mission. The developed roughness retrieval algorithm shows independence of permittivity for > 10 [-] due to the covariation formalism. Results reveal that s and l can be estimated simultaneously by the proposed approach since surface patterns of non-vegetated areas become evident on global scale. In regions with sandy deserts, like the Sahara or the outback in Australia, determined and confirm rather smooth to semi-rough surface roughness patterns with small vertical RMS heights and corresponding higher horizontal correlation lengths.

correlation length↗

Practical Start-Up Process of Multiple Grid-Tied Voltage-Sourced Inverters in Laboratory

Starting up inverter-based resources (IBRs) and integrating them into a grid are important in real-world engineering and there are many challenges to be tackled. Using a laboratory-scale hardware test bed (50 Watt and 20 V), this paper demonstrates the challenges and provides a practical start-up process that can smoothly energize two grid-tied voltage-sourced inverters (VSI) and synchronize the VSIs into a 60-Hz grid. The hardware test bed consists of two SiC VSIs and a 45-kW Chroma grid simulator to act as the grid. The inverter controls are implemented in OPAL-RT OP5600, which has the measurements sensed, control algorithms processed, and gate signals generated and sent to the VSI switches. This research focuses on examining how to energize VSIs and how to synchronize the VSIs into the grid. The best energizing approach is summarized based on comparison and analysis of the experiment results. The presented approach can prevent large transients during the start-up process.

Wang, Zhengyu↗

Multidimensional difference schemes with fourth-order accuracy

An explicit finite-difference algorithm is presented for the solution of quasilinear divergence free multidimensional hyperbolic systems. The method consists of four steps per time level. The resulting scheme is fourth-order accurate in both space and time, though the intermediate steps are only first-order accurate. The family of schemes introduced is dissipative, and hence, suitable for both smooth flows and flows containing shocks. This method is compared, in several numerical examples, with both second-order schemes and others that are fourth order in space, but second order in time.

Turkel, E.↗

An algorithm for generating an m-ary summation tree

An algorithm is presented for generating an m-ary summation tree. The algorithm is completely general and may be applied to any length input string. For an N length sequence summed in groups of m sub l at each level l a maximum of 3L - 2 storage is required. A special case of the general m-ary tree where all m sub l are equal is used to smooth data in a radio frequency interference experiment. The maximum storage required when m l sub l = m for all l reduces to the closed form 3 log m N - 2.

Sievers, M.↗

Upwind control volumes - A new upwind approach

This paper presents a new algorithm for the solution of the steady Euler equations in two-dimensional flow. It uses an upwind control volume approach in which a standard flux residual on triangular cells is distributed with a downwind bias. The resulting scheme is second order accurate on unstructured triangular meshes and produces sharp shocks without the addition of any shock smoothing. Results demonstrate its performance on a standard AGARD test case using both smooth and irregular unstructured meshes.

Giles, Michael↗

Investigation of advanced counterrotation blade configuration concepts for high speed turboprop systems. Task 4: Advanced fan section aerodynamic analysis

The purpose of this study is the development of a three-dimensional Euler/Navier-Stokes flow analysis for fan section/engine geometries containing multiple blade rows and multiple spanwise flow splitters. An existing procedure developed by Dr. J. J. Adamczyk and associates and the NASA Lewis Research Center was modified to accept multiple spanwise splitter geometries and simulate engine core conditions. The procedure was also modified to allow coarse parallelization of the solution algorithm. This document is a final report outlining the development and techniques used in the procedure. The numerical solution is based upon a finite volume technique with a four stage Runge-Kutta time marching procedure. Numerical dissipation is used to gain solution stability but is reduced in viscous dominated flow regions. Local time stepping and implicit residual smoothing are used to increase the rate of convergence. Multiple blade row solutions are based upon the average-passage system of equations. The numerical solutions are performed on an H-type grid system, with meshes being generated by the system (TIGG3D) developed earlier under this contract. The grid generation scheme meets the average-passage requirement of maintaining a common axisymmetric mesh for each blade row grid. The analysis was run on several geometry configurations ranging from one to five blade rows and from one to four radial flow splitters. Pure internal flow solutions were obtained as well as solutions with flow about the cowl/nacelle and various engine core flow conditions. The efficiency of the solution procedure was shown to be the same as the original analysis.

Crook, Andrew J.↗

Investigating the Impact of Power-Take-Off System Parameters and Control Law on a Rotational Wave Energy Converter’s Peak-to-Average Power Ratio Reduction

Due to the irregular nature of real waves, the power captured in a wave energy converter (WEC) system is highly variable. This is an important barrier to the effective use of WECs. To address this challenge, this study focuses on a rotational WEC power-take-off system in which high-speed and high-efficiency generators along with a torque/power smoothing inertia element can be effectively utilized. In the first phase of this study, the U.S. Department of Energy’s reference model 3 (WEC-Sim RM3; two-body point absorber), along with a slider-crank WEC, were integrated for linear to rotational conversion. Relative motion between the float and spar in RM3 was the driving force for this slider-crank WEC, which is connected to a motor/generator set through a gearbox. RM3 geometry was scaled down by 25 times to work within the limits of the physical motor/generator set used in the experimentation. Once the integration in a hardware-in-the-loop simulation environment was successfully completed, data on the peak-to-average power ratio was collected for various wave conditions including regular and irregular waves. The control algorithm designed to keep the system in resonance with waves was able to maintain relatively high speed depending on the specific gear ratio and wave period. Initial results with hardware-in-the-loop simulations reveal that gear ratio and crank radius have a strong impact on the peak-to-average power ratio. In addition, it was found that output power from the generator was maximized at a larger gear ratio, as the crank radius was increased.

50 EE - Wind and Water Power Program - Water (EE-4↗

Bias-Variance Trade-Off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs

Physics-Informed Neural Networks (PINNs) have triggered a paradigm shift in scientific computing, leveraging mesh-free properties and robust approximation capabilities. While proving effective for low-dimensional partial differential equations (PDEs), the computational cost of PINNs remains a hurdle in high-dimensional scenarios. This is particularly pronounced when computing high-order and high-dimensional derivatives in the physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian noise for stochastic smoothing of the original neural net model, enabling the use of Monte Carlo methods for derivative approximation, which eliminates the need for costly automatic differentiation. Despite its computational efficiency, especially in the approximation of high-dimensional derivatives, RS-PINN introduces biases in both loss and gradients, negatively impacting convergence, especially when coupled with stochastic gradient descent (SGD) algorithms. We present a comprehensive analysis of biases in RS-PINN, attributing them to the nonlinearity of the Mean Squared Error (MSE) loss as well as the intrinsic nonlinearity of the PDE itself. We propose tailored bias correction techniques, delineating their application based on the order of PDE nonlinearity. The derivation of an unbiased RS-PINN allows for a detailed examination of its advantages and disadvantages compared to the biased version. Specifically, the biased version has a lower variance and runs faster than the unbiased version, but it is less accurate due to the bias. To optimize the bias-variance trade-off, we combine the two approaches in a hybrid method that balances the rapid convergence of the biased version with the high accuracy of the unbiased version. In addition to methodological contributions, we present an enhanced implementation of RS-PINN. Extensive experiments on diverse high-dimensional PDEs, including Fokker-Planck, Hamilton-Jacobi-Bellman (HJB), viscous Burgers’, Allen-Cahn, and Sine-Gordon equations, illustrate the bias-variance trade-off and highlight the effectiveness of the hybrid RS-PINN. Empirical guidelines are provided for selecting biased, unbiased, or hybrid versions, depending on the dimensionality and nonlinearity of the specific PDE problem.

97 MATHEMATICS AND COMPUTING↗

Solving Eco-Driving Problems Using Indirect Collocation Method and Smooth Representation

Here, we discuss the eco-driving problem, considering both electric and conventional powertrains, and presents a pathway to solving it numerically using an indirect collocation method. Despite the low-order system dynamics, the piecewise fuel/efficiency map, gear shifting, and real-world traffic/road situations bring system discontinuities/switchings and pure state constraints into the problem formulation, which make the problem highly nonlinear and nontrivial to solve. This letter introduces smooth approximations to convert the original problem to an unconstrained (and penalized) smooth boundary-value problem. This approach eliminates the discussion of the switching structure and leads to a lightweight Newton-method-based solution procedure.

42 ENGINEERING↗

Advancement of hybrid fluid-kinetic modeling for HEDP and ICF science

We report on the development progress of a hybrid fluid-kinetic code for simulating fluids and plasmas in a wide range of environments, such as laser–matter interactions, inertial confinement fusion, magnetic confinement fusion, and pulsed power. The suite of numerical tools under development utilizes heterogeneous computer architectures and leverages the benefits of particle–based simulation techniques. By working to combine the kinetic particle-in-cell (PIC) model with a particle-based fluid simulation technique, such as smoothed particle hydrodynamics, we are developing a flexible framework capable of accurately modeling complex flows within and between kinetic and fluid regimes. The TriForce code is under development as a C++ framework for parallel, 3D, particle-based, hybrid fluid-kinetic plasma simulations. The fluid half of TriForce will be based upon the meshless smoothed-particle-hydrodynamics (SPH) approach, well-suited for shear, mixing, and turbulence, whereas the kinetic half resembles a traditional particle-in-cell (PIC) code; other particle-based approaches to fluid modeling that do use a mesh are also possible to use and are under investigation. Maxwell’s electromagnetic field equations are solved either via explicit or implicit algorithms or approximated via resistive magnetohydrodynamics (MHD) using an Ohm’s law and resulting induction equation (extended MHD is under development). A primary goal of enabling direct comparisons, from the same code, between results from the variants of MHD and implicit electromagnetic solutions is to improve our fundamental understanding of systems with magnetic fields. The code is under development to recover results from both radiation-MHD and fully kinetic codes in those limits, and is continuing to be developed from other follow-on grants to operate in between where both descriptions may co-exist and interact. For certain applications, it is desired for a simulation to contain fluid ions and electrons as well as kinetic ions and electrons. Typically, it is too computationally intensive to model a full-scale ICF or HEDP experiment fully kinetically since many cycles are expended with very small time steps on modeling the fluid part of a material that is well treated by the fluid approximation. In this case, many traditional PIC particles can be replaced with a single fluid particle representing the thermal part of the distribution function, and there are fewer needed kinetic particles, which describe the non-thermal part and can be sub-cycled relative to the fluid particle advance. Furthermore, a pure fluid code may, depending on the problem, simply lack many physically important details that are beyond the scope of its reduced approximations and assumptions. In this report, we summarize the objectives achieved in the development of the collisional and kinetic half of the code, and the physics problems to which the code has been applied in the areas of advanced and innovative fusion concepts, pulsed power, and magneto-inertial fusion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Freeboard, Snow Depth and Sea-Ice Roughness in East Antarctica from In Situ and Multiple Satellite Data

In October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50 500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. The results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

Markus, Thorsten↗