A computational method for the prediction of the three-dimensional viscous flow pressure distribution over finite wings using strip analysis
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Shubert, Gary Leonard↗
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Results for “prediction methods”
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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Computational methods in the prediction of advanced subsonic and supersonic propeller induced noise: ASSPIN users' manual
This document describes the computational aspects of propeller noise prediction in the time domain and the use of high speed propeller noise prediction program ASSPIN (Advanced Subsonic and Supersonic Propeller Induced Noise). These formulations are valid in both the near and far fields. Two formulations are utilized by ASSPIN: (1) one is used for subsonic portions of the propeller blade; and (2) the second is used for transonic and supersonic regions on the blade. Switching between the two formulations is done automatically. ASSPIN incorporates advanced blade geometry and surface pressure modelling, adaptive observer time grid strategies, and contains enhanced numerical algorithms that result in reduced computational time. In addition, the ability to treat the nonaxial inflow case has been included.
Dunn, M. H.↗
Theoretical and experimental investigation of additive drag
The significance of additive drag is discussed and equations for determining its approximate value are derived. Charts are presented giving values of additive drag for open-nose inlets and for annular-nose inlets with conical flow at the inlet. The effects of variable inlet total-pressure recovery and static pressures on the center body are investigated, and an analytical method of predicting the variation of pressure on the center body with mass-flow ratio is given. Experimental values of additive drag are compared with values predicted by the methods.
AIR INLETS↗
A new method for failure prediction of SR-200 beryllium sheet structures
Contemporary applications of failure criteria frequently incorporate two-dimensional or simplified three-dimensional methodologies for prediction of stresses. Motivation behind the development of a new multi-dimensional failure criterion is due mainly to the lack of a sufficiently accurate mathematical tool that accounts for the behavior of brittle material with anisotropic properties. Such a criterion should be able to provide a reliable maximum load estimate so that design of the structure is not penalized in terms of excessive weight requirements. The failure criterion developed is represented by a fracture surface in a six-dimensional stress space. The criterion is applied for failure prediction of SR-200 beryllium sheet structures, a non-homogeneous orthotropic material used widely in space applications. Two experiments are used to verify the criterion.
Papados, P. P.↗
Scale-Resolving Methods for Accurately Predicting the Aerodynamics of In-Flight Icing
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AATT↗
Impact of Building Characteristics on Energy Demand Profile Forecasting: An Evaluation of Predictive Accuracy and Methods
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Chowdhury, Shovan [ORNL]↗
Method of Testing and Predicting Failures of Electronic Mechanical Systems
A method employing a knowledge base of human expertise comprising a reliability model analysis implemented for diagnostic routines is disclosed. The reliability analysis comprises digraph models that determine target events created by hardware failures human actions, and other factors affecting the system operation. The reliability analysis contains a wealth of human expertise information that is used to build automatic diagnostic routines and which provides a knowledge base that can be used to solve other artificial intelligence problems.
Iverson, David L.↗
Displacement incrementation in non-linear structural analysis by the self-correcting method
The prediction of nonlinear structural behavior by the finite element method wherein buckling does not occur has received considerable attention and, with it, reasonable success has been achieved. However, the post-buckling problem has been less actively pursued probably because of the inherent numerical difficulties encountered. This note reviews very briefly the numerical methods currently being used for pre- and post-buckling analysis and presents a self-correcting approach based on load and displacement incrementation which is shown to be efficient, reliable, and easy to program. Numerical solutions are presented which demonstrate the effectiveness of the method.
Haisler, W. E.↗
Efficient Unstructured Grid Adaptation Methods for Sonic Boom Prediction
This paper examines the use of two grid adaptation methods to improve the accuracy of the near-to-mid field pressure signature prediction of supersonic aircraft computed using the USM3D unstructured grid flow solver. The first method (ADV) is an interactive adaptation process that uses grid movement rather than enrichment to more accurately resolve the expansion and compression waves. The second method (SSGRID) uses an a priori adaptation approach to stretch and shear the original unstructured grid to align the grid with the pressure waves and reduce the cell count required to achieve an accurate signature prediction at a given distance from the vehicle. Both methods initially create negative volume cells that are repaired in a module in the ADV code. While both approaches provide significant improvements in the near field signature (< 3 body lengths) relative to a baseline grid without increasing the number of grid points, only the SSGRID approach allows the details of the signature to be accurately computed at mid-field distances (3-10 body lengths) for direct use with mid-field-to-ground boom propagation codes.
Campbell, Richard L.↗
Development of a simplified procedure for rocket engine thrust chamber life prediction with creep
An analytical method for predicting engine thrust chamber life is developed. The method accounts for high pressure differentials and time-dependent creep effects both of which are significant in limiting the useful life of the shuttle main engine thrust chamber. The hot-gas-wall ligaments connecting adjacent cooling channels ribs and separating the coolant flow from the combustion gas are subjected to a high pressure induced primary stress superimposed on an alternating cyclic thermal strain field. The pressure load combined with strain-controlled cycling produces creep ratcheting and consequent bulging and thinning of these ligaments. This mechanism of creep-enhanced ratcheting is analyzed for determining the hot-gas-wall deformation and accumulated strain. Results are confirmed by inelastic finite element analysis. Fatigue and creep rupture damage as well as plastic tensile instability are evaluated as potential failure modes. It is demonstrated for the NARloy Z cases analyzed that when pressure differentials across the ligament are high, creep rupture damage is often the primary failure mode for the cycle times considered.
Badlani, M. L.↗
Improved naive Bayesian probability classifier in predictions of nuclear mass
Recently, novel statistical methods such as neural networks and Bayesian learning methods are implemented to describe the nuclear masses. Based on previous studies, an improved naive Bayesian probability (iNBP) classifier is proposed to study the nuclear masses by refining the results of sophisticated nuclear models. In the iNBP method, the prediction for nuclear masses is treated as a classification problem. The residuals are classified into several groups to generate prior and conditional probabilities, and the posterior probabilities are further determined by the Bayesian formula. We choose the expectation with maximum probability as the final prediction. Reliability of the iNBP method is assessed by analyzing the global optimizations and the extrapolating capabilities. Here, the iNBP method exhibits impressive improvements on global descriptions for different mass models. Moreover, the method shows robust extrapolating capabilities. Results demonstrate the iNBP method can be applied to predict the nuclear masses of unknown regions. Considering the local mass relations, the iNBP method can offer considerable fine-tuning of the mass descriptions from nuclear models. The methodology proposed in this paper can also be applied to other model-based extrapolations of nuclear observables.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion
Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.
Hoffman, Lauren [Scripps Institution of Oceanograp↗
Predicting Unsteady Aeroelastic Behavior
New method for predicting subsonic flutter, static deflections, and aeroelastic divergence developed. Unsteady aerodynamic loads determined by unsteady-vortex-lattice method. Accounts for aspect ratio and angle of attack. Equations for motion of wing and flow field solved iteratively and simultaneously. Used to predict transient responses to initial disturbances, and to predict steady-state static and oscillatory responses. Potential application for research in such unsteady structural/flow interactions as those in windmills, turbines, and compressors.
Strganac, Thomas W.↗
Prediction of Landing Gear Noise Reduction and Comparison to Measurements
Noise continues to be an ongoing problem for existing aircraft in flight and is projected to be a concern for next generation designs. During landing, when the engines are operating at reduced power, the noise from the airframe, of which landing gear noise is an important part, is equal to the engine noise. There are several methods of predicting landing gear noise, but none have been applied to predict the change in noise due to a change in landing gear design. The current effort uses the Landing Gear Model and Acoustic Prediction (LGMAP) code, developed at The Pennsylvania State University to predict the noise from landing gear. These predictions include the influence of noise reduction concepts on the landing gear noise. LGMAP is compared to wind tunnel experiments of a 6.3%-scale Boeing 777 main gear performed in the Quiet Flow Facility (QFF) at NASA Langley. The geometries tested in the QFF include the landing gear with and without a toboggan fairing and the door. It is shown that LGMAP is able to predict the noise directives and spectra from the model-scale test for the baseline configuration as accurately as current gear prediction methods. However, LGMAP is also able to predict the difference in noise caused by the toboggan fairing and by removing the landing gear door. LGMAP is also compared to far-field ground-based flush-mounted microphone measurements from the 2005 Quiet Technology Demonstrator 2 (QTD 2) flight test. These comparisons include a Boeing 777-300ER with and without a toboggan fairing that demonstrate that LGMAP can be applied to full-scale flyover measurements. LGMAP predictions of the noise generated by the nose gear on the main gear measurements are also shown.
Lopes, Leonard V.↗
A new method of very long-term prediction of solar activity
A method of solar activity prediction based on the statistical relationship between the minimum value of geomagnetic activity in each 11 year cycle and the height of the next sunspot cycle is proposed. The method is applied to predict sunspot cycle 21.
Ohl, A. I.↗
Predicting mesoscale spectral thermal conductivity using advanced deterministic phonon transport techniques
We present a review, demonstration and simulation of phonon transport for the purposes of predicting materials performance at the mesoscale. We focus primarily on the development and implementation of a unified methodology to enable predictive heat transport. We report on the current state of the art as it pertains to deterministic phonon transport methodologies, discussing various topics concerning phonons. In application, we focus on the Self-Adjoint Angular Flux (SAAF) formulation of the Boltzmann transport equation for phonons, and develop the spatial, angular, and material property discretization required to accurately simulate the predictive physics of heat transport in dielectrics. We discuss thermal interfacial resistance and present our formulation of the diffuse mismatch model for simulating phonon interactions at internal boundaries. We have recently developed a deterministic, spectral phonon transport method for predicting effective thermal conductivity ($\kappa_{\textrm{eff}}$), using Bose-Einstein source terms coupled through an average material temperature. In this method, we introduce a closure term to the phonon transport system which acts as a redistribution function for the total energy of the system, and serves as a guide for the amount of non-equilibrium behavior occurring in the system. This method predicts thermal conductivity and equilibrium temperature distributions in homogeneous and heterogeneous materials using data generated by ab initio density functional theory methods. We employ polarization, density of states and full dispersion spectra to resolve thermal conductivity with numerous angular and spatial discretizations. Our implementation utilizes a Richardson iteration on a modified version of the phonon scattering source. The equations associated with this method are solved via a modification of traditional source iteration. We compare the performance of source iteration applied to an existing uncoupled, traditional SAAF method to our new method and comment on the iterative performance of each. We observe ballistic and diffusive phonon scattering as acoustic thickness of the domain changes, and are able to make comparisons between the accuracy and efficiency of both methods.
36 MATERIALS SCIENCE↗
Least-Squares Prediction of Solar Activity
Studies of statistical methods for predicting future solar activity described in report containing extensive graphical and tabular presentations of analyses. Lagrangian-least-squares method, which gives best predictions presented in detail. FORTRAN source code for method given.
Holland, R. L.↗
Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
60 APPLIED LIFE SCIENCES↗