A Proxy-Labeling Method for Data in the Absence of Ground Truth with Domain Adaptation
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Whetzel, Jonathan Hunt↗
SEARCH · Engineering Papers
Results for “adaptive 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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Neural network based adaptive control of nonlinear plants using random search optimization algorithms
This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.
Boussalis, Dhemetrios↗
Ensemble Data Assimilation Without Ensembles: Methodology and Application to Ocean Data Assimilation
Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time.
Data Assimilation↗
Laser Additive Manufacturing Process Development for Bismuth Telluride Thermoelectric Material
Thermoelectric generators have strong potential for aerospace and aircraft applications. However, traditional manufacturing methods for thermoelectric devices severely limit device adaptability and consequently marketability. Laser powder bed fusion is an additive manufacturing method which shows promising potential for producing thermoelectric devices. Thermoelectric materials pose unique challenges compared to metals because of low thermal conductivity, brittle fracture characteristics, and irregular powder particle morphologies. Herein, we communicate processing procedures to fabricate thermoelectric parts of Bi 2 Te 3 through laser powder bed fusion. In this study, we identify the successful combination of key process parameters—laser power, scan speed, hatch distance, and powder layer thickness—to achieve high-quality parts in terms of density and physical properties, and we demonstrate the impact of process parameter variation on built part quality. While it cannot exclusively determine part quality, the volumetric energy density is a useful guide in deciding process parameters for thermoelectric materials, and the optimal value is between 9 and 11 J/mm 3 for Bi 2 Te 3 . We demonstrate successful fabrication of different freeform geometries of Bi 2 Te 3 powders. The results suggest it is possible to extend this method more broadly to other semiconductor materials, including thermoelectric power generation materials applicable for space applications.
36 MATERIALS SCIENCE↗
Maritime Aviation
This report presents some studies of maritime aviation which cover the following principal points: employment of landplanes on maritime aerial warfare; their adaption to peculiar requirements of the Navy; and the establishment of a method of aerial pursuit and bombardment, likewise adapted to military aviation over land.
Ravennes, Jean↗
A wall interference assessment/correction system
A Wall Signature method, the Hackett method, has been selected to be adapted for the 12-ft Wind Tunnel wall interference assessment/correction (WIAC) system in the present phase. This method uses limited measurements of the static pressure at the wall, in conjunction with the solid wall boundary condition, to determine the strength and distribution of singularities representing the test article. The singularities are used in turn for estimating wall interferences at the model location. The Wall Signature method will be formulated for application to the unique geometry of the 12-ft Tunnel. The development and implementation of a working prototype will be completed, delivered and documented with a software manual. The WIAC code will be validated by conducting numerically simulated experiments rather than actual wind tunnel experiments. The simulations will be used to generate both free-air and confined wind-tunnel flow fields for each of the test articles over a range of test configurations. Specifically, the pressure signature at the test section wall will be computed for the tunnel case to provide the simulated 'measured' data. These data will serve as the input for the WIAC method-Wall Signature method. The performance of the WIAC method then may be evaluated by comparing the corrected parameters with those for the free-air simulation. Each set of wind tunnel/test article numerical simulations provides data to validate the WIAC method. A numerical wind tunnel test simulation is initiated to validate the WIAC methods developed in the project. In the present reported period, the blockage correction has been developed and implemented for a rectangular tunnel as well as the 12-ft Pressure Tunnel. An improved wall interference assessment and correction method for three-dimensional wind tunnel testing is presented in the appendix.
Lo, Ching F.↗
Using Distributed Fiber-Optic Strain Sensing to Estimate Modal Generalized Modal Coordinates from Flight-Test Data
Background and Motivation - Problem - Contemporary aircraft carry around structural mass so that the flutter instabilities lie well outside of the operational envelope. - Better methods of measuring the structural state could allow reduction of the extra structural weight. - Modal filtering - Standard method in structural analysis - Deformations are a combination of mode shapes - Modal filtering is estimating these modal coordinates from data - Often ordinary least squares methods - Often applied to simpler test articles - Factor Analysis - Classic (in psychology) analysis method - Measurements are a combination of small number of unmeasurable variables - Lessons from this factor can be adapted to improve the modal filtering methods
Jeffrey Ouellette↗
Dyn$\mathrm{AMO}$: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws
Here we introduce DynAMO, a reinforcement learning paradigm for Dynamic Anticipatory Mesh Optimization. Adaptive mesh refinement is an effective tool for optimizing computational cost and solution accuracy in numerical methods for partial differential equations. However, traditional adaptive mesh refinement approaches for time-dependent problems typically rely only on instantaneous error indicators to guide adaptivity. As a result, standard strategies often require frequent remeshing to maintain accuracy. In the DynAMO approach, multi-agent reinforcement learning is used to discover new local refinement policies that can anticipate and respond to future solution states by producing meshes that deliver more accurate solutions for longer time intervals. By applying DynAMO to discontinuous Galerkin methods for the linear advection and compressible Euler equations in two dimensions, we demonstrate that this new mesh refinement paradigm can outperform conventional threshold-based strategies while also generalizing to different mesh sizes, remeshing and simulation times, and initial conditions.
97 MATHEMATICS AND COMPUTING↗
Cosmic Inference: Constraining Parameters with Observations and a Highly Limited Number of Simulations
We look at cosmological probes that pose an inverse problem where the measurement result is obtained through observations, and the objective is to infer values of model parameters that characterize the underlying physical system-our universe, from these observations and theoretical forward-modeling. The only way to accurately forward-model physical behavior on small scales is via expensive numerical simulations, which are further “emulated” due to their high cost. Emulators are commonly built with a set of simulations covering the parameter space with Latin hypercube sampling and an interpolation procedure; the aim is to establish an approximately constant prediction error across the hypercube. In this paper, we provide a description of a novel statistical framework for obtaining accurate parameter constraints. The proposed framework uses multi-output Gaussian process emulators that are adaptively constructed using Bayesian optimization methods with the goal of maintaining a low emulation error in the region of the hypercube preferred by the observational data. In this paper, we compare several approaches for constructing multi-output emulators that enable us to take possible inter-output correlations into account while maintaining the efficiency needed for inference. Using a Lyα forest flux power spectrum, we demonstrate that our adaptive approach requires considerably fewer-by a factor of a few in the Lyα P(k) case considered here-simulations compared to the emulation based on Latin hypercube sampling, and that the method is more robust in reconstructing parameters and their Bayesian credible intervals.
79 ASTRONOMY AND ASTROPHYSICS↗
NWPEsSe: an Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems
Global optimization constitutes an important and fundamental problem in theoretical studies in many chemical fields, such as catalysis, materials or separations problems. In this paper, a novel algorithm has been developed for the global optimization of large systems including neat and ligated clusters in gas phase, and supported clusters in periodic boundary conditions. The method is based on an updated artificial bee colony (ABC) algorithm method, that allows for adaptive-learning during the search process. The new algorithm is tested against four classes of systems of diverse chemical nature: gas phase Au_55, ligated Au_8^(2+), Au_8 supported on graphene oxide and defected rutile, and a large cluster assembly ?[Co?_6 Te_8 (PEt_3 )_6][C_60 ]_n, with sizes ranging between 1 to 3 nm and containing up to 1300 atoms. Reliable global minima (GMs) are obtained for all cases, either confirming published data or reporting new lower energy structures. The algorithm and interface to other codes in the form of an independent program, Northwest Potential Energy Search Engine (NWPEsSe), is freely available and it provides a powerful and efficient approach for global optimization of nanosized cluster systems. The work described in this publication was performed at Pacific Northwest National Laboratory (PNNL), which is operated by Battelle for the United States Department of Energy (DOE) under Contract DE-AC05-76RL0180. J. Z. and V.-A. G. acknowledge support from DOE, Office of Science, Office of Basic Energy Sci-ences, Chemical, Geological and Biological Sciences Division and computing resources from PNNL’s Research Computing Facility and the National Energy Research Scientific Computing Center.
Zhang, Jun↗
The controlled growth method - A tool for structural optimization
An adaptive design variable linking scheme in a NLP based optimization algorithm is proposed and evaluated for feasibility of application. The present scheme, based on an intuitive effectiveness measure for each variable, differs from existing methodology in that a single dominant variable controls the growth of all others in a prescribed optimization cycle. The proposed method is implemented for truss assemblies and a wing box structure for stress, displacement and frequency constraints. Substantial reduction in computational time, even more so for structures under multiple load conditions, coupled with a minimal accompanying loss in accuracy, vindicates the algorithm.
Hajela, P.↗
Estimating and Calibrating DER Model Parameters Using Levenberg–Marquardt Algorithm in Renewable Rich Power Grid
The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57 × 10 –7 .
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Recent Efforts to Improve the Near Real Time Forest Disturbance Monitoring Capabilities of the ForWarn System
This presentation discusses the development of anew method for computing NDVI temporal composites from near real time eMODIS data This research is being conducted to improve forest change products used in the ForWarn system for monitoring regional forest disturbances in the United States. ForWarn provides nation-wide NDVI-based forest disturbance detection products that are refreshed every 8 days. Current eMODIS and historical MOD13 24 day NDVI data are used to compute the disturbance detection products. The eMODIS 24 day NDVI data re-aggregated from 7 day NDVI products. The 24 day eMODIS NDVIs are generally cloud free, but do not necessarily use the freshest quality data. To shorten the disturbance detection time, a method has been developed that performs adaptive length/maximum value compositing of eMODIS NDVI, along with cloud and shadow "noise" mitigation. Tests indicate that this method can reduce detection rates by 8-16 days for known recent disturbance events, depending on the cloud frequencies and disturbance type. The noise mitigation in these tests, though imperfect, helped to improve quality of the resulting NDVI and forest change products.
Spruce, Joseph↗
Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator
In this work, we present a general adaptive latent space tuning approach for improving the robustness of machine learning tools with respect to time variation and distribution shift. We demonstrate our approach by developing an encoder-decoder convolutional neural network-based virtual 6D phase space diagnostic of charged particle beams in the HiRES ultrafast electron diffraction (UED) compact particle accelerator with uncertainty quantification. Our method utilizes model-independent adaptive feedback to tune a low dimensional 2D latent space representation of ~1 million dimensional objects which are the 15 unique 2D projections (x, y),...,(z, p z ) of the 6D phase space (x, y, z, p x , p y , p z ) of the charged particle beams. We demonstrate our method with numerical studies of short electron bunches utilizing experimentally measured UED input beam distributions.
43 PARTICLE ACCELERATORS↗
Comparison of genetic algorithms with conjugate gradient methods
Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.
Bosworth, J. L.↗
Accelerated statistical failure analysis of multifidelity TRISO fuel models
Statistical nuclear fuel failure analysis is critical for the design and development of advanced reactor technologies. Although Monte Carlo Sampling (MCS) is a standard method of statistical failure analysis for fuels, the low failure probabilities of some advanced fuel forms and the correspondingly large number of required model evaluations limit its application to low-fidelity (e.g., 1-D) fuel models. In this paper, we present four other statistical methods for fuel failure analysis in Bison, considering tri-structural isotropic (TRISO)-coated particle fuel as a case study. The statistical methods considered are Latin hypercube sampling (LHS), adaptive importance sampling (AIS), subset simulation (SS), and the Weibull theory. Using these methods, we analyzed both 1-D and 2-D representations of TRISO models to compute failure probabilities and the distributions of fuel properties that result in failures. The results of these methods compare well across all TRISO models considered. Overall, SS and the Weibull theory were deemed the most efficient, and can be applied to both 1-D and 2-D TRISO models to compute failure probabilities. Moreover, since SS also characterizes the distribution of parameters that cause TRISO failures, and can consider failure modes not described by the Weibull criterion, it may be preferred over the other methods. Finally, a discussion on the efficacy of different statistical methods of assessing nuclear fuel safety is provided.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
"Grinding" cavities in polyurethane foam
Grinding tool installed on conventional milling machine cuts precise cavities in foam blocks. Method is well suited for prototype or midsize production runs and can be adapted to computer control for mass production. Method saves time and materials compared to bonding or hot wire techniques.
Brower, J. R.↗
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Physics-informed neural networks (PINNs) have shown to be effective tools for solving both forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of the neural network using automatic differentiation, and this PDE loss is evaluated at a set of scattered spatio-temporal points (called residual points). The location and distribution of these residual points are highly important to the performance of PINNs. However, in the existing studies on PINNs, only a few simple residual point sampling methods have mainly been used. Here, we present a comprehensive study of two categories of sampling for PINNs: non-adaptive uniform sampling and adaptive nonuniform sampling. We consider six uniform sampling methods, including (1) equispaced uniform grid, (2) uniformly random sampling, (3) Latin hypercube sampling, (4) Halton sequence, (5) Hammersley sequence, and (6) Sobol sequence. We also consider a resampling strategy for uniform sampling. To improve the sampling efficiency and the accuracy of PINNs, we propose two new residual-based adaptive sampling methods: residual-based adaptive distribution (RAD) and residual-based adaptive refinement with distribution (RAR-D), which dynamically improve the distribution of residual points based on the PDE residuals during training. Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling. We extensively tested the performance of these sampling methods for four forward problems and two inverse problems in many setups. Our numerical results presented in this study are summarized from more than 6000 simulations of PINNs. Here, we show that the proposed adaptive sampling methods of RAD and RAR-D significantly improve the accuracy of PINNs with fewer residual points for both forward and inverse problems. Furthermore, the results obtained in this study can also be used as a practical guideline in choosing sampling methods.
97 MATHEMATICS AND COMPUTING↗