Search NASA⌕ Search

SEARCH · Search NASA

Results for “Adaptable”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

An adaptive lattice Boltzmann scheme for modeling two-fluid-phase flow in porous medium systems

In this paper, we formulate a multiple-relaxation-time (MRT) lattice-Boltzmann method (LBM) to simulate two-fluid-phase flow in porous medium systems. The MRT LBM is applied to simulate the displacement of a wetting fluid by a nonwetting fluid in a system corresponding to a microfluidic cell. Analysis of the simulation shows widely varying time scales for the dynamics of fluid pressures, fluid saturations, and interfacial curvatures that are typical characteristics of such systems. Displacement phenomena include Haines jumps, which are relatively short duration isolated events of rapid fluid displacement driven by capillary instability. An adaptive algorithm is advanced using a level-set method to locate interfaces and estimate their rate of advancement. Because the displacement dynamics are confined to the interfacial regions for a majority of the relaxation time, the computational effort is focused on these regions. The proposed algorithm is shown to reduce computational effort by an order of magnitude, while yielding essentially identical solutions to a conventional fully coupled approach. Finally, the challenges posed by Haines jumps are also resolved by the adaptive algorithm. Possible extensions to the advanced method are discussed.

42 ENGINEERING↗
DOE OSTI2018

Enzymatic Adaptation of Podospora anserina to Different Plant Biomass Provides Leads to Optimized Commercial Enzyme Cocktails

As a late colonizer of herbivore dung, Podospora anserina has evolved an enzymatic machinery to degrade the more recalcitrant fraction of plant biomass, suggesting a great potential for biotechnology applications. The authors investigated its transcriptome during growth on two industrial feedstocks, soybean hulls (SBH) and corn stover (CS). Initially, CS and SBH results in the expression of hemicellulolytic and amylolytic genes, respectively, while at later time points a more diverse gene set is induced, especially for SBH. Substrate adaptation is also observed for carbon catabolism. Overall, SBH resulted in a larger diversity of expressed genes, confirming previous proteomics studies. The results not only provide an in depth view on the transcriptomic adaptation of P. anserina to substrate composition, but also point out strategies to improve saccharification of plant biomass at the industrial level.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

In‐memory integration of existing software components for parallel adaptive unstructured mesh workflows

Summary Reliable mesh‐based simulations are needed to solve complex engineering problems. Mesh adaptivity can increase reliability by reducing discretization errors but requires multiple software components to exchange information. Often, components exchange information by reading and writing a common file format. This file‐based approach becomes a problem on massively parallel computers where filesystem bandwidth is a critical performance bottleneck. Our approach using data streams and component interfaces avoids the filesystem bottleneck. In this paper, we present these techniques and their use for coupling mesh adaptivity to the PHASTA computational fluid dynamics solver, the Albany multi‐physics framework, and the Omega3P linear accelerator frequency analysis applications. Performance results are reported on up to 16,384 cores of an Intel Knights Landing‐based system.

Smith, Cameron W.↗
DOE OSTI2016

A parallel adaptive numerical method with generalized curvilinear coordinate transformation for compressible Navier–Stokes equations

A fourth-order finite-volume method for solving the Navier–Stokes equations on a mapped grid with adaptive mesh refinement is proposed, implemented, and demonstrated for the prediction of unsteady compressible viscous flows. The method employs fourth-order quadrature rules for evaluating face-averaged fluxes. Our approach is freestream preserving, guaranteed by the way of computing the averages of the metric terms on the faces of cells. The standard Runge–Kutta marching method is used for time discretization. Solutions of a smooth flow are obtained in order to verify that the method is formally fourth-order accurate when applying the nonlinear viscous operators on mapped grids. Solutions of a shock tube problem are obtained to demonstrate the effectiveness of adaptive mesh refinement in resolving discontinuities. A Mach reflection problem is solved to demonstrate the mapped algorithm on a non-rectangular physical domain. The simulation is compared against experimental results. Future work will consider mapped multiblock grids for practical engineering geometries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Adaptive downstream tensorial eddy viscosity for hybrid large‐eddy simulations‐Reynolds‐averaged‐Navier–Stokes simulations

An adaptive method is developed to improve the accuracy of eddy‐viscosity Reynolds‐averaged‐Navier–Stokes (RANS) model in hybrid large‐eddy simulations (LES)‐RANS simulations by using available upstream LES results. The method first gets the tensorial eddy viscosity from the upstream LES solution at the LES‐RANS interface and then uses that information to improve the downstream RANS model by invoking the weak‐equilibrium assumption. The proposed method was evaluated via two test problems—flow in a channel and over a periodic hill. Results obtained show the proposed approach to increase the accuracy and stability of hybrid LES‐RANS simulations. Since the modification of the downstream RANS model is based on the tensorial eddy viscosity from the upstream LES solution, the method is adaptive to the problem being studied.

Zhang, Wanjia↗
DOE OSTI2022

Hybrid large-eddy simulation with adaptive downstream anisotropic eddy viscosity model

Turbulence pervades most flows of engineering interest, and its prediction remains a challenge on both accuracy and cost. One promising predictive approach that reduces cost combines large-eddy simulation (LES) with simulation based on Reynolds averaged Navier–Stokes equations (RANS). This study presents a method to overcome stability and accuracy issues associated with these hybrid LES-RANS methods. The method developed involves extracting the Reynolds stresses from the upstream LES solution and then using that information to convert the downstream RANS model from a scalar eddy-viscosity model to an anisotropic nonlinear eddy-viscosity model. The method developed differs from the downstream tensorial eddy-viscosity model by being independent of the coordinate system. The method developed was evaluated by computing film cooling of a flat plate with the coolant injected through one row of circular holes. Results obtained show instabilities at the LES-to-RANS interface to be eliminated. Also, the method developed yielded solutions that compare reasonably well with those from LES, even though a significant portion of the flow is computed by the adapted anisotropic RANS model instead of LES, which significantly reduced the number of grid points and computational time needed. Since modification of the downstream RANS model is based on information extracted from the upstream LES solution, the method developed is adaptive to the problem being studied.

42 ENGINEERING↗
DOE OSTI2023

Caspian Tern Management to Increase Survival of Juvenile Salmonids in the Columbia River Basin: Progress and Adaptive Management Considerations

Abstract Predation by Caspian terns Hydroprogne caspia is a factor limiting the recovery of some Endangered Species Act-listed populations of salmonids from the Columbia River basin, especially steelhead Oncorhynchus mykiss. This prompted the development and implementation of two separate management plans, one in the Columbia River estuary and the other in the Columbia Plateau region, to reduce the impact of Caspian tern predation on smolt survival. Caspian terns nesting at managed breeding colonies within the basin were relocated to alternative nesting islands created for terns outside the basin. Both plans were successful in significantly reducing smolt losses to Caspian terns nesting at managed sites; however, new developments have led to regression in smolt survival gains associated with tern management. Adaptive management to prevent terns at managed colonies from relocating to nest elsewhere in the basin and to improve nesting opportunities for Caspian terns outside the basin are needed to maximize the survival benefits to Endangered Species Act-listed salmonids from management. Adaptive management is also necessary to ensure the long-term viability of the Pacific Flyway population of Caspian terns, whose breeding population has declined by more than 50% since the two management plans were implemented.

Collis, Ken↗
DOE OSTI2023

Adaptive changes in sexual behavior in the high–risk population in response to human monkeypox transmission in Canada can help control the outbreak: Insights from a two–group, two–route epidemic model

Monkeypox, a zoonotic disease, is emerging as a potential sexually transmitted infection/disease, with underlying transmission mechanisms still unclear. We devised a risk-structured, compartmental model, incorporating sexual behavior dynamics. We compared different strategies targeting the high-risk population: a scenario of control policies geared toward the use of condoms and/or sexual abstinence (robust control strategy) with risk compensation behavior change, and a scenario of control strategies with behavior change in response to the doubling rate (adaptive control strategy). Monkeypox's basic reproduction number is 1.464, 0.0066, and 1.461 in the high-risk, low-risk, and total populations, respectively, with the high-risk group being the major driver of monkeypox spread. Policies imposing condom use or sexual abstinence need to achieve a 35% minimum compliance rate to stop further transmission, while a combination of both can curb the spread with 10% compliance to abstinence and 25% to condom use. With risk compensation, the only option is to impose sexual abstinence by at least 35%. Adaptive control is more effective than robust control where the daily sexual contact number is reduced proportionally and remains constant thereafter, shortening the time to epidemic peak, lowering its size, facilitating disease attenuation, and playing a key role in controlling the current outbreak.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Limited‐memory adaptive snapshot selection for proper orthogonal decomposition

Summary Reduced order models are useful for accelerating simulations in many‐query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models (ROMs) can have prohibitively expensive memory and floating‐point operation costs in high‐performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time‐stepping ordinary differential equation solvers. The error estimator used in this work is related to theory bounding the approximation error in time of proper orthogonal decomposition‐based ROMs, and memory usage is minimized by computing the singular value decomposition using a single‐pass incremental algorithm. Results for a viscous Burgers' test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full‐order model is recovered to within discretization error. A parallel version of the resulting method can be used on supercomputers to generate proper orthogonal decomposition‐based ROMs, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space. Copyright © 2016 John Wiley & Sons, Ltd.

Oxberry, Geoffrey M.↗
DOE OSTI2023

Deformation Rate‐Adaptive Conducting Polymers and Composites

Abstract Materials are more easily damaged during accidents that involve rapid deformation. Here, a design strategy is described for electronic materials comprised of conducting polymers that defies this orthodox property, making their extensibility and toughness dynamically adaptive to deformation rates. This counterintuitive property is achieved through a morphology of interconnected nanoscopic core–shell micelles, where the chemical interactions are stronger within the shells than the cores. As a result, the interlinked shells retain material integrity under strain, while the rate of dissociation of the cores controls the extent of micelle elongation, which is a process that adapts to deformation rates. A prototype based on polyaniline shows a 7.5‐fold increase in ultimate elongation and a 163‐fold increase in toughness when deformed at increasing rates from 2.5 to 10 000% min −1 . This concept can be generalized to other conducting polymers and highly conductive composites to create “self‐protective” soft electronic materials with enhanced durability under dynamic movement or deformation.

Hernandez, Victor↗
DOE OSTI2018

Adaptive mesh refinement in stress-constrained topology optimization

We present a topology structural optimization framework with adaptive mesh refinement and stress-constraints. Finite element approximation and geometry representation benefit from such refinement by enabling more accurate stress field predictions and greater resolution of the optimal structural boundaries. We combine a volume fraction filter to impose a minimum design feature size, the RAMP penalization to generate “black-and-white designs” and a RAMP-like stress definition to resolve the “stress singularity problem.” Regions with stress concentrations dominate the optimized design. As such, rigorous simulations are required to accurately approximate the stress field. To achieve this goal, we invoke a threshold operation and mesh refinement during the optimization. We do so in an optimal fashion, by applying adaptive mesh refinement techniques that use error indicators to refine and coarsen the mesh as needed. In this way, we obtain more accurate simulations and greater resolution of the design domain. We present results in two dimensions to demonstrate the efficiency of our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A posteriori error estimation and adaptivity in hp virtual elements

An explicit and computable error estimator for the hp version of the virtual element method (VEM), together with lower and upper bounds with respect to the exact energy error, is presented. Such error estimator is employed to provide, following the approach of Melenk and Wohlmuth (Adv Comput Math 15(1–4):311–331, 2001), hp adaptive mesh refinements for very general polygonal meshes. In addition, a novel VEM hp Clément quasi-interpolant, instrumental for the a posteriori error analysis, is introduced. The performances of the adaptive method are validated by a number of numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Weight-adapted ultra-low-dose pancreatic perfusion CT: radiation dose, image quality, and perfusion parameters

Purpose: We evaluate the reliability and feasibility of weight-adapted ultra-low-dose pancreatic perfusion CT. Methods: A total of 100 (47 men, 53 women) patients were enrolled prospectively and were assigned to five groups (A, B, C, D, and E) with different combination of tube voltage and tube current according to their body weight. Radiation dose parameters including volume CT dose index (CTDI) and dose-length product (DLP) were recorded. Image quality was evaluated both subjectively and objectively (noise, signal-to-noise ratio, contrast-to-noise ratio). Perfusion parameters including blood flow (BF), blood volume (BV), and permeability (PMB) were measured. The dose, image quality measurements, and perfusion parameters were compared between the five groups using one-way analysis of variance (ANOVA). Results: Radiation dose reached 8.7 mSv in patients under 50 kg and was 18.9 mSv in patients above 80 kg. The mean subjective image quality score was above 4.45 on a 5-point scale with good agreement between two radiologists. Groups A–D had equivalent performance on objective image quality (P > 0.05), while Group E performed even better (P < 0.05). No significant differences emerged in comparison with perfusion parameters (BF, BV, PMB) of normal pancreas parenchyma between the five groups. Conclusion: Weight-adapted ultra-low-dose pancreatic perfusion CT can effectively reduce radiation dose without prejudice to image quality, and the perfusion parameters of normal parenchyma are accurate and reliable.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Parallel anisotropic mesh adaptation with boundary layers for automated viscous flow simulations

This paper presents a set of parallel procedures for anisotropic mesh adaptation accounting for mixed element types used in boundary layer meshes, i.e., the current procedures operate in parallel on distributed boundary layer meshes. The procedures accept anisotropic mesh metric field as an input for the desired mesh size field and apply local mesh modifications to adapt the mesh to match/satisfy the specified mesh size field. The procedures fully account for the parametric geometry of curved domains and maintain the semi-structured nature of the boundary layer elements. The effectiveness of the procedures is demonstrated on three viscous flow examples that include the ONERA M6 wing, a heat transfer manifold, and a scramjet engine.

Sahni, Onkar↗
DOE OSTI2018

Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets

New manufacturing technologies such as additive manufacturing require research and development to minimize the uncertainties in the produced parts. The research involves experimental measurements and large simulations, which result in huge quantities of data to store and analyze. We address this challenge by alleviating the data storage requirements using lossy data compression. We select wavelet bases as the mathematical tool for compression. Unlike images, additive manufacturing data is often represented on irregular geometries and unstructured meshes. Thus, we use Alpert tree-wavelets as bases for our data compression method. We first analyze different basis functions for the wavelets and find the one that results in maximal compression and miminal error in the reconstructed data. We then devise a new adaptive thresholding method that is data-agnostic and allows a priori estimation of the reconstruction error. Finally, we propose metrics to quantify the global and local errors in the reconstructed data. One of the error metrics addresses the preservation of physical constraints in reconstructed data fields, such as divergence-free stress field in structural simulations. While our compression and decompression method is general, we apply it to both experimental and computational data obtained from measurements and thermal/structural modeling of the sintering of a hollow cylinder from metal powders using a Laser Engineered Net Shape process. The results show that monomials achieve optimal compression performance when used as wavelet bases. The new thresholding method results in compression ratios that are two to seven times larger than the ones obtained with commonly used thresholds. Overall, adaptive Alpert tree-wavelets can achieve compression ratios between one and three orders of magnitude depending on the features in the data that are required to preserve. Furthermore, these results show that Alpert tree-wavelet compression is a viable and promising technique to reduce the size of large data structures found in both experiments and simulations.

42 ENGINEERING↗
DOE OSTI2018

Convergence and Efficiency of Adaptive Importance Sampling Techniques with Partial Biasing

We propose a new Monte Carlo method to efficiently sample a multimodal distribution (known up to a normalization constant). We consider a generalization of the discrete-time Self Healing Umbrella Sampling method, which can also be seen as a generalization of well-tempered metadynamics. The dynamics is based on an adaptive importance technique. The importance function relies on the weights (namely the relative probabilities) of disjoint sets which form a partition of the space. These weights are unknown but are learnt on the fly yielding an adaptive algorithm. In the context of computational statistical physics, the logarithm of these weights is, up to an additive constant, the free-energy, and the discrete valued function defining the partition is called the collective variable. The algorithm falls into the general class of Wang–Landau type methods, and is a generalization of the original Self Healing Umbrella Sampling method in two ways: (i) the updating strategy leads to a larger penalization strength of already visited sets in order to escape more quickly from metastable states, and (ii) the target distribution is biased using only a fraction of the free-energy, in order to increase the effective sample size and reduce the variance of importance sampling estimators. We prove the convergence of the algorithm and analyze numerically its efficiency on a toy example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Adaptive Wavelet Decomposition of Matrix Flows

Adaptive algorithms for constructing spline-wavelet decompositions of matrix flows from a linear space of matrices over a normed field are presented. The algorithms suggested provides for an a priori prescribed estimate of the deviation of the basic flow from the initial one. Comparative bounds of the volumes of data in the basic flow for various irregularity characteristics of the initial flow are obtained in the cases of pseudo-equidistant and adaptive grids. Limit characteristics of the above-mentioned volumes are given in the cases where the initial flow is generated by differentiable functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Memory-efficient nonsmooth dynamic optimization using adaptive randomized compression

Dynamic optimization problems arise in many applications including flow control, full waveform inversion, and medical imaging. These problems are plagued by significant computational challenges. One such challenge — and the focus of this work — is the memory limitation induced by the size of the underlying dynamical system. In particular, the entire dynamic trajectory is required for derivative computation and therefore must be stored or recomputed using, e.g., checkpointing. Although recent work demonstrated the use of adaptive randomized sketching to overcome the memory challenge, that work only applies to smooth unconstrained problems, prohibiting its use for nonsmooth regularized and constrained problems. The inclusion of nonsmooth regularizers and constraints is critical as they often arise in an attempt to preserve certain physical properties or to promote sparsity. To solve these problems, we introduce a trust-region algorithm for minimizing the sum of a smooth nonconvex function and a nonsmooth convex function that leverages randomized sketching to compress the dynamical system trajectories and adaptively adjust the sketch rank to satisfy a gradient inexactness condition. We prove convergence of this algorithm and demonstrate that it achieves substantial memory reduction on three discretized PDE-constrained optimization applications.

97 MATHEMATICS AND COMPUTING↗