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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

WE-DE-BRA-01: SCIENCE COUNCIL JUNIOR INVESTIGATOR COMPETITION WINNER: Acceleration of a Limited-Angle Intrafraction Verification (LIVE) System Using Adaptive Prior Knowledge Based Image Estimation

Purpose: To develop an adaptive prior knowledge based image estimation method to reduce the scan angle needed in the LIVE system to reconstruct 4D-CBCT for intrafraction verification. Methods: The LIVE system has been previously proposed to reconstructs 4D volumetric images on-the-fly during arc treatment for intrafraction target verification and dose calculation. This system uses limited-angle beam’s eye view (BEV) MV cine images acquired from the treatment beam together with the orthogonally acquired limited-angle kV projections to reconstruct 4D-CBCT images for target verification during treatment. In this study, we developed an adaptive constrained free-form deformation reconstruction technique in LIVE to further reduce the scanning angle needed to reconstruct the CBCT images. This technique uses free form deformation with energy minimization to deform prior images to estimate 4D-CBCT based on projections acquired in limited angle (orthogonal 6°) during the treatment. Note that the prior images are adaptively updated using the latest CBCT images reconstructed by LIVE during treatment to utilize the continuity of patient motion.The 4D digital extended-cardiac-torso (XCAT) phantom was used to evaluate the efficacy of this technique with LIVE system. A lung patient was simulated with different scenario, including baseline drifts, amplitude change and phase shift. Limited-angle orthogonal kV and beam’s eye view (BEV) MV projections were generated for each scenario. The CBCT reconstructed by these projections were compared with the ground-truth generated in XCAT.Volume-percentage-difference (VPD) and center-of-mass-shift (COMS) were calculated between the reconstructed and the ground-truth tumors to evaluate the reconstruction accuracy. Results: Using orthogonal-view of 6° kV and BEV- MV projections, the VPD/COMS values were 12.7±4.0%/0.7±0.5 mm, 13.0±5.1%/0.8±0.5 mm, and 11.4±5.4%/0.5±0.3 mm for the three scenarios, respectively. Conclusion: The technique enables LIVE to accurately reconstruct 4D-CBCT images using only orthogonal 6° angle, which greatly improves the efficiency and reduces dose of LIVE for intrafraction verification.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-202-04: Auto-Adaptive Margin Generation for MLC-Tracked Radiotherapy

Purpose: To develop an auto-adaptive margin generator for MLC tracking. The generator is able to estimate errors arising in image guided radiotherapy, particularly on an MR-Linac, which depend on the latencies of machine and image processing, as well as on patient motion characteristics. From the estimated error distribution, a segment margin is generated, able to compensate errors up to a user-defined confidence. Method: In every tracking control cycle (TCC, 40ms), the desired aperture D(t) is compared to the actual aperture A(t), a delayed and imperfect representation of D(t). Thus an error e(t)=A(T)-D(T) is measured every TCC. Applying kernel-density-estimation (KDE), the cumulative distribution (CDF) of e(t) is estimated. With CDF-confidence limits, upper and lower error limits are extracted for motion axes along and perpendicular leaf-travel direction and applied as margins. To test the dosimetric impact, two representative motion traces were extracted from fast liver-MRI (10Hz). The traces were applied onto a 4D-motion platform and continuously tracked by an Elekta Agility 160 MLC using an artificially imposed tracking delay. Gafchromic film was used to detect dose exposition for static, tracked, and error-compensated tracking cases. The margin generator was parameterized to cover 90% of all tracking errors. Dosimetric impact was rated by calculating the ratio between underexposed points (>5% underdosage) to the total number of points inside FWHM of static exposure. Results: Without imposing adaptive margins, tracking experiments showed a ratio of underexposed points of 17.5% and 14.3% for two motion cases with imaging delays of 200ms and 300ms, respectively. Activating the margin generated yielded total suppression (<1%) of underdosed points. Conclusion: We showed that auto-adaptive error compensation using machine error statistics is possible for MLC tracking. The error compensation margins are calculated on-line, without the need of assuming motion or machine models. Further strategies to reduce consequential overdosages are currently under investigation. This work was funded by the SoRTS consortium, which includes the industry partners Elekta, Philips and Technolution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Adaptation to Environmental Extremes Structures Functional Traits in Biological Soil Crust and Hypolithic Microbial Communities

Biological soil crusts (biocrusts) are widespread in drylands and deserts. At the microhabitat scale, they also host hypolithic communities that live under semitranslucent stones. Both environmental niches experience exposure to extreme conditions such as high UV radiation, desiccation, temperature fluctuations, and resource limitation. However, hypolithic communities are somewhat protected from extremes relative to biocrust communities. Conditions are otherwise similar, so comparing them can answer outstanding questions regarding adaptations to environmental extremes. Using metagenomic sequencing, we assessed the functional potential of dryland soil communities and identified the functional underpinnings of ecological niche differentiation in biocrusts versus hypoliths. We also determined the effect of the anchoring photoautotroph (moss or cyanobacteria). Genes and pathways differing in abundance between biocrusts and hypoliths indicate that biocrust communities adapt to the higher levels of UV radiation, desiccation, and temperature extremes through an increased ability to repair damaged DNA, sense and respond to environmental stimuli, and interact with other community members and the environment. Intracellular competition appears to be crucial to both communities, with biocrust communities using the Type VI Secretion System (T6SS) and hypoliths favoring a diversity of antibiotics. The dominant primary producer had a reduced effect on community functional potential compared with niche, but an abundance of genes related to monosaccharide, amino acid, and osmoprotectant uptake in moss-dominated communities indicates reliance on resources provided to heterotrophs by mosses. Our findings indicate that functional traits in dryland communities are driven by adaptations to extremes and we identify strategies that likely enable survival in dryland ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

High-Order Mesh r-Adaptivity with Tangential Relaxation and Guaranteed Mesh Validity

High-order meshes are crucial for achieving optimal convergence rates in curvilinear domains, preserving symmetry, and aligning with key flow features in moving mesh simulations [1], but their quality is challenging to control. In prior work, we have developed techniques based on Target-Matrix Optimization Paradigm (TMOP) to adapt a given high-order mesh to the geometry and solution of the partial differential equation (PDE) [2, 3]. Here, we extend this framework to address two key gaps in the literature for highorder mesh 𝑟-adaptivity. First, we introduce tangential relaxation on curved surfaces using solely the discrete mesh representation, eliminating the need for access to underlying geometry (e.g., CAD model). Second, we ensure a continuously positive Jacobian determinant throughout the domain. This determinant positivity is essential for using the high-order mesh resulting from 𝑟-adaptivity with arbitrary quadrature schemes in simulations. The proposed approach is demonstrated to be robust using a variety of numerical experiments.

Mathematics and Computing↗
DOE OSTI2019

Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators

We present an adaptive grid matrix-free operator approximation scheme based on a "product-convolution" interpolation of convolution operators. This scheme is appropriate for operators that are locally translation-invariant, even if these operators are high rank or full rank. Such operators arise in Schur complement methods for solving partial differential equations (PDEs), as Hessians in PDE-constrained optimization and inverse problems, as integral operators, as covariance operators, and as Dirichlet-to-Neumann maps. Constructing the approximation requires computing the impulse responses of the operator to point sources centered on nodes in an adaptively refined grid of sample points. A randomized a posteriori error estimator drives the adaptivity. Once constructed, the approximation can be efficiently applied to vectors using the fast Fourier transform. The approximation can be efficiently converted to hierarchical matrix (H-matrix) format, then inverted or factorized using scalable H-matrix arithmetic. The quality of the approximation degrades gracefully as fewer sample points are used, allowing cheap lower quality approximations to be used as preconditioners. Furthermore, this yields an automated method to construct preconditioners for locally translation-invariant Schur complements. We directly address issues related to boundaries and prove that our scheme eliminates boundary artifacts. We test the scheme on a spatially varying blurring kernel, on the nonlocal component of an interface Schur complement for the Poisson operator, and on the data misfit Hessian for an advection dominated advection-diffusion inverse problem. Numerical results show that the scheme outperforms existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Making Data Prefetch Smarter: Adaptive Prefetching on POWER7

Hardware data prefetch engines are integral parts of many general purpose server-class microprocessors in the eld to- day. Some prefetch engines allow the user to change some of their parameters. The prefetcher, however, is usually enabled in a default conguration during system bring-up and dynamic reconguration of the prefetch engine is not an autonomic feature of current machines. Conceptually, however, it is easy to infer that commonly used prefetch algorithms, when applied in a xed mode will not help per- formance in many cases. In fact, they may actually de- grade performance due to useless bus bandwidth consump- tion and cache pollution. In this paper, we present an adap- tive prefetch scheme that dynamically modies the prefetch settings in order to adapt to the workload requirements. We implement and evaluate adaptive prefetching in the con- text of an existing, commercial processor, namely the IBM POWER7. Our adaptive prefetch mechanism improves per- formance with respect to the default prefetch setting up to 2.7X and 30% for single-threaded and multiprogrammed workloads, respectively. Categories

Jimenez, Victor↗
DOE OSTI2021

PAGANI: a parallel adaptive GPU algorithm for numerical integration

We present a new adaptive parallel algorithm for the challenging problem of multi-dimensional numerical integration on massively parallel architectures. Adaptive algorithms have demonstrated the best performance, but efficient many-core utilization is difficult to achieve because the adaptive work-load can vary greatly across the integration space and is impossible to predict a priori. Existing parallel algorithms utilize sequential computations on independent processors, which results in bottlenecks due to the need for data redistribution and processor synchronization. Our algorithm employs a high-throughput approach in which all existing sub-regions are processed and sub-divided in parallel. Repeated sub-region classification and filtering improves upon a brute-force approach and allows the algorithm to make efficient use of computation and memory resources. A CUDA implementation shows orders of magnitude speedup over the fastest open-source CPU method and extends the achievable accuracy for difficult integrands. Our algorithm typically outperforms other existing deterministic parallel methods.

Sakiotis, Ioannis↗
DOE OSTI2026

Adaptive Sampling Trust Region Method for Bi-fidelity Simulation Optimization [SWR-25-166]

Adaptive Sampling Trust Region Method for Bi-fidelity Simulation Optimization aims to demonstrate the effect of adaptive sampling-based bi-fidelity stochastic trust region method (ASTRO-BFDF). ASTRO-BFDF, derived from a derivative-free adaptive sampling trust-region optimization (ASTRO-DF) (Shashaani et al. 2018, Ha and Shashaani 2023), intended to efficiently solve the bi-fidelity simulation optimization.

Mueller, Juliane [National Laboratory of the Rocki↗
DOE OSTI2024

Long-term Multimodal Recording Reveals Epigenetic Adaptation Routes in Dormant Breast Cancer Cells

Patients with estrogen receptor–positive breast cancer receive adjuvant endocrine therapies (ET) that delay relapse by targeting clinically undetectable micrometastatic deposits. Yet, up to 50% of patients relapse even decades after surgery through unknown mechanisms likely involving dormancy. To investigate genetic and transcriptional changes underlying tumor awakening, we analyzed late relapse patients and longitudinally profiled a rare cohort treated with long-term neoadjuvant ETs until progression. Next, we developed an in vitro evolutionary study to record the adaptive strategies of individual lineages in unperturbed parallel experiments. Our data demonstrate that ETs induce nongenetic cell state transitions into dormancy in a stochastic subset of cells via epigenetic reprogramming. Single lineages with divergent phenotypes awaken unpredictably in the absence of recurrent genetic alterations. Targeting the dormant epigenome shows promising activity against adapting cancer cells. Overall, this study uncovers the contribution of epigenetic adaptation to the evolution of resistance to ETs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A Scale-Adaptive Turbulent Kinetic Energy Closure for the Dry Convective Boundary Layer

Abstract A pragmatic scale-adaptive turbulent kinetic energy (TKE) closure is proposed to simulate the dry convective boundary layer for a variety of horizontal grid resolutions: from 50 m, typical of large-eddy simulation models that use three-dimensional turbulence parameterizations/closures, up to 100 km, typical of climate models that use one-dimensional turbulence and convection parameterizations/closures. Since parameterizations/closures using the TKE approach have been frequently used in these two asymptotic limits, a simple method is proposed to merge them with a mixing-length-scale formulation for intermediate resolutions. This new scale-adaptive mixing length naturally increases with increasing grid length until it saturates as the grid length reaches mesoscale-model resolution. The results obtained using this new approach for dry convective boundary layers are promising. The mean vertical profiles of potential temperature and heat flux remain in good agreement for different resolutions. A continuous transition (in terms of resolution) across the gray zone is illustrated through the partitioning between the model-resolved and the subgrid-scale transports as well as by documenting the transition of the subgrid-scale TKE source/sink terms. In summary, a natural and continuous transition across resolutions (from 50 m to 100 km) is obtained, for dry convection, using exactly the same atmospheric model for all resolutions with a simple scale-adaptive mixing-length formulation.

Kurowski, Marcin J.↗
DOE OSTI2023

Multisensor Agile Adaptive Sampling (MAAS): A Methodology to Collect Radar Observations of Convective Cell Life Cycle

Abstract Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at subkilometer scale), and temporal evolution (at ∼2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect three sector plan position indicator (PPI) scans toward the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of three to six range–height indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a predetermined set of criteria. Between 1 June and 30 September 2022 over 315 000 vertical cross-section observations were collected by the C-band radars through ∼1300 unique isolated convective cells, most of which were observed for over 15 min of their life cycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Gonadal transcriptomics elucidate patterns of adaptive evolution within marine rockfishes (Sebastes)

Background: The genetic mechanisms of speciation and adaptation in the marine environment are not well understood. The rockfish genus Sebastes provides a unique model system for studying adaptive evolution because of the extensive diversity found within this group, which includes morphology, ecology, and a broad range of life spans. Examples of adaptive radiations within marine ecosystems are considered an anomaly due to the absence of geographical barriers and the presence of gene flow. Using marine rockfishes, we identified signatures of natural selection from transcriptomes developed from gonadal tissue of two rockfish species (Sebastes goodei and S. saxicola). We predicted orthologous transcript pairs, and estimated their distributions of nonsynonymous (Ka) and synonymous (Ks) substitution rates. Results: We identified 144 genes out of 1079 orthologous pairs under positive selection, of which 11 are functionally annotated to reproduction based on gene ontologies (GOs). One orthologous pair of the zona pellucida gene family, which is known for its role in the selection of sperm by oocytes, out of ten was identified to be evolving under positive selection. In addition to our results in the protein coding-regions of transcripts, we found substitution rates in 3’ and 5’ UTRs to be significantly lower than Ks substitution rates implying negative selection in these regions. Conclusions: We were able to identify a series of candidate genes that are useful for the assessment of the critical genes that diverged and are responsible for the radiation within this genus. Genes associated with longevity hold potential for understanding the molecular mechanisms that have contributed to the radiation within this genus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Large-scale 3D geoelectromagnetic modeling using parallel adaptive high-order finite element method

Here, we have investigated the use of the adaptive high-order finite-element method (FEM) for geoelectromagnetic modeling. Because high-order FEM is challenging from the numerical and computational points of view, most published finite-element studies in geoelectromagnetics use the lowest order formulation. Solution of the resulting large system of linear equations poses the main practical challenge. We have developed a fully parallel and distributed robust and scalable linear solver based on the optimal block-diagonal and auxiliary space preconditioners. The solver was found to be efficient for high finite element orders, unstructured and nonconforming locally refined meshes, a wide range of frequencies, large conductivity contrasts, and number of degrees of freedom (DoFs). Furthermore, the presented linear solver is in essence algebraic; i.e., it acts on the matrix-vector level and thus requires no information about the discretization, boundary conditions, or physical source used, making it readily efficient for a wide range of electromagnetic modeling problems. To get accurate solutions at reduced computational cost, we have also implemented goal-oriented adaptive mesh refinement. The numerical tests indicated that if highly accurate modeling results were required, the high-order FEM in combination with the goal-oriented local mesh refinement required less computational time and DoFs than the lowest order adaptive FEM.

58 GEOSCIENCES↗
DOE OSTI2017

Extreme scale unstructured adaptive CFD for aerodynamic flow control

Understanding the flow of fluid, either liquid or gas, through and around solid bodies has challenged man since the dawn of scientific inquiry. Many of the great minds of science and mathematics have progressively built up a hierarchy of fluid models since fluid flow impacts our lives in so many fundamental ways - from the early days of flow of water in viaducts to cities, to today's flight of planes. This chapter is concerned with the computational modeling of turbulent flow around aerodynamic bodies such as planes and wind turbines. In this case, viscous effects near the solid bodies create very thin boundary layers that yield highly anisotropic (gradients normal to the surface may be 10 6 larger than gradients along the surface) solutions to the governing non-linear partial differential equations (PDE); the Navier-Stokes equations. Furthermore, turbulent flows develop extremely broad ranges of length and time scales motivating the use of discretization methods capable of employing adaptivity and implicit time integration. The combination of these features - non-linear, anisotropy, adaptivity, and implicit - dramatically raise the complexity of the discretization, posing large challenges to efficient scalable parallel implementation. However, through careful design, the more complex algorithms can provide great reductions in computational cost relative to simpler methods (e.g., Cartesian grids with explicit time integration) that are easier to mate efficiently to hardware. In this chapter, we not only describe our approach but we also address the fact that while complex algorithms may never be as efficient flop-for-flop as simple methods, in the important measure of science-per-core-hour, they can still win big by making complex features like adaptivity and implicit methods as efficient and scalable as possible.

42 ENGINEERING↗
DOE OSTI2022

Random adaptive tool path for zonal optics fabrication

Deterministic optics fabrication using sub-aperture tools has been vital for manufacturing precision optical surfaces. The fabrication process requires the tool influence function and the tool path to calculate the dwell time that guides the tool to bring surface quality within tight design tolerances. Widely used spiral and raster paths may leave excess waviness from the tool path, and the unavoidable constant removal layer is added to obtain positive dwell time. This waviness can be removed by either using smaller tools sequentially or randomizing the tool path. However, the existing tool-path solutions can hardly adapt to different surface aperture shapes and localized surface errors. Process efficiency and accuracy are also not well considered in tool-path planning. We propose an innovative zonal Random Adaptive Path (RAP) to solve these problems in this study. Firstly, RAP can be flexibly adapted to different surface aperture shapes by introducing part boundary. Secondly, an average threshold strategy is used in the RAP planning to improve efficiency, enabling the surface errors to be selectively corrected. Finally, the threshold is performed in several passes within one processing cycle, each with its RAP, until the desired residual is achieved. The performance of the proposed RAP is studied by comparing it with the conventional tool paths. The results demonstrated that RAP takes the least processing time and achieves the best surface quality, which verifies the effectiveness of RAP in deterministic optics fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Spatial soft sweeps: Patterns of adaptation in populations with long-range dispersal

Adaptation in extended populations often occurs through multiple independent mutations responding in parallel to a common selection pressure. As the mutations spread concurrently through the population, they leave behind characteristic patterns of polymorphism near selected loci—so-called soft sweeps—which remain visible after adaptation is complete. These patterns are well-understood in two limits of the spreading dynamics of beneficial mutations: the panmictic case with complete absence of spatial structure, and spreading via short-ranged or diffusive dispersal events, which tessellates space into distinct compact regions each descended from a unique mutation. However, spreading behaviour in most natural populations is not exclusively panmictic or diffusive, but incorporates both short-range and long-range dispersal events. Here, we characterize the spatial patterns of soft sweeps driven by dispersal events whose jump distances are broadly distributed, using lattice-based simulations and scaling arguments. We find that mutant clones adopt a distinctive structure consisting of compact cores surrounded by fragmented “haloes” which mingle with haloes from other clones. As long-range dispersal becomes more prominent, the progression from diffusive to panmictic behaviour is marked by two transitions separating regimes with differing relative sizes of halo to core. We analyze the implications of the core-halo structure for the statistics of soft sweep detection in small genomic samples from the population, and find opposing effects of long-range dispersal on the expected diversity in global samples compared to local samples from geographic subregions of the range. We also discuss consequences of the standing genetic variation induced by the soft sweep on future adaptation and mixing.

Genetics & Heredity↗
DOE OSTI2013

Hierarchy-Direction Selective Approach for Locally Adaptive Sparse Grids

We consider the problem of multidimensional adaptive hierarchical interpolation. We use sparse grids points and functions that are induced from a one dimensional hierarchical rule via tensor products. The classical locally adaptive sparse grid algorithm uses an isotropic refinement from the coarser to the denser levels of the hierarchy. However, the multidimensional hierarchy provides a more complex structure that allows for various anisotropic and hierarchy selective refinement techniques. We consider the more advanced refinement techniques and apply them to a number of simple test functions chosen to demonstrate the various advantages and disadvantages of each method. While there is no refinement scheme that is optimal for all functions, the fully adaptive family-direction-selective technique is usually more stable and requires fewer samples.

Stoyanov, Miroslav K↗
DOE OSTI2014

Training Adaptive Decision-Making.

Adaptive Thinking has been defined here as the capacity to recognize when a course of action that may have previously been effective is no longer effective and there is need to adjust strategy. Research was undertaken with human test subjects to identify the factors that contribute to adaptive thinking. It was discovered that those most effective in settings that call for adaptive thinking tend to possess a superior capacity to quickly and effectively generate possible courses of action, as measured using the Category Generation test. Software developed for this research has been applied to develop capabilities enabling analysts to identify crucial factors that are predictive of outcomes in fore-on-force simulation exercises.

Abbott, Robert G.↗