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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

EXCHANGE Campaign Degradation (ECD): Understanding Decomposition Dynamics Across Mid-Atlantic and Great Lakes Coastal Ecosystems

The EXploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments (EXCHANGE) Degradation Experiment (EXCHANGE-D) is an in situ experiment designed to assess organic matter decomposition rates across coastal terrestrial-aquatic interfaces (TAIs), from coastal uplands through transition zones to wetlands. Through a network of partner scientists and coastal sites, we are testing how environmental gradients shape decomposition and carbon dynamics across terrestrial-aquatic interfaces. Using standardized tea bag substrates deployed across a network of diverse coastal sites, we compare decomposition rates at different fresh- and salt-water TAIs to develop transferable knowledge that improves the representation of organic matter degradation in coastal ecosystem models. For more information, please see https://compass.pnnl.gov/FME/EXCHANGE. This is Version 1 of the data package, which includes: ecd_README.pdf flmd.csv dd.csv ecd_soil_weom_L2.csv ecd_soil_ph_conductivity_L2.csv ecd_soil_gwc_L2.csv ecd_soil_teabag_degradation_L2.csv ecd_readme.pdf

coastal soils↗
NASA NTRS1986

A multifunction recognition operator for telerobotic vision

Research on developing an operator capable of performing the various subtasks required of a telerobot's vision sensor is reported. The operator uses a goal-driven matching technique which is an application of a linear programming method that readily adapts to the elastic template matching approach to pattern recognition. Four applications of the operator are discussed: (1) three-space location of an isolated object; (2) shape determination of isolated planar figures; (3) image compression/restoration; and (4) shape decomposition.

Goode, P. W., IV↗
DOE OSTI2024

Human activities shape global patterns of decomposition rates in rivers

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf litter decomposition rates with high accuracy (70% variance explained). Finally, our global map provides estimates of rates across vast understudied areas of Earth and reveals rapid decomposition across continental-scale areas dominated by human activities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1968

Decomposition vessel

Stainless steel crucible-shaped vessel permits rapid decomposition of silicates and other refractory compounds by acids at relatively low temperatures. The vessel is lined with tetrafluoroethylene fluorocarbon resin and sealed by a sheet of the same material retained in a stainless steel screw cap.

Bernas, B.↗
NASA NTRS1992

Hierarchical image coding with diamond-shaped sub-bands

We present a sub-band image coding/decoding system using a diamond-shaped pyramid frequency decomposition to more closely match visual sensitivities than conventional rectangular bands. Filter banks are composed of simple, low order IIR components. The coder is especially designed to function in a multiple resolution reconstruction setting, in situations such as variable capacity channels or receivers, where images must be reconstructed without the entire pyramid of sub-bands. We use a nonlinear interpolation technique for lost subbands to compensate for loss of aliasing cancellation.

Li, Xiaohui↗
DOE OSTI2022

Decomposition Kinetics of H 2 O 2 on Pd Nanocrystals with Different Shapes and Surface Strains

Direct synthesis of hydrogen peroxide (H 2 O 2 ) from H 2 and O 2 on a Pd-based catalyst has emerged as a promising route to replace the energy-consuming, highly inefficient anthraquinone process. However, Pd is also a good catalyst for the decomposition of H 2 O 2 , thereby compromising the selectivity toward the desired product. The coupling between the formation and decomposition reactions makes it difficult to single out the most important parameter that controls the selectivity toward direct synthesis of H 2 O 2 . Herein, support-free monometallic Pd nanocrystals with different shapes and surface strains are used to investigate their impacts on the decomposition kinetics of H 2 O 2 . The kinetics are analyzed by tracking the concentration of the remaining H 2 O 2 using infrared spectroscopy. The data indicates that both surface structure and strain affect the decomposition kinetics of H 2 O 2 , but their impacts are inferior to that caused by Br – , a surface capping agent for the Pd{100} facets. The experimental results are consistent with the trend obtained through density functional theory calculations. Furthermore, this work helps shed light on the development of Pd-based catalysts for the direct synthesis of H 2 O 2 by offering strategies to mitigate the decomposition of the desired product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2021

Non-Causal Controller Approximation Methodology Augmented with Model Reference Control – Robust Design

This paper describes the development of a new control design methodology based on a non-causal controller structure and its approximation. The non-causal controller design is based on a loop shaping approach. Partial fraction decomposition is used, along with derivative approximations, to derive a proper transfer function design for a causal control structure. The premise for this non-causal control methodology is that the phase delay as a function of frequency ideally remains within approximately -90o, which tends to make the system robustly stable to uncertainties in the plant dynamics. To have a predictable control system response in lieu of the tolerance of this methodology to relatively large uncertainty in the plant dynamics, the approach is augmented with model reference control. The addition of model reference control can potentially further enhance the robustness of the design. A control system design example is presented in this paper, along with simulation results, to demonstrate the high degree of robustness of this non-causal methodology and its augmentation.

Control Systems↗
DOE OSTI2022

Long-Term Cellulose Enrichment Selects for Highly Cellulolytic Consortia and Competition for Public Goods

Microbial communities are a key driver of the carbon cycle through the breakdown of complex polysaccharides in diverse environments including soil, marine systems, and the mammalian gut. However, due to the complexity of these communities, the species-species interactions that impact community structure and ultimately shape the rate of decomposition are difficult to define.

microbial interactions↗
NASA NTRS1994

Rigorous Justification of the Localized Approximation to the Beam-Shape Coefficients in Generalized Lorenz-Mie Theory: On-Axis Beams - Part 1

Generalized Lorenz-Mie theory describes electromagnetic scattering of an arbitrary light beam by a spherical particle. The computationally most expensive feature of the theory is the evaluation of the beam-shape coefficients, which give the decomposition of the incident light beam into partial waves. The so-called localized approximation to these coefficients for a focused Gaussian beam is an analytical function whose use greatly simplifies Gaussian-beam scattering calculations. A mathematical justification and physical interpretation of the localized approximation is presented for on-axis beams.

Lock, James A.↗
NASA NTRS1993

Single block three-dimensional volume grids about complex aerodynamic vehicles

This paper presents an alternate approach for the generation of volumetric grids for supersonic and hypersonic flows about complex configurations. The method uses parametric two-dimensional block-face grid definitions, within the framework of GRIDGEN2D. The incorporation of a face decomposition reduces complex surfaces to simple shapes. These simple shapes are recombined to obtain the final face definition. The advantages of this method include the reduction of overall grid generation time through the use of vectorized computer code, the elimination of generating matching block faces, and simplified boundary conditions.

Alter, Stephen J.↗
NASA NTRS1993

Single block three-dimensional volume grids about complex aerodynamic vehicles

This paper presents an alternate approach for the generation of volumetric grids for supersonic and hypersonic flows about complex configurations. The method uses parametric two dimensional block face grid definition within the framework of GRIDGEN2D. The incorporation of face decomposition reduces complex surfaces to simple shapes. These simple shapes are combined to obtain the final face definition. The advantages of this method include the reduction of overall grid generation time through the use of vectorized computer code, the elimination of the need to generate matching block faces, and the implementation of simplified boundary conditions. A simple axisymmetric grid is used to illustrate this method. In addition, volume grids for two complex configurations, the Langley Lifting Body (HL-20) and the Space Shuttle Orbiter, are shown.

Alter, Stephen J.↗
NASA NTRS1995

Implementation of a multiblock sensitivity analysis method in numerical aerodynamic shape optimization

A multiblock sensitivity analysis method is applied in a numerical aerodynamic shape optimization technique. The Sensitivity Analysis Domain Decomposition (SADD) scheme which is implemented in this study was developed to reduce the computer memory requirements resulting from the aerodynamic sensitivity analysis equations. Discrete sensitivity analysis offers the ability to compute quasi-analytical derivatives in a more efficient manner than traditional finite-difference methods, which tend to be computationally expensive and prone to inaccuracies. The direct optimization procedure couples CFD analysis based on the two-dimensional thin-layer Navier-Stokes equations with a gradient-based numerical optimization technique. The linking mechanism is the sensitivity equation derived from the CFD discretized flow equations, recast in adjoint form, and solved using direct matrix inversion techniques. This investigation is performed to demonstrate an aerodynamic shape optimization technique on a multiblock domain and its applicability to complex geometries. The objectives are accomplished by shape optimizing two aerodynamic configurations. First, the shape optimization of a transonic airfoil is performed to investigate the behavior of the method in highly nonlinear flows and the effect of different grid blocking strategies on the procedure. Secondly, shape optimization of a two-element configuration in subsonic flow is completed. Cases are presented for this configuration to demonstrate the effect of simultaneously reshaping interfering elements. The aerodynamic shape optimization is shown to produce supercritical type airfoils in the transonic flow from an initially symmetric airfoil. Multiblocking effects the path of optimization while providing similar results at the conclusion. Simultaneous reshaping of elements is shown to be more effective than individual element reshaping due to the inclusion of mutual interference effects.

Lacasse, James M.↗
DOE OSTI2023

Intensive and extensive margins of the peak load: Measuring adaptation with mixed frequency panel data

In this work we investigate the response of daily electricity peak load to daily maximum temperatures across states in Europe and India. We propose a method that decomposes short- from medium/long-run effects, retains the high frequency nature of the load-weather covariation and treats economic growth as a modulating factor. By simultaneously exploiting variation in unexpected daily weather anomalies and decade-long climatic changes in each location we decompose transitory - intensive margin - adjustments from permanent - extensive margin - adjustments. We find that the shocks over the long-run differ substantially from the short-run dynamics. Furthermore, we find evidence that per capita income modulates the adjustments over the short- and long-run. We project that in response to climate change around 2050 the peak load may increase by up to 20%-30% in Southern Europe and in several states in India, depending on the degree of warming and the evolution of socio-economic conditions. Even with a limited scope to two world regions, we identify that the structure of the economy and differences in future income growth matter in shaping the adaptation to climate change. Our decomposition allows to identify how future weather anomalies can further amplify the relative increase associated to the shift in the climate norm. Assuming that the interannual variability of maximum temperatures follows the distribution observed in the past, we find a doubling of the impacts of climate change during the summer in Europe. Uncertainty around the distribution of future weather anomalies may lead to further unexpected peak load amplifications. Our results have important policy implications for power systems’ generation capacity, transmission and storage, as we show that the challenges to accommodate the peak load in days with extreme temperatures may substantially increase already around mid-century.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Surface Variability Mapping and Roughness Analysis of the Moon Using a Coarse–Graining Decomposition

The lunar surface contains a wide variety of topographic shapes and features, each with different distributions and scales, and any analysis technique to objectively measure roughness must respect these qualities. Coarse-graining is a naturally scale-dependent filtering technique that preserves scale-dependent symmetries and produces coarse elevation maps that gradually erase the smaller features from the original topography. In this study of the lunar surface, we present two surface variability metrics obtained from coarse-graining lunar topography: fine elevation and coarse curvature. Both metrics are isotropic, deterministic, slope-independent, and coordinate-agnostic. Fine (detrended) elevation is acquired by subtracting the coarse elevation from the original topography and contains features that are smaller than the coarse-graining length-scale. Coarse curvature is the Laplacian of coarsened topography, and naturally quantifies the curvature at any scale and indicates whether a location is elevated or depressed relative to its neighborhood at that scale. We find that highlands and maria have distinct roughness characteristics at all length-scales. Our topographic spectra reveal four scale-breaks that mark characteristic shifts in surface roughness: 100, 300, 1,000, and 4,000 km. Comparing fine elevation distributions between maria and highlands, we show that maria fine elevation is biased toward smaller-magnitude elevations and that the maria–highland discrepancies are more pronounced at larger length-scales. Here, we also provide local examples of selected regions to demonstrate that these metrics can successfully distinguish geological features of different length-scales.

58 GEOSCIENCES↗
NASA NTRS1988

Structural shape optimization in multidisciplinary system synthesis

Structural shape optimization couples with other discipline optimization in the design of complex engineering systems. For instance, the wing structural weight and elastic deformations couple to aerodynamic loads and aircraft performance through drag. This coupling makes structural shape optimization a subtask in the overall vehicle synthesis. Decomposition methods for optimization and sensitivity analysis allow the specialized disciplinary methods to be used while the disciplines are temporarily decoupled, after which the interdisciplinary couplings are restored at the system level. Application of decomposition methods to structures-aerodynamics coupling in aircraft is outlined and illustrated with a numerical example of a transport aircraft. It is concluded that these methods may integrate structural and aerodynamic shape optimizations with the unified objective of the maximum aircraft performance.

Sobieszczanski-Sobieski, Jaroslaw↗
DOE OSTI2025

Precursor Identity and Surfactant Concentration Influence Shape of UO 2 Nanoparticles

A fundamental step toward studying the unique properties of actinide nanomaterials is control over the shape of actinide nanoparticles. Toward this goal, this work demonstrates the effects of precursor identity and surfactant concentration on the shape of uranium dioxide (UO 2 ) nanoparticles. UO 2 nanoparticles were synthesized by thermal decomposition of different precursors in the presence of oleylamine and oleic acid as surfactants. The size, shape, phase, and chemical composition of the nanoparticles was evaluated using transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS), electron diffraction (ED), and U L 3 -edge X-ray absorption fine-structure (XAFS) spectroscopy. Anisotropic UO 2 nanocubes of ∼ 4 nm were obtained only with low surfactant concentrations, while increasing the surfactant concentration resulted in formation of nanoparticles with an isotropic, sphere morphology. The importance of precursor identity was also investigated by employing U(hfa) 4 , U(acac) 4 , UO 2 (acac) 2 , and UO 2 (hfa) 2 ·H 2 O (where hfa = hexafluoracetylacetone and acac = acetylacetone) as precursors. The nanocube morphology was only observed when U(hfa) 4 was used as a precursor. XAS allowed for comparison of the disorder in anisotropic vs isotropic UO 2 nanoparticles.

Nanocubes↗
DOE OSTI2026

Deep learning for time series forecasting: a survey of recent advances

Time series forecasting plays a critical role in numerous real-world applications, such as finance, healthcare, transportation, and scientific computing. In recent years, deep learning has become a powerful tool for modeling complex temporal patterns and improving forecasting accuracy. This survey provides an overview of recent deep learning approaches for time series forecasting, involving various architectures including RNNs, CNNs, GNNs, transformers, large language models, MLP-based models, and diffusion models. We first identify key challenges in the field, such as temporal dependency, efficiency, and cross-variable dependency, which drive the development of forecasting techniques. Then, the general advantages and limitations of each architecture are discussed to contextualize their adaptation in time series forecasting. Furthermore, we highlight promising design trends like multi-scale modeling, decomposition, and frequency-domain techniques, which are shaping the future of the field. This paper serves as a compact reference for researchers and practitioners seeking to understand the current landscape and future trajectory of deep learning in time series forecasting.

97 MATHEMATICS AND COMPUTING↗