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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Image coding using entropy-constrained residual vector quantization

The residual vector quantization (RVQ) structure is exploited to produce a variable length codeword RVQ. Necessary conditions for the optimality of this RVQ are presented, and a new entropy-constrained RVQ (ECRVQ) design algorithm is shown to be very effective in designing RVQ codebooks over a wide range of bit rates and vector sizes. The new EC-RVQ has several important advantages. It can outperform entropy-constrained VQ (ECVQ) in terms of peak signal-to-noise ratio (PSNR), memory, and computation requirements. It can also be used to design high rate codebooks and codebooks with relatively large vector sizes. Experimental results indicate that when the new EC-RVQ is applied to image coding, very high quality is achieved at relatively low bit rates.

Kossentini, Faouzi↗
NASA NTRS2012

OpenMDAO: Framework for Flexible Multidisciplinary Design, Analysis and Optimization Methods

The OpenMDAO project is underway at NASA to develop a framework which simplifies the implementation of state-of-the-art tools and methods for multidisciplinary design, analysis and optimization. Foremost, OpenMDAO has been designed to handle variable problem formulations, encourage reconfigurability, and promote model reuse. This work demonstrates the concept of iteration hierarchies in OpenMDAO to achieve a flexible environment for supporting advanced optimization methods which include adaptive sampling and surrogate modeling techniques. In this effort, two efficient global optimization methods were applied to solve a constrained, single-objective and constrained, multiobjective version of a joint aircraft/engine sizing problem. The aircraft model, NASA's nextgeneration advanced single-aisle civil transport, is being studied as part of the Subsonic Fixed Wing project to help meet simultaneous program goals for reduced fuel burn, emissions, and noise. This analysis serves as a realistic test problem to demonstrate the flexibility and reconfigurability offered by OpenMDAO.

Kriging↗
NASA NTRS2003

Robust Airfoil Optimization in High Resolution Design Space

The robust airfoil shape optimization is a direct method for drag reduction over a given range of operating conditions and has three advantages: (1) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (2) it uses a large number of B-spline control points as design variables yet the resulting airfoil shape is fairly smooth, and (3) it allows the user to make a trade-off between the level of optimization and the amount of computing time consumed. The robust optimization method is demonstrated by solving a lift-constrained drag minimization problem for a two-dimensional airfoil in viscous flow with a large number of geometric design variables. Our experience with robust optimization indicates that our strategy produces reasonable airfoil shapes that are similar to the original airfoils, but these new shapes provide drag reduction over the specified range of Mach numbers. We have tested this strategy on a number of advanced airfoil models produced by knowledgeable aerodynamic design team members and found that our strategy produces airfoils better or equal to any designs produced by traditional design methods.

Li, Wu↗
DOE OSTI2025

Improving the Quasi‐Biennial Oscillation via a Surrogate‐Accelerated Multi‐Objective Optimization

Accurate simulation of the quasi-biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM for a realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency from complex observations, we extract interpretable and physically meaningful quantities capturing key attributes. Building on this, we train a probabilistic surrogate model that approximates the fundamental characteristics of the QBO as functions of critical physics parameters governing gravity wave generation. Leveraging the Karhunen–Loève decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, capturing cross-correlations among multiple physics quantities evaluated at different pressure levels and enabling rapid surrogate-based inference at a fraction of the computational cost of full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution. To navigate this, we quantify the bi-criteria trade-off and generate a set of Pareto optimal parameter values that balance the conflicting objectives. This integrated workflow improves the fidelity of QBO simulations and offers a versatile template for uncertainty quantification in complex geophysical models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

The Ground Flash Fraction Retrieval Algorithm Employing Differential Evolution: Simulations and Applications

The ability to estimate the fraction of ground flashes in a set of flashes observed by a satellite lightning imager, such as the future GOES-R Geostationary Lightning Mapper (GLM), would likely improve operational and scientific applications (e.g., severe weather warnings, lightning nitrogen oxides studies, and global electric circuit analyses). A Bayesian inversion method, called the Ground Flash Fraction Retrieval Algorithm (GoFFRA), was recently developed for estimating the ground flash fraction. The method uses a constrained mixed exponential distribution model to describe a particular lightning optical measurement called the Maximum Group Area (MGA). To obtain the optimum model parameters (one of which is the desired ground flash fraction), a scalar function must be minimized. This minimization is difficult because of two problems: (1) Label Switching (LS), and (2) Parameter Identity Theft (PIT). The LS problem is well known in the literature on mixed exponential distributions, and the PIT problem was discovered in this study. Each problem occurs when one allows the numerical minimizer to freely roam through the parameter search space; this allows certain solution parameters to interchange roles which leads to fundamental ambiguities, and solution error. A major accomplishment of this study is that we have employed a state-of-the-art genetic-based global optimization algorithm called Differential Evolution (DE) that constrains the parameter search in such a way as to remove both the LS and PIT problems. To test the performance of the GoFFRA when DE is employed, we applied it to analyze simulated MGA datasets that we generated from known mixed exponential distributions. Moreover, we evaluated the GoFFRA/DE method by applying it to analyze actual MGAs derived from low-Earth orbiting lightning imaging sensor data; the actual MGA data were classified as either ground or cloud flash MGAs using National Lightning Detection Network[TM] (NLDN) data. Solution error plots are provided for both the simulations and actual data analyses.

Koshak, William↗
DOE OSTI2022

CO 2 fertilization of terrestrial photosynthesis inferred from site to global scales

Significance The magnitude of the CO 2 fertilization effect on terrestrial photosynthesis is uncertain because it is not directly observed and is subject to confounding effects of climatic variability. We apply three well-established eco-evolutionary optimality theories of gas exchange and photosynthesis, constraining the main processes of CO 2 fertilization using measurable variables. Using this framework, we provide robust observationally inferred evidence that a strong CO 2 fertilization effect is detectable in globally distributed eddy covariance networks. Applying our method to upscale photosynthesis globally, we find that the magnitude of the CO 2 fertilization effect is comparable to its in situ counterpart but highlight the potential for substantial underestimation of this effect in tropical forests for many reflectance-based satellite photosynthesis products.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2012

Top-down Estimate of Dust Emissions Through Integration of MODIS and MISR Aerosol Retrievals With the Geos-chem Adjoint Model

Predicting the influences of dust on atmospheric composition, climate, and human health requires accurate knowledge of dust emissions, but large uncertainties persist in quantifying mineral sources. This study presents a new method for combined use of satellite-measured radiances and inverse modeling to spatially constrain the amount and location of dust emissions. The technique is illustrated with a case study in May 2008; the dust emissions in Taklimakan and Gobi deserts are spatially optimized using the GEOSChem chemical transport model and its adjoint constrained by aerosol optical depth (AOD) that are derived over the downwind dark-surface region in China from MODIS (Moderate Resolution Imaging Spectroradiometer) reflectance with the aerosol single scattering properties consistent with GEOS-chem. The adjoint inverse modeling yields an overall 51% decrease in prior dust emissions estimated by GEOS-Chem over the Taklimakan-Gobi area, with more significant reductions south of the Gobi Desert. The model simulation with optimized dust emissions shows much better agreement with independent observations from MISR (Multi-angle Imaging SpectroRadiometer) AOD and MODIS Deep Blue AOD over the dust source region and surface PM10 concentrations. The technique of this study can be applied to global multi-sensor remote sensing data for constraining dust emissions at various temporal and spatial scales, and hence improving the quantification of dust effects on climate, air quality, and human health.

MODIS aerosol retrievals↗
DOE OSTI2020

Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings

Insufficient flexibility in system operation caused by traditional "heat-set" operating modes of combined heat and power (CHP) units in winter heating periods is a key issue that limits renewable energy consumption. In order to reduce the curtailment of renewable energy resources through improving the operational flexibility, a novel optimal scheduling model based on chance-constrained programming (CCP), aiming at minimizing the lowest generation cost, is proposed for a small-scale integrated energy system (IES) with CHP units, thermal power units, renewable generations and representative auxiliary equipments. In this model, due to the uncertainties of renewable generations including wind turbines and photovoltaic units, the probabilistic spinning reserves are supplied in the form of chance-constrained; from the perspective of user experience, a heating load model is built with consideration of heat comfort and inertia in buildings. To solve the model, a solution approach based on sequence operation theory (SOT) is developed, where the original CCP-based scheduling model is tackled into a solvable mixed-integer linear programming (MILP) formulation by converting a chance constraint into its deterministic equivalence class, and thereby is solved via the CPLEX solver. We report the simulation results on the modified IEEE 30-bus system demonstrate that the presented method manages to improve operational flexibility of the IES with uncertain renewable generations by comprehensively leveraging thermal inertia of buildings and different kinds of auxiliary equipments, which provides a fundamental way for promoting renewable energy consumption.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Quantifying Subsurface Flow and Solute Transport in a Snowmelt‐Recharged Hillslope With Multiyear Water Balance

Abstract Quantifying flow and transport from hillslopes is vital for understanding water quantity and quality in rivers, but remains obscure because of limited subsurface measurements. Using measured hydraulic conductivity K profiles and water balance over a single year to calibrate a transmissivity feedback model for a hillslope in the East River watershed (Colorado) proved unsatisfactory for predicting flow over the subsequent years. Well‐constrained field‐scale K were obtained by optimizing subsurface flux predictions over years having large differences in recharge, and by including estimates of interannual transfer of excess snowmelt recharge. Water and solute exports during high snowmelt recharge occur predominantly via shallow groundwater flow through weathered rock and soil because of their enlarged transmissivities under saturated conditions. Conversely, these shallow pathways are less active in snow drought years when the water table remains deeper within the weathering zone. Hillslope soil water monitoring showed that rainfall does not infiltrate deeply during summer and fall months, and revealed water losses consistent with model ET predictions. By combining water table‐dependent fluxes with pore water chemistry in different zones, time‐dependent rates of solute exports become predictable. As an example, calibrated K were combined with dissolved nitrogen concentrations in pore waters to show the snowmelt‐dependence of reactive nitrogen exported from the hillslope, further supporting the recent finding that the weathering zone is the dominant source of reactive nitrogen at this site. Subsurface export predictions can now be obtained for wide ranges of recharge based on measurements of water table elevation and profiles of pore water chemistry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Mapping the global distribution of C 4 vegetation using observations and optimality theory

Plants with the C 4 photosynthesis pathway typically respond to climate change differently from more common C 3 -type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C 4 and C 3 vegetation distributions. However, current C 4 vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C 4 vegetation. We find that global C 4 vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C 4 natural grass cover due to elevated CO 2 favoring C 3 -type photosynthesis, and an increase in C 4 crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C 4 vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C 4 plants in the contemporary global carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accelerated Constrained Sparse Tensor Factorization on Massively Parallel Architectures

This study presents the first constrained sparse tensor factorization (cSTF) framework that optimizes and fully offloads computation to massively parallel GPU architectures, and the first performance characterization of cSTF on GPU architectures. In contrast to prior work on tensor factorization, where the matricized tensor times Khatri-Rao product (MTTKRP) is the primary performance bottleneck, our systematic analysis of the cSTF algorithm on GPUs reveals that adding constraints creates an additional bottleneck in the update operation for many real-world sparse tensors. While executing the update operation on the GPU brings significant speedup over its CPU counterpart, it remains a significant bottleneck. To further accelerate the update operation, we propose cuADMM, a new update algorithm that leverages algorithmic and code optimization strategies to minimize both computation and data movement on GPUs. As a result, our framework delivers significantly improved performance compared to prior state-of-the-art. On 10 real-world sparse tensors, our framework achieves geometric mean speedup of 5.1 × (max 41.59 ×) and 7.01 × (max 58.05 ×) on the NIVIDA A100 and H100 GPUs, respectively, over the state-of-the-art SPLATT library running on a 26-core Intel Ice Lake Xeon CPU.

Soh, Yongseok↗
NASA NTRS1972

Control of linear uncertain systems utilizing mismatched state observers

The control of linear continuous dynamical systems is investigated as a problem of limited state feedback control. The equations which describe the structure of an observer are developed constrained to time-invarient systems. The optimal control problem is formulated, accounting for the uncertainty in the design parameters. Expressions for bounds on closed loop stability are also developed. The results indicate that very little uncertainty may be tolerated before divergence occurs in the recursive computation algorithms, and the derived stability bound yields extremely conservative estimates of regions of allowable parameter variations.

Goldstein, B.↗
NASA NTRS1987

Nonlinear programming extensions to rational function approximations of unsteady aerodynamics

This paper deals with approximating unsteady generalized aerodynamic forces in the equations of motion of a flexible aircraft. Two methods of formulating these approximations are extended to include both the same flexibility in constraining them and the same methodology in optimizing nonlinear parameters as another currently used 'extended least-squares' method. Optimal selection of 'nonlinear' parameters is made in each of the three methods by use of the same nonlinear (nongradient) optimizer. The objective of the nonlinear optimization is to obtain rational approximations to the unsteady aerodynamics whose state-space realization is of lower order than that required when no optimization of the nonlinear terms is performed. The free 'linear' parameters are determined using least-squares matrix techniques on a Lagrange multiplier formulation of an objective function which incorporates selected linear equality constraints. State-space mathematical models resulting from the different approaches are described, and results are presented which show comparative evaluations from application of each of the extended methods to a numerical example. The results obtained for the example problem show a significant (up to 63 percent) reduction in the number of differential equations used to represent the unsteady aerodynamic forces in linear time-invariant equations of motion as compared to a conventional method in which nonlinear terms are not optimized.

Tiffany, Sherwood H.↗
NASA NTRS2004

Fleet Assignment Using Collective Intelligence

Product distribution theory is a new collective intelligence-based framework for analyzing and controlling distributed systems. Its usefulness in distributed stochastic optimization is illustrated here through an airline fleet assignment problem. This problem involves the allocation of aircraft to a set of flights legs in order to meet passenger demand, while satisfying a variety of linear and non-linear constraints. Over the course of the day, the routing of each aircraft is determined in order to minimize the number of required flights for a given fleet. The associated flow continuity and aircraft count constraints have led researchers to focus on obtaining quasi-optimal solutions, especially at larger scales. In this paper, the authors propose the application of this new stochastic optimization algorithm to a non-linear objective cold start fleet assignment problem. Results show that the optimizer can successfully solve such highly-constrained problems (130 variables, 184 constraints).

Antoine, Nicolas E.↗
NASA NTRS2011

Kinetic Simulations of Current-Sheet Formation and Reconnection at a Magnetic X Line

The integration of kinetic effects into macroscopic numerical models is currently of great interest to the plasma physics community, particularly in the context of magnetic reconnection. We are examining the formation and reconnection of current sheets in a simple, two-dimensional X-line configuration using high resolution particle-in-cell (PIC) simulations. The initial potential magnetic field is perturbed by thermal pressure introduced into the particle distribution far from the X line. The relaxation of this added stress leads to the development of a current sheet, which reconnects for imposed stress of sufficient strength. We compare the evolution and final state of our PIC simulations with magnetohydrodynamic simulations assuming both uniform and localized resistivities, and with force-free magnetic-field equilibria in which the amount of reconnect ion across the X line can be constrained to be zero (ideal evolution) or optimal (minimum final magnetic energy). We will discuss implications of our results for reconnection onset and cessation at kinetic scales in dynamically formed current sheets, such as those occurring in the terrestrial magnetotail and solar corona.

Black, C.↗
NASA NTRS2011

Current-Sheet Formation and Reconnection at a Magnetic X Line in Particle-in-Cell Simulations

The integration of kinetic effects into macroscopic numerical models is currently of great interest to the heliophysics community, particularly in the context of magnetic reconnection. Reconnection governs the large-scale energy release and topological rearrangement of magnetic fields in a wide variety of laboratory, heliophysical, and astrophysical systems. We are examining the formation and reconnection of current sheets in a simple, two-dimensional X-line configuration using high-resolution particle-in-cell (PIC) simulations. The initial minimum-energy, potential magnetic field is perturbed by excess thermal pressure introduced into the particle distribution function far from the X line. Subsequently, the relaxation of this added stress leads self-consistently to the development of a current sheet that reconnects for imposed stress of sufficient strength. We compare the time-dependent evolution and final state of our PIC simulations with macroscopic magnetohydrodynamic simulations assuming both uniform and localized electrical resistivities (C. R. DeVore et al., this meeting), as well as with force-free magnetic-field equilibria in which the amount of reconnection across the X line can be constrained to be zero (ideal evolution) or optimal (minimum final magnetic energy). We will discuss implications of our results for understanding magnetic-reconnection onset and cessation at kinetic scales in dynamically formed current sheets, such as those occurring in the solar corona and terrestrial magnetotail.

Black, C.↗
DOE OSTI2022

Systems and methods for tensor scheduling

A technique for efficient scheduling of operations in a program for parallelized execution thereof using a multi-processor runtime environment having two or more processors includes constraining the type or number of loop optimization transforms that may be explored such that memory and processing capacity available for the scheduling task are not exceeded, while facilitating a tradeoff between memory locality, parallelization, and/or data communication between memory modules of the multi-processor runtime environment.

Meister, Benoit J.↗
NASA NTRS1981

Interplanetary trajectory optimization

A procedure for minimizing total impulsive Delta-V for constrained multiple-flyby trajectories, which was originally developed for application to satellite tours, has been modified for application to interplanetary trajectories. The modification includes adding to the cost function the Delta-V required to escape from a parking orbit about the launch planet and the Delta-V required for insertion into orbit about the arrival planet. The hyperbolic excess velocity vector with respect to the launch planet and the launch date have been added to the set of independent variables for the optimization. Each trajectory originates at departure from the parking orbit rather than at a fixed position in space, as is the case for the satellite tour application. The multi-conic trajectory propagation techniques and the Newton optimization algorithm of the original method have been retained. Examples of the application of this new method are given for several types of Galileo interplanetary trajectory options, including Mars powered flyby, broken plane, VEGA, and Delta VEGA trajectories.

Damario, L. A.↗