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NASA NTRS2019

Retrieval of Vegetation Water Content Using Brightness Temperatures from the Soil Moisture Active Passive (SMAP) Mission

In this paper, we explore a time series approach to using the tau-omega (τ-ω) model to retrieve vegetation water content (kg/m2) with minimal use of ancillary data. Analytically, this approach calls for nonlinear optimization in two steps. First, multiple days of co-located brightness temperature observations are used to retrieve the effective vegetation opacity, which incorporates the combined radiometric and polarization effects of surface roughness and vegetation opacity. The resulting effective vegetation opacity is then used to retrieve vegetation water content to within a gain factor α and an offset factor β. By using a climatological vegetation water content ancillary database as the one adopted in the development of the SMAP standard and enhanced soil moisture products, α and β can be determined globally using the annual minimum and annual maximum of vegetation water content. The resulting values of α and β can then be used to reconstruct the retrieved vegetation water content. Formulation, assumptions, and limitations of this approach are presented alongside the preliminary global retrieval of vegetation water content using one year (2016) ofSMAP brightness temperature observations.

radiative transfer↗

DOE OSTI2024

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗

NASA NTRS1994

A new look at the simultaneous analysis and design of structures

The minimum weight optimization of structural systems, subject to strength and displacement constraints as well as size side constraints, was investigated by the Simultaneous ANalysis and Design (SAND) approach. As an optimizer, the code NPSOL was used which is based on a sequential quadratic programming (SQP) algorithm. The structures were modeled by the finite element method. The finite element related input to NPSOL was automatically generated from the input decks of such standard FEM/optimization codes as NASTRAN or ASTROS, with the stiffness matrices, at present, extracted from the FEM code ANALYZE. In order to avoid ill-conditioned matrices that can be encountered when the global stiffness equations are used as additional nonlinear equality constraints in the SAND approach (with the displacements as additional variables), the matrix displacement method was applied. In this approach, the element stiffness equations are used as constraints instead of the global stiffness equations, in conjunction with the nodal force equilibrium equations. This approach adds the element forces as variables to the system. Since, for complex structures and the associated large and very sparce matrices, the execution times of the optimization code became excessive due to the large number of required constraint gradient evaluations, the Kreisselmeier-Steinhauser function approach was used to decrease the computational effort by reducing the nonlinear equality constraint system to essentially a single combined constraint equation. As the linear equality and inequality constraints require much less computational effort to evaluate, they were kept in their previous form to limit the complexity of the KS function evaluation. To date, the standard three-bar, ten-bar, and 72-bar trusses have been tested. For the standard SAND approach, correct results were obtained for all three trusses although convergence became slower for the 72-bar truss. When the matrix displacement method was used, correct results were still obtained, but the execution times became excessive due to the large number of constraint gradient evaluations required. Using the KS function, the computational effort dropped, but the optimization seemed to become less robust. The investigation of this phenomenon is continuing. As an alternate approach, the code MINOS for the optimization of sparse matrices can be applied to the problem in lieu of the Kreisselmeier-Steinhauser function. This investigation is underway.

Striz, Alfred G.↗

NASA NTRS1997

Satellite Sensed Skin Sea Surface Temperature

Quantitative predictions of spatial and temporal changes the global climate rely heavily on the use of computer models. Unfortunately, such models cannot provide the basis for climate prediction because key physical processes are inadequately treated. Consequently, fine tuning procedures are often used to optimize the fit between model output and observational data and the validation of climate models using observations is essential if model based predictions of climate change are to be treated with any degree of confidence. Satellite Sea Surface Temperature (SST) observations provide high spatial and temporal resolution data which is extremely well suited to the initialization, definition of boundary conditions and, validation of climate models. In the case of coupled ocean-atmosphere models, the SST (or more correctly the 'Skin' SST (SSST)) is a fundamental diagnostic variable to consider in the validation process. Daily global SST maps derived from satellite sensors also provide adequate data for the detection of global patterns of change which, unlike any other SST data set, repeatedly extend into the southern hemisphere extra-tropical regions. Such data are essential to the success of the spatial 'fingerprint' technique, which seeks to establish a north-south asymmetry where warming is suppressed in the high latitude Southern Ocean. Some estimates suggest that there is a greater than 80% chance of directly detecting significant change (97.5 % confidence level) after 10-12 years of consistent global observations of mean sea surface temperature. However, these latter statements should be qualified with the assumption that a negligible drift in the observing system exists and that biases between individual instruments required to derive a long term data set are small. Given that current estimates for the magnitude of global warming of 0.015 K yr(sup -1) - 0.025 K yr(sup -1), satellite SST data sets need to be both accurate and stable if such a warming trend is to be confidently detected. Some of these activities are focussed to develop and deploy instrumentation suitable for the collection of precise in situ measurements of the SSST which can be used to improve the accuracy of satellite measurements, while others develop techniques to generate improved global analyses of sea surface temperature using historical data.

Donlon, Craig↗

NASA NTRS2023

The optimization of model ensemble composition and size can enhance the robustness of crop yield projections

Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.

Agriculture↗

NASA NTRS2016

Infrared Observations of the Orion Capsule During EFT-1 Hypersonic Reentry

High-resolution infrared observations of the Orion capsule during its atmospheric reentry on December 5, 2015 were made from a US Navy NP-3D. This aircraft, equipped with a long-range optical sensor system, tracked the capsule from Mach 10 to 7 from a distance of approximately 60 nmi. Global surface temperatures of the capsule's thermal heatshield were derived from near infrared intensity measurements. The global surface temperature measurements complemented onboard instrumentation and were invaluable to the interpretation of the in-depth thermocouple measurements which rely on inverse heat transfer methods and material response codes to infer the desired surface temperature from the sub-surface measurements. The full paper will address the motivations behind the NASA Engineering Safety Center sponsored observation and highlight premission planning processes with an emphasis on aircraft placement, optimal instrument configuration and sensor calibrations. Critical aspects of mission operations coordinated from the NASA Johnson Spaceflight Center and integration with the JSC Flight Test Management Office will be discussed. A summary of the imagery that was obtained and processed to global surface temperature will be presented. At the capsule's point of closest approach relative to the imaging system, the spatial resolution was estimated to be approximately 15-inches per pixel and was sufficient to identify localized temperature increases associated with compression pad support hardware on the heatshield. The full paper will discuss the synergy of the quantitative imagery derived temperature maps with in-situ thermocouple measurements. Comparison of limited onboard surface thermocouple data to the image derived surface temperature will be presented. The two complimentary measurements serve as an example of the effective leveraging of resources to advance the understanding of high Mach number environments associated with an ablated heatshield and provide unique data for the validation of design tools and numerical flight simulation techniques. Collaborative opportunities and technology investments in support of planned observations of NASA's next Orion flight test in 2018 will be explored in the full manuscript.

Horvath, Thomas J.↗

DOE OSTI2026

BLEECAM™ (Benchmarking Life Cycle Environmental, Economic, and Social Metrics for Critical and Advanced Minerals and Materials) [SWR-25-125]

The National Laboratory of the Rockies' (NLR) Benchmarking Life Cycle Environmental, Economic, and Social Metrics for Critical and Advanced Minerals and Materials (BLEECAM™) is an open-source, integrated decision-support tool for evaluating the impacts, risks, and trade-offs across U.S. and global materials supply chains. Funded by the U.S. Department of Energy, BLEECAM supports supply chain and market analysis. The tool integrates multi-objective supply chain optimization, system dynamics, network design, lifecycle assessment, techno-economic modeling, and social impact assessment methods to evaluate how supply chains evolve over time, geography, and deployment scenarios. BLEECAM also supports analysis related to energy infrastructure, data centers and digital infrastructure, advanced manufacturing, and other sectors that depend on critical materials.

Khalifa, SherifA. [National Laboratory of the Rock↗

NASA NTRS1982

An automated approach to the design of decision tree classifiers

An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

Argentiero, P.↗

NASA NTRS1984

On the utilization of engineering knowledge in design optimization

Some current research work conducted at the University of Michigan is described to illustrate efforts for incorporating knowledge in optimization in a nontraditional way. The incorporation of available knowledge in a logic structure is examined in two circumstances. The first examines the possibility of introducing global design information in a local active set strategy implemented during the iterations of projection-type algorithms for nonlinearly constrained problems. The technique used algorithms for nonlinearly constrained problems. The technique used combines global and local monotinicity analysis of the objective and constraint functions. The second examines a knowledge-based program which aids the user to create condigurations that are most desirable from the manufacturing assembly viewpoint. The data bank used is the classification scheme suggested by Boothroyd. The important aspect of this program is that it is an aid for synthesis intended for use in the design concept phase in a way similar to the so-called idea-triggers in creativity-enhancement techniques like brain-storming. The idea generation, however, is not random but it is driven by the goal of achieving the best acceptable configuration.

Papalambros, P.↗

NASA NTRS2017

Reconfigurable Model Execution in the OpenMDAO Framework

NASA's OpenMDAO framework facilitates constructing complex models and computing their derivatives for multidisciplinary design optimization. Decomposing a model into components that follow a prescribed interface enables OpenMDAO to assemble multidisciplinary derivatives from the component derivatives using what amounts to the adjoint method, direct method, chain rule, global sensitivity equations, or any combination thereof, using the MAUD architecture. OpenMDAO also handles the distribution of processors among the disciplines by hierarchically grouping the components, and it automates the data transfer between components that are on different processors. These features have made OpenMDAO useful for applications in aircraft design, satellite design, wind turbine design, and aircraft engine design, among others. This paper presents new algorithms for OpenMDAO that enable reconfigurable model execution. This concept refers to dynamically changing, during execution, one or more of: the variable sizes, solution algorithm, parallel load balancing, or set of variables-i.e., adding and removing components, perhaps to switch to a higher-fidelity sub-model. Any component can reconfigure at any point, even when running in parallel with other components, and the reconfiguration algorithm presented here performs the synchronized updates to all other components that are affected. A reconfigurable software framework for multidisciplinary design optimization enables new adaptive solvers, adaptive parallelization, and new applications such as gradient-based optimization with overset flow solvers and adaptive mesh refinement. Benchmarking results demonstrate the time savings for reconfiguration compared to setting up the model again from scratch, which can be significant in large-scale problems. Additionally, the new reconfigurability feature is applied to a mission profile optimization problem for commercial aircraft where both the parametrization of the mission profile and the time discretization are adaptively refined, resulting in computational savings of roughly 10% and the elimination of oscillations in the optimized altitude profile.

Hwang, John T.↗

NASA NTRS1989

Optimized orbits for the constellation of Space Station polar platforms

The orbital configuration of the Eos polar-platform constellation is examined to determine if baseline orbital parameters are appropriately specified to meet the mission task of measuring global oceanic mesoscale variability. Using the Nyquist sampling theorem, spatial and temporal measurement requirements are first established for the constellation. Based upon these requirements, the optimum solution indicates that a total of five spacecraft equipped with nadir-pointing altimeters, or two multibeam-equipped platforms, would be required to meet the mesoscale mapping task. Additional analyses were performed on both one- and and two-platform configurations as well as the complete four-platform Eos constellation. The results of these calculations show that the preferred scenario would be to equip all four Eos polar platforms with multibeam altimeters.

Chase, Robert↗

DOE OSTI2025

Uncertainty propagation and sensitivity analysis for constrained optimization of nuclear waste vitrification

Abstract The vitrification of high‐level waste (HLW) by heating a mixture of glass‐forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization algorithm that requires derivative information. Other UP methods often used for optimization under uncertainty (OUU) can be designed to work within an established constrained optimization framework. Methods of UP are evaluated in this study including iterative sampling approaches, first‐order approximations, and surrogate modeling with machine learning (ML). A method of dimensional reduction based on global sensitivity analysis is introduced to support the UP methods for the large dimensionality of the problem. Analytical UP methods able to achieve similar optimums 10 times faster than the baseline MC approach, and produce 93.9% similar output distributions are reported.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2000

A Hybrid Satellite-Terrestrial Approach to Aeronautical Communication Networks

Rapid growth in air travel has been projected to continue for the foreseeable future. To maintain a safe and efficient national and global aviation system, significant advances in communications systems supporting aviation are required. Satellites will increasingly play a critical role in the aeronautical communications network. At the same time, current ground-based communications links, primarily very high frequency (VHF), will continue to be employed due to cost advantages and legacy issues. Hence a hybrid satellite-terrestrial network, or group of networks, will emerge. The increased complexity of future aeronautical communications networks dictates that system-level modeling be employed to obtain an optimal system fulfilling a majority of user needs. The NASA Glenn Research Center is investigating the current and potential future state of aeronautical communications, and is developing a simulation and modeling program to research future communications architectures for national and global aeronautical needs. This paper describes the primary requirements, the current infrastructure, and emerging trends of aeronautical communications, including a growing role for satellite communications. The need for a hybrid communications system architecture approach including both satellite and ground-based communications links is explained. Future aeronautical communication network topologies and key issues in simulation and modeling of future aeronautical communications systems are described.

Kerczewski, Robert J.↗

DOE OSTI2026

Biocatalyst discovery and design for plastics deconstruction: A multi‐scale perspective

Plastic waste accumulation poses significant environmental challenges due to a lack of economical solutions for the molecular deconstruction of diverse synthetic polymers. Biological‐based degradation offers promise but is hindered by the crystallinity, hydrophobicity, and additive complexity of plastics, which restrict biocatalyst access and activity. To address these problems, we propose a multi‐scale framework that combines detailed materials characterization, optimization of plastic‐biomolecular interfacial interactions, and enhancement of biocatalytic kinetics to develop effective plastic‐deconstructing enzymes. This approach leverages principles from reaction kinetics, transport and interfacial phenomena, and enzyme engineering to systematically address barriers across diverse plastic types. Our framework aims to accelerate the discovery and optimization of biocatalysts capable of scalable, selective, and efficient deconstruction of plastic waste. These advances hold potential to enable sustainable biological recycling and upcycling pathways, contributing to global efforts in mitigating plastic pollution and promoting circular material economies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1986

Equivalent plate analysis of aircraft wing box structures with general planform geometry

A new equilvalent plate analysis formulation is described which is capable of modeling aircraft wing structures with a general planform such as cranked wing boxes. Multiple trapezoidal segments are used to represent such planforms. A Ritz solution technique is used in conjunction with global displacement functions which encompass all the segments. This Ritz solution procedure is implemented efficiently into a computer program so that it can be used by rigorous optimization algorithms for application in early preliminary design. A direct method to interface this structural analysis procedure with aerodynamic programs for use in aeroelastic calculations is described. This equivalent plate analysis procedure is used to calculate the static deflections and stresses and vibration frequencies and modes of an example wing configuration. The numerical results are compared with results from a finite element model of the same configuration to illustrate typical levels of accuracy and computation times resulting from use of this procedure.

Giles, G. L.↗

NASA NTRS1986

Equivalent plate analysis of aircraft wing box structures with general planform geometry

A new equivalent plate analysis formulation is described which is capable of modeling aircraft wing structures with a general planform such as cranked wing boxes. Multiple trapezoidal segments are used to represent such planforms. A Ritz solution technique is used in conjunction with global displacement functions which encompass all the segments. This Ritz solution procedure is implemented efficiently into a computer program so that it can be used by rigorous optimization algorithms for application in early preliminary design. A direct method to interface this structural analysis procedure with aerodynamic programs for use in aeroelastic calculations is described. This equivalent plate analysis procedure is used to calculate the static deflections and stresses and vibration frequencies and modes of an example wing configuration. The numerical results are compared with results from a finite element model of the same configuration to illustrate typical levels of accuracy and computation times resulting from use of this procedure.

Giles, G. L.↗

NASA NTRS1998

Comparison of Response Surface and Kriging Models for Multidisciplinary Design Optimization

In this paper, we compare and contrast the use of second-order response surface models and kriging models for approximating non-random, deterministic computer analyses. After reviewing the response surface method for constructing polynomial approximations, kriging is presented as an alternative approximation method for the design and analysis of computer experiments. Both methods are applied to the multidisciplinary design of an aerospike nozzle which consists of a computational fluid dynamics model and a finite-element model. Error analysis of the response surface and kriging models is performed along with a graphical comparison of the approximations, and four optimization problems m formulated and solved using both sets of approximation models. The second-order response surface models and kriging models-using a constant underlying global model and a Gaussian correlation function-yield comparable results.

Simpson, Timothy W.↗

NASA NTRS2021

Evaluation of Global Surface Water Temperature Data Sets for use in Passive Remote Sensing of Soil Moisture

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

Runze Zhang↗