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At least 505 records · Page 28

Development of X-33/X-34 Aerothermodynamic Data Bases: Lessons Learned and Future Enhancements

A synoptic of programmatic and technical lessons learned in the development of aerothermodynamic data bases for the X-33 and X-34 programs is presented in general terms and from the perspective of the NASA Langley Research Center Aerothermodynamics Branch. The format used is that of the "aerothermodynamic chain," the links of which are personnel, facilities, models/test articles, instrumentation, test techniques, and computational fluid dynamics (CFD). Because the aerodynamic data bases upon which the X-33 and X-34 vehicles will fly are almost exclusively from wind tunnel testing, as opposed to CFD, the primary focus of the lessons learned is on ground-based testing. The period corresponding to the development of X-33 and X-34 aerothermodynamic data bases was challenging, since a number of other such programs (e.g., X-38, X-43) competed for resources at a time of downsizing of personnel, facilities, etc., outsourcing, and role changes as NASA Centers served as subcontractors to industry. The impact of this changing environment is embedded in the lessons learned. From a technical perspective, the relatively long times to design and fabricate metallic force and moment models, delays in delivery of models, and a lack of quality assurance to determine the fidelity of model outer mold lines (OML) prior to wind tunnel testing had a major negative impact on the programs. On the positive side, the application of phosphor thermography to obtain global, quantitative heating distributions on rapidly fabricated ceramic models revolutionized the aerothermodynamic optimization of vehicle OMLs, control surfaces, etc. Vehicle designers were provided with aeroheating information prior to, or in conjunction with, aerodynamic information early in the program, thereby allowing trades to be made with both sets of input; in the past only aerodynamic data were available as input. Programmatically, failure to include transonic aerodynamic wind tunnel tests early in the assessment phase led to delays in the optimization phase, as OMLs required modification to provide adequate transonic aerodynamic performance without sacrificing subsonic and hypersonic performance. Funding schedules for industry, based on technical milestones, also presented challenges to aerothermodynamics seeking optimum flying characteristics across the subsonic to hypersonic speed regimes and minimum aeroheating. This paper is concluded with a brief discussion of enhancements in ground-based testing/CFD capabilities necessary to partially/fully satisfy future requirements.

Miller, C. G.↗

Estimating the Inertia Matrix of a Spacecraft

A paper presents a method of utilizing some flight data, aboard a spacecraft that includes reaction wheels for attitude control, to estimate the inertia matrix of the spacecraft. The required data are digitized samples of (1) the spacecraft attitude in an inertial reference frame as measured, for example, by use of a star tracker and (2) speeds of rotation of the reaction wheels, the moments of inertia of which are deemed to be known. Starting from the classical equations for conservation of angular momentum of a rigid body, the inertia-matrix-estimation problem is formulated as a constrained least-squares minimization problem with explicit bounds on the inertia matrix incorporated as linear matrix inequalities. The explicit bounds reflect physical bounds on the inertia matrix and reduce the volume of data that must be processed to obtain a solution. The resulting minimization problem is a semidefinite optimization problem that can be solved efficiently, with guaranteed convergence to the global optimum, by use of readily available algorithms. In a test case involving a model attitude platform rotating on an air bearing, it is shown that, relative to a prior method, the present method produces better estimates from few data.

Acikmese, Behcet↗

Stationkeeping of Lissajous Trajectories in the Earth-Moon System with Applications to ARTEMIS

In the last few decades, several missions have successfully exploited trajectories near the.Sun-Earth L1 and L2 libration points. Recently, the collinear libration points in the Earth-Moon system have emerged as locations with immediate application. Most libration point orbits, in any system, are inherently unstable. and must be controlled. To this end, several stationkeeping strategies are considered for application to ARTEMIS. Two approaches are examined to investigate the stationkeeping problem in this regime and the specific options. available for ARTEMIS given the mission and vehicle constraints. (I) A baseline orbit-targeting approach controls the vehicle to remain near a nominal trajectory; a related global optimum search method searches all possible maneuver angles to determine an optimal angle and magnitude; and (2) an orbit continuation method, with various formulations determines maneuver locations and minimizes costs. Initial results indicate that consistent stationkeeping costs can be achieved with both approaches and the costs are reasonable. These methods are then applied to Lissajous trajectories representing a baseline ARTEMIS libration orbit trajectory.

Folta, D. C.↗

GLDAS-2 Land Surface Model Data and Data Services at NASA GES DISC

The goal of the NASA Global Land Data Assimilation System (GLDAS, https://ldas.gsfc.nasa.gov/gldas(https://ldas.gsfc.nasa.gov/gldas)) is to generate optimal fields of land surface states and fluxes by ingesting satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques (Rodell et al., 2004).The GLDAS dataset currently archived at and distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC, https://disc.gsfc.nasa.gov/ (https://disc.gsfc.nasa.gov/)) is GLDAS Version 2 (GLDAS-2). It contains a series of output fields from the upgraded Noah-3.6, Catchment-F2.5, and VIC-4.1.2 Land Surface Models (LSMs) in the Land Information System (LIS-V7, https://lis.gsfc.nasa.gov/ (https://lis.gsfc.nasa.gov/)). GLDAS-2 has three components:GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the upgraded Princeton Meteorological ForcingV2.2 Dataset and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data, with data spanning from 2000 to the present. The GLDAS-2.2 product suite uses data assimilation(DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, varies for different GLDAS-2.2 products. The currently availableGLDAS-2.2 data contain a daily 0.25-degree output from the Catchment-F2.5 LSM in LIS-V7. The data are forced with the meteorological analysis fields from the operational European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS) and assimilated with GRACE and GRACE-FO data, ranging from February 1, 2003 to the present. The current GLDAS-2.0 and 2.1 Noah LSM data were reprocessed in November 2019 and January 2020 respectively and their data from Catchment and VIC LSMs are new to the GLDAS-2 collection. This presentation provides a summary of theGLDAS-2 data products, their land surface fields, and their related data services at the GES DISC; and a description of the majorGLDAS-2 climatological characteristics as well as the intercomparison with the data of the previous version.

Hydrology↗

Merging Analytic Collaborative Frameworks with New Observing Strategies Toward a Digital Twin: Earth – Episodic Pulse Event Impacts on Ocean Carbon Cycle as an Example

Virtual representations of the Earth will allow us to address some of the most critical environmental issues of our time. Here, we show the first steps toward representation of riverine, estuarine, and coastal carbon processes to enable scenario driven “what-if” analyses of the carbon system and human footprint. Excess sediment and nutrient runoff from land-based human activities impact water quality and can pose serious threats to coastal and marine ecosystems. Episodic pulse events, such as extreme precipitation events, can increase the amount of nutrients entering estuaries and coastal regions, potentially leading to large phytoplankton blooms followed by anoxic conditions. Consequences of coastal runoff are predicted to increase with the higher intensity and frequency of extreme events. Beyond the threat to coastal ecosystems, recent findings suggest these episodic pulses might play a significant role for biological production influencing regional and global carbon fluxes and budgets. An improved understanding of these events through optimal, dynamic observing strategies will increase our knowledge of the land-ocean continuum and how regional events and nutrient fluxes affect the carbon cycle and ocean ecosystem. This conceptual framework enables focused science investigations by pairing data analytics and artificial intelligence tools (otherwise termed an Analytic Center Framework, ACF) with targeted measurement acquisition through distributed sensing and intelligent asset tasking (or New Observing Strategies, NOS). This NOS and ACF iterative approach acquires and integrates complementary and coincident satellite, in-situ and model data to build a more complete and in-depth picture of science phenomena. Specifically, Apache Science Data Analytic Platform (SDAP) is extended to incorporate relevant datasets for data access, harmonized analysis, and anomaly detection. When conditions are met for a likely pulse event, NASA’s D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) tool is triggered to optimize asset overpass frequency and schedule observations for persistent monitoring. Targeted data is ingested by SDAP for enhanced investigation via iterative analysis until the trigger criteria is no longer met - steps toward a digital twin.

Laura Rogers↗

Systems Analysis and Optimization of Circular PET Packaging Supply Chains in the United States: Environmental and Socioeconomic Impacts

Many actions are underway at global, national, and local levels to address the plastic waste problem and transition toward a circular economy of plastics. Studies evaluating environmental and socioeconomic impacts of such a transition are lacking. Here, the purpose of this study is to conduct a national systems analysis of polyethylene terephthalate (PET) packaging supply chains in the United States. Material flow data was combined with environmental and socioeconomic indicators to evaluate and compare the sustainability of the linear PET packaging supply chain, current (2019) supply chain, and possible future circular supply chain options in the United States. Environmentally optimal circular US PET packaging material flows showed 31% and 38% savings of GHG emissions and energy demand, respectively, with a circularity of 77% when compared with a linear supply chain. Additionally, the environmentally optimal system showed higher employment (29%) and wages (31%) than a linear system, but with a 5% decrease in revenue generation. A socioeconomically optimal circular PET supply chain showed increased employment (by 52%), wages (by 67%), and revenues (by 1%), with a circularity of 59% when compared with the linear system. However, it showed 14% higher GHG emissions than a linear system, indicating a trade-off between environmentally and socioeconomically optimal circular PET packaging systems. Overall, linear-to-circular material flow transition may not necessarily lead to increased revenues and decreased environmental impacts of the entire system, but it does benefit society due to increased employment and wages. Future systems analysis work should focus on improving data quality for environmental and socioeconomic dimensions.

PET↗

Implementing a unified solver for nonlinearly constrained optimization

SQP and interior-point methods (also referred to as Lagrange-Newton methods) typically share key algorithmic components, such as strategies for computing descent directions and mechanisms that promote global convergence. Building on this insight, we introduce a unifying framework with eight building blocks that abstracts the workflows of Lagrange-Newton methods. We then present Uno, a modular C++ solver that implements our unifying framework and allows the automatic combination of a wide range of strategies with no programming effort from the user. Uno is meant to (1) organize mathematical optimization strategies into a coherent hierarchy; (2) offer a wide range of efficient and robust methods that can be compared for a given instance; (3) enable researchers to experiment with novel optimization strategies; and (4) reduce the cost of development and maintenance of multiple optimization solvers. Uno’s software design allows user to compose new customized solvers for emerging optimization areas such as robust optimization or optimization problems with complementarity constraints, while building on reliable nonlinear optimization techniques. We demonstrate that Uno is highly competitive against state-of-the-art solvers filterSQP, IPOPT, SNOPT, MINOS, LANCELOT, LOQO, and CONOPT on a subset of 429 small problems from the CUTE collection. Uno is available as open-source software under the MIT license at https://github.com/cvanaret/Uno and via its C, Julia, Python, Fortran, and AMPL interfaces.

97 MATHEMATICS AND COMPUTING↗

Constraining Human Contributions to Observed Warming Since the Pre-industrial Period

Parties to the Paris Agreement agreed to holding global average temperature increases “well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels”. Monitoring the contributions of human-induced climate forcings to warming so far is key to understanding progress towards these goals. Here we use climate model simulations from the Detection and Attribution Model Intercomparison Project, as well as regularized optimal fingerprinting, to show that anthropogenic forcings caused 0.9 to 1.3 °C of warming in global mean near-surface air temperature in 2010–2019 relative to 1850–1900, compared with an observed warming of 1.1 °C. Greenhouse gases and aerosols contributed changes of 1.2 to 1.9 °C and −0.7 to −0.1 °C, respectively, and natural forcings contributed negligibly. These results demonstrate the substantial human influence on climate so far and the urgency of action needed to meet the Paris Agreement goals.

Attribution↗

Distributed quantum approximate optimization algorithm on a quantum-centric supercomputing architecture

Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than are necessary to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1000-bit problem), achieving a high solution quality and short time-to-solution, outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA’s applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.

Kim, Seongmin [ORNL] (ORCID:0000000159063004)↗

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗

Single step optimization of feedback-decoupled spacecraft attitude maneuvers

A next-state optimization technique for monitoring spacecraft maneuvers is examined. The optimization technique permits simultaneous trajectory planning and tracking, incorporates actuator saturation and attitude constraints, and prevents overshoot and undershoot. The global linearization of the dynamics and kinematics for a spacecraft driven by reaction wheels is considered. The equations utilized to derive the spacecraft trajectories are presented. The applications of the next-state optimization procedure to a spacecraft maneuvered by momentum transfer devices and the tracking of the nominal trajectory of a critically damped harmonic oscillator response are described.

Dwyer, T. A. W., III↗

NASA's Planned Return to the Moon: Global Access and Anytime Return Requirement Implications on the Lunar Orbit Insertion Burns

Lunar orbit insertion LOI is a critical maneuver for any mission going to the Moon. Optimizing the geometry of this maneuver is crucial to the success of the architecture designed to return humans to the Moon. LOI burns necessary to meet current NASA Exploration Constellation architecture requirements for the lunar sortie missions are driven mainly by the requirement for global access and "anytime" return from the lunar surface. This paper begins by describing the Earth-Moon geometry which creates the worst case (delta)V for both the LOI and the translunar injection (TLI) maneuvers over the full metonic cycle. The trajectory which optimizes the overall (delta)V performance of the mission is identified, trade studies results covering the entire lunar globe are mapped onto the contour plots, and the effects of loitering in low lunar orbit as a means of reducing the insertion (delta)V are described. Finally, the lighting conditions on the lunar surface are combined with the LOI and TLI analyses to identify geometries with ideal lighting conditions at sites of interest which minimize the mission (delta)V.

Garn, Michelle↗

Optimization of a Parallel Ocean General Circulation Model

One of the principal roles of the ocean in the global heat balance is storage of heat, which moderates seasonal extremes and leads to the contrast between ocean and continent temperatures.

3-dimensional ocean general circulation models (OG↗

Development of Multi-Sensor Global Cloud and Radiance Composites for Earth Radiation Budget Monitoring from DSCOVR

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-kilometer resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95 percent of the globe.

Khlopenkov, Konstantin↗

Machine Learning Techniques in Optimal Design

Many important applications can be formalized as constrained optimization tasks. For example, we are studying the engineering domain of two-dimensional (2-D) structural design. In this task, the goal is to design a structure of minimum weight that bears a set of loads. A solution to a design problem in which there is a single load (L) and two stationary support points (S1 and S2) consists of four members, E1, E2, E3, and E4 that connect the load to the support points is discussed. In principle, optimal solutions to problems of this kind can be found by numerical optimization techniques. However, in practice [Vanderplaats, 1984] these methods are slow and they can produce different local solutions whose quality (ratio to the global optimum) varies with the choice of starting points. Hence, their applicability to real-world problems is severely restricted. To overcome these limitations, we propose to augment numerical optimization by first performing a symbolic compilation stage to produce: (a) objective functions that are faster to evaluate and that depend less on the choice of the starting point and (b) selection rules that associate problem instances to a set of recommended solutions. These goals are accomplished by successive specializations of the problem class and of the associated objective functions. In the end, this process reduces the problem to a collection of independent functions that are fast to evaluate, that can be differentiated symbolically, and that represent smaller regions of the overall search space. However, the specialization process can produce a large number of sub-problems. This is overcome by deriving inductively selection rules which associate problems to small sets of specialized independent sub-problems. Each set of candidate solutions is chosen to minimize a cost function which expresses the tradeoff between the quality of the solution that can be obtained from the sub-problem and the time it takes to produce it. The overall solution to the problem, is then obtained by solving in parallel each of the sub-problems in the set and computing the one with the minimum cost. In addition to speeding up the optimization process, our use of learning methods also relieves the expert from the burden of identifying rules that exactly pinpoint optimal candidate sub-problems. In real engineering tasks it is usually too costly to the engineers to derive such rules. Therefore, this paper also contributes to a further step towards the solution of the knowledge acquisition bottleneck [Feigenbaum, 1977] which has somewhat impaired the construction of rulebased expert systems.

Cerbone, Giuseppe↗

A photovoltaic catenary-tent array for the Martian surface

To provide electrical power during an exploration mission to Mars, a deployable tent-shaped structure with a flexible photovoltaic (PV) blanket is proposed. The array is designed with a self-deploying mechanism utilizing pressurized gas expansion. The structural design for the array uses a combination of cables, beams, and columns to support and deploy the PV blanket. Under the force of gravity, a cable carrying a uniform load will take the shape of a catenary curve. A catenary-tent collector is self shading which must be taken into account in the solar radiation calculation. The shape and the area of the shadow on the array was calculated and used in the determination of the global irradiance on the array. The PV blanket shape and structure dimensions were optimized to achieve a configuration which maximizes the specific power (W/kg). The optimization was performed for three types of PV blankets (silicon, gallium arsenide over germanium, and amorphous silicon) and two types of structural materials (carbon composite and arimid fiber composite). The results show that the catenary shape of the PV blanket corresponding to zero end angle at the base with respect to the horizontal results in the highest specific power. The tent angle is determined by optimizing the specific mass and the output power for maximum specific power. The combination of carbon fiber structural material and amorphous silicon blanket produces the highest specific power.

Colozza, Anthony J.↗

A photovoltaic catenary-tent array for the Martian surface

To provide electrical power during an exploration mission to Mars, a deployable tent-shaped structure with a flexible photovoltaic (PV) blanket is proposed. The array is designed with a self-deploying mechanism utilizing pressurized gas expansion. The structural design for the array uses a combination of cables, beams, and columns to support and deploy the PV blanket. Under the force of gravity a cable carrying a uniform load will take the shape of a catenary curve. A catenary-tent collector is self shadowing which must be taken into account in the solar radiation calculation. The shape and the area of the shadow on the array was calculated and used in the determination of the global radiation on the array. The PV blanket shape and structure dimension were optimized to achieve a configuration which maximizes the specific power (W/kg). The optimization was performed for four types of PV blankets (Si, GaAs/Ge, GaAs CLEFT, and amorphous Si) and four types of structure materials (Carbon composite, Aramid Fiber composite, Aluminum, and Magnesium). The results show that the catenary shape of the PV blanket, which produces the highest specific power, corresponds to zero end angle at the base with respect to the horizontal. The tent angle is determined by the combined effect of the array structure specific mass and the PV blanket output power. The combination of carbon composite structural material and GaAs CLEFT solar cells produce the highest specific power. The study was carried out for two sites on Mars corresponding to the Viking Lander locations. The designs were also compared for summer, winter, and yearly operation.

Crutchik, M.↗

Stereo imaging based particle velocimeter

Three dimensional coordinates of an object are determined from it's two dimensional images for a class of points on the object. Two dimensional images are first filtered by a Laplacian of Gaussian (LOG) filter in order to detect a set of feature points on the object. The feature points on the left and the right images are then matched using a Hopfield type optimization network. The performance index of the Hopfield network contains both local and global properties of the images. Parallel computing in stereo matching can be achieved by the proposed methodology.

Batur, Celal↗