SEARCH · Search NASA

Results for “multidimensional array”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

23 records · Page 2

All sources NASA NTRS DOE OSTI

NASA NTRS2020

RainCube and its legacy for the next generation of spaceborne cloud and precipitation radars

The multidimensional challenge of observing cloud and precipitation processes from space has motivated and focused a number of technological developments in the last decade. They span from novel compact instrument architectures to modular millimeter wave phased array solutions. In this paper we summarize the main capabilities offered by three instruments from this new generation, and discuss how they affect formulation of future observing systems.

Rodriguez-Monje, Raquel↗

NASA NTRS2026

Mathematical Model of a Regenerative Fuel Cell for System Optimization

This thesis developed a system-level optimization model of a regenerative fuel cell (RFC) system for long-duration, off-world energy storage applications. Prior RFC design studies have typically been limited to reduced parameter sets and simplified constraints due to computational limitations relative to the number of relevant degrees of freedom. As a result, important nonlinear interactions between subsystems have not been fully captured. This work began to address that gap by developing a higher-fidelity, nonlinear optimization framework that incorporates a broader set of design variables and coupled constraints, enabling a multidimensional model that captures the coupled behavior of RFC subsystems and demonstrates the feasibility of applying optimization to such systems. An expanded system-level optimization approach was established that captures interactions between electrochemical performance, structural requirements, and storage design. This enabled a more comprehensive evaluation of trade-offs than conventional formulations. The model integrates four coupled subsystems: a fuel cell, an electrolyzer, reactant gas, and high-pressure storage tanks, and was formulated to accommodate a wide range of mission parameters, including operational time and required output power. It incorporates constraints on available solar array power, reactant mass balance between production and consumption, and pressure-dependent storage requirements. To enable reliable convergence, the optimization problem was reformulated to reduce dimensionality and improve numerical stability, with subsystem models organized for efficient evaluation. Problem dimensionality was reduced by consolidating lower-level design variables into higher-level representative quantities, and subsystem behavior was evaluated within the optimization loop. A multi-start initialization strategy was employed to mitigate sensitivity to local minima and improve solution quality, while nonlinear relationships were solved using robust numerical methods. The results showed that convergence was achieved across a range of required output power values. Specific energy reached a maximum at a critical mission power level, where the electrolyzer power matched the available solar input and operated near its voltage and current density limits. Beyond this point, further increases in required power resulted in less mass-efficient operation, increasing total system mass and reducing overall performance. The developed model represents an advancement in RFC system-level optimization by enabling analysis of a broader and more tightly coupled design space than previous considerations. While convergence behavior and computational cost remain challenges, the methods introduced improve solvability and allow inclusion of additional design variables with minimal loss of physical fidelity. However, the numerical results should not be interpreted as definitive design recommendations, as the model includes simplifying assumptions and omits several higher-order effects. Future work should extend this framework by incorporating additional subsystems and loss mechanisms, such as thermal management, parasitic power consumption, and reactant losses, to improve fidelity and ensure more representative design conclusions.

Electrochemistry↗

NASA NTRSDate not supplied

NASA’s Earth System Observatory Formulation Progress

The 2017 Earth Science Decadal Survey by the National Academies of Science, Engineering, and Medicine identified five foundational observations, or Designated Observables (DO), to be implemented as missions by NASA. To address these five DO areas – Surface Biology and Geology; Mass Change; Aerosols; Cloud, Convection, and Precipitation; and Surface Deformation and Change – NASA is developing the Earth System Observatory (ESO) as an array of Earth-focused, interconnected satellite missions. The ESO will build on the strong history of international partnerships in NASA Earth science, with initial participation and collaboration on these missions across space agency partners including the Japanese Aerospace Exploration Agency (JAXA), Centre National D'Etudes Spatiales (CNES), Canadian Space Agency (CSA), Deutsches Zentrum für Luft- und Raumfahrt (DLR), and Agenzia Spaziale Italiana (ASI). Targeting launch dates in the late 2020s and early 2030s, the ESO will work as a single observatory, with each satellite in the ESO delivering its own valuable information, but taken together, the data and imagery will provide the global community with a multidimensional, holistic view of the Earth. In addition to furthering the world’s scientific knowledge, data gathered by ESO missions will provide actionable science to inform decisions related to climate change, disaster mitigation, and wildfires, improve real-time agricultural processes, and many other applications. This paper will provide an overview of the ESO development activities, as well as an update on the individual ESO missions currently in formulation – Atmosphere Observing System, Gravity Recovery and Climate Experiment-Continuity, and Surface Biology and Geology – including current mission architectures and concepts, international partner collaboration, science community engagement, and applications efforts, as well as how ESO data will be freely open and accessible to users all over the world.

NASA Earth Science↗

NASA NTRS1992

Adaptation of multidimensional group particle tracking and particle wall-boundary condition model to the FDNS code

A particulate two-phase flow CFD model was developed based on the FDNS code which is a pressure based predictor plus multi-corrector Navier-Stokes flow solver. Turbulence models with compressibility correction and the wall function models were employed as submodels. A finite-rate chemistry model was used for reacting flow simulation. For particulate two-phase flow simulations, a Eulerian-Lagrangian solution method using an efficient implicit particle trajectory integration scheme was developed in this study. Effects of particle-gas reaction and particle size change to agglomeration or fragmentation were not considered in this investigation. At the onset of the present study, a two-dimensional version of FDNS which had been modified to treat Lagrangian tracking of particles (FDNS-2DEL) had already been written and was operational. The FDNS-2DEL code was too slow for practical use, mainly because it had not been written in a form amenable to vectorization on the Cray, nor was the full three-dimensional form of FDNS utilized. The specific objective of this study was to reorder to calculations into long single arrays for automatic vectorization on the Cray and to implement the full three-dimensional version of FDNS to produce the FDNS-3DEL code. Since the FDNS-2DEL code was slow, a very limited number of test cases had been run with it. This study was also intended to increase the number of cases simulated to verify and improve, as necessary, the particle tracking methodology coded in FDNS.

Chen, Y. S.↗

NASA NTRS1994

VLSI neuroprocessors

Electronic and optoelectronic hardware implementations of highly parallel computing architectures address several ill-defined and/or computation-intensive problems not easily solved by conventional computing techniques. The concurrent processing architectures developed are derived from a variety of advanced computing paradigms including neural network models, fuzzy logic, and cellular automata. Hardware implementation technologies range from state-of-the-art digital/analog custom-VLSI to advanced optoelectronic devices such as computer-generated holograms and e-beam fabricated Dammann gratings. JPL's concurrent processing devices group has developed a broad technology base in hardware implementable parallel algorithms, low-power and high-speed VLSI designs and building block VLSI chips, leading to application-specific high-performance embeddable processors. Application areas include high throughput map-data classification using feedforward neural networks, terrain based tactical movement planner using cellular automata, resource optimization (weapon-target assignment) using a multidimensional feedback network with lateral inhibition, and classification of rocks using an inner-product scheme on thematic mapper data. In addition to addressing specific functional needs of DOD and NASA, the JPL-developed concurrent processing device technology is also being customized for a variety of commercial applications (in collaboration with industrial partners), and is being transferred to U.S. industries. This viewgraph p resentation focuses on two application-specific processors which solve the computation intensive tasks of resource allocation (weapon-target assignment) and terrain based tactical movement planning using two extremely different topologies. Resource allocation is implemented as an asynchronous analog competitive assignment architecture inspired by the Hopfield network. Hardware realization leads to a two to four order of magnitude speed-up over conventional techniques and enables multiple assignments, (many to many), not achievable with standard statistical approaches. Tactical movement planning (finding the best path from A to B) is accomplished with a digital two-dimensional concurrent processor array. By exploiting the natural parallel decomposition of the problem in silicon, a four order of magnitude speed-up over optimized software approaches has been demonstrated.

Kemeny, Sabrina E.↗