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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

FY2019 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2019 focused on analytical research to understand the impacts that new mobility technologies and services will have at the vehicle, traveler, and overall transportation system-level. This research included the development of vehicle and transportation system simulation models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results for projects within four of the five EEMS activity areas: (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) High Performance Computing and Big Data Solutions for Mobility Data, (3) Advanced R&D Projects conducted by industry and academia, and (4) Core Modeling, Simulation, and Evaluation, Similarly, the remaining EEMS activity area – (5) Living Labs (managed under VTO’s Technology Integration Program). Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

FY2020 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2020 focused on analytical research and large-scale modeling and simulation to understand the impacts that new mobility technologies and services will have at the vehicle-, traveler-, and overall transportation system-level. This research included the development of a multi-fidelity, end-to-end transportation system models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results from projects within each of the EEMS activity areas. The Computational Modeling and Simulation key activity area summarizes work within the sub-areas of (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) Artificial Intelligence, High-Performance Computing, and Data Analytics, and (3) Core Simulation and Evaluation Tools. Additionally, the program’s advanced R&D projects are summarized within (4) the Connectivity and Automation Technology key activity area. Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
NASA NTRS1976

A new computer approach to mixed feature classification for forestry application

A computer approach for mapping mixed forest features (i.e., types, classes) from computer classification maps is discussed. Mixed features such as mixed softwood/hardwood stands are treated as admixtures of softwood and hardwood areas. Large-area mixed features are identified and small-area features neglected when the nominal size of a mixed feature can be specified. The computer program merges small isolated areas into surrounding areas by the iterative manipulation of the postprocessing algorithm that eliminates small connected sets. For a forestry application, computer-classified LANDSAT multispectral scanner data of the Sam Houston National Forest were used to demonstrate the proposed approach. The technique was successful in cleaning the salt-and-pepper appearance of multiclass classification maps and in mapping admixtures of softwood areas and hardwood areas. However, the computer-mapped mixed areas matched very poorly with the ground truth because of inadequate resolution and inappropriate definition of mixed features.

Kan, E. P.↗
NASA NTRS1989

Robot acting on moving bodies (RAMBO): Preliminary results

A robot system called RAMBO is being developed. It is equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a moving object. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations nearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enchancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows the use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using parametric cubic splines between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors.

Davis, Larry S.↗
NASA NTRS1989

Robot Acting on Moving Bodies (RAMBO): Interaction with tumbling objects

Interaction with tumbling objects will become more common as human activities in space expand. Attempting to interact with a large complex object translating and rotating in space, a human operator using only his visual and mental capacities may not be able to estimate the object motion, plan actions or control those actions. A robot system (RAMBO) equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a tumbling object, is being developed. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations rearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enhancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using dynamic interpolations between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors.

Davis, Larry S.↗
NASA NTRS1990

Applications of artificial intelligence to mission planning

The following subject areas are covered: object-oriented programming task; rule-based programming task; algorithms for resource allocation; connecting a Symbolics to a VAX; FORTRAN from Lisp; trees and forest task; software data structure conversion; software functionality modifications and enhancements; portability of resource allocation to a TI MicroExplorer; frontier of feasibility software system; and conclusions.

Ford, Donnie R.↗
NASA NTRS2013

Tool for Automated Retrieval of Generic Event Tracks (TARGET)

Methods have been developed to identify and track tornado-producing mesoscale convective systems (MCSs) automatically over the continental United States, in order to facilitate systematic studies of these powerful and often destructive events. Several data sources were combined to ensure event identification accuracy. Records of watches and warnings issued by National Weather Service (NWS), and tornado locations and tracks from the Tornado History Project (THP) were used to locate MCSs in high-resolution precipitation observations and GOES infrared (11-micron) Rapid Scan Operation (RSO) imagery. Thresholds are then applied to the latter two data sets to define MCS events and track their developments. MCSs produce a broad range of severe convective weather events that are significantly affecting the living conditions of the populations exposed to them. Understanding how MCSs grow and develop could help scientists improve their weather prediction models, and also provide tools to decision-makers whose goals are to protect populations and their property. Associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Any storm-tracking method should include the following processes: storm identification, storm tracking, and quantification of storm intensity and activity. The spatiotemporal coordinates of the tracks will enable researchers to obtain other coincident observations to conduct more thorough studies of these events. In addition to their tracked locations, their areal extents, precipitation intensities, and accumulations all as functions of their evolutions in time were also obtained and recorded for these events. All parameters so derived can be catalogued into a moving object database (MODB) for custom queries. The purpose of this software is to provide a generalized, cross-platform, pluggable tool for identifying events within a set of scientific data based upon specified criteria with the possibility of storing identified events into a searchable database. The core of the application uses an implementation of the connected component labeling (CCL) algorithm to identify areas of interest, then uses a set of criteria to establish spatial and temporal relationships between identified components. The CCL algorithm is used for identifying objects within images for computer vision. This application applies it to scientific data sets using arbitrary criteria. The most novel concept was applying a generalized CCL implementation to scientific data sets for establishing events both spatially and temporally. The combination of several existing concepts (pluggable components, generalized CCL algorithm, etc.) into one application is also novel. In addition, how the system is designed, i.e., its extensibility with pluggable components, and its configurability with a simple configuration file, is innovative. This allows the system to be applied to new scenarios with ease.

Clune, Thomas↗
NASA NTRS2018

The Impact of Stellar Variability on the Detection of Transiting Earth-like Planets

Stellar variability represents a key limitation on the detectability of weak transit signatures of small, Earth-size planets with space-based missions such as NASA's Kepler Mission or ESA's PLATO Mission. The expectations for the performance of the Kepler Mission in the face of solar-like variability were predicated on observations of the Sun with the Variability of Solar Irradiance and Gravity Oscillations (VIRGO) instrument aboard NASA and ESA's joint Solar and Heliospheric Observatory (SOHO) mission, which was launched in December 1995. Analyses of SOHO data indicated that solar-like variability would contribute approx.10 ppm to Kepler's noise budget at 6.5 ours, a typical grazing transit timescale. This proved to be optimistic as the typical 12th mag G2V star in the Kepler field of view exhibited 20 ppm of stellar variability-related noise at 6.5 hours, making the detection of true Earth-Sun analogs more difficult. While the consequent increase in the total noise at 6.5 hours from 20 ppm to 30 ppm could have been overcome by extending the mission to a total duration of 7 to 8 years. Unfortunately, a second reaction wheel failed in May 2013, ending the Kepler primary mission. Nevertheless, Kepler proved to be a veritable cornucopia of science results, both for exoplanets and for astrophysics. The phenomenal photometric precision and continuous observations required in order to identify small, rocky transiting planets enables the study of a large range of phenomena contributing to stellar variability for many thousands of solar-like stars in Kepler's field of view in exquisite detail. These effects range from less than 1 ppm acoustic oscillations on timescales from a few minutes and longward, to flares on timescales of hours, to spot-induced modulation on timescales of days to weeks to activity cycles on timescales of months to years. Kepler discovered over 2600 validated and confirmed exoplanets, and measured the pressure-mode oscillations of over 15000 stars over the course of its mission and the reconstituted K2 mission. We present the adaptive, wavelet-based matched filter used for both the Kepler and the TESS missions, and discuss the connections between this detection algorithm and fundamental detection theory, which allows the detector to function as a noise characterization engine. This provides a dynamical measurement of the photometric noise at transit timescales and thus, a key performance metric for missions like PLATO and Kepler. We give an overview of the stellar variability we see across the full range of spectral types observed by Kepler, from the cool, small red M stars to the hot, large late A stars, both in terms of amplitude as well as timescale.

transit surveys↗
NASA NTRS2018

The Impact of Stellar Variability on the Detection of Transiting Earth-Like Planets

Stellar variability represents a key limitation on the detectability of weak transit signatures of small, Earth-size planets with space-based missions such as NASA's Kepler Mission or ESA's PLATO Mission. The expectations for the performance of the Kepler Mission in the face of solar-like variability were predicated on observations of the Sun with the Variability of Solar Irradiance and Gravity Oscillations (VIRGO) instrument aboard NASA and ESA's joint Solar and Heliospheric Observatory (SOHO) mission, which was launched in December 1995. Analyses of SOHO data indicated that solar-like variability would contribute ~10 ppm to Kepler's noise budget at 6.5 ours, a typical grazing transit timescale. This proved to be optimistic as the typical 12th mag G2V star in the Kepler field of view exhibited 20 ppm of stellar variability-related noise at 6.5 hours, making the detection of true Earth-Sun analogs more difficult. While the consequent increase in the total noise at 6.5 hours from 20 ppm to 30 ppm could have been overcome by extending the mission to a total duration of 7 to 8 years. Unfortunately, a second reaction wheel failed in May 2013, ending the Kepler primary mission. Nevertheless, Kepler proved to be a veritable cornucopia of science results, both for exoplanets and for astrophysics. The phenomenal photometric precision and continuous observations required in order to identify small, rocky transiting planets enables the study of a large range of phenomena contributing to stellar variability for many thousands of solar-like stars in Kepler's field of view in exquisite detail. These effects range from <1 ppm acoustic oscillations on timescales from a few minutes and longward, to flares on timescales of hours, to spot-induced modulation on timescales of days to weeks to activity cycles on timescales of months to years. Kepler discovered over 2600 validated and confirmed exoplanets, and measured the pressure-mode oscillations of over 15000 stars over the course of its mission and the reconstituted K2 mission. We present the adaptive, wavelet-based matched filter used for both the Kepler and the TESS missions, and discuss the connections between this detection algorithm and fundamental detection theory, which allows the detector to function as a noise characterization engine. This provides a dynamical measurement of the photometric noise at transit timescales and thus, a key performance metric for missions like PLATO and Kepler. We give an overview of the stellar variability we see across the full range of spectral types observed by Kepler, from the cool, small red M stars to the hot, large late A stars, both in terms of amplitude as well as timescale.

exoplanets↗
NASA NTRS2017

Implementation of (O-)CGR in The ONE

Routing in Delay-/Disruption-Tolerant Networking (DTN) requires specific solutions as link impairments prevent the use of ordinary Internet algorithms, based on a timely dissemination of network topology information. Among DTN routing algorithms there is a dichotomy between opportunistic and deterministic (scheduled) solutions. The former are numerous and apply to terrestrial environments; CGR is the most widely supported algorithm designed for scheduled connectivity, and it is usually applied to space networks. However, in an attempt to provide a unified approach, an opportunistic variant of CGR, Opportunistic CGR (OCGR) has been recently proposed by some of the authors. Performance evaluations are normally carried out for opportunistic solutions by means of simulators, such as The ONE considered in this paper. CGR by contrast is more often studied by means of small testbeds. As the simulation approach could be complementary for CGR, and essential for OCGR, the authors have recently ported both of them into The ONE, by developing and releasing as free software a specific additional package. The aim of this paper is to show the rationale of this choice and discuss the many challenges that needed to be tackled to achieve this primary goal.

Tempesta, G.↗
NASA NTRS1988

Introduction to a system for implementing neural net connections on SIMD architectures

Neural networks have attracted much interest recently, and using parallel architectures to simulate neural networks is a natural and necessary application. The SIMD model of parallel computation is chosen, because systems of this type can be built with large numbers of processing elements. However, such systems are not naturally suited to generalized communication. A method is proposed that allows an implementation of neural network connections on massively parallel SIMD architectures. The key to this system is an algorithm permitting the formation of arbitrary connections between the neurons. A feature is the ability to add new connections quickly. It also has error recovery ability and is robust over a variety of network topologies. Simulations of the general connection system, and its implementation on the Connection Machine, indicate that the time and space requirements are proportional to the product of the average number of connections per neuron and the diameter of the interconnection network.

Tomboulian, Sherryl↗
NASA NTRS1988

Introduction to a system for implementing neural net connections on SIMD architectures

Neural networks have attracted much interest recently, and using parallel architectures to simulate neural networks is a natural and necessary application. The SIMD model of parallel computation is chosen, because systems of this type can be built with large numbers of processing elements. However, such systems are not naturally suited to generalized elements. A method is proposed that allows an implementation of neural network connections on massively parallel SIMD architectures. The key to this system is an algorithm permitting the formation of arbitrary connections between the neurons. A feature is the ability to add new connections quickly. It also has error recovery ability and is robust over a variety of network topologies. Simulations of the general connection system, and its implementation on the Connection Machine, indicate that the time and space requirements are proportional to the product of the average number of connections per neuron and the diameter of the interconnection network.

Tomboulian, Sherryl↗
NASA NTRS1985

Three-dimensional algorithms for grid restructuring in Free-Lagrangian calculations

Grid restructuring algorithms which lower the price of three-dimensional Free-Lagrange calculations are presented. The algorithms are first given for the case of planar triangulated surfaces embedded in and spanning a three-dimensional region. The tetrahedra generated by this technique form a Delaunay mesh if the interplane spacing is comparable to the resolution within the planes. The algorithm can therefore be used for efficient determinations of Voronoi connections for initial grids. Modifications of the algorithm for the case of closely spaced surfaces are demonstrated in the context of restructuring algorithms which can accommodate colliding surfaces. Then, the restriction to planar surfaces is removed and regular surfaces are examined. The basic algorithm is the same, with an additional operation to project the vertices of one surface onto another. Finally, vertices on the surface are allowed to migrate anywhere in space.

Fritts, M.↗
DOE OSTI2026

EASY-SHIFT v Alpha

The software is a generic, price- and load-responsive control algorithm integrating heat pumps with thermal energy storage. The algorithm leverages simple models of the system and easily accessible data to schedule operation of heat pumps and thermal energy storage in ways that minimize the cost of operating the heating/cooling system. This tool is specifically designed to be easy to interact with, and something that industry partners are able to adopt. There are two current state of the art approaches. Industry tends to develop very simple algorithms, with predetermined schedules that are not capable of changing operation in response to changes in operating environment. For example, a control designed to avoid high-price electricity from 5-8 PM will not be able to adapt if the high-price period changes to 4-9 PM. Academia commonly develops algorithms called Model predictive control (MPC). MPC requires extensive data and highly trained staff to develop a specific type of simulation model of the building, connect the building to optimization algorithms, and leverage powerful computers. Industry, with limited time/finance budgets for any project, is resistant to adopting MPC due to the associated high complexity and cost.

Grant, Peter [Lawrence Berkeley National Laborator↗
NASA NTRSDate not supplied

Quantum-accelerated Global Constraint Filtering

Motivated by recent advances in quantum algorithms and gate-model quantum computation, we introduce quantum-accelerated filtering algorithms for global constraints in constraint programming. We adapt recent work in quantum algorithms for graph problems and identify quantum subroutines that accelerate the main domain consistency algorithms for the all different constraint and the global cardinality constraint (gcc). The subroutines are based on quantum algorithms for finding maximum matchings and strongly connected components in graphs, and provide speedups over the best classical algorithms. We detail both complete and bounded-probability frameworks for quantum-accelerated global constraint filtering algorithms within backtracking search.

Quantum algorithms↗
DOE OSTI2024

Rapidly convergent quantum Monte Carlo using a Chebyshev projector

The multireference coupled-cluster Monte Carlo (MR-CCMC) algorithm is a determinant-based quantum Monte Carlo (QMC) algorithm that is conceptually similar to Full Configuration Interaction QMC (FCIQMC). It has been shown to offer a balanced treatment of both static and dynamic correlation while retaining polynomial scaling, although application to large systems with significant strong correlation remained impractical. In this paper, we document recent algorithmic advances that enable rapid convergence and a more black-box approach to the multireference problem. These include a logarithmically scaling metric-tree-based excitation acceptance algorithm to search for determinants connected to the reference space at the desired excitation level and a symmetry-screening procedure for the reference space. We show that, for moderately sized reference spaces, the new search algorithm brings about an approximately 8-fold acceleration of one MR-CCMC iteration, while the symmetry screening procedure reduces the number of active reference space determinants with essentially no loss of accuracy. We also introduce a stochastic implementation of an approximate wall projector, which is the infinite imaginary time limit of the exponential projector, using a truncated expansion of the wall function in Chebyshev polynomials. Notably, this wall-Chebyshev projector can be used to accelerate any projector-based QMC algorithm. We show that it requires significantly fewer applications of the Hamiltonian to achieve the same statistical convergence. We benchmark these acceleration methods on the beryllium and carbon dimers, using initiator FCIQMC and MR-CCMC with basis sets up to cc-pVQZ quality.

Zhao, Zijun↗
NASA NTRS1990

On the suitability of the connection machine for direct particle simulation

The algorithmic structure was examined of the vectorizable Stanford particle simulation (SPS) method and the structure is reformulated in data parallel form. Some of the SPS algorithms can be directly translated to data parallel, but several of the vectorizable algorithms have no direct data parallel equivalent. This requires the development of new, strictly data parallel algorithms. In particular, a new sorting algorithm is developed to identify collision candidates in the simulation and a master/slave algorithm is developed to minimize communication cost in large table look up. Validation of the method is undertaken through test calculations for thermal relaxation of a gas, shock wave profiles, and shock reflection from a stationary wall. A qualitative measure is provided of the performance of the Connection Machine for direct particle simulation. The massively parallel architecture of the Connection Machine is found quite suitable for this type of calculation. However, there are difficulties in taking full advantage of this architecture because of lack of a broad based tradition of data parallel programming. An important outcome of this work has been new data parallel algorithms specifically of use for direct particle simulation but which also expand the data parallel diction.

Dagum, Leonard↗