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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Design and Operational Evaluation of the Traffic Management Advisor at the Ft. Worth Air Route Traffic Control Center

NASA and the FAA have designed and developed and an automation tool known as the Traffic Management Advisor (TMA). The system was operationally evaluated at the Ft. Worth Air Route Traffic Control Center (ARTCC). The TMA is a time-based strategic planning tool that provides Traffic Management Coordinators and En Route Air Traffic Controllers the ability to efficiently optimize the capacity of a demand impacted airport. The TMA consists of trajectory prediction, constraint-based runway scheduling, traffic flow visualization and controllers advisories. The TMA was used and operationally evaluated for forty-one rush traffic periods during a one month period in the Summer of 1996. The evaluations included all shifts of air traffic operations as well as periods of inclement weather. Performance data was collected for engineering and human factor analysis and compared with similar operations without the TMA. The engineering data indicates that the operations with the TMA show a one to two minute per aircraft delay reduction during rush periods. The human factor data indicate a perceived reduction in en route controller workload as well as an increase in job satisfaction. Upon completion of the evaluation, the TMA has become part of the normal operations at the Ft. Worth ARTCC.

Swenson, Harry N.↗
NASA NTRS2010

Data Access Services that Make Remote Sensing Data Easier to Use

This slide presentation reviews some of the processes that NASA uses to make the remote sensing data easy to use over the World Wide Web. This work involves much research into data formats, geolocation structures and quality indicators, often to be followed by coding a preprocessing program. Only then are the data usable within the analysis tool of choice. The Goddard Earth Sciences Data and Information Services Center is deploying a variety of data access services that are designed to dramatically shorten the time consumed in the data preparation step. On-the-fly conversion to the standard network Common Data Form (netCDF) format with Climate-Forecast (CF) conventions imposes a standard coordinate system framework that makes data instantly readable through several tools, such as the Integrated Data Viewer, Gridded Analysis and Display System, Panoply and Ferret. A similar benefit is achieved by serving data through the Open Source Project for a Network Data Access Protocol (OPeNDAP), which also provides subsetting. The Data Quality Screening Service goes a step further in filtering out data points based on quality control flags, based on science team recommendations or user-specified criteria. Further still is the Giovanni online analysis system which goes beyond handling formatting and quality to provide visualization and basic statistics of the data. This general approach of automating the preparation steps has the important added benefit of enabling use of the data by non-human users (i.e., computer programs), which often make sub-optimal use of the available data due to the need to hard-code data preparation on the client side.

Lynnes, Christopher↗
DOE OSTI2025

Machine Learning for Optimized Polarization at Jefferson Lab

Polarized cryo-targets and polarized photon beams are widely used in experiments at Jefferson Lab. Traditional methods for maintaining the optimal polarization involve manual adjustments throughout data taking by human shift takers. This may introduce some level of inconsistency simply due to the wide variety of experience and expertise of the shift takers themselves. Implementing machine learning-based control systems can improve the stability of the polarization without relying on human intervention. The cryo-target polarization is influenced by temperature, microwave energy, the distribution of paramagnetic radicals, as well as operational conditions including the radiation dose. Diamond radiators are used to generate linearly polarized photons from a primary electron beam. The energy spectrum of these photons can drift over time due to changes in the primary electron beam conditions and diamond degradation. As a first step towards automating the continuous optimization and control processes, uncertainty aware surrogate models have been developed to predict the polarization based on historical data. This talk will provide an overview of the use cases and models developed, highlighting the collaboration between data scientists and physicists at Jefferson Lab.

Jeske, Torri [Thomas Jefferson National Accelerato↗
DOE OSTI2024

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Furthermore, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.

Microscopy↗
DOE OSTI2023

High-Throughput Characterization and Optimization of Polyamide Hydrolase Activity Using Open Port Sampling Interface Mass Spectrometry

Enzymatic biodegradation of polymers, such as polyamides (PA), has the potential to cost-effectively reduce plastic waste, but enhancements in degradation efficiency are needed. Engineering enzymes through directed evolution is one pathway toward identification of critical domains needed for improving activity. However, screening such enzymatic libraries (100s-to-1000s of samples) is time-consuming. Here we demonstrate the use of robotic autosampler (PAL) and immediate drop on demand technology (I.DOT) liquid handling systems coupled with open-port sampling interface-mass spectrometry (OPSI-MS) to screen for PA6 and PA66 hydrolysis by 6-aminohexanoate-oligomer endo-hydrolase (nylon hydrolase, NylC) in a high-throughput (8–20 s/sample) manner. The OPSI-MS technique required minimal sample preparation and was amenable to 96-well plate formats for automated processing. Enzymatic hydrolysis of PA characteristically produced soluble linear oligomer products that could be identified by OPSI-MS. Incubation temperatures and times were optimized for PA6 (65 °C, 24 h) and PA66 (75 °C, 24 h) over 108 experiments. In addition, the I.DOT/OPSI-MS quantified production of PA6 linear dimer (8.3 ± 1.6 μg/mL) and PA66 linear monomer (13.5 ± 1.5 μg/mL) by NylC with a lower limit of detection of 0.029 and 0.032 μg/mL, respectively. For PA6 and PA66, linear oligomer production corresponded to 0.096 ± 0.018% and 0.204 ± 0.028% conversion of dry pellet mass, respectively. The developed methodology is expected to be utilized to assess enzymatic hydrolysis of engineered enzyme libraries, comprising hundreds to thousands of individual samples.

47 OTHER INSTRUMENTATION↗
NASA NTRS2022

Environmental Testing of the OVEN System for Lunar Water Extraction and Prospecting

Introduction: The presence of water ice in permanently shadowed regions on the lunar surface [1] may enable a sustained human presence on the Moon with minimal need for consumables. However, in order to develop a long term utilization plan that includes the usage of in-situ water we must first understand the abundance, stratigraphy and distribution of this re-source. Multiple space agencies currently have plans for lunar water prospecting missions. The Optimized Volatile Extraction Node (OVEN) was designed for water prospecting missions that require samples to be weighed, sealed, and heated as the means of determining water concentration. This method of water quantification necessitates a fair amount of automation, so a rigorous environmental test program was performed in order to build confidence in the performance of the OVEN design. The work presented here describes the OVEN environmental test program as well as ongoing efforts to improve on the design. Vibration: The OVEN participated in two rounds of random vibration tests. The first test was a stand-alone test performed at the Energy Systems Test Area of the Johnson Space Center. The second test was an integrated test with the mobile platform developed for the Resource Prospector project. The OVEN survived both tests without damage, but the tests did provide valuable lessons learned with regards to specific operations. Thermal Vacuum: The OVEN was successfully demonstrated at a temperature range of -50 to 75 C in a thermal vacuum chamber. The need to heat motor gearboxes at lower temperatures was predetermined so this test program was completed by implementing a method of gearbox heating that used the existing circuitry within the motors. Dust: A custom dynamometer was built in order to determine the torque required to move the various mechanisms within the OVEN at a range of temperatures. The OVEN system was coated with lunar dust simulant in order to determine mechanism torques under a worst-case operating condition. Sublimation: Sublimation losses within the OVEN were quantified through a series of test configurations, including an integrated test in a thermal vacuum chamber at Glenn Research Center [2]. Current Work: The OVEN subsystem is currently not a component of any existing prospecting missions, but work continues that will take the lessons learned from previous environmental tests and improve on the design in order to be considered for future prospecting opportunities. References: [1] Colaprete, A., Schultz, P., Heldmann, J., Wooden, D., Shirley, M., Ennico, K., ... & Sollitt, L. (2010). Detection of water in the LCROSS ejecta plume. science, 330(6003), 463-468. [2] Kleinhenz, J., Smith, J., Roush, T., Colaprete, A., Zacny, K., Paulsen, G., ... & Paz, A. (2018). Volatiles Loss from water bearing regolith simulant at Lunar Environments. In Earth and Space 2018: Engineering for Extreme Environments (pp. 454-466). Reston, VA: American Society of Civil Engineers.

A Paz↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2023

MrHyDE v.1.0

SAND2024-01324O MrHyDE, which stands for Multi-resolution Hybridized Differential Equations, is a general-purpose C++ package for the solution of coupled multiphysics and multiscale systems on massively parallel computing systems. MrHyDE is designed to enable moving beyond forward simulation for multiscale applications which includes optimization, control, uncertainty quantification, and stochastic inversion. The framework provides interfaces to several packages within the Trilinos framework and leverages automatic differentiation to enable adjoint capabilities for large-scale, gradient-based optimization. MrHyDE provides automated multiscale capabilities through a subgrid model interface and multiscale Dirichlet-to-Neumann maps. For extreme-scale applications, MrHyDE provides in situ data-compression algorithms to reduce memory requirements while maintaining performance. MrHyDE is a general-purpose, computational framework for the solution of multiscale and multiphysics applications. It uses a combination of structure-preserving, physics-compatible discretizations, fully implicit methods, multi-resolution schemes, or fully explicit methods. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
NASA NTRS2002

Macromolecular Crystal Growth by Means of Microfluidics

We have performed a feasibility study in which we show that chip-based, microfluidic (LabChip(TM)) technology is suitable for protein crystal growth. This technology allows for accurate and reliable dispensing and mixing of very small volumes while minimizing bubble formation in the crystallization mixture. The amount of (protein) solution remaining after completion of an experiment is minimal, which makes this technique efficient and attractive for use with proteins, which are difficult or expensive to obtain. The nature of LabChip(TM) technology renders it highly amenable to automation. Protein crystals obtained in our initial feasibility studies were of excellent quality as determined by X-ray diffraction. Subsequent to the feasibility study, we designed and produced the first LabChip(TM) device specifically for protein crystallization in batch mode. It can reliably dispense and mix from a range of solution constituents into two independent growth wells. We are currently testing this design to prove its efficacy for protein crystallization optimization experiments. In the near future we will expand our design to incorporate up to 10 growth wells per LabChip(TM) device. Upon completion, additional crystallization techniques such as vapor diffusion and liquid-liquid diffusion will be accommodated. Macromolecular crystallization using microfluidic technology is envisioned as a fully automated system, which will use the 'tele-science' concept of remote operation and will be developed into a research facility for the International Space Station as well as on the ground.

vanderWoerd, Mark↗
DOE OSTI2024

Voltage Optimization

Voltage optimization refers to a volt-var optimization technique which was originally designed to minimize energy consumption and improve end-use efficiency on the distribution system. This reduces source voltage at a substation, which lowers generation demand, reduces system losses, and improves system stability. This has been the conventional implementation of voltage optimization: focusing on regulation of voltages throughout the distribution system via coordinated adjustments of load tap changers, line-voltage regulators, switched shunt compensation, and the targeted placement of new shunt compensation. These methods have received increased focus following a growth in distributed-energy resources (DERs) interconnecting at the distribution level, given their developing capability to participate in voltage regulation. This is paired with the intermittency challenges they pose, load imbalances, and voltage fluctuations. Voltage optimization has long been an area of extensive research within the distribution system, with various strategies and philosophies demonstrating a range of effects. The conventional focus on the distribution system owes to the typical operation of transmission systems, which assumes firm local resources and flows strictly from generation resources to loads. The strategies employed in distribution have also been optimized for this typically radial configuration and operation of the distribution system.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2024

Optimized bandpasses for the Habitable Worlds Observatory's exoEarth survey

A primary scientific goal of the future Habitable Worlds Observatory will be the direct detection and characterization of Earth-like planets. Estimates of the exoplanet yields for this concept will help guide mission design through detailed trade studies. It is therefore critical that yield estimation codes optimally adapt observations to the mission’s performance parameters to ensure accurate trade studies. To aid in this, we implement wavelength optimization in yield calculations for the first time, allowing the yield code to determine the ideal detection and characterization bandpasses. We use this capability to confirm the observational wavelength assumptions made for the large UV/Optical/IR surveyor, design B (LUVOIR-B) study, namely that the optimum detection wavelength is 500 nm for the majority of targets and the optimum wavelength to detect water is near 1000 nm, given LUVOIR-B’s assumed instrument performance parameters. We show that including the wavelength-dependent albedo of an Earth twin as a prior provides no significant benefit to the yields of exoEarth candidates and caution against tuning observations to modern Earth twins. We also show that coronagraphs whose inner working angles are similar to step functions may benefit from wavelength optimization and demonstrate how wavelength-dependent instrument performance can impact the optimum wavelengths for detection and characterization. The optimization methods we implement automate wavelength selection and remove uncertainties regarding these choices, helping to adapt the observations to the instrument’s performance parameters.

telescopes↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
NASA NTRS1977

The IMS Satellite Situation Center

Following a brief historical review of the SSC (Satellite Situation Center), created by the U.S. for the IMS (International Magnetospheric Study), its main functions are discussed. The services of the SSC include the accurate orbit determination of the satellites, 12-18 months in advance and the coordination of simultaneous observations by a multispacecraft system, which are essential for the optimization of the scientific gains from experiments conducted with limited resources. For 1976 SSC generated plots of the satellites Vela 5B, Vela 6A, Vela 6B, Hawkeye 1, Imp H (7), Imp J (8) by computing certain projections of the solar ecliptic, solar magnetospheric, and solar magnetic coordinate systems. The SSC system was automated by the addition of a computer system capable of interactive graphics. The SSC can also provide the ground-based campaigns with a graphical or tabular information about the position low-altitude satellites in any coordinate system. The possible participation of the SSC in future Electrodynamics Explorer mission, Space Shuttle programs is also being explored.

Sugiura, M.↗
NASA NTRS1987

Expert systems tools for Hubble Space Telescope observation scheduling

The utility of expert systems techniques for the Hubble Space Telescope (HST) planning and scheduling is discussed and a plan for development of expert system tools which will augment the existing ground system is described. Additional capabilities provided by these tools will include graphics-oriented plan evaluation, long-range analysis of the observation pool, analysis of optimal scheduling time intervals, constructing sequences of spacecraft activities which minimize operational overhead, and optimization of linkages between observations. Initial prototyping of a scheduler used the Automated Reasoning Tool running on a LISP workstation.

Miller, Glenn↗
NASA NTRS1995

An empirical evaluation of graphical interfaces to support flight planning

Whether optimization techniques or expert systems technologies are used, the underlying inference processes and the model or knowledge base for a computerized problem-solving system are likely to be incomplete for any given complex, real-world task. To deal with the resultant brittleness, it has been suggested that 'cooperative' rather than 'automated' problem-solving systems be designed. Such cooperative systems are proposed to explicitly enhance the collaboration of people and the computer system when working in partnership to solve problems. This study evaluates the impact of alternative design concepts on the performance of airline pilots interacting with such a cooperative system designed to support enroute flight planning. Thirty pilots were studied using three different versions of the system. The results clearly demonstrate that different system design concepts can strongly influence the cognitive processes of users. Indeed, one of the designs studied caused four times as many pilots to accept a poor flight amendment. Based on think-aloud protocols, cognitive models are proposed to account for how features of the computer system interacted with specific types of scenarios to influence exploration and decision-making by the pilots. The results are then used to develop recommendations for guiding the design of cooperative systems.

Smith, Philip J.↗
NASA NTRS1996

A Model-based Approach to Reactive Self-Configuring Systems

This paper describes Livingstone, an implemented kernel for a self-reconfiguring autonomous system, that is reactive and uses component-based declarative models. The paper presents a formal characterization of the representation formalism used in Livingstone, and reports on our experience with the implementation in a variety of domains. Livingstone's representation formalism achieves broad coverage of hybrid software/hardware systems by coupling the concurrent transition system models underlying concurrent reactive languages with the discrete qualitative representations developed in model-based reasoning. We achieve a reactive system that performs significant deductions in the sense/response loop by drawing on our past experience at building fast prepositional conflict-based algorithms for model-based diagnosis, and by framing a model-based configuration manager as a prepositional, conflict-based feedback controller that generates focused, optimal responses. Livingstone automates all these tasks using a single model and a single core deductive engine, thus making significant progress towards achieving a central goal of model-based reasoning. Livingstone, together with the HSTS planning and scheduling engine and the RAPS executive, has been selected as the core autonomy architecture for Deep Space One, the first spacecraft for NASA's New Millennium program.

Williams, Brian C.↗
NASA NTRSDate not supplied

Parametric Optimization of Rigid Wheels for Planetary Surface Mobility Applications

Design-Build-Test approaches for spaceflight hardware are time and cost intensive, which can result in suboptimal mechanism designs. Optimization-based approaches that utilize high-fidelity models and physics simulation could overcome these limitations while simultaneously speeding up the mechanical design process and reducing cost. In this work, we present a toolchain that enables the multi-objective optimization of rigid rover wheels for planetary surface mobility applications. The toolchain uses Chrono’s Continuous Representation Model (CRM) functionality to simulate granular soil and performs multi-objective parametric optimization on candidate rover wheels to meet a desired performance criterion. The resulting wheel design is then evaluated experimentally using a single-wheel testbed. We end with a discussion of how the toolchain can be extended to simultaneously co-optimize other system parameters, such as system power consumption and feedback control gains.

Optimization↗