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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Solving problems by interrogating sets of knowledge systems: Toward a theory of multiple knowledge systems

The main purpose is to develop a theory for multiple knowledge systems. A knowledge system could be a sensor or an expert system, but it must specialize in one feature. The problem is that we have an exhaustive list of possible answers to some query (such as what object is it). By collecting different feature values, in principle, it should be possible to give an answer to the query, or at least narrow down the list. Since a sensor, or for that matter an expert system, does not in most cases yield a precise value for the feature, uncertainty must be built into the model. Also, researchers must have a formal mechanism to be able to put the information together. Researchers chose to use the Dempster-Shafer approach to handle the problems mentioned above. Researchers introduce the concept of a state of recognition and point out that there is a relation between receiving updates and defining a set valued Markov Chain. Also, deciding what the value of the next set valued variable is can be phrased in terms of classical decision making theory such as minimizing the maximum regret. Other related problems are examined.

Dekorvin, Andre↗
NASA NTRS1990

Electric power scheduling - A distributed problem-solving approach

Space Station Freedom's power system, along with the spacecraft's other subsystems, needs to carefully conserve its resources and yet strive to maximize overall Station productivity. Due to Freedom's distributed design, each subsystem must work cooperatively within the Station community. There is a need for a scheduling tool which will preserve this distributed structure, allow each subsystem the latitude to satisfy its own constraints, and preserve individual value systems while maintaining Station-wide integrity.

Mellor, Pamela A.↗
NASA NTRS1993

Design of a cooperative problem-solving system for enroute flight planning: An empirical study of its use by airline dispatchers

In a previous report, an empirical study of 30 pilots using the Flight Planning Testbed was reported. An identical experiment using the Flight Planning Testbed (FPT), except that 27 airline dispatchers were studied, is described. Five general questions were addressed in this study: (1) under what circumstances do the introduction of computer-generated suggestions (flight plans) influence the planning behavior of dispatchers (either in a beneficial or adverse manner); (2) what is the nature of such influences (i.e., how are the person's cognitive processes changed); (3) how beneficial are the general design concepts underlying FPT (use of a graphical interface, embedding graphics in a spreadsheet, etc.); (4) how effective are the specific implementation decisions made in realizing these general design concepts; and (5) how effectively do dispatchers evaluate situations requiring replanning, and how effectively do they identify appropriate solutions to these situations.

Smith, Philip J.↗
NASA NTRS2002

Solving Problems With SINDA/FLUINT

SINDA/FLUINT, the NASA standard software system for thermohydraulic analysis, provides computational simulation of interacting thermal and fluid effects in designs modeled as heat transfer and fluid flow networks. The product saves time and money by making the user's design process faster and easier, and allowing the user to gain a better understanding of complex systems. The code is completely extensible, allowing the user to choose the features, accuracy and approximation levels, and outputs. Users can also add their own customizations as needed to handle unique design tasks or to automate repetitive tasks. Applications for SINDA/FLUINT include the pharmaceutical, petrochemical, biomedical, electronics, and energy industries. The system has been used to simulate nuclear reactors, windshield wipers, and human windpipes. In the automotive industry, it simulates the transient liquid/vapor flows within air conditioning systems.

Source record↗
NASA NTRS2011

Houston, We Have a Problem Solving Model for Training

In late 2006, the Mission Operations Directorate (MOD) at NASA began looking at ways to make training more efficient for the flight controllers who support the International Space Station. The average certification times for flight controllers spanned from 18 months to three years and the MOD, responsible for technical training, was eager to develop creative solutions that would reduce the time to 12 months. Additionally, previously trained flight controllers sometimes participated in more than 50 very costly, eight-hour integrated simulations before becoming certified. New trainees needed to gain proficiency with far fewer lessons and training simulations than their predecessors. This poster presentation reviews the approach and the process that is currently in development to accomplish this goal.

Schmidt, Lacey↗
NASA NTRS2012

Using M and S to Improve Human Decision Making and Achieve Effective Problem Solving in an International Environment

In the international arena, decision makers are often swayed away from fact-based analysis by their own individual cultural and political bias. Modeling and Simulation-based training can raise awareness of individual predisposition and improve the quality of decision making by focusing solely on fact vice perception. This improved decision making methodology will support the multinational collaborative efforts of military and civilian leaders to solve challenges more effectively. The intent of this experimental research is to create a framework that allows decision makers to "come to the table" with the latest and most significant facts necessary to determine an appropriate solution for any given contingency.

Christie, Vanessa L.↗
NASA NTRS2017

What Machines Need to Learn to Support Human Problem-Solving

In the development of intelligent systems that interact with humans, there is often confusion between how the system functions with respect to the humans it interacts with and how it interfaces with those humans. The former is a much deeper challenge than the latter it requires a system-level understanding of evolving human roles as well as an understanding of what humans need to know (and when) in order to perform their tasks. This talk will focus on some of the challenges in getting this right as well as on the type of research and development that results in successful human-autonomy teaming. Brief Bio: Dr. Alonso Vera is Chief of the Human Systems Integration Division at NASA Ames Research Center. His expertise is in human-computer interaction, information systems, artificial intelligence, and computational human performance modeling. He has led the design, development and deployment of mission software systems across NASA robotic and human space flight missions, including Mars Exploration Rovers, Phoenix Mars Lander, ISS, Constellation, and Exploration Systems. Dr. Vera received a Bachelor of Science with First Class Honors from McGill University in 1985 and a Ph.D. from Cornell University in 1991. He went on to a Post-Doctoral Fellowship in the School of Computer Science at Carnegie Mellon University from 1990-93.

intelligent systems↗
DOE OSTI2019

Pilot-Scale CFP Commissioning: Creative Problem Solving and Lessons Learned

We have commissioned a pilot-scale, ex-situ catalytic fast pyrolysis (CFP) reactor system in the Thermal and Catalytic Process Development Unit (TCPDU) at the National Renewable Energy Laboratory. The FCC-style CFP system, including a riser and continuous regeneration, was added to the existing half-ton-per-day biomass fast pyrolysis unit and was designed to run a zeolite-type catalyst. The system is unique due to its scale and catalyst flow control scheme. As the design of this system was informed by early data from a bench-scale reactor, several technical scale-up challenges had to be overcome. Additionally, physical constraints and the complexities of biomass vapors required and inspired creative solutions. This talk will discuss the specific challenges we found and addressed, including an oxygen deficiency in the regenerator and plugging in the exit streams. We explored several options for increasing oxygen availability and selected a cyclone-catalyst return design on the exit line so we could increase air flow through the regenerator. We found that hot gas filtration is essential in any exit streams containing water vapor and trace amounts of catalyst; any combination of catalyst and condensate quickly makes mud that plugs equipment and severely limits run time. Duplicate filters are also required to extend run time. Most of the equipment and solutions were designed and installed by our team of engineers and technicians, working closely together in an iterative process to design, install, operate, then redesign. Collaborations with other national laboratories with computational fluid dynamics expertise helped inform designs and identify creative solutions to these scale-up challenges. Ultimately, data from the reactor system will be used to validate a comprehensive kinetic model of CFP that can be used to better design and inform new systems at various scales.

09 BIOMASS FUELS↗
DOE OSTI2025

A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem

Solving problems related to planning and operations of large-scale power systems is challenging on classical computers due to their inherent nature as mixed-integer and nonlinear problems. Quantum computing provides new avenues to approach these problems. We develop a hybrid quantum-classical algorithm for the Unit Commitment (UC) problem in power systems which aims at minimizing the total cost while optimally allocating generating units to meet the hourly demand of the power loads. The hybrid algorithm combines a variational quantum algorithm (VQA) with a classical Benders-type heuristic. The resulting algorithm computes approximate solutions to UC in three stages: i) a collection of UC vectors capable meeting the power demand with lowest possible operating costs is generated based on VQA; ii) a classical sequential least squares programming (SLSQP) routine is leveraged to find the optimal power level corresponding to a predetermined number of candidate vectors; iii) in the last stage, the approximate solution of UC along with generating units power level combination is given. To demonstrate the effectiveness of the presented method, three different systems with 3 generating units, 10 generating units, and 26 generating units were tested for different time periods. In addition, convergence of the hybrid quantum-classical algorithm for select time periods is proven out on IonQ's Forte system.

Aboumrad, Willie [IonQ, Inc]↗