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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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The application of NASA management approach to solving complex problems on earth is discussed. The management of the Apollo program is presented as an example of effective management techniques. Four key elements of effective management are analyzed. Photographs of the Cape Kennedy launch sites and supporting equipment are included to support the discussions.
Quantum computing is a new and rapidly evolving paradigm for solving chemistry problems. In previous work, we developed the Quantum Annealer Eigensolver (QAE) and applied it to the calculation of the vibrational spectrum of a molecule on the D-Wave quantum annealer. However, the original QAE methodology was applicable to real symmetric matrices only. For many physics and chemistry problems, the diagonalization of complex matrices is required. For example, the calculation of quantum scattering resonances can be formulated as a complex eigenvalue problem where the real part of the eigenvalue is the resonance energy and the imaginary part is proportional to the resonance width. In the present work, we generalize the QAE to treat complex matrices: first complex Hermitian matrices and then complex symmetric matrices. These generalizations are then used to compute a quantum scattering resonance state in a 1D model potential for O + O collisions. These calculations are performed using both a software (classical) annealer and hardware annealer (the D-Wave 2000Q). The results of the complex QAE are also benchmarked against a standard linear algebra library (LAPACK). Furthermore, this work presents the first numerical solution of a complex eigenvalue problem of any kind on a quantum annealer, and it is the first treatment of a quantum scattering resonance on any quantum device.
A process-oriented control model is described for distributed problem solving. The model coordinates the transfer and manipulation of information across independent networked applications, both intelligent and conventional. The model was implemented using SOCIAL, a set of object-oriented tools for distributing computing. Complex sequences of distributed tasks are specified in terms of high level scripts. Scripts are executed by SOCIAL objects called Manager Agents, which realize an intelligent coordination model that routes individual tasks to suitable server applications across the network. These tools are illustrated in a prototype distributed system for decision support of ground operations for NASA's Space Shuttle fleet.
A coordinated effort has been underway over the past four years to elevate unstructured-grid methodology to a mature level. The goal of this endeavor is to provide a validated capability to non-expert users for performing rapid aerodynamic analysis and design of complex configurations. The Euler component of the system is well developed, and is impacting a broad spectrum of engineering needs with capabilities such as rapid grid generation and inviscid flow analysis, inverse design, interactive boundary layers, and propulsion effects. Progress is also being made in the more tenuous Navier-Stokes component of the system. A robust grid generator is under development for constructing quality thin-layer tetrahedral grids, along with a companion Navier-Stokes flow solver. This paper presents an overview of this effort, along with a perspective on the present and future status of the methodology.
Abstract not provided.
The intrinsic similarity between the firing of a rule and the firing of a neuron has been captured in this research to provide a neural network development system within an existing production system (CLIPS). A very important by-product of this research has been the emergence of an integrated technique of using rule based systems in conjunction with the neural networks to solve complex problems. The systems provides a tool kit for an integrated use of the two techniques and is also extendible to accommodate other AI techniques like the semantic networks, connectionist networks, and even the petri nets. This integrated technique can be very useful in solving complex AI problems.
It is often argued that problem-solving behavior in a complex environment is determined as much by the features of the environment as by the goals of the problem solver. This article explores a technique to determine the extent to which measured features of a complex environment influence problem-solving behavior observed within that environment. In this study, the technique is used to determine how complex flight deck and air traffic control environment influences the strategies used by airline pilots when controlling the flight path of a modern jetliner. Data collected aboard 16 commercial flights are used to measure selected features of the task environment. A record of the pilots' problem-solving behavior is analyzed to determine to what extent behavior is adapted to the environmental features that were measured. The results suggest that the measured features of the environment account for as much as half of the variability in the pilots' problem-solving behavior and provide estimates on the probable effects of each environmental feature.
Genetic Programming (GP) is an evolutionary algorithm that generates computer programs, or mathematical expressions, to solve complex problems. In this Guide, we demonstrate how to use GP to develop surrogate models to mitigate the computational costs of modeling atomic nuclei with ever increasing complexity. The computational burden escalates when uncertainty quantification is pursued, or when observables must be globally computed for thousands of nuclei. By studying three models in which the mean field depends on the total particle density self-consistently, we show that by constructing reduced order models supported by GP one can speed up many-body computations by several orders of magnitude with a negligible loss in accuracy.
Here, this article presents fusion intelligence (FI), a bio-inspired paradigm that synergistically integrates the intrinsic capabilities of intelligent biological organisms with the advanced potential of artificial intelligence (AI)-driven systems. FI harnesses the unique intelligence, sensing, actuation, and mobility attributes of living organisms, such as honeybees, blending these with the sophisticated data-driven problem-solving functionalities of AI. By bridging the gap between natural intelligence (NI) and AI, FI can transform how humans interact with and harness the capabilities of both natural and artificial systems. This article presents the model of FI and its application to solve practical problems, discusses the challenges and future directions of FI research, emphasizing a generalized approach to solve complex problems, where AI can observe/control NI in a closed-loop system. We demonstrate the potential for FI to enhance the performance of an agricultural IoT system via a simulated case study, which achieves 50% improvement in the efficacy of insect pollination (entomophily).
Several modifications aimed at improving physical accuracy are proposed for solving axially symmetric problems building on the DSMC (2007) algorithms introduced by Bird. Originally developed to solve nonequilibrium, rarefied flows, the DSMC method is now regularly used to solve complex problems over a wide range of Knudsen numbers. These new algorithms include features such as nearest neighbor collisions excluding the previous collision partners, separate collision and sampling cells, automatically adaptive variable time steps, a modified no-time counter procedure for collisions, and discontinuous and event-driven physical processes. Axially symmetric solutions require radial weighting for the simulated molecules since the molecules near the axis represent fewer real molecules than those farther away from the axis due to the difference in volume of the cells. In the present methodology, these radial weighting factors are continuous, linear functions that vary with the radial position of each simulated molecule. It is shown that how one defines the number of tentative collisions greatly influences the mean collision time near the axis. The method by which the grid is treated for axially symmetric problems also plays an important role near the axis, especially for scalar pressure. A new method to treat how the molecules are traced through the grid is proposed to alleviate the decrease in scalar pressure at the axis near the surface. Also, a modification to the duplication buffer is proposed to vary the duplicated molecular velocities while retaining the molecular kinetic energy and axially symmetric nature of the problem.
Using automated and standardized computer tools to calculate the pertinent test result values has several advantages such as: 1. allowing high-fidelity solutions to complex nonlinear phenomena that would be impractical to express in written equation form, 2. eliminating errors associated with the interpretation and programing of analysis procedures from the text of test standards, 3. lessening the need for expertise in the areas of solid mechanics, fracture mechanics, numerical methods, and/or finite element modeling, to achieve sound results, 4. and providing one computer tool and/or one set of solutions for all users for a more "standardized" answer. In summary, this approach allows a non-expert with rudimentary training to get the best practical solution based on the latest understanding with minimum difficulty.Other existing ASTM standards that cover complicated phenomena use standard computer programs: 1. ASTM C1340/C1340M-10- Standard Practice for Estimation of Heat Gain or Loss Through Ceilings Under Attics Containing Radiant Barriers by Use of a Computer Program 2. ASTM F 2815 - Standard Practice for Chemical Permeation through Protective Clothing Materials: Testing Data Analysis by Use of a Computer Program 3. ASTM E2807 - Standard Specification for 3D Imaging Data Exchange, Version 1.0 The verification, validation, and round-robin processes required of a computer tool closely parallel the methods that are used to ensure the solution validity for equations included in test standard. The use of automated analysis tools allows the creation and practical implementation of advanced fracture mechanics test standards that capture the physics of a nonlinear fracture mechanics problem without adding undue burden or expense to the user. The presented approach forms a bridge between the equation-based fracture testing standards of today and the next generation of standards solving complex problems through analysis automation.
With the development of smart grid technologies an increasing number of new devices and participants have joined modern energy systems and are inevitably making them more complicated and interdependent than ever. Optimally controlling such a complex energy system and maintaining its operation in a high-efficient, secure, and resilient manner are challenging tasks to the system operators. Fortunately, the revolution in deep learning and artificial intelligence (AI), both from hardware and algorithms perspectives, has provided new ideas and solutions to many previously intractable problems. As a result, this advance in computer science also sparked great research interests in utilizing AI in solving engineering problems related to the modern energy systems. Among many AI techniques, deep reinforcement learning (DRL) has demonstrated great potential for solving sequential optimization problems, which are very common in the engineering domains. Its ability to handle nonlinearity and stochasticity in controlled systems has out-competed many traditional optimal control algorithms. Therefore in this chapter, we focus on the state-of-the-art of DRL concepts and related algorithms, compare their pros and cons with traditional optimal control approaches and discuss the typical workflow for leveraging RL in solving complex problems in modern energy systems.
Teamwork can harness the collective energy of people to solve complex problems. Yet, teamwork requires more than just putting people together. Complexity is added when people from different organizations or areas of expertise form a team to accomplish specific ends. Every organization is designed to fulfill certain purposes and, as such, it has unique objectives and values and it operates according to methods, procedures, and norms intended to fulfill those objectives and values. And each field of expertise, with its specialized knowledge, uses certain methods to approach and solve problems. Teams that consist of people from multiple organizations or areas of expertise often have the added demands of reconciling dissimilar methods of getting the work done, of sharing information and knowledge that everyone may not have, and of taking into account the goals and objectives of multiple parties. The ideas presented here help teams meet these added demands. This paper introduces a framework that highlights three aspects of teamwork: the physical aspects (what team members do), the mental/intellectual aspects (what team members know and think), and the social/spiritual aspects (what team members feel about others' needs and objectives). These three aspects are easy to remember and intuitive to work with, and when one aspect of a team's work is strengthened, the other two are strengthened as well. (authors)
To date six long duration astronauts have experienced in flight visual changes and post flight signs of optic disc edema, globe flattening, choroidal folds, hyperoptic shifts and or raised intracranial pressure. In some cases the changes were transient while in others they are persistent with varying degrees of visual impairment. Given that all astronauts exposed to microgravity experience a cephalad fluid shift, and that both symptomatic and asymptomatic patients have exhibited optic nerve sheath edema on MRI, there is a high probability that all astronauts develop in-flight idiopathic intracranial hypertension to some degree. Those who are susceptible, have an increased likelihood of developing treatment resistant papilledema resulting in visual impairment and possible long-term vision loss. Such an acquired disability would have a profound mission impact and would be detrimental to the long term health of the astronaut. The visual impairment and increased intracranial pressure phenomenon appears to have multiple contributing factors. Consequently, the working "physiological fault bush" with elevated intracranial pressure at its center, is divided into ocular effects, and CNS and other effects. Some of these variables have been documented and or measured through operational data gathering, while others are unknown, undocumented and or hypothetical. Both the complexity of the problem and the urgency to find a solution require that a unique, non-traditional research model be employed such as the Accelerated Research Collaboration(TM) (ARC) model that has been pioneered by the Myelin Repair Foundation. In the ARC model a single entity facilitates and manages all aspects of the basic, translational, and clinical research, providing expert oversight for both scientific and managerial efforts. The result is a comprehensive research plan executed by a multidisciplinary team and the elimination of stove-piped research. The ARC model emphasizes efficient and effective communication between management and investigators; and real-time sharing of scientific discoveries in an effort to solve complex problems.
To solve complex problems one can often use the decomposition principle. However, a problem is seldom decomposable into completely independent subproblems. System integration deals with problem of resolving the interdependencies and the integration of the subsolutions. A natural method of decomposition is the hierarchical one. High-level specifications are broken down into lower level specifications until they can be transformed into solutions relatively easily. By automating the hierarchical decomposition and solution generation an integrated system is obtained in which the declaration of high level specifications is enough to solve the problem. We offer a knowledge-based approach to integrate the development and building of control systems. The process modeling is supported by using graphic editors. The user selects and connects icons that represent subprocesses and might refer to prewritten programs. The graphical editor assists the user in selecting parameters for each subprocess and allows the testing of a specific configuration. Next, from the definitions created by the graphical editor, the actual control program is built. Fault-diagnosis routines are generated automatically as well. Since the user is not required to write program code and knowledge about the process is present in the development system, the user is not required to have expertise in many fields.
Basic formulations, analyses, and numerical procedures are presented to investigate radiative heat interactions in diatomic and polyatomic gases under local and nonlocal thermodynamic equilibrium conditions. Essential governing equations are presented for both gray and nongray gases. Information is provided on absorption models, relaxation times, and transfer equations. Radiative flux equations are developed which are applicable under local and nonlocal thermodynamic equilibrium conditions. The problem is solved for fully developed laminar incompressible flows between two parallel plates under the boundary condition of a uniform surface heat flux. For specific applications, three diatomic and three polyatomic gases are considered. The results are obtained numerically by employing the method of variation of parameters. The results are compared under local and nonlocal thermodynamic equilibrium conditions at different temperature and pressure conditions. Both gray and nongray studies are conducted extensively for all molecular gases considered. The particular gases selected for this investigation are CO, NO, OH, CO2, H2O, and CH4. The temperature and pressure range considered are 300-2000 K and 0.1-10 atmosphere, respectively. In general, results demonstrate that the gray gas approximation overestimates the effect of radiative interaction for all conditions. The conditions of NLTE, however, result in underestimation of radiative interactions. The method developed for this study can be extended to solve complex problems of radiative heat transfer involving nonequilibrium phenomena.
Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.
Quantum computing stands as a revolutionary frontier in information technology, with the potential to solve complex problems far beyond the capacity of classical computers. At the heart of this disruptive innovation are qubits, forming the fundamental backbone of quantum computing. A leading-edge solution for constructing robust, enduring qubits involves embedding a Josephson junction within a high Q-factor, superconducting three-dimensional cavity. Further, our recent innovation lies in developing a uniquely optimized, quarter-wave resonator-based superconducting cavity, functioning at 6 GHz, specifically tailored for quantum computers. In this work, we elucidate our advancement towards elevating the Q-factor tenfold, an achievement made possible through the enhancement of machining precision, the application of rigorous postprocessing techniques—including mechanical, chemical, and surface treatments—as well as the refinement of our testing methods.