A Critique of Optimization Modeling Environments for Complex Engineered Systems.
Abstract not provided.
Hart, William↗
SEARCH · Engineering Papers
Results for “complex environments”
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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Paracousti: 3-D Hydroacoustic Modeling in the Complex Marine Environment.
Abstract not provided.
Preston, Leiph↗
Time analysis of polydisperse sprays in complex turbulent environments
The two-phase flow field of a small swirl-stabilized burner has been investigated, using a phase Doppler particle analyzer to obtain the velocity, drop size, and time of arrival components. Water/kerosene comparisons were made for the case of nonreacting flow. It is found that the spray flow field is strongly influenced by the aerodynamic flow field and by the reaction and fluid properties. It is noted that vortex shedding produces clusters of drops.
Rudoff, R. C.↗
A methodology for evaluating the reliability and risk of structures under complex service environments
The theoretical basis and numerical implementation of NESSUS (Numerical Evaluation of Stochastic Structures Under Stress; Cruse et al., 1988), a computer code for probabilistic structural analyses of aerospace components, are described, with an emphasis on the use of NESSUS for reliability and risk assessment. Topics addressed include the structure of NESSUS, the material-properties model, computational procedures, probabilistic models of fatigue-crack initiation, risk/cost evaluation, fatigue-fracture analyses, and fatigue-crack propagation. Numerical results from typical applications are presented in extensive graphs and briefly characterized; the usefulness of NESSUS predictions for establishing inspection and retirement schedules and for component certification is indicated.
Shiao, Michael C.↗
A methodology for evaluating the reliability and risk of structures under complex service environments
The theoretical basis and numerical implementation of NESSUS (Numerical Evaluation of Stochastic Structures Under Stress), a computer code for probabilistic structural analysis of aerospace components, are described, with an emphasis on the use of NESSUS for reliability and risk assessment. Topics addressed include the structure of probabilistic models of fatigue-crack initiation, risk/cost evaluation, fatigue-fracture analysis, and fatigue-crack initiation. Numerical results from typical applications are presented in graphs and briefly characterized. The usefulness of NESSUS predictions for establishing inspection and retirement schedules and for component certification is indicated.
Shiao, Michael C.↗
Effective Team Support: From Task and Cognitive Modeling to Software Agents for Time-Critical Complex Work Environments
The purpose of this research contract was to perform multidisciplinary research between CMU psychologists, computer scientists and NASA researchers to design a next generation collaborative system to support a team of human experts and intelligent agents. To achieve robust performance enhancement of such a system, we had proposed to perform task and cognitive modeling to thoroughly understand the impact technology makes on the organization and on key individual personnel. Guided by cognitively-inspired requirements, we would then develop software agents that support the human team in decision making, information filtering, information distribution and integration to enhance team situational awareness. During the period covered by this final report, we made substantial progress in completing a system for empirical data collection, cognitive modeling, and the building of software agents to support a team's tasks, and in running experiments for the collection of baseline data.
Remington, Roger W.↗
A novel approach to information distribution in a complex distributed environment
In this presentation, we will describe how the system uses CIRs in terms of subscriptions, filters and priorities, and how client interests are propagated throughout the system, and we will describe the novel information flows that result from this CIR propagation.
information distribution publish subscribe↗
Complexity Computational Environment: Data Assimilation SERVOGrid
We are using Web (Grid) service technology to demonstrate the assimilation of multiple distributed data sources (a typical data grid problem) into a major parallel high-performance computing earthquake forecasting code. Such a linkage of Geoinformatics with Geocomplexity demonstrates the value of the Solid Earth Research Virtual Observatory (SERVO) Grid concept, and advance Grid technology by building the first real-time large-scale data assimilation grid Here we develop the next steps for both the SERVO concept and the identified need for a Solid Earth problem-solving environment. We use a challenging motivating problem of importance to NASA namely integrating NASA space geodetic observations with numerical simulations of a changing earth.
data assimiliation↗
Investigation of Liner Axial Displacement in a Complex Acoustic Environment
This paper investigates the effects of axial liner displacement on modal attenuation and scattering. Tests are conducted with a uniform liner mounted in the NASA Langley Curved Duct Test Rig, with a multimode source at flow conditions of Mach 0.0 and 0.3. The impedance of the liner is educed from data acquired in the NASA Langley Grazing Flow Impedance Tube. This impedance is then used as input to the CDUCT-LaRC propagation code to enable comparisons of predicted and measured modal content upstream and downstream of the liner as the axial displacement is varied. Results show that the liner position has a clear effect on the sound field as the number of modes is increased from two to six. The comparison of measured and predicted modal content is very good when only a few modes are present. For frequencies in which more modes are present, this comparison is less favorable very near resonance, but improves for frequencies away from resonance.
Brown, M. C.↗
Human controller modeling in environments that include non-control tasks
In complex environments where the human operator is a supervisor, he must allocate his attention between different kinds of tasks for optimum overall performance. When a portion of the future reference trajectory for the control task is available for preview, scheduling various activities is possible. A model has been developed for this situation using dynamic programming to solve an optimal control problem. An experiment was conducted where subjects controlled an airplane symbol over a map, shown a fixed distance into the future. Discrete, non-control tasks were introduced as number entry tasks. Results from the model are compared with experimental results.
Govindaraj, T.↗
Modeling the human controller in environments that include continuous and discrete tasks
In complex environments where the human operator is a supervisor, he must allocate his attention between different kinds of tasks for satisfactory overall performance. When a portion of the future reference trajectory for a continuous control task is available for preview, scheduling various other discrete activities is possible. A model has been developed for this situation using dynamic programming to solve an optimal control problem. An experiment was conducted where subjects controlled an airplane symbol over a map, shown a fixed distance into the future. Discrete tasks were introduced as data entry tasks. Results showed that the model compared favorably with experimental results.
Govindaraj, T.↗
Mechanisms of transport enhancement for self-propelled nanoswimmers in a porous matrix
Significance As with living microorganisms, many real-world applications of synthetic micro/nanoswimmers are likely to rely on transport in complex environments, a phenomenon that remains poorly understood. Here, we report anomalously rapid transport of self-propelled particles in a porous medium, where nanoswimmers escaped from cavities more than an order of magnitude faster than expected compared to corresponding Brownian particles. This phenomenon was determined to be due to nuanced effects associated with self-propulsion, including enhanced mobility in confinement, efficient surface-mediated searching for holes, and the abolition of barriers at cavity exits. These mechanisms are broadly generalizable, and the previously unknown transport efficiency of nanoswimmers in complex porous environments suggests tremendous opportunities in forward-looking applications, including drug delivery and environmental remediation.
59 BASIC BIOLOGICAL SCIENCES↗
Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility
Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.
machine learningautonomous systems; flight simulat↗
Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility
Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.
machine learning↗
Emergent Capabilities Converging into M and S 2.0
The continued operational environment complexity faced by the Department of Defense, despite a restricted resource environment, is a mandate for greater adaptability and availability in joint training. To address these constraints, this paper proposes a model for the potential integration of adaptability training, virtual world capabilities and immersive training into the wider Joint Live Virtual and Constructive (JLVC) Federation, supported by human, social, cultural and behavior modeling, and measurement and assessment. By fusing those capabilities and modeling and simulation enhancements into the JLVC federation, it will create a force who is more apt to arrive at and implement correct decisions, and more able to appropriately seize initiative in the field. The model would allow for the testing and training of capabilities and TTPs that cannot be reasonably explored to their logical conclusions in a 'live' environment, as well as enhance training fidelity for all echelons and tasks.
Reitz, Emilie↗
Kilonova light-curve interpolation with neural networks
Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22 248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multiband inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle. Published by the American Physical Society 2024
79 ASTRONOMY AND ASTROPHYSICS↗
Space Weather Effects on Spacecraft Systems
Space-based systems are developing into critical infrastructure required to support the quality of life on Earth. Hence, spacecraft reliability is a serious issue that is complicated by exposure to the space environment. Complex mission designs along with rapidly evolving technologies have outpaced efforts to accommodate detrimental space environment impacts on systems. Hazardous space environments, the effects on systems, and the accommodation of the effects are described with a focus on the need to predict space environments.
Barth, Janet L.↗
Neurosymbolic Hybrid Approach to Driver Collision Warning
There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.
Wang, Pei↗