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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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One of the first questions facing the designer of the control system for a large space structure is how many components actuators and sensors - to specify and where to place them on the structure. This paper presents a methodology which is intended to assist the designer in making these choices. A measure of controllability is defined which is a quantitative indication of how well the system can be controlled with a given set of actuators. Similarly, a measure of observability is defined which is a quantitative indication of how well the system can be observed with a given set of sensors. Then the effect of component unreliability is introduced by computing the average expected degree of controllability (observability) over the operating lifetime of the system accounting for the likelihood of various combinations of component failures. The problem of component location is resolved by optimizing this performance measure over the admissible set of locations. The variation of this optimized performance measure with number of actuators (sensors) is helpful in deciding how many components to use.
The usefulness of the concept of spent-beam refocusing in optimizing the performance of low- and medium-power helix TWTs (traveling-wave tubes) equipped with small multistage depressed collectors (MDCs) is examined. Several direct comparisons of the performance of individually optimized TWT-MDC combinations with and without controlled beam expansion and recollimation are presented. The collector efficiency of a number of representative space and airborne TWT-MDCs, which do not use refocusing, is compared to a measure of quality (standard of excellence) established by an examination of irrecoverable MDC losses. The results suggest that the application of the traditional concept of spent-beam refocusing to most helix TWTs that are equipped with small MDCs in order to obtain maximum efficiency is not required (or even desired). Two effects combine to obviate the need for refocusing: 1) the unexpanded beam more nearly meets the `point source' ideal at the input to the small MDC, and 2) the larger space-charge spreading and the slightly larger (but manageable) injection angles of the unexpanded beam can reduce the amount of backstreaming current and the attendant loss in efficiency.
Sensitivity to variations in electrical and mechanical characteristics reduced. Proposed control system for motor-driven rotary actuator includes three nested feedback loops which, when properly designed, decoupled from each other. Intended to increase accuracy of control by mitigating such degrading effects as vibrations and variations in electrical and mechanical characteristics of structure rotated. Lends itself to optimization of performance via independent optimization of each of three loops. Includes outer, actuator, and driver feedback loops, configured so that actuator is subsystem, and driver is subsystem of actuator.
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It is demonstrated how the design of graded-reflectance output coupler unstable laser cavities may be tailored to significantly enhance the overall power transmission efficiency of a given laser system relative to that of a conventional diffractively coupled unstable resonator. The importance of these findings in coherent lidar applications is explained with particular emphasis on projected space-based systems.
The viability of photovoltaics on the Lunar and Martian surfaces may be determined by their ability to withstand significant degradation in the Lunar and Martian environments. One of the greatest threats is posed by fine dust particles which are continually blown about the surfaces. In an effort to determine the extent of the threat, and to investigate some abatement strategies, a series of experiments were conducted outdoors and in the Moon and Mars environmental chamber at the Florida Solar Energy Center. Electrodynamic dust shield prototypes based on the electric curtain concept have been developed by our collaborators at the Kennedy Space Center [1]. These thin film layers can remove dust from surfaces and prevent dust accumulation. Several types of dust shields were designed, built and tested under high vacuum conditions and simulated lunar gravity to validate the technology for lunar exploration applications. Gallium arsenide, single crystal and polycrystalline silicon photovoltaic integrated devices were designed, built and tested under Moon and Mars environmental conditions as well as under ambient conditions. Photovoltaic efficiency measurements were performed on each individual cell with the following configurations; without an encapsulation layer, with a glass covering, and with various thin film dust shields. It was found that the PV efficiency of the hybrid systems was unaffected by these various thin film dust shields, proving that the optical transmission of light through the device is virtually uninhibited by these layers. The future goal of this project is to incorporate a photovoltaic cell as the power source for the electrodynamic dust shield system, and experimentally show the effective removal of dust obstructing any light incident on the cell, thus insuring power production is maximized over time.
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In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.
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The performance of the quasi-procedural analysis system in optimization tasks is investigated. In particular, the quasi-procedural method is applied to the complete mission optimization of a medium-sized transport aircraft using a vortex-lattice aerodynamic model and finite element structural analysis of the wing and the tail. Some modifications and improvements to the system, required in order to complete this task, are described. The performance of the system is compared with that of the standard procedural system, and it is shown that the quasi-procedural approach reduces the analysis required to reach the optimum. The overall cost is shown to be strongly dependent on the analysis architecture and data structure.
With connections to bounded rational game theory, information theory and statistical mechanics, Product Distribution (PD) theory provides a new framework for performing distributed optimization. Furthermore, PD theory extends and formalizes Collective Intelligence, thus connecting distributed optimization to distributed Reinforcement Learning (FU). This paper provides an overview of PD theory and details an algorithm for performing optimization derived from it. The approach is demonstrated on two unconstrained optimization problems, one with discrete variables and one with continuous variables. To highlight the connections between PD theory and distributed FU, the results are compared with those obtained using distributed reinforcement learning inspired optimization approaches. The inter-relationship of the techniques is discussed.
The Evolutionary aLgorithm for Iterative Studies of Aeromechanics (ELISA) was developed in support of the Rotorcraft Optimization for the Advancement of Mars eXploration (ROAMX) project. ELISA was developed to enable aerodynamic rotor hover optimization for low Reynolds number flows in the Mars atmosphere. The first objective of the algorithm allows for unconventional airfoil parameterization and multi-objective airfoil geometry optimization using OVERFLOW. The Pareto-optimal airfoil sets are converted to a set of Pareto-optimal airfoil decks, providing the lowest drag air foil geometry for each angle of attack, removing the need to arbitrarily select the airfoils to be used in the rotor optimization. The second objective allows for rotor geometry optimization with simultaneous maximization of blade loading and minimization of rotor power using the comprehensive analysis CAMRADII. The result is a Pareto-optimal rotor set, providing the lowest power rotor for each attainable blade loading, and one of the first tools for hover-optimized rotors for high-subsonic low Reynolds number conditions. The airfoil thickness can be modified after the airfoil optimization is complete, allowing for a post-airfoil-optimization adjustment of blade thickness to facilitate conforming to external structural analyses requirements. The relevance of the code is demonstrated with case studies for the ROAMX rotor optimization for Ingenuity-sized single rotors in the Mars atmosphere, a performance study optimizing the chord and twist of Ingenuity’s coaxial rotor resulting in the Sample Recovery Helicopters candidate rotor, and high-subsonic low Reynolds number airfoil optimization providing novel insights for higher-efficiency low Reynolds number airfoil geometries and flow physics.
Many scientific and engineering applications require repeated calculations of derivatives of output functions with respect to input parameters. Automatic Differentiation (AD) is a method that automates derivative calculations and can significantly speed up code development. In Computational Fluid Dynamics (CFD), derivatives of flux functions with respect to state variables (Jacobian) are needed for efficient solutions of the nonlinear governing equations. AD of flux functions on graphics processing units (GPUs) is challenging as flux computations involve many intermediate variables that create high register pressure and require significant memory traffic because of the need to store the derivatives. This paper presents a forward-mode AD method based on multivariate dual numbers that addresses these challenges and simultaneously reduces the floating-point operation count. The dimension of the multivariate dual numbers is optimized for performance. The flux computations are restructured to minimize the number of temporary variables and reduce register pressure. For effective utilization of memory bandwidth, shared memory is used to store the local flux Jacobian. This AD implementation is compared with several other Jacobian implementations on an NVIDIA V100 GPU (V100). For three-dimensional perfect-gas compressible-flow equations implemented in a practical CFD code, the AD implementation of a flux Jacobian based on multivariate dual numbers of dimension 5 outperforms all other GPU AD implementations on V100. Its performance is comparable with the optimized hand-differentiated version. Finally, the implementation achieves 75% of the peak floating-point throughput and 61 % of the peak global device memory bandwidth usage.
Application of optimal control theory to practical problems has been limited by the difficulty of prescribing a performance index which accurately reflects design requirements. The task of deriving equivalent performance indices is considered in the present paper for a plant that is a completely controllable, scalar linear system with state feedback. A quadratic index is developed which leads to an optimal design performance satisfying some of the classical performance criteria.
This paper presents a novel modular, reconfigurable battery energy storage system. The proposed design is characterized by a tight integration of reconfigurable power switches and DC/DC converters. This characteristic enables isolation of faulty cells from the system and allows fine power control for individual cells toward optimal system-level performance. An optimal power management approach is developed to extensively exploit the merits of the proposed design. Based on receding-horizon convex optimization, this approach aims to minimize the total power losses in charging/discharging while allocating the power in line with each cell’s condition to achieve state of-charge (SoC) and temperature balancing. By appropriate design, the approach manages to regulate the power of a cell across its full SoC range and guarantee the feasibility of the optimization problem. Here, we perform extensive simulations and further develop a lab-scale prototype to validate the proposed system design and power management approach.