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At least 883 records · Page 49

Solar dynamic power system definition study

The solar dynamic power system design and analysis study compared Brayton, alkali-metal Rankine, and free-piston Stirling cycles with silicon planar and GaAs concentrator photovoltaic power systems for application to missions beyond the Phase 2 Space Station level of technology for all power systems. Conceptual designs for Brayton and Stirling power systems were developed for 35 kWe and 7 kWe power levels. All power systems were designed for 7-year end-of-life conditions in low Earth orbit. LiF was selected for thermal energy storage for the solar dynamic systems. Results indicate that the Stirling cycle systems have the highest performance (lowest weight and area) followed by the Brayton cycle, with photovoltaic systems considerably lower in performance. For example, based on the performance assumptions used, the planar silicon power system weight was 55 to 75 percent higher than for the Stirling system. A technology program was developed to address areas wherein significant performance improvements could be realized relative to the current state-of-the-art as represented by Space Station. In addition, a preliminary evaluation of hardenability potential found that solar dynamic systems can be hardened beyond the hardness inherent in the conceptual designs of this study.

Wallin, Wayne E.↗

Computer program system for dynamic simulation and stability analysis of passive and actively controlled spacecraft. Volume 1. Theory

A theoretical development and associated digital computer program system is presented. The dynamic system (spacecraft) is modeled as an assembly of rigid and/or flexible bodies not necessarily in a topological tree configuration. The computer program system may be used to investigate total system dynamic characteristics including interaction effects between rigid and/or flexible bodies, control systems, and a wide range of environmental loadings. Additionally, the program system may be used for design of attitude control systems and for evaluation of total dynamic system performance including time domain response and frequency domain stability analyses. Volume 1 presents the theoretical developments including a description of the physical system, the equations of dynamic equilibrium, discussion of kinematics and system topology, a complete treatment of momentum wheel coupling, and a discussion of gravity gradient and environmental effects. Volume 2, is a program users' guide and includes a description of the overall digital program code, individual subroutines and a description of required program input and generated program output. Volume 3 presents the results of selected demonstration problems that illustrate all program system capabilities.

Bodley, C. S.↗

Mars: A Planet with a Dynamic Climate System

Mars is a well-observed planet. Since the 1960s orbiters, landers, rovers, and earth-based telescopic observations show that its climate system is dynamic. Its dynamic nature, largely the result of atmosphere-surface interactions, is most obvious in the seasonal cycles of dust, water, and carbon dioxide that define the planet's climate system. These cycles are linked through the global circulation and MGS, Odyssey, Phoenix, MER, Mars Express, MRO, and now MSL have continuously observed them at Mars for the past 16 years. Their observations show that while the seasonal cycles are largely annually repeatable, there are interannual variations. Planet-encircling dust storms, for example, are quasi-triennial and originate over a broader range of seasons and locations than previously thought. Water moves from pole-to-pole each year in a largely, but not precisely, repeatable pattern that suggests but does not demand non-polar surface reservoirs. And the seasonal CO2 polar caps grow and retreat in a very predictable way with only minor deviations from year-to-year in spite of significant differences in atmospheric dust content. These behaviors suggest a complicated but robust coupled system in which these cycles interact to produce the greatest interannual variability in the dust cycle and least variability in the CO2 cycle. The nature of these interactions is the subject of ongoing research, but clouds, both water ice and CO2 ice, now appear to play a bigger role than believed at the end of the 20th century. There may also be some long-term trends in these cycles as there is evidence from imaging data, for example, that the south polar residual cap may not be stable on decadal to centennial time scales. On even longer time scales, the discovery of as much as 5 mb global equivalent of buried CO2 ice near the south pole, the detection of vast quantities of subsurface water ice at very shallow depths in midlatitudes of both hemispheres, and the presence of remnant glacial features at almost all latitudes, strongly suggests the possibility of significant climate change associated with orbital variations. Some of the major questions these data raise concern how closed the seasonal cycles are and which reservoirs are gaining or loosing, the cause of the large interannual variability of the dust cycle and how it couples to the water and CO2 cycles, and the mechanisms for the origin of past glacial activity and the emplacement and removal of subsurface ice. While many of these questions can be addressed with continued research based on existing data, new observations focused on atmosphere surface-interactions would provide valuable constraints on how dust, water, and CO2 move between the surface and atmosphere.

CO2 cycles↗

A Chaos Theoretic Approach to Animal Activity Recognition

Animal activity is a descriptor that can be potentially used to track the health and well-being, which is obviously very important to improve the management process and productivity of farms. This paper deals with an animal behavior recognition problem using a chaos theory approach where we adopt such a technique for automatic classification of calves behavioral states. Two main mutually exclusive behaviors are of interest, namely, lying and standing/walking with six possible activities: feeding, drinking water, drinking milk, playing, rumination, and neutral. The time series generated by ear-tags with a 3D-accelerometer after a wavelet denoising transformation and frequency stabilization are treated as representations of the nonlinear dynamical system. The dynamical system of a certain animal state exhibits specific strange attractor in a phase space. A characterization of such an attractor is performed through metric, dynamic, and topological invariants including Lyapunov exponent, correlation dimension, length of a phase trajectory, sum of edges forming a convex hull and others. These measures are used as a feature vector for the subsequent classification. In a cross validation scheme, six classifiers are built on each training set, and the hyper-parameters are optimized using an inner validation set. The classifier that reaches the highest accuracy on the inner validation set is used to classify the outer validation set. It is shown that this approach can be useful at predicting activity states as an alternative methodology for the animal behavior state recognition problem with acceptable classification accuracy. Furthermore it is possible to include this procedure as part of an ensemble method in machine learning where a combination of different models is used.

97 MATHEMATICS AND COMPUTING↗

Future Opportunities for Dynamic Power Systems for NASA Missions

Dynamic power systems have the potential to be used in Radioisotope Power Systems (RPS) and Fission Surface Power Systems (FSPS) to provide high efficiency, reliable and long life power generation for future NASA applications and missions. Dynamic power systems have been developed by NASA over the decades, but none have ever operated in space. Advanced Stirling convertors are currently being developed at the NASA Glenn Research Center. These systems have demonstrated high efficiencies to enable high system specific power (>8 W(sub e)/kg) for 100 W(sub e) class Advanced Stirling Radioisotope Generators (ASRG). The ASRG could enable significant extended and expanded operation on the Mars surface and on long-life deep space missions. In addition, advanced high power Stirling convertors (>150 W(sub e)/kg), for use with surface fission power systems, could provide power ranging from 30 to 50 kWe, and would be enabling for both lunar and Mars exploration. This paper will discuss the status of various energy conversion options currently under development by NASA Glenn for the Radioisotope Power System Program for NASA s Science Mission Directorate (SMD) and the Prometheus Program for the Exploration Systems Mission Directorate (ESMD).

Shaltens, Richard K.↗

Optical measurements pertaining to Space Station solar dynamic power systems

The Space Station solar dynamic power system is a hybrid of solar photovoltaic and solar dynamic systems, the latter of which uses a parabolic reflector to collect solar energy. This paper describes analytical results of an off-axis solar illumination on the intensity distribution in arbitrary target planes perpendicular to the axis of a parabolic reflector. Such computational capability would make it possible to predict optical intensity distributions resulting from off-axis angles of incident radiation on such target planes. To validate the computer code, experimental optical measurements were performed on the multifaceted paraboloidal collecor at the Solar Dynamic Test Facility at Rockedyne's Santa Susana Field Laboratory. The experimental data compared reasonably well with the calculated values.

Holly, S.↗

Observability Analysis of a Power System Stochastic Dynamic Model Using a Derivative-Free Approach

Serving as a prerequisite to power system dynamic state estimation, the observability analysis of a power system dynamic model has recently attracted the attention of many power engineers. However, because this model is typically nonlinear and large-scale, the analysis of its observability is a challenge to the traditional derivative-based methods. Indeed, the linear-approximation-based approach may provide unreliable results while the nonlinear-technique-based approach inevitably faces extremely complicated derivations. Furthermore, because power systems are intrinsically stochastic, the traditional deterministic approaches may lead to inaccurate observability analyses. In this work, facing these challenges, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only is free of any linear approximations, but also accounts for the stochasticity of the dynamic model while bringing a low implementation complexity. Furthermore, this approach enables us to quantify the degree of observability of a stochastic model, what conventional deterministic methods cannot do. The excellent performance of the proposed method has been demonstrated by performing extensive simulations using a synchronous generator model with IEEE-DC1A exciter and the TGOV1 turbine governor.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Correlation of dynamic dielectric measurements with viscosity in polymeric resin systems

Dynamic dielectric analysis has been used to study curing polymer systems and thermoplastics. Measurements have been made over a frequency range of six decades. This wide range of frequencies increases the amount of information which can be obtained. The data are analyzed in terms of the frequency dependence of the specific conductivity sigma and the relaxation time tau, parameters which are characteristic of the cure state of the material and independent of size of the sample. Dynamic dielectric measurements have been correlated with viscosity for the polysulfone thermoplastic UDEL-P1700 and with viscosity and ultrasonic measurements on the DGEBA-type epoxy Epon 828 cured with Agent U. The experimental results suggest that when ionic processes dominate the dielectric response, the intensive property sigma is a good monitor of the resin's viscosity. The results show that the dielectric relaxation time tau can be used to monitor the state of the system and the extent and rate of the reaction.

Kranbuehl, D.↗

Power System Frequency Dynamics Modeling, State Estimation, and Control using Neural Ordinary Differential Equations (NODEs) and Soft Actor-Critic (SAC) Machine Learning Approaches

With the global energy transition of the electric power system, grid control, supervision, and protection is becoming more challenging. With the increasing integration of renewable energy sources (RES), the system dynamics are changing, causing traditional power system dynamic modeling with swing equation-based modeling approaches to fail. Additionally, the converter-dominated power grid is decreasing the system inertia, making the power system more fragile to the frequency swings. This paper first investigates and compares the application of a model-based Kalman filter state estimation approach with (i) a model-free machine learning approach --- neural ordinary differential equations (NODEs) --- and (ii) a data-driven system identification (SysId) approach to model and infer critical state values of the power system frequency dynamics. Then a model predictive control (MPC) framework is compared to a model-free Soft Actor-Critic (SAC) reinforcement learning (RL) control algorithm in providing efficient fast frequency response (FFR) to the power system frequency dynamics. The approaches are compared in terms of their performance goals as well as their per-timestep computational efficiency. Furthermore, the comparative study for state estimation shows that for the model-free requirement, both NODEs and SysId can provide accurate state estimates; however, with increasing model complexity, NODEs can be a better choice for model identification. Similarly, the results from the FFR comparative study show that the SAC RL-based FFR, once trained, outperforms MPC with better control signals and faster computation time, making the SAC RL-based FFR better option for providing FFR to the power system.

97 MATHEMATICS AND COMPUTING↗

Optimal Power Sharing Speed Compensation in On-road Wireless EV Charging Systems

Dynamic wireless charging of electric vehicles (EV) is an emerging technology with the potential to address range anxiety and reduce the size of batteries or provide charge-sustaining operation. Charging demand for dynamic wireless charging systems (DWCS) varies greatly in response to location-specific traffic behaviors including the number and speed of vehicles. This paper highlights the potential reduction of load variation with speed compensation and simulates opportunities to maximize the number of cars charged concurrently through “power sharing” or altering power output to slower cars while maintaining a maximum delivered energy. An improved sub-minute model for synthetic traffic is proposed to effectively model DWCS load on high speed roadways and a power electronics model is created based on an existing prototype developed by ORNL to investigate the potential for power sharing. Reductions in the average speed of traffic can greatly increase instantaneous DWCS load by as much as 26%, complicating capacity sizing. Parametric studies with a LCC-S power electronics model shows feasible power reduction of more than 20%, while maintaining a maximum achievable efficiency greater than 90%. Speed compensation on a roadway with large speed variation can reduce average power expected by 21% and increase the maximum number of cars charged simultaneously by 30%. The application of power sharing may significantly reduce load variability due to speed, allow for increased car hosting capability, and guarantee a maximum energy delivered.

Lewis, Donovin D.↗

A Gamma Memory Neural Network for System Identification

A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

Motter, Mark A.↗

Learning to Control Advanced Life Support Systems

Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for advanced life support.

Subramanian, Devika↗

Dynamic interferometer alignment and its utility in UV Fourier transform spectrometer systems

Dynamic alignment has been demonstrated as a practical approach to alignment maintenance for systems in the infrared region of the spectrum. On the basis of work done by OPTRA, this technique was introduced in commercial Fourier transform spectrometer systems in 1982 and in various forms is now available from a number of manufacturers. This paper reports on work by OPTRA to extend the basic technique to systems operating in the ultraviolet. In addition, this paper reports the preliminary results of the development of an alignment system using a laser diode in place of a gas laser normally found in dynamic alignment systems. A unique optical system and spatial heterodyne technique allows for achievement of a metrology system with characteristics that fully satisfy the requirements of an ultraviolet spectrometer system.

Dorval, Rick K.↗