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If the Deep Space Network is to exist in a cost effective and reliable manner in the next decade, the problems presented by international energy cost increases and energy availability must be addressed. The Deep Space Network Energy Program was established to implement solutions compatible with the ongoing development of the total network.
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This paper discusses the motivation, opportunities, and problems associated with implementing digital logic at very low voltages, including the challenge of making use of the available real estate in 3D multichip modules, energy requirements of very large neural networks, energy optimization metrics and their impact on system design, modeling problems, circuit design constraints, possible fabrication process modifications to improve performance, and barriers to practical implementation.
A computer program called ECPVER (Energy Consumption Program - Verification) was developed to simulate all energy loads for any number of buildings. The program computes simulated daily, monthly, and yearly energy consumption which can be compared with actual meter readings for the same time period. Such comparison can lead to validation of the model under a variety of conditions, which allows it to be used to predict future energy saving due to energy conservation measures. Predicted energy saving can then be compared with actual saving to verify the effectiveness of those energy conservation changes. This verification procedure is planned to be an important advancement in the Deep Space Network Energy Project, which seeks to reduce energy cost and consumption at all DSN Deep Space Stations.
Large-scale applications of high-power lasers are considered (special experiments are described to confirm the feasibility of these applications) to achieve also large-scale environmental advantages. The possibility of producing electric energy by Laser-Solar Power Satellites in the near future is discussed. A full-scale experimental L-SPS satellite is suggested as a module of a global space energy network. Electric power of about 10 MW at the surface of the Earth is achievable as a result of energy conversion of laser radiation. L-SPS is based on the greatest advantages of present optics and laser techniques. Specialized-scale experiments are carried out and described. L-SPS project could provide real electricity for consumers not later than by highly developed fusion techniques, and the environmental aftereffects are quite favorable. A new method of power supply for satellites is suggested, based on the connection of an on-board electric circuit directly with the ground-based power grid by means of laser beams.
Accurate measurements for the purpose of estimating wind energies are proposed in those regions of the USA where the greatest potentials exists. Preliminary wind characteristic calculations from weather station data are provided for the Southern Great Plains region; wind energies from 153 to 212 kW-hr/(ft squared-per year) for 1970 to 1972 are determined. It is concluded that a wind energy survey based on data compiled from weather service stations is feasible for determining the energy potential of a windpowered integrated energy network.
One of the prospective, practical applications of high-temperature superconductors is the fault-current limitation in electrical energy networks. The development and testing of experimental HTSC serial current limiters have been reported in the literature. A Hungarian electric power company has proposed the development of a parallel equipment for arc suppressing both in the industrial and customers' networks. On the basis of the company's proposal the authors have outlined the scheme of a compound circuit that can be applied both for current limitation and arc suppressing. In this paper the design principles and methods of the shunt equipment are presented. These principles involve the electrical, mechanical and cryogenic aspects with the special view on the electrical and mechanical connection between the HTSC material and the current lead. Preliminary experiments and tests have been carried out to demonstrate the validity of the design principles developed. The results of the experiments and of the technological investigations are presented.
Several energy functions for synthesizing neural networks are tested on 2-D synthetic data and on Landsat-4 Thematic Mapper data. These new energy functions, designed specifically for minimizing misclassification error, in some cases yield significant improvements in classification accuracy over the standard least mean squares energy function. In addition to operating on networks with one output unit per class, a new energy function is tested for binary encoded outputs, which result in smaller network sizes. The Thematic Mapper data (four bands were used) is classified on a single pixel basis, to provide a starting benchmark against which further improvements will be measured. Improvements are underway to make use of both subpixel and superpixel (i.e. contextual or neighborhood) information in tile processing. For single pixel classification, the best neural network result is 78.7 percent, compared with 71.7 percent for a classical nearest neighbor classifier. The 78.7 percent result also improves on several earlier neural network results on this data.
Generalized disjunctive programming (GDP) models with bilinear and concave constraints, often seen in water network design, are challenging optimization problems. This work proposes quadratic and piecewise linear approximations for nonlinear terms to reformulate GDP models into quadratic GDP (QGDP) models that suitable solvers may solve more efficiently. We illustrate the benefits of the quadratic reformulation with a water treatment network design problem in which nonconvexities arise from bilinear terms in the mixers’ mass balances and concave investment cost functions of treatment units. Given the similarities with water network design problems, we suggest quadratic approximation for the GDP model for the optimal design of a large-scale reverse electrodialysis (RED) process. This power technology can recover energy from salinity differences between by-product streams of the water sector, such as desalination brine mixed with regenerated wastewater effluents. The solver Gurobi excels in handling QGDP problems, but weighing the problem’s precision and tractability balance is crucial. The piecewise linear approximation yields more accurate, yet larger QGDP models that may require longer optimization times in large-scale process synthesis problems.
Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.
Energy transfer from pulse network to mass associated with propagating current sheet in linear pinch discharge, discussing pulsed plasma accelerator efficiency
Energy transfer from pulse network to mass associated with propagating current sheet in linear pinch discharge, discussing pulsed plasma accelerator efficiency
Examination of the relation of stored electric and magnetic energy within a system to the terminal behavior of nonreciprocal passive networks shows both similarities and important differences between wholly reciprocal systems and systems containing nonreciprocal elements.
NASA’s Artemis Program outlines the need for a habitat capable of sustaining human life as well as mining and producing raw materials on the lunar surface. This mission is viewed as a means towards deeper space exploration, with plans for reaching Mars and beyond. Human presence on the moon is not possible without the ability to generate and distribute energy, namely electricity, through a network of energy sources, loads, and power converters called a microgrid. Multiple microgrids can be deployed on the moon based on location and need. Separate microgrids will require interconnection to increase resiliency and reliability given the mission’s high criticality. A method for adaptive control over power converters connecting two dc microgrids is proposed. A simulation is modeled after the lunar power system with two approaches to power converter droop control, allowing for a more flexible and adaptive microgrid architecture. Further experiments are conducted using the control methods in a power hardware-in-the-loop test environment to study the performance of hardware converter control used in this application.
Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.
A systems planning methodology was developed for analyzing and synthesizing Deep Space Network (DSN) energy systems. The resultant product, the Deep Space Network Planning and Analysis Methodology supports DSN energy planning evaluations. It addresses a broad spectrum of tradeoff and dispatching scenarios. It evaluates a variety of energy generation configurations and also includes a capability to evaluate conservation measures.
A neural network controller has been developed to accommodate disturbances and nonlinearities and improve the robustness of a magnetically suspended flywheel energy storage system. The controller is trained using the back propagation-through-time technique incorporated with a time-averaging scheme. The resulting nonlinear neural network controller improves system performance by adapting flywheel stiffness and damping based on operating speed. In addition, a hybrid multi-layered neural network controller is developed off-line which is capable of improving system performance even further. All of the research presented in this paper was implemented via a magnetic bearing computer simulation. However, careful attention was paid to developing a practical methodology which will make future application to the actual bearing system fairly straightforward.
The NASA Ames-Dryden Flight Research Facility developed a computerized aircraft battery servicing facility called the Aerospace Energy Systems Laboratory (AESL). This system employs distributed processing with communications provided by a 2.4-megabit BITBUS local area network. Customized handlers provide real time status, remote command, and file transfer protocols between a central system running the iRMX-II operating system and ten slave stations running the iRMX-I operating system. The hardware configuration and software components required to implement this BITBUS application are required.