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Multi-objective optimization involves finding one or more optimal solutions when there is more than one conflicting objective. This means that a solution that is better in one objective compromises or trades-off, other objectives. Trade Studies are conducted by flight projects to create mission concepts with different trade-off solutions for mass, cost, performance and risk.
This paper considers the dynamic spectrum access and power control problem in a single-hop point-to-point Air-Air Communication Network (AACN). Due to spectrum scarcity, we assume the number of Aircraft-to-Aircraft (A2A) communication links is greater than that of the available channels, such that some communication links need to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the Weighted Sum Spectral Efficiency (WSSE). A distributed and dynamic deep Q learning-based algorithm is proposed to find the optimal solution. Specifically, we design two different policies that are trained by conducting a trial-and-error scheme. Each communication link can achieve the optimal policy by exploiting the local information from its neighbors, and this distributive approach make it scalable to large networks. Finally, our experimental results demonstrate the effectiveness of the proposed solution in various AACN scenarios.
This paper considers the dynamic spectrum access and power control problem in a single-hop point-to-point Air-Air Communication Network (AACN). Due to spectrum scarcity, we assume the number of Aircraft-to-Aircraft (A2A) communication links is greater than that of the available channels, such that some communication links need to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the Weighted Sum Spectral Efficiency (WSSE). A distributed and dynamic deep Q learning-based algorithm is proposed to find the optimal solution. Specifically, we design two different policies that are trained by conducting a trial-and-error scheme. Each communication link can achieve the optimal policy by exploiting the local information from its neighbors, and this distributive approach make it scalable to large networks. Finally, our experimental results demonstrate the effectiveness of the proposed solution in various AACN scenarios.
This paper studies learning-based edge computing and communication in a dynamic Air-to-Air Ad-hoc Network (AAAN). Due to spectrum scarcity, we assume the number of Air-to-Air (A2A) communication links is greater than that of the available frequency channels, such that some communication links have to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the aggregate spectrum utilization efficiency under resource and fairness constraints. A distributed deep Q learning-based edge computing and communication algorithm is proposed to find the optimal solution. In particular, we design two different neural network structures and each communication link can converge to the optimal operation by exploiting only the local information from its neighbors, making it scalable to large networks. Finally, experimental results demonstrate the effectiveness of the proposed solution in various AAAN scenarios.
A test in the Unitary Plan Wind Tunnel at NASA Ames Research Center is scheduled for early 2024 with the intent of showcasing improvements made to unsteady Pressure Sensitive Paint technology. Analysis was performed to investigate how to maximize the system resolving power by optimizing the camera parameters, specifically focal length and focusing distance, while still subject to other system requirements such as field of view and relatively low illumination from the paint. Additionally, the system is upgrading to an 8-camera system from a 4-camera system, so analysis was performed to determine the best way to extend that system to maximize camera coverage and how to most effective reduce the system uncertainty by averaging multiple measurement using camera overlap. These analyses have uncovered new opportunities to improve the unsteady Pressure Sensitive Paint technology without the need for new or expensive hardware and will allow the system to collect better data with simple changes to the design process.
An analysis of the coherent mode decomposition of an optical field after propagation through atmospheric turbulence is presented. The coherent modes represent an ideal basis by which to decompose the field for design of mode-limited optical systems. Using the rotational symmetry of the Fredholm integral operator for Kolmogorov optical turbulence, it is shown that the coherent modes exhibit separable solutions classified by radial and azimuthal quantum numbers. The study of the coherent modes is then reduced that of the radial functions determined by the aperture-coherence diameter ratio and obscuration ratio. Analysis of the spectrum of eigenvalues yields sharp bounds on the efficiency of receivers using incoherent or coherent combining with mode-limited photonic devices in the presence of Kolmogorov turbulence. The effective number of modes needed to represent Kolmogorov optical turbulence is studied via the von Neumann entropy, purity, and largest eigenvalue, and the differences in the different definitions is discussed. The similarity of the coherent modes to linearly polarized (LP) fiber modes is quantified yielding a precise characterization of the maximum gain that can be achieved in mode-limited systems via mode shaping techniques. As a final application, a mode sorting technique is presented for optimally splitting power from atmospherically degraded light into a finite number of modes simultaneously maximizing total coupling efficiency and minimizing the average and instantaneous power ratio between channels.
An analysis of the coherent mode decomposition of an optical field after propagation through atmospheric turbulence is presented. The coherent modes represent an ideal basis by which to decompose the field for design of mode-limited optical systems. Due to rotational symmetry of the Fredholm integral operator for Kolmogorov optical turbulence, the coherent modes exhibit separable solutions classified by a radial and azimuthal mode index. The study of the coherent modes is then reduced to that of the radial functions determined by the coherence ratio and obscuration ratio. Analysis of the spectrum of eigenvalues yields sharp bounds on the efficiency of receivers using incoherent or coherent combining with mode-limited photonic devices in the presence of Kolmogorov turbulence. The effective number of modes needed to represent Kolmogorov optical turbulence is studied via the von Neumann entropy, purity, and largest eigenvalue, and the differences in the different definitions is discussed. The similarity of the coherent modes to linearly polarized (LP) fiber modes is quantified yielding a precise characterization of the maximum gain that can be achieved in mode-limited systems via mode shaping techniques. As a final application, a mode sorting technique is presented for optimally splitting power from atmospherically degraded light into a finite number of modes simultaneously maximizing total coupling efficiency and minimizing the difference in average power between channels.
General features of laser engines and laser piston engines are discussed in terms of optimization of operation. The efficiency of laser powered turbine and Stirling engines is also considered.
A development status evaluation is presented for methods that allow accurate preliminary design and optimization of closed Brayton cycle engines for space electrical power generation. The basis for such work is the Closed Cycle Engine Performance simulation code, in conjunction with the optimization code COPES/ADS; the joining of the two codes has greatly expedited the optimization process. Attention is given to a variety of other model-versatility enhancers.
As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.
Optimization of electrical power system for ATS
A comparison is made between projected electricity costs of an optimized space solar power station (SPS) in GEO and a moon-based SPS (LPS). A single SPS would receive sunlight on an almost uninterruptible basis. The LPS would be illumined 50 percent of the time, thus requiring two stations on opposite sides of the moon as well as three terrestrial rectennas spaced 120 deg apart. The 5 percent efficient LPS cells would be manufactured from lunar materials and built into arrays mounted on tall mounds. The steerable GEO SPS would have 15 percent efficient cells. Construction costs would be significantly lower on the moon, but the total antenna area would need to be two orders of magnitude larger to produce the same output as the SPS. It is estimated that the LPS would therefore deliver 5 GW electricity to earth stations at a cost of 511.3 mills/kW, compared to 46.8 mills/kW from the SPS. It is concluded that lunar materials are suitable for building an SPS, not a LPS.
The design optimization of a reactor-Stirling heat-pipe-cooled radiator is presented. The radiator is a self-deploying concept that uses individual finned heat pipe 'petals' to reject waste heat from a Stirling engine. Radiator optimization methodology is presented, and the results of a parametric analysis of the radiator design variables for a 100-kW(e) system are given. The additional steps of optiminzing the radiator resulted in a net system mass savings of 3 percent.
An effective form of wireless power transmission (WPT) has been developed to enable extended mission durations, increased coverage and added capabilities for both space and terrestrial applications that may benefit from optically delivered electrical energy. The high intensity laser power beaming (HILPB) system enables long range optical 'refueling" of electric platforms such as micro unmanned aerial vehicles (MUAV), airships, robotic exploration missions and spacecraft platforms. To further advance the HILPB technology, the focus of this investigation is to determine the optimal laser wavelength to be used with the HILPB receiver, which utilizes vertical multi-junction (VMJ) photovoltaic cells. Frequency optimization of the laser system is necessary in order to maximize the conversion efficiency at continuous high intensities, and thus increase the delivered power density of the HILPB system. Initial spectral characterizations of the device performed at the NASA Glenn Research Center (GRC) indicate the approximate range of peak optical-to-electrical conversion efficiencies, but these data sets represent transient conditions under lower levels of illumination. Extending these results to high levels of steady state illumination, with attention given to the compatibility of available commercial off-the-shelf semiconductor laser sources and atmospheric transmission constraints is the primary focus of this paper. Experimental hardware results utilizing high power continuous wave (CW) semiconductor lasers at four different operational frequencies near the indicated band gap of the photovoltaic VMJ cells are presented and discussed. In addition, the highest receiver power density achieved to date is demonstrated using a single photovoltaic VMJ cell, which provided an exceptionally high electrical output of 13.6 W/sq cm at an optical-to-electrical conversion efficiency of 24 percent. These results are very promising and scalable, as a potential 1.0 sq m HILPB receiver of similar construction would be able to generate 136 kW of electrical power under similar conditions.
Power point tracker for maintaining optimal output voltage of power source
Magnetic bearings have demonstrated the capability for achieving positioning accuracies at the nanometer level in precision motion control stages. This makes possible the positioning of a wafer in six degrees of freedom with the precision necessary for photolithography. To control the position of an object at the nanometer level, a model of the magnetic bearing actuator force-current-airgap relationship must be accurately obtained. Additionally, to reduce thermal effects the design of the actuator should be optimized to achieve maximum power efficiency and flux density. Optimization of the actuator is accomplished by proper pole face sizing and utilizing a magnetic core material which can be magnetized to the highest flux density with low magnetic loss properties. This paper describes the construction of a magnetic bearing calibration fixture designed for experimental measurement of the actuator force characteristics. The results of a material study that review the force properties of nickel-steel, silicon-steel, and cobalt-vanadium-iron, as they apply to magnetic bearing applications are also presented.
A human's ability to perform physical tasks is limited, not only by his intelligence, but by his physical strength. If, in an appropriate environment, a machine's mechanical power is closely integrated with a human arm's mechanical power under the control of the human intellect, the resulting system will be superior to a loosely integrated combination of a human and a fully automated robot. Therefore, we must develop a fundamental solution to the problem of 'extending' human mechanical power. The work presented here defines 'extenders' as a class of robot manipulators worn by humans to increase human mechanical strength, while the wearer's intellect remains the central control system for manipulating the extender. The human, in physical contact with the extender, exchanges power and information signals with the extender. The aim is to determine the fundamental building blocks of an intelligent controller, a controller which allows interaction between humans and a broad class of computer-controlled machines via simultaneous exchange of both power and information signals. The prevalent trend in automation has been to physically separate the human from the machine so the human must always send information signals via an intermediary device (e.g., joystick, pushbutton, light switch). Extenders, however are perfect examples of self-powered machines that are built and controlled for the optimal exchange of power and information signals with humans. The human wearing the extender is in physical contact with the machine, so power transfer is unavoidable and information signals from the human help to control the machine. Commands are transferred to the extender via the contact forces and the EMG signals between the wearer and the extender. The extender augments human motor ability without accepting any explicit commands: it accepts the EMG signals and the contact force between the person's arm and the extender, and the extender 'translates' them into a desired position. In this unique configuration, mechanical power transfer between the human and the extender occurs because the human is pushing against the extender. The extender transfers to the human's hand, in feedback fashion, a scaled-down version of the actual external load which the extender is manipulating. This natural feedback force on the human's hand allows him to 'feel' a modified version of the external forces on the extender. The information signals from the human (e.g., EMG signals) to the computer reflect human cognitive ability, and the power transfer between the human and the machine (e.g., physical interaction) reflects human physical ability. Thus the information transfer to the machine augments cognitive ability, and the power transfer augments motor ability. These two actions are coupled through the human cognitive/motor dynamic behavior. The goal is to derive the control rules for a class of computer-controlled machines that augment human physical and cognitive abilities in certain manipulative tasks.
An antenna optimization technique is applied to the environmental requirements pertaining to SPS (solar power satellite) systems to create high efficiency, multiple beams from a single antenna. Effects of rectenna spacings, subarray sizing, multiple beam degradations, antenna electrical and mechanical errors, and satellite motion are investigated. Advantages of multiple beam SPS systems include operational flexibility, lower power per rectenna, fewer satellites, smaller rectennas, and adherence to a 0.01 mW/sq cm environmental guidelines for sidelobe levels. These advantages suggest multiple beam systems are attractive alternatives to the present single beam system.