Index to NASA Tech Briefs, 1975

This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Model Predictive Control Based on Deep Learning for Solar Parabolic-Trough Plants

En la actualidad, cada vez es mayor el interés por utilizar energías renovables, entre las que se encuentra la energía solar. Las plantas de colectores cilindro-parabólicos son un tipo de planta termosolar donde se hace incidir la radiación del Sol en unos tubos mediante el uso de unos espejos con forma de parábola. En el interior de estos tubos circula un fluido, generalmente aceite o agua, que se calienta para generar vapor y hacer girar una turbina, produciendo energía eléctrica. Uno de los métodos más utilizados para manejar estas plantas es el control predictivo basado en modelo (model predictive control, MPC), cuyo funcionamiento consiste en obtener las señales de control óptimas que se enviarán a la planta basándose en el uso de un modelo de la misma. Este método permite predecir el estado que adoptará el sistema según la estrategia de control escogida a lo largo de un horizonte de tiempo. El MPC tiene como desventaja un gran coste computacional asociado a la resolución de un problema de optimización en cada instante de muestreo. Esto dificulta su implementación en plantas comerciales y de gran tamaño, por lo que, actualmente, uno de los principales retos es la disminución de estos tiempos de cálculo, ya sea tecnológicamente o mediante el uso de técnicas subóptimas que simplifiquen el problema. En este proyecto, se propone el uso de redes neuronales que aprendan offline de la salida proporcionada por un controlador predictivo para luego poder aproximarla. Se han entrenado diferentes redes neuronales utilizando un conjunto de datos de 30 días de simulación y modificando el número de entradas. Los resultados muestran que las redes neuronales son capaces de proporcionar prácticamente la misma potencia que el MPC con variaciones más suaves de la salida y muy bajas violaciones de las restricciones, incluso disminuyendo el número de entradas. El trabajo desarrollado se ha publicado en Renewable Energy, una revista del primer cuartil en Green & sustainable science & technology y Energy and fuels.Nowadays, there is an increasing interest in using renewable energy sources, including solar energy. Parabolic trough plants are a type of solar thermal power plant in which solar radiation is reflected onto tubes with parabolic mirrors. Inside these tubes circulates a fluid, usually oil or water, which is heated to generate steam and turn a turbine to produce electricity. One of the most widely used methods to control these plants is model predictive control (MPC), which obtains the optimal control signals to send to the plant based on the use of a model. This method makes it possible to predict its future state according to the chosen control strategy over a time horizon. The MPC has the disadvantage of a significant computational cost associated with resolving an optimization problem at each sampling time. This makes it challenging to implement in commercial and large plants, so currently, one of the main challenges is to reduce these computational times, either technologically or by using suboptimal techniques that simplify the problem. This project proposes the use of neural networks that learn offline from the output provided by a predictive controller to then approximate it. Different neural networks have been trained using a 30-day simulation dataset and modifying the number of irradiance and temperature inputs. The results show that the neural networks can provide practically the same power as the MPC with smoother variations of the output and very low violations of the constraints, even when decreasing the number of inputs. The work has been published in Renewable Energy, a first quartile journal in Green & sustainable science & technology and Energy and fuels.Universidad de Sevilla. Máster en Ingeniería Industria

Improved modelling of microgrid distributed energy resources with machine learning algorithms

Mención Internacional en el título de doctorRenewable energy technologies are being increasingly adopted in many countries around the world. However, the deployment of these power generation systems is becoming more diverse than ever, from small generation units in individual houses to massive power production plants. In that spectrum, distributed energy resources (DERs) cover systems from the low- to the middle-power ranges. The operation, control and assessment of these technologies is becoming more complex, and structures such as microgrids (MGs) may provide a suitable ecosystem to manage them. The beginning of this thesis covers the fundamentals of MG systems. A review was conducted by analysing the MG in a layer perspective, where each layer corresponded to a topic such as operation, business or standards, among others. The advancements in electronics, computer power and storage capability have created a paradigm in which massive amounts of data are generated and computed. The electrical sector has introduced many data acquisition technologies to assess the grid and its components. Classical modelling approaches have applied physical, chemical or electrical algorithms to model the behaviours of DERs. Nevertheless, with the extensive amount of information at our disposal, data-driven techniques such as machine learning (ML) may provide more individualised models to simulate the behaviour of these power generation technologies with the particularities of both their components and their location. Following the MG review, its power generation technologies were analysed. The information from 1,618 MGs around the world have been aggregated and studied. Also, two MG infrastructure model generators have been proposed (considering the infrastructure as the power generation technology and their rated power of an MG.). One of the models is based on the statistical data aggregated in tables and the other is based on ML techniques. The latter, which provides more particularised results, is able to generate the most typical MG infrastructure for a given location and segment of operation. Ideally, each of the DERs of a MG should be modelled, but, given the time constraints of a PhD, only the principal renewable generation technologies have been studied. Hence, ML models of photovoltaic (PV) systems plus a battery and wind energy conversion systems (WECS) have been proposed. Various ML models for PV systems were developed in two studies. First, an ML model for PV power estimation was performed using data from two real PV farms and validated using deterministic models from the literature. The ML algorithm was performed using neural networks and automatic strategies to clean the data. Neural network accuracy when trained and tested in the same location yields solid results which can be applied in performance ratio tools for PV power stations. In the second study, various mathematical models are proposed. This study provides several models for computing the annual optimum tilt angle for both fixed PV arrays and solar collectors. The optimum tilt algorithm proposed can be calculated in the absence of meteorological or software tools. To generate these models, data were collected from 2,551 sites across the world. A regression analysis with polynomial fitting, neural networks and decision trees was performed. Despite the better performance of the ML models, the ease of use of polynomial algorithms is recommended for those sites with no access to computational tools or meteorological data. The performances of the models were validated using previous research algorithms. Also, an ML algorithm was proposed to estimate the state of charge of a lithium-ion battery. The available capacity in a battery is an important feature when operating these kinds of systems. Given the complex behaviours of a battery, data-driven algorithms are able to capture the dynamic behaviours of a battery. Based on the data obtained in different experiments performed in a laboratory, an ensemble method, gradient boosting algorithm, was trained to model the state of charge of the battery. Even though the state of health of the storage system was below the theoretical life expectancy, the model was able to provide solid results. The model was validated with non-trained data. Finally, data-driven techniques were applied to model different elements of WECS. The first study provided two power coefficient algorithms, one based on polynomial fitting and another based on neural networks. To train the models, data from a corrected blade element momentum algorithm was used and three sets of data representing different wind turbine ranges, from 2 to 10 MW, were generated. Both models were validated with three datasets of real wind turbines and compared with the existing literature equations. Compared to previous equations, errors were reduced by at least 55% with the best numerical approximation from the literature. This type of reduction has a great impact for WECS dynamic and transient studies. The second study proposed for WECS develops three different ML models: one estimates the power of individual WECS, the second aggregates the data from all the WECS and estimates their power and the last one estimates the power of an entire wind farm. Given the stochastic and dynamic behaviours of the systems modelled, data pre-processing should be performed. Along with default cleaning techniques, a Student-t copula has been proposed so outliers can be automatically removed. Results show that the neural network algorithms’ performances for the three models can be improved without excessive manual intervention in the development process. Traditionally, electrical, physical and chemical models have been applied to mimic the behaviour of power systems. Now, with the power of computer and storage systems, a new era of more customised models has begun. It is time to review the existing models and provide better solutions by using ML techniques. In this thesis, only a few DERs have been modelled, but the results show that huge improvements can be made and future work in this subject should be done. The ML models proposed can be applied either as individual models for performance assessment of each DER or as a complementary tool to dynamic or static studies, unit commitment and planning software, among others.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Veganzones Nicolás.- Secretaria: María Ángeles Moreno López de Saa.- Vocal: Athanasios Kolio

NASA Tech Briefs Index, 1977, volume 2, numbers 1-4

Announcements of new technology derived from the research and development activities of NASA are presented. Abstracts, and indexes for subject, personal author, originating center, and Tech Brief number are presented for 1977

Active control of split system domestic solar water heaters

Solar water heaters have the potential to make large savings in greenhouse gas emissions in Australia. Long financial payback periods are the main reason that uptake of solar water heating is not more significant. This thesis investigates the potential improvement in performance of split-system solar water heaters by the addition of an active control system. This work builds upon "low flow" collector circulation theory and addresses the poor control available from the storage tank thermostat. Modelling suggests that the thermal efficiency of the water heater can be improved by about 25°/o, primarily through reduction of tank standing losses, if the thermostat is replaced by a smart controller. Auxiliary energy consumption is reduced proportionally. If realisable, these savings recover the capital cost of the additional controller in several years. The consumer will benefit from further savings in auxiliary energy consumption over the life of the system and so the payback will be more attractive. The active control strategy is based upon predicting and controlling the energy content of the storage tank. The control strategy is energy tariff sensitive and may be set by the householder to behave in an energy efficient or a cost effective manner. A number of technologies and design improvements regarding forecasting of the energy supply and demand were also developed in this work. The auxiliary heater was moved outside of the tank and placed in series with the solar collector via a switching valve arrangement. The collector circulation pump was also used to circulate water through the auxiliary heater effectively providing a variable volume, variable temperature thermostat. A new variable power pump controller was developed for the existing circulation pump to allow fine temperature control of water returning from both the auxiliary and solar heat sources so that disruption to thermal stratification in the tank was minimised. The predictive performance of the collector could then be decoupled from the state of the tank. This thesis explores a practical implementation of the active control strategy and provides an insight into the actual performance and areas of sensitivity of the technology. The proposed design changes require more thorough validation including field trials to evaluate the load learning algorithms. Performance of the active controller would be improved if the heating circuit intake position could be actuated vertically within the tank or if hot and cold water could be fully separated in the tank

Modern optical astronomy: technology and impact of interferometry

The present `state of the art' and the path to future progress in high spatial resolution imaging interferometry is reviewed. The review begins with a treatment of the fundamentals of stellar optical interferometry, the origin, properties, optical effects of turbulence in the Earth's atmosphere, the passive methods that are applied on a single telescope to overcome atmospheric image degradation such as speckle interferometry, and various other techniques. These topics include differential speckle interferometry, speckle spectroscopy and polarimetry, phase diversity, wavefront shearing interferometry, phase-closure methods, dark speckle imaging, as well as the limitations imposed by the detectors on the performance of speckle imaging. A brief account is given of the technological innovation of adaptive-optics (AO) to compensate such atmospheric effects on the image in real time. A major advancement involves the transition from single-aperture to the dilute-aperture interferometry using multiple telescopes. Therefore, the review deals with recent developments involving ground-based, and space-based optical arrays. Emphasis is placed on the problems specific to delay-lines, beam recombination, polarization, dispersion, fringe-tracking, bootstrapping, coherencing and cophasing, and recovery of the visibility functions. The role of AO in enhancing visibilities is also discussed. The applications of interferometry, such as imaging, astrometry, and nulling are described. The mathematical intricacies of the various `post-detection' image-processing techniques are examined critically. The review concludes with a discussion of the astrophysical importance and the perspectives of interferometry.Comment: 65 pages LaTeX file including 23 figures. Reviews of Modern Physics, 2002, to appear in April issu

Numerical modelling of a parabolic trough solar collector

Concentrated Solar Power (CSP) technologies are gaining increasing interest in electricity generation due to the good potential for scaling up renewable energy at the utility level. Parabolic trough solar collector (PTC) is economically the most proven and advanced of the various CSP technologies. The modelling of these devices is a key aspect in the improvement of their design and performances which can represent a considerable increase of the overall efficiency of solar power plants. In the subject of modelling and improving the performances of PTCs and their heat collector elements (HCEs), the thermal, optical and aerodynamic study of the fluid flow and heat transfer is a powerful tool for optimising the solar field output and increase the solar plant performance. This thesis is focused on the implementation of a general methodology able to simulate the thermal, optical and aerodynamic behaviour of PTCs. The methodology followed for the thermal modelling of a PTC, taking into account the realistic non-uniform solar heat flux in the azimuthal direction is presented. Although ab initio, the finite volume method (FVM) for solving the radiative transfer equation was considered, it has been later discarded among other reasons due to its high computational cost and the unsuitability of the method for treating the finite angular size of the Sun. To overcome these issues, a new optical model has been proposed. The new model, which is based on both the FVMand ray tracing techniques, uses a numerical-geometrical approach for considering the optic cone. The effect of different factors, such as: incident angle, geometric concentration and rim angle, on the solar heat flux distribution is addressed. The accuracy of the new model is verified and better results than the Monte Carlo Ray Tracing (MCRT) model for the conditions under study are shown. Furthermore, the thermal behaviour of the PTC taking into account the nonuniform distribution of solar flux in the azimuthal direction is analysed. A general performance model based on an energy balance about the HCE is developed. Heat losses and thermal performances are determined and validated with Sandia Laboratories tests. The similarity between the temperature profile of both absorber and glass envelope and the solar flux distribution is also shown. In addition, the convection heat losses to the ambient and the effect of wind flow on the aerodynamic forces acting on the PTC structure are considered. To do this, detailed numerical simulations based on Large Eddy simulations (LES) are carried out. Simulations are performed at two Reynolds numbers of ReW1 = 3.6 × 105 and ReW2 = 1 × 106. These values corresponds to working conditions similar to those encountered in solar power plants for an Eurotrough PTC. The study has also considered different pitch angles mimicking the actual conditions of the PTC tracking mechanism along the day. Aerodynamic loads, i.e. drag and lift coefficients, are calculated and validatedwith measurements performed in wind tunnels. The indepen-dence of the aerodynamic coefficients with Reynolds numbers in the studied range is shown. Regarding the convection heat transfer taking place around the receiver, averaged local Nusselt number for the different pitch angles and Reynolds numbers have been computed and the influence of the parabola in the heat losses has been analysed. Last but not the least, the detailed analysis of the unsteady forces acting on the PTC structure has been conducted by means of the power spectra of several probes. The analysis has led to detect an increase of instabilities when moving the PTC to intermediate pitch angles. At these positions, the shear-layers formed at the sharp corners of the parabola interact shedding vortices with a high level of coherence. The coherent turbulence produces vibrations and stresses on the PTC structure which increase with the Reynolds number and eventually, might lead to structural failure under certain conditions

A double-diffusive interface tank for dynamic-response studies

Author Posting. © Sears Foundation for Marine Research, 2005. This article is posted here by permission of Sears Foundation for Marine Research for personal use, not for redistribution. The definitive version was published in Journal of Marine Research 63 (2005): 263-289, doi:10.1357/0022240053693842.A large tank capable of long-term maintenance of a sharp temperature-salinity interface has been developed and applied to measurements of the dynamical response of oceanographic sensors. A two-layer salt-stratified system is heated from below and cooled from above to provide two convectively mixed layers with a thin double-diffusive interface separating them. A temperature jump exceeding 10°C can be maintained over 1–2 cm (a vertical temperature gradient of order 103°C/m) for several weeks. A variable speed-lowering system allows testing of the dynamic response of conductivity and temperature sensors in full-size oceanographic instruments. An acoustic echo sounder and shadowgraph system provide nondisruptive monitoring of the interface and layer microstructure. Tests of several sensor systems show how data from the facility is used to determine sensor response times using several fitting techniques and the speed dependence of thermometer time constants is illustrated. The linearity of the conductivity–temperature relationship across the interface is proposed as a figure of merit for design of lag-correction filters to accurately match temperature and conductivity sensors for the computation of salinity. The effects of finite interface thickness, slow sensor sampling rates and the thermal mass of the conductivity cell are treated. Sensor response characterization is especially important for autonomous instruments where data processing and compression must be performed in-situ, but is also helpful in the development of new sensors and in assuring accurate salinity records from traditional wire-lowered and towed systems.This research was supported by the National Science Foundation, grants OCE-97-11869 and OCE-02-40956, NOAA CORC grant 154368 and a WHOI Mellon Technical Staff Award