A Review of using Data Mining Techniques in Power Plants

Data mining techniques and their applications have developed rapidly during the last two decades. This paper reviews application of data mining techniques in power systems, specially in power plants, through a survey of literature between the year 2000 and 2015. Keyword indices, articles’ abstracts and conclusions were used to classify more than 86 articles about application of data mining in power plants, from many academic journals and research centers. Because this paper concerns about application of data mining in power plants; the paper started by providing a brief introduction about data mining and power systems to give the reader better vision about these two different disciplines. This paper presents a comprehensive survey of the collected articles and classifies them according to three categories: the used techniques, the problem and the application area. From this review we found that data mining techniques (classification, regression, clustering and association rules) could be used to solve many types of problems in power plants, like predicting the amount of generated power, failure prediction, failure diagnosis, failure detection and many others. Also there is no standard technique that could be used for a specific problem. Application of data mining in power plants is a rich research area and still needs more exploration

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Investigation of the chemical vapor deposition of Cu from copper amidinate through data driven efficient CFD modelling

peer reviewedA chemical reaction model, consisting of two gas-phase and a surface reaction, for the deposition of copper from copper amidinate is investigated, by comparing results of an efficient, reduced order CFD model with experiments. The film deposition rate over a wide range of temperatures, 473K-623K, is accurately captured, focusing specifically on the reported drop of the deposition rate at higher temperatures, i.e above 553K that has not been widely explored in the literature. This investigation is facilitated by an efficient computational tool that merges equation-based analysis with data-driven reduced order modeling and artificial neural networks. The hybrid computer-aided approach is necessary in order to address, in a reasonable time-frame, the complex chemical and physical phenomena developed in a three-dimensional geometry that corresponds to the experimental set-up. It is through this comparison between the experiments and the derived simulation results, enabled by machine-learning algorithms that the prevalent theoretical hypothesis is tested and validated, illuminating the possible underlying dominant phenomena

Small business innovation research. Abstracts of 1988 phase 1 awards

Non-proprietary proposal abstracts of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA are presented. Projects in the fields of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robots, computer sciences, information systems, data processing, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

Accurate pressure drop estimation in forced boiling phenomena is important during the thermal analysis and the geometric design of cryogenic heat exchangers. However, current methods to predict the pressure drop have one of two problems: lack of accuracy or generalization to different situations. In this work, we present the correlated-informed neural networks (CoINN), a new paradigm in applying the artificial neural network (ANN) technique combined with a successful pressure drop correlation as a mapping tool to predict the pressure drop of zeotropic mixtures in micro-channels. The proposed approach is inspired by Transfer Learning, highly used in deep learning problems with reduced datasets. Our method improves the ANN performance by transferring the knowledge of the Sun & Mishima correlation for the pressure drop to the ANN. The correlation having physical and phenomenological implications for the pressure drop in micro-channels considerably improves the performance and generalization capabilities of the ANN. The final architecture consists of three inputs: the mixture vapor quality, the micro-channel inner diameter, and the available pressure drop correlation. The results show the benefits gained using the correlated-informed approach predicting experimental data used for training and a posterior test with a mean relative error (mre) of 6%, lower than the Sun & Mishima correlation of 13%. Additionally, this approach can be extended to other mixtures and experimental settings, a missing feature in other approaches for mapping correlations using ANNs for heat transfer applications

Modeling Methods for Hydraulic Fracturing Wastewater Treatment Applications

Modeling of hydraulic fracturing wastewater treatment systems must be very accurate so it can be used for real-time modeling and control of the process. The treatment of hydraulic fracturing wastewater is complex because of the variability of the composition of hydraulic fracturing wastewater. In this work, literature review of hydraulic fracturing wastewater and its existing treatment options is presented to demonstrate the difficulties of treating this type of wastewater, including the presence of azeotropic contaminants that cannot be removed by straightforward distillation. Additionally, modeling of individual water treatment system components, in particular low-pressure Venturi mixing nozzles, was conducted using several modeling methods, including analytical, empirical, and neural network models. Both analytical and empirical models were determined to be insufficiently accurate for wastewater treatment applications despite the empirical models being at least twice as accurate as the analytical model, for this application. To create a more general, computationally efficient, and accurate model, artificial neural networks were implemented. The concept of physics-guided artificial neural networks is introduced and evaluated on three different multi-species mixing applications. It was found that physics-guided artificial neural networks can reduce the error of the model by up to 40% for a given network architecture or can reduce the network architecture, and thus computational intensity, by up to 60% for a given error value, as compared to traditional black box artificial neural networks. In order to train the physics-guided network model, both the analytical and empirical models must be used. Once the physics-guided network model is trained it can be applied to any case within its training range with low error and computational intensity. The now-proven physics-guided network model concept can be applied to full wastewater treatment technologies, with sufficiently low error and computational intensity to be used for real-time modeling, control, and optimization of the system, using critical input parameters identified by literature review of existing treatment options.

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Current status and future trends of computational methods to predict frost formation

Nowadays, the increasing energy prices and associated environmental concerns lead the refrigeration systems’ developers and manufacturers to develop more energy efficient and sustainable equipment and devices. On the most demanding systems, intense usage results in the fast accumulation of ice on the evaporator fins that reduces the efficiency and may even clog the system. These systems often have time-controlled defrost cycles, that heat the evaporator, melting the ice and allowing the system to keep working normally after the defrost cycle. This cycle consumes extra energy and causes a thermal imbalance on the refrigerated space, that may result in a worst refrigeration quality. If it was possible to avoid the defrosting cycle passively (without energy consumption) its efficiency would greatly increase, and the refrigeration temperature would be more stable. Currently defrost cycles cannot be avoided in an economically viable way, although new designs, materials and configurations show promising results, and are currently being investigated. These studies require experimental tests that may become expensive as several geometries, topologies, materials and surface treatment combinations should be evaluated. To access the efficiency before these experimental tests, computational models that simulate frost formation could predict with some accuracy which of the most promising configurations should be then tested experimentally. The present paper aims to review the computational methods to predict frost formation and compare them for possible usage in the computational study of evaporators. Additionally, the future trends of the simulations are discussed, taking into account physical and mathematical models, numerical procedures and the accuracy of the dynamic pattern of the predictions.info:eu-repo/semantics/publishedVersio