1.

Record Nr.

UNINA9910716883003321

Autore

Allison John M.

Titolo

Tank tests of Model 36 flying-boat hull / / by John M. Allison

Pubbl/distr/stampa

Washington, [D.C.] : , : National Advisory Committee for Aeronautics, , 1938

Descrizione fisica

1 online resource (9 pages, 16 unnumbered pages) : illustrations

Collana

Technical note / National Advisory Committee for Aeronautics ; ; No. 638

Soggetti

Seaplanes - Hulls

Seaplanes - Models - Testing

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

"March 1938."

No Federal Depository Library Program (FDLP) item number.

Nota di bibliografia

Includes bibliographical references (page 8).



2.

Record Nr.

UNINA9910253992703321

Autore

Majumder Mrinmoy

Titolo

Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques / / by Mrinmoy Majumder, Apu K. Saha

Pubbl/distr/stampa

Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2016

ISBN

981-287-308-2

Edizione

[1st ed. 2016.]

Descrizione fisica

1 online resource (58 p.)

Collana

SpringerBriefs in Energy, , 2191-5539

Disciplina

621.042

Soggetti

Renewable energy sources

Computational intelligence

Electric power production

Environmental economics

Climatology

Renewable Energy

Computational Intelligence

Electrical Power Engineering

Mechanical Power Engineering

Environmental Economics

Climate Sciences

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references at the end of each chapters.

Nota di contenuto

Introduction -- Justification -- Solar Energy -- Solar Energy -- Importance -- Benefits of Solar energy -- MCDM -- Definitions -- Applications -- Artificial Neural Network -- Definition -- Development Procedure of Models -- Development of the Feasibility Model -- Application of MCDM -- Development of Feasibility Index -- Model Validation of the Model -- Sensitivity Analysis -- Case Studies -- Locations -- Why this location ? -- Results and Discussion -- MCDM Results -- ANN Results -- Conclusion.

Sommario/riassunto

This Brief highlights a novel model to find out the feasibility of any location to produce solar energy. The model utilizes the latest multi-criteria decision making techniques and artificial neural networks to predict the suitability of a location to maximize allocation of available



energy for producing optimal amount of electricity which will satisfy the demand from the market. According to the results of the case studies further applications are encouraged.