Khem Gyanwali

Khem Gyanwali

orcid

Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 March 2022 in International Journal of Hydrogen Energy
Verified
Journal Article
Closed access
KGKhem Gyanwali
Khem Gyanwali
ABAadya Bhattarai
Aadya Bhattarai
TBTri Ratna Bajracharya
Tri Ratna Bajracharya
RKRyoichi Komiyama
Ryoichi Komiyama
YFYasumasa Fujii
Yasumasa Fujii

1 July 2020 in Energy
Verified
Journal Article
Closed access
KGKhem Gyanwali
Khem Gyanwali
RKRyoichi Komiyama
Ryoichi Komiyama
YFYasumasa Fujii
Yasumasa Fujii

12 June 2023 in Renewable and Sustainable Energy Reviews
Verified
Journal Article
Closed access
KGKhem Gyanwali
Khem Gyanwali
PAPadam Adhikari
Padam Adhikari
SKSonu Khanal
Sonu Khanal
NBNawraj Bhattarai
Nawraj Bhattarai
TBTri Ratna Bajracharya
Tri Ratna Bajracharya
YFYasumasa Fujii
Yasumasa Fujii

3 August 2023 in Journal of Cleaner Production
Verified
Journal Article
Closed access
KGKhem Gyanwali
Khem Gyanwali
SKSaroj Karki
Saroj Karki
PAPadam Adhikari
Padam Adhikari
SDSijan Devkota
Sijan Devkota
Prakash AryalPrakash Aryal
Prakash Aryal

16 November 2021 in Metals and Materials International
Verified
Journal Article
Open access
PDF file
USUpadesh Subedi
Upadesh Subedi
AKAnil Kunwar
Anil Kunwar
YCYuri Amorim Coutinho
Yuri Amorim Coutinho
KGKhem Gyanwali
Khem Gyanwali
Multi-principal element alloys (MPEAs) occur at or nearby the centre of the multicomponent phase space, and they have the unique potential to be tailored with a blend of several desirable properties for the development of materials of future. The lack of universal phase diagrams for MPEAs has been a major challenge in the accelerated design of products with these materials. This study aims to solve this issue by employing data-driven approaches in phase prediction. A MPEA is first represented by numerical fingerprints (composition, atomic size difference , electronegativity , enthalpy of mixing , entropy of mixing , dimensionless $$\Omega$$ Ω parameter, valence electron concentration and phase types ), and an artificial neural network (ANN) is developed upon the datasets of these numerical descriptors. A pyMPEALab GUI interface is developed on the top of this ANN model with a computational capability to associate composition features with remaining other input features. With the GUI interface, an user can predict the phase(s) of a MPEA by entering solely the information of composition. It is further explored on how the knowledge of phase(s) prediction in composition-varied $$\hbox {Al}_x$$ Al x CrCoFeMnNi and $$\hbox {CoCrNiNb}_x$$ CoCrNiNb x can help in understanding the mechanical behavior of these MPEAs. Graphic Abstract

27 September 2020 in International Journal of Sustainable Energy
Verified
Journal Article
Closed access
KGKhem Gyanwali
Khem Gyanwali
RKRyoichi Komiyama
Ryoichi Komiyama
YFYasumasa Fujii
Yasumasa Fujii
TBTri Ratna Bajracharya
Tri Ratna Bajracharya
Although the BBIN (Bangladesh-Bhutan-India-Nepal) sub-region shares identical socio-economic characteristics and secures considerable benefits from energy co-operation and power grid connectivity, it is still far behind to grasp its full potential. This paper reviews existing publications and papers from a viewpoint of resource distribution, energy supply, prospects, and policies related to power sector development and cross-border power trading with a detailed consideration of the spatial variation of the largest country, India. The member countries face similar energy problems and the challenge remains in overcoming growing energy demand by attaining emission reduction targets. Limited production but increasing use of imported fuel have also raised serious environmental and energy security concerns. The wide variation in the energy resource endowments among the countries, particularly with hydropower, coal, and natural gas presents both opportunity and challenge in sub-regional energy collaboration.