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Engineering and Technology

D-Index
60
Citations
15165
World Ranking
2157
National Ranking
684

Overview

Juan J. Alonso is a researcher affiliated with Stanford University in the United States, specializing in fields related to engineering, with a significant focus on computational mechanics and aerospace engineering. Their research contributes substantially to areas including applied mathematics, global and planetary change, and statistics, probability, and uncertainty.

The main topics of Juan J. Alonso's work include:

  • Computational Fluid Dynamics and Aerodynamics
  • Gas Dynamics and Kinetic Theory
  • Advanced Aircraft Design and Technologies
  • Aerodynamics and Acoustics in Jet Flows
  • Probabilistic and Robust Engineering Design
  • Fluid Dynamics and Turbulent Flows
  • Wind and Air Flow Studies

Their recent publications reflect a range of themes in fluid mechanics, aerospace applications, and design under uncertainty, highlighted by:

  • "A universal velocity profile for turbulent wall flows including adverse pressure gradient boundary layers" (2021), published in Journal of Fluid Mechanics
  • "Lithium-Ion Battery Modeling for Aerospace Applications" (2021), published in Journal of Aircraft
  • "SU2-NEMO: An Open-Source Framework for High-Mach Nonequilibrium Multi-Species Flows" (2021), published in Aerospace
  • "Design exploration and optimization under uncertainty" (2020), published in Physics of Fluids
  • "A Low-Cost Aero-Propulsive Analysis of Distributed Electric Propulsion Aircraft" (2021), published in AIAA Scitech 2021 Forum

Juan J. Alonso frequently collaborates with several coauthors, notably:

  • Matthew Clarke
  • Jayant Mukhopadhaya
  • Racheal M. Erhard
  • Walter Maier
  • Catarina Garbacz

Their work is regularly published in venues focused on aerospace and engineering topics, including:

  • AIAA SCITECH 2022 Forum
  • AIAA Scitech 2021 Forum
  • AIAA Scitech 2020 Forum
  • AIAA SCITECH 2023 Forum
  • AIAA AVIATION 2021 FORUM

Best Publications

  • CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences

    Jeffrey P Slotnick;Abdollah Khodadoust;Juan Alonso;David Darmofal

  • SU2: An Open-Source Suite for Multiphysics Simulation and Design

    Thomas D. Economon;Francisco Palacios;Sean R. Copeland;Trent W. Lukaczyk

  • The complex-step derivative approximation

    Joaquim R. R. A. Martins;Peter Sturdza;Juan J. Alonso

  • Stanford University Unstructured (SU 2 ): An open-source integrated computational environment for multi-physics simulation and design

    Francisco Palacios;Juan Alonso;Karthikeyan Duraisamy;Michael Colonno

  • Constrained Multipoint Aerodynamic Shape Optimization Using an Adjoint Formulation and Parallel Computers

    James Reuther;Antony Jameson;Juan Jose Alonso;Mark J. Rimlinger

  • High-Fidelity Aerostructural Design Optimization of a Supersonic Business Jet

    Joaquim R. R. A. Martins;Juan J. Alonso;James J. Reuther

  • Aircraft Gas Turbine Engine Simulations

    William Reynolds;Juan Alonso;Massimiliano Fatica

  • A Machine Learning Strategy to Assist Turbulence Model Development

    Brendan D. Tracey;Karthikeyan Duraisamy;Juan J. Alonso

  • Liszt: a domain specific language for building portable mesh-based PDE solvers

    Zachary DeVito;Niels Joubert;Francisco Palacios;Stephen Oakley

  • Fully-implicit time-marching aeroelastic solutions

    Juan Alonso;Antony Jameson

  • A Coupled-Adjoint Sensitivity Analysis Method for High-Fidelity Aero-Structural Design

    Joaquim R.R.A. Martins;Juan J. Alonso;James J. Reuther

  • AN AUTOMATED METHOD FOR SENSITIVITY ANALYSIS USING COMPLEX VARIABLES

    Joaquim R. R. A. Martins;Ilan M. Kroo;Juan J. Alonso

  • Application of a Non-Linear Frequency Domain Solver to the Euler and Navier-Stokes Equations

    Matthew McMullen;Antony Jameson;Juan J. Alonso

  • Stanford University Unstructured (SU2): Analysis and Design Technology for Turbulent Flows

    Francisco Palacios;Thomas D. Economon;Aniket Aranake;Sean R. Copeland

  • ADjoint: An Approach for the Rapid Development of Discrete Adjoint Solvers

    Charles A. Mader;Joaquim R. R. A. Martins;Juan J. Alonso;Edwin van der Weide

  • Using gradients to construct cokriging approximation models for high-dimensional design optimization problems

    H.-S. Chung;J. Alonso

  • Airfoil design optimization using reduced order models based on proper orthogonal decomposition

    Patrick LeGresley;Juan Alonso

  • Investigation of non-linear projection for POD based reduced order models for Aerodynamics

    Patrick LeGresley;Juan Alonso

  • Aerodynamic shape optimization of supersonic aircraft configurations via an adjoint formulation on distributed memory parallel computers

    J. Reuther;J.J. Alonso;M.J. Rimlinger;A. Jameson

  • Active Subspaces for Shape Optimization

    Trent Lukaczyk;Francisco Palacios;Juan J. Alonso;Paul G. Constantine

  • Constrained multipoint aerodynamic shape optimization using an adjoint formulation and parallel computers

    J. Reuther;A. Jameson;J. Alonso;M. Rimlinger

Frequent Co-Authors

Antony Jameson
Antony Jameson Texas A&M University
Heinz Pitsch
Heinz Pitsch RWTH Aachen University
Joaquim R. R. A. Martins
Joaquim R. R. A. Martins University of Michigan–Ann Arbor
Xiaohua Wu
Xiaohua Wu University of Geneva
Gianluca Iaccarino
Gianluca Iaccarino Stanford University
Ilan Kroo
Ilan Kroo Stanford University
Rafael Palacios
Rafael Palacios Imperial College London
Dimitri J. Mavriplis
Dimitri J. Mavriplis University of Wyoming
Parviz Moin
Parviz Moin Stanford University

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