Derivatives are key to numerous science, engineering, and machine learning applications.
While existing tools generate derivatives of programs in a single language, modern parallel …

The present work proposes a modified Pressure-Implicit Split-Operator (PISO) solver
integrating the recent Immersed Boundary Method (IBM) proposed by [1] in order to perform …

Digital twins (DTs) have attracted widespread attention in academia and industry in recent
years. It can accurately reflect the physical world in real-time, enabling online monitoring …

Fluid flow in the vadose zone is governed by the Richards equation; it is parameterized by
hydraulic conductivity, which is a nonlinear function of pressure head. Investigations in the …

Deep reinforcement learning (DRL) has emerged as a promising approach for handling
highly dynamic and nonlinear active flow control (AFC) problems. However, the …

The open-source multiphysics suite SU2 features discrete adjoints by means of operator
overloading automatic differentiation (AD). While both primal and discrete adjoint solvers …

Topology optimization methods have been vastly applied to fluid problems with different
methods. In this work, the discrete adjoint (DA) approach is used in combination with a finite …

A discrete adjoint method implemented in a coupled pressure-based RANS solver is
presented in this paper. The adjoint equations are solved using an adjoint fixed point …

Highly resolved computations using Delayed Detached Eddy Simulations (DDES) of a
canonical Windsor body are carried out. The flow around the car model, which is …

Algorithmic differentiation (AD) is a tool for generating discrete adjoint solvers, which
efficiently compute gradients of functions with many inputs, for example for use in gradient …