Adjoint computations by algorithmic differentiation of a parallel solver for time-dependent PDEs

A computational fluid dynamics code is differentiated using algorithmic differentiation (AD) in both tangent and adjoint modes. The two novelties of the present approach are 1) the adjoint code is obtained by letting the AD tool Tapenade invert the complete layer of message passing interface (MPI) communications, and 2) the adjoint code integrates time-dependent, non-linear and dissipative (hence physically irreversible) PDEs with an explicit time integration loop running for ca. $10^{6}$ time steps. The approach relies on using the Adjoinable MPI library to reverse the non-blocking communication patterns in the original code, and by controlling the memory overhead induced by the time-stepping loop with binomial checkpointing. A description of the necessary code modifications is provided along with the validation of the computed derivatives and a performance comparison of the tangent and adjoint codes.Comment: Submitted to Journal of Computational Scienc

Open-loop control of cavity noise using Proper Orthogonal Decomposition reduced-order model.

Flow over open cavities is mainly governed by a feedback mechanism due to the interaction of shear layer instabilities and acoustic forcing propagating upstream in the cavity. This phenomenon is known to lead to resonant tones that can reach 180 dB in the far-field and may cause structural fatigue issues and annoying noise emission. This paper concerns the use of optimal control theory for reducing the noise level emitted by the cavity. Boundary control is introduced at the cavity upstream corner as a normal velocity component. Model-based optimal control of cavity noise involves multiple simulations of the compressible Navier–Stokes equations and its adjoint, which makes it a computationally expensive optimization approach. To reduce the computational costs, we propose to use a reduced-order model (ROM) based on Proper Orthogonal Decomposition (POD) as a surrogate model of the forward simulation. For that, a control input separation method is first used to introduce explicitly the control effect in the model. Then, an accurate and robust POD ROM is derived by using an optimization-based identification procedure and generalized POD modes, respectively. Since the POD modes describe only velocities and speed of sound, we minimize a noise-related cost functional characteristic of the total enthalpy unsteadiness. After optimizing the control function with the reduced-order model, we verify the optimality of the solution using the original, high-fidelity model. A maximum noise reduction of 4.7 dB is reached in the cavity and up to 16 dB at the far-field

Evidence for the Role of Proton Shell Closure in Quasifission Reactions from X-Ray Fluorescence of Mass-Identified Fragments

The atomic numbers and the masses of fragments formed in quasifission reactions are simultaneously measured at scission in Ti48+U238 reactions at a laboratory energy of 286 MeV. The atomic numbers are determined from measured characteristic fluorescence x rays, whereas the masses are obtained from the emission angles and times of flight of the two emerging fragments. For the first time, thanks to this full identification of the quasifission fragments on a broad angular range, the important role of the proton shell closure at Z=82 is evidenced by the associated maximum production yield, a maximum predicted by time-dependent Hartree-Fock calculations. This new experimental approach gives now access to precise studies of the time dependence of the N/Z (neutron over proton ratios of the fragments) evolution in quasifission reactions.The authors acknowledge support from the Australian Research Council through Discovery Grants No. FL110100098, No. FT120100760, No. DP130101569, No. DE140100784, No. DP160 101254, and No. DP170102318. Support for accelerator operations through the NCRIS program is acknowledged. Two of us (C. S. and M. A.) acknowledge support from the Scientific Mobility Program of the Embassy of France in Australia. This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government

Development and application of a reduced order model for the control of self-sustained instabilities in cavity flows

Flow around a cavity is characterized by a self-sustained mechanism in which the shear layer impinges on the downstream edge of the cavity resulting in a feedback mechanism. Direct Numerical Simulations of the flow at low Reynolds number has been carried out to get pressure and velocity fluctuations, for the case of un-actuated and multi frequency actuation. A Reduced Order Model for the isentropic compressible equations based on the method of Proper Orthogonal Decomposition has been constructed. The model has been extended to include the effect of control. The Reduced Order dynamical system shows a divergence in time integration. A method of calibration based on them inimization of a linear functional of error, to the sensitivity of the modes, is proposed. The calibrated low order model is used to design a feedback control of cavity flows based on an observer design. For the experimental implementation of the controller, a state estimate based on the observed pressure measurements is obtained through a linear stochastic estimation. Finally the obtained control is introduced into the Direct Numerical Simulation to obtain a decrease in spectra of the cavity acoustic mode

Learning Context Conditions for BDI Plan Selection

An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element of learning from experience. In particular, the so-called context conditions of plans, on which the whole model relies for plan selection, are restricted to be boolean formulas that are to be specified at design/implementation time. To address these limitations, we propose a novel BDI programming framework that, by suitably modeling context conditions as decision trees, allows agents to learn the probability of success for plans based on previous execution experiences. By using a probabilistic plan selection function, the agents can balance exploration and exploitation of their plans. We develop and empirically investigate two extreme approaches to learning the new context conditions and show that both can be advantageous in certain situations. Finally, we propose a generalization of the probabilistic plan selection function that yields a middle-ground between the two extreme approaches, and which we thus argue is the most flexible and simple approach

Enhancing the adaptation of BDI agents using learning techniques

Belief, Desire, and Intentions (BDI) agents are well suited for complex applications with (soft) real-time reasoning and control requirements. BDI agents are adaptive in the sense that they can quickly reason and react to asynchronous events and act accordingly. However, BDI agents lack learning capabilities to modify their behavior when failures occur frequently. We discuss the use of past experience to improve the agent¿s behavior. More precisely, we use past experience to improve the context conditions of the plans contained in the plan library, initially set by a BDI programmer. First, we consider a deterministic and fully observable environment and we discuss how to modify the BDI agent to prevent re-occurrence of failures, which is not a trivial task. Then, we discuss how we can use decision trees to improve the agent¿s behavior in a non-deterministic environment

Monitoring batch processes with the STATIS approach

Structuration des Tableaux A Trois Indices de la Statistique (STATIS), a method which can be seen as a 3-way exploratory analysis method, is proposed and investigated for the purpose of batch process monitoring. It is applied to batch process data to monitor the evolution in time of batches, through what are called the compromise plots. Because all batches do not have the same length, a particular procedure has to be used to obtain the compromise for batches. The monitoring is then based on the comparison between reference batches and new batches being processed. Three different real industrial data sets (with both process variables and spectroscopic variables) are studied in this paper and yield good results.</p