FATODE: A Library for Forward, Adjoint, and Tangent Linear Integration of ODEs

FATODE is a FORTRAN library for the integration of ordinary differential equations with direct and adjoint sensitivity analysis capabilities. The paper describes the capabilities, implementation, code organization, and usage of this package. FATODE implements four families of methods -- explicit Runge-Kutta for nonstiff problems and fully implicit Runge-Kutta, singly diagonally implicit Runge-Kutta, and Rosenbrock for stiff problems. Each family contains several methods with different orders of accuracy; users can add new methods by simply providing their coefficients. For each family the forward, adjoint, and tangent linear models are implemented. General purpose solvers for dense and sparse linear algebra are used; users can easily incorporate problem-tailored linear algebra routines. The performance of the package is demonstrated on several test problems. To the best of our knowledge FATODE is the first publicly available general purpose package that offers forward and adjoint sensitivity analysis capabilities in the context of Runge Kutta methods. A wide range of applications are expected to benefit from its use; examples include parameter estimation, data assimilation, optimal control, and uncertainty quantification

Towards a Holistic, Total Engineering Cost Model

In this paper, we explore a new approach for a unified and interdisciplinary model for estimating the total engineering effort in developing and delivering a parametric software-intensive complex system. We begin by reviewing some of the limitations of using existing engineering discipline-focused tools for estimating total engineering cost and by articulating the benefits of such a holistic model. Applying a two step method combining heuristic analysis and data validation, we propose three hypotheses to expand the basic cost estimating relationship of COSYSMO, a systems engineering model, to the total engineering scope by including software size drivers. The implementation of the hypotheses and the validation approach are also discussed. We conclude the discussion by outlining the future work required to realize such a model and to apply it to supporting successful system development endeavours

Mechatronics Design Process with Energy Optimization for Industrial Machines

The need for designing industrial machines with higher energy efficiency, reliability, flexibility, and accuracy has increased to satisfy market demand for higher productivity at reduced costs in a sustainable manner. As machines become more complex, model-based design is essential to overcome the challenges in mechatronic system design. However, a well-designed mechanical system with a well-designed and tuned control system are not sufficient for machines to operate at high-performance conditions; this also heavily depends on trajectory planning and the appropriate selection of the motors controlling the axes of the machine. In this work, a model-based design approach to properly select motors for single-axes or multi-axes coordinated systems was proposed. Additionally, a trajectory planning approach was also proposed to improve performance of industrial machines. The proposed motor selection process and trajectory planning approach were demonstrated via modeling, simulation, and experimental validation for three systems: two-inertia system, planar robot, and self-balancing transporter. Over 25% of the electric energy delivered in the U.S. in 2013 was used in the industrial sector according to the U.S. Energy Information Administration, with an estimated efficiency of 80% according to the Lawrence Livermore National Laboratory. This entails major responsibility by the industry to utilize energy efficiently and promote sustainable energy usage. To help improve the energy efficiency in the industrial sector, a novel method to optimize the energy of single-axis and multi-axis coordinated systems of industrial machines was developed. Based on trajectory boundaries and the kinetic model of the mechanism and motors, this proposed energy optimization method performs iterations to recalculate the shape of the motion profile for each motor of the system being optimized until it converges to a motion profile with optimal energy cost and within these boundaries. This method was validated by comparing the energy consumption of those three systems while commanded by the optimized motion profile and then by motion profiles typically used in industrial applications. The energy saved was between 5% and 10%. The implementation cost of this method in industrial systems resides in machine-code changes; no physical changes are needed

Technology Shocks and Aggregate Fluctuations: How Well Does the RBS Model Fit Postwar U.S. Data?

Our answer: not so well. We reach that conclusion after reviewing recent research on the role of technology as a source of economic fluctuations. The bulk of the evidence suggests a limited role for aggregate technology shocks, pointing instead to demand factors as the main force behind the strong positive comovement between output and labor input measures.

A Practical Guide to Surface Kinetic Monte Carlo Simulations

This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently used for surface and interface applications. We will provide worked out examples using the kmos code, where we highlight the central approximations made in implementing a KMC model as well as possible pitfalls. This includes the mapping of the problem onto a lattice and the derivation of rate constant expressions for various elementary processes. Example KMC models will be presented within the application areas surface diffusion, crystal growth and heterogeneous catalysis, covering both transient and steady-state kinetics as well as the preparation of various initial states of the system. We highlight the sensitivity of KMC models to the elementary processes included, as well as to possible errors in the rate constants. For catalysis models in particular, a recurrent challenge is the occurrence of processes at very different timescales, e.g. fast diffusion processes and slow chemical reactions. We demonstrate how to overcome this timescale disparity problem using recently developed acceleration algorithms. Finally, we will discuss how to account for lateral interactions between the species adsorbed to the lattice, which can play an important role in all application areas covered here.Comment: This document is the final Author's version of a manuscript that has been peer reviewed and accepted for publication in Frontiers in Chemistry. To access the final edited and published work see https://www.frontiersin.org/articles/10.3389/fchem.2019.00202/abstrac

Computational Steering in the Problem Solving Environment WBCSim

Computational steering allows scientists to interactively control a numerical experiment and adjust parameters of the computation on-the-fly and explore “what if ” analysis. Computational steering effectively reduces computational time, makes research more efficient, and opens up new product design opportunities. There are several problem solving environments (PSEs) featuring computational steering. However, there is hardly any work explaining how to enable computational steering for PSEs embedded with legacy simulation codes. This paper describes a practical approach to implement computational steering for such PSEs by using WBCSim as an example. WBCSim is a Web based simulation system designed to increase the productivity of wood scientists conducting research on wood-based composites manufacturing processes. WBCSim serves as a prototypical example for the design, construction, and evaluation of small-scale PSEs. Various changes have been made to support computational steering across the three layers—client, server, developer—comprising the WBCSim system. A detailed description of the WBCSim system architecture is presented, along with a typical scenario of computational steering usage

The Welfare Implications of Costly Information Provision

We study information acquisition in a framework characterized by strategic complementarity or substitutability. Agents’ actions are based on costly public and private signals, the precisions of which are set by a policy maker and by private agents, respectively. The policy maker – acting as a von Stackelberg leader – takes into account that an increase in the precision of public information reduces the incentives for private information acquisition. The precisions of both the public and private information available to each agent are shown to depend crucially on the degree of strategic complementarity or substitutability. We explore the welfare and policy implications of our results in economies with beauty contests, price setting complementarities, and negative externalities entailing strategic substitutability.Incomplete information, strategic complementarity, strategic substitutability, welfare