Shock tube instrumentation techniques for study of hypervelocity entry problems

Shock tube instrumentation methods for convective heat transfer study and radiative properties of high temperature gas at conditions simulating hypervelocity entr

Fast sampling control of a class of differential linear repetitive processes

Repetitive processes are a distinct class of 2D linear systems of practical and theoretical interest. Most of the available control theory for them is for the case of linear dynamics and focuses on systems theoretic properties such as stability and controllability/observability. This paper uses an extension of standard, or 1D, feedback control schemes to control a physically relevant sub-class of these processes

Material Flow Analysis: Outcome Focus (MFA:OF) for Elucidating the Role of Infrastructure in the Development of a Liveable City

Engineered infrastructures (i.e., utilities, transport & digital) underpin modern society. Delivering services via these is especially challenging in cities where differing infrastructures form a web of interdependencies. There must be a step change in how infrastructures deliver services to cities, if those cities are to be liveable in the future (i.e., provide for citizen wellbeing, produce less CO2 & ensure the security of the resources they use). Material Flow Analysis (MFA) is a useful methodology for understanding how infrastructures transfer resources to, within and from cities and contribute to the city’s metabolism. Liveable Cities, a five-year research programme was established to identify & test radical engineering interventions leading to liveable cities of the future. In this paper, the authors propose an outcome-focussed variation on the MFA methodology (MFA: OF), evidenced through work on the resource flows of Birmingham, UK. These flows include water, energy, food & carbon-intensive materials (e.g., steel, paper, glass), as well as their associated waste. The contribution MFA: OF makes to elucidating the interactions & interdependencies between the flows is highlighted and suggestions are made for how it can contribute to the (radical) rethinking of the engineered infrastructure associated with such flow

Stability Tests for a Class of 2D Continuous-Discrete Linear Systems with Dynamic Boundary Conditions

Repetitive processes are a distinct class of 2D systems of both practical and theoretical interest. Their essential characteristic is repeated sweeps, termed passes, through a set of dynamics defined over a finite duration with explicit interaction between the outputs, or pass profiles, produced as the system evolves. Experience has shown that these processes cannot be studied/controlled by direct application of existing theory (in all but a few very restrictive special cases). This fact, and the growing list of applications areas, has prompted an on-going research programme into the development of a 'mature' systems theory for these processes for onward translation into reliable generally applicable controller design algorithms. This paper develops stability tests for a sub-class of so-called differential linear repetitive processes in the presence of a general set of initial conditions, where it is known that the structure of these conditions is critical to their stability properties

ODE parameter inference using adaptive gradient matching with Gaussian processes

Parameter inference in mechanistic models based on systems of coupled differential equa- tions is a topical yet computationally chal- lenging problem, due to the need to fol- low each parameter adaptation with a nu- merical integration of the differential equa- tions. Techniques based on gradient match- ing, which aim to minimize the discrepancy between the slope of a data interpolant and the derivatives predicted from the differen- tial equations, offer a computationally ap- pealing shortcut to the inference problem. The present paper discusses a method based on nonparametric Bayesian statistics with Gaussian processes due to Calderhead et al. (2008), and shows how inference in this model can be substantially improved by consistently inferring all parameters from the joint dis- tribution. We demonstrate the efficiency of our adaptive gradient matching technique on three benchmark systems, and perform a de- tailed comparison with the method in Calder- head et al. (2008) and the explicit ODE inte- gration approach, both in terms of parameter inference accuracy and in terms of computa- tional efficiency

INS3D: An incompressible Navier-Stokes code in generalized three-dimensional coordinates

The operation of the INS3D code, which computes steady-state solutions to the incompressible Navier-Stokes equations, is described. The flow solver utilizes a pseudocompressibility approach combined with an approximate factorization scheme. This manual describes key operating features to orient new users. This includes the organization of the code, description of the input parameters, description of each subroutine, and sample problems. Details for more extended operations, including possible code modifications, are given in the appendix

On Similarities between Inference in Game Theory and Machine Learning

In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution)

Pyrotechnic shock at the orbiter/external tank forward attachment

During the initial certification test of the forward structural attachment of the space shuttle orbiter to the external tank, pyrotechnic shock from actuation of the separation device resulted in structural failure of the thermal protection tiles surrounding the attachment. Because of the high shock associated with the separation bolt, the development of alternative low shock separation designs was initiated. Two concepts that incorporate a 5.08 centimeter frangible nut as the release device were developed and tested

Restoring Public Trust and Confidence in New Zealand’s Intelligence and Security Agencies: Is a Parliamentary Commissioner for Security the missing key?

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