The influence of injection conditions and soil types on soil improvement by microbial functions

Research has begun on Microbial Carbonate Precipitation (MCP), which shows promise as a soil improvement method because of its low carbon dioxide emission compared to cement stabilized agents. MCP produces calcium carbonate from carbonates and calcium in soil voids through ureolysis by "Bacillus Pasteurii". This study focuses on how the amount of calcium carbonate precipitation is affected by the injection conditions of the microorganism and nutrient salt, such as the number of injections and the soil type. Experiments were conducted to simulate soil improvement by bio-grouting soil in a syringe. The results indicate that the amount of precipitation is affected by injection conditions and soil type, suggesting that, in order for soil improvement by MCP to be effective, it is necessary to set injection conditions that are in accordance with the soil conditions. © 2011 ASCE.

Face-based Selection of Corners in 3D Substructuring

In most recent substructuring methods, a fundamental role is played by the coarse space. For some of these methods (e.g. BDDC and FETI-DP), its definition relies on a 'minimal' set of coarse nodes (sometimes called corners) which assures invertibility of local subdomain problems and also of the global coarse problem. This basic set is typically enhanced by enforcing continuity of functions at some generalized degrees of freedom, such as average values on edges or faces of subdomains. We revisit existing algorithms for selection of corners. The main contribution of this paper consists of proposing a new heuristic algorithm for this purpose. Considering faces as the basic building blocks of the interface, inherent parallelism, and better robustness with respect to disconnected subdomains are among features of the new technique. The advantages of the presented algorithm in comparison to some earlier approaches are demonstrated on three engineering problems of structural analysis solved by the BDDC method.

The non-local nature of structure functions

Kolmogorov's two-thirds, ((Δv) 2) ∼ e 2/ 3r 2/ 3, and five-thirds, E ∼ e 2/ 3k -5/ 3, laws are formally equivalent in the limit of vanishing viscosity, v → 0. However, for most Reynolds numbers encountered in laboratory scale experiments, or numerical simulations, it is invariably easier to observe the five-thirds law. By creating artificial fields of isotropic turbulence composed of a random sea of Gaussian eddies whose size and energy distribution can be controlled, we show why this is the case. The energy of eddies of scale, s, is shown to vary as s 2/ 3, in accordance with Kolmogorov's 1941 law, and we vary the range of scales, γ = s max/s min, in any one realisation from γ = 25 to γ = 800. This is equivalent to varying the Reynolds number in an experiment from R λ = 60 to R λ = 600. While there is some evidence of a five-thirds law for g > 50 (R λ > 100), the two-thirds law only starts to become apparent when g approaches 200 (R λ ∼ 240). The reason for this discrepancy is that the second-order structure function is a poor filter, mixing information about energy and enstrophy, and from scales larger and smaller than r. In particular, in the inertial range, ((Δv) 2) takes the form of a mixed power-law, a 1+a 2r 2+a 3r 2/ 3, where a 2r 2 tracks the variation in enstrophy and a 3r 2/ 3 the variation in energy. These findings are shown to be consistent with experimental data where the polution of the r 2/ 3 law by the enstrophy contribution, a 2r 2, is clearly evident. We show that higherorder structure functions (of even order) suffer from a similar deficiency.

On the functions of products

Understanding the performance and manner of functioning of existing products is at the base of new product development activities. In engineering design the term function is generally used to refer to the technical actions performed by a product. However, products accomplish a wider range of goals. This research explores the opportunity to describe and model, through the concept of function, product actions across four dimensions including technical, aesthetic, social and economic. The research demonstrates that non-technical functions can be represented through active verbs and nouns and modelled using a method known as the Function Analysis Diagram (FAD). The research argues that when technical, aesthetic, social and economic perspectives on product development are considered as different types of function, stakeholders have a common language to communicate which can benefit design collaboration.

Additive Gaussian processes

We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesian Hierarchical Kernel Learning (HKL). We introduce an expressive but tractable parameterization of the kernel function, which allows efficient evaluation of all input interaction terms, whose number is exponential in the input dimension. The additional structure discoverable by this model results in increased interpretability, as well as state-of-the-art predictive power in regression tasks.

700V smart trench IGBT with monolithic over-voltage and over-current protecting functions

An advanced 700V Smart Trench IGBT with monolithically integrated over-voltage and over-current protecting circuits is presented in this paper. The proposed Smart IGBT comprises a sense IGBT, a low voltage lateral n-channel MOSFET (M 1), an avalanche diode (D av), and poly-crystalline Zener diodes (ZD) and resistor (R poly). Mix-mode transient simulations with MEDICI have proven the functionalities of the protecting circuits when the device is operating under abnormal conditions, such as Unclamped Inductive Switching (UIS) and Short Circuit (SC) condition. A Trench IGBT process is used to fabricate this device with total 11 masks including one metal mask only. The characterizations of the fabricated device exhibit the clamping capability of the avalanche diode and voltage pull-down ability of the MOSFET. © 2012 IEEE.

Generalized vector control for brushless doubly fed machines with nested-loop rotor

This paper presents a generalized vector control system for a generic brushless doubly fed (induction) machine (BDFM) with nested-loop type rotor. The generic BDFM consists of p1/p2 pole-pair stator windings and a nested-loop rotor with N number of loops per nest. The vector control system is derived based on the basic BDFM equation in the synchronous mode accompanied with an appropriate synchronization approach to the grid. An analysis is performed for the vector control system using the generic BDFM vector model. The analysis proves the efficacy of the proposed approach in BDFM electromagnetic torque and rotor flux control. In fact, in the proposed vector control system, the BDFM torque can be controlled very effectively promising a high-performance BDFM shaft speed control system. A closed-loop shaft speed control system is composed based on the presented vector control system whose performance is examined both in simulations and experiments. The results confirm the high performance of the proposed approach in BDFM shaft speed control as well as a very close agreement between the simulations and experiments. Tests are performed on a 180-frame prototype BDFM. © 2012 IEEE.

Elemental unbiased estimators for the Generalized Pareto tail

Unbiased location- and scale-invariant `elemental' estimators for the GPD tail parameter are constructed. Each involves three log-spacings. The estimators are unbiased for finite sample sizes, even as small as N=3. It is shown that the elementals form a complete basis for unbiased location- and scale-invariant estimators constructed from linear combinations of log-spacings. Preliminary numerical evidence is presented which suggests that elemental combinations can be constructed which are consistent estimators of the tail parameter for samples drawn from the pure GPD family.

Elemental estimators for the Generalized Extreme Value tail

In a companion paper (McRobie(2013) arxiv:1304.3918), a simple set of `elemental' estimators was presented for the Generalized Pareto tail parameter. Each elemental estimator: involves only three log-spacings; is absolutely unbiased for all values of the tail parameter; is location- and scale-invariant; and is valid for all sample sizes $N$, even as small as $N= 3$. It was suggested that linear combinations of such elementals could then be used to construct efficient unbiased estimators. In this paper, the analogous mathematical approach is taken to the Generalised Extreme Value (GEV) distribution. The resulting elemental estimators, although not absolutely unbiased, are found to have very small bias, and may thus provide a useful basis for the construction of efficient estimators.