Building operational energy consumption of Chongqing and its exergy assessment

Building energy consumption(BEC) accounting and assessment is fundamental work for building energy efficiency(BEE) development. In existing Chinese statistical yearbook, there is no specific item for BEC accounting and relevant data are separated and mixed with other industry consumption. Approximate BEC data can be acquired from existing energy statistical yearbook. For BEC assessment, caloric values of different energy carriers are adopted in energy accounting and assessment field. This methodology obtained much useful conclusion for energy efficiency development. While the traditional methodology concerns only on the energy quantity, energy classification issue is omitted. Exergy methodology is put forward to assess BEC. With the new methodology, energy quantity and quality issues are both concerned in BEC assessment. To illustrate the BEC accounting and exergy assessment, a case of Chongqing in 2004 is shown. Based on the exergy analysis, BEC of Chongqing in 2004 accounts for 17.3% of the total energy consumption. This result is quite common to that of traditional methodology. As far as energy supply efficiency is concerned, the difference is highlighted by 0.417 of the exergy methodology to 0.645 of the traditional methodology.

Optimal maintenance policies under different operational schedules

In the reliability literature, maintenance time is usually ignored during the optimization of maintenance policies. In some scenarios, costs due to system failures may vary with time, and the ignorance of maintenance time will lead to unrealistic results. This paper develops maintenance policies for such situations where the system under study operates iteratively at two successive states: up or down. The costs due to system failure at the up state consist of both business losses & maintenance costs, whereas those at the down state only include maintenance costs. We consider three models: Model A, B, and C: Model A makes only corrective maintenance (CM). Model B performs imperfect preventive maintenance (PM) sequentially, and CM. Model C executes PM periodically, and CM; this PM can restore the system as good as the state just after the latest CM. The CM in this paper is imperfect repair. Finally, the impact of these maintenance policies is illustrated through numerical examples.

Solving optimal control problems with state constraints using nonlinear programming and simulation tools

This paper illustrates how nonlinear programming and simulation tools, which are available in packages such as MATLAB and SIMULINK, can easily be used to solve optimal control problems with state- and/or input-dependent inequality constraints. The method presented is illustrated with a model of a single-link manipulator. The method is suitable to be taught to advanced undergraduate and Master's level students in control engineering.

Input constraints handling in an MPC/feedback linearization scheme

The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.

A new RBF neural network with boundary value constraints

We present a novel topology of the radial basis function (RBF) neural network, referred to as the boundary value constraints (BVC)-RBF, which is able to automatically satisfy a set of BVC. Unlike most existing neural networks whereby the model is identified via learning from observational data only, the proposed BVC-RBF offers a generic framework by taking into account both the deterministic prior knowledge and the stochastic data in an intelligent manner. Like a conventional RBF, the proposed BVC-RBF has a linear-in-the-parameter structure, such that it is advantageous that many of the existing algorithms for linear-in-the-parameters models are directly applicable. The BVC satisfaction properties of the proposed BVC-RBF are discussed. Finally, numerical examples based on the combined D-optimality-based orthogonal least squares algorithm are utilized to illustrate the performance of the proposed BVC-RBF for completeness.

Using continuous ground-based radar and lidar measurements for evaluating the representation of clouds in four operational models

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw. Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud

Force distribution in manipulation by a robot hand with equality and inequality constraints

The problem of the appropriate distribution of forces among the fingers of a four-fingered robot hand is addressed. The finger-object interactions are modelled as point frictional contacts, hence the system is indeterminate and an optimal solution is required for controlling forces acting on an object. A fast and efficient method for computing the grasping and manipulation forces is presented, where computation has been based on using the true model of the nonlinear frictional cone of contact. Results are compared with previously employed methods of linearizing the cone constraints and minimizing the internal forces.

Development and application of a novel algorithm for steady-state optimising control using dynamic information

A novel optimising controller is designed that leads a slow process from a sub-optimal operational condition to the steady-state optimum in a continuous way based on dynamic information. Using standard results from optimisation theory and discrete optimal control, the solution of a steady-state optimisation problem is achieved by solving a receding-horizon optimal control problem which uses derivative and state information from the plant via a shadow model and a state-space identifier. The paper analyzes the steady-state optimality of the procedure, develops algorithms with and without control rate constraints and applies the procedure to a high fidelity simulation study of a distillation column optimisation.

The distribution of solar wind speeds during solar minimum: calibration for numerical solar wind modeling constraints on the source of the slow solar wind

It took the solar polar passage of Ulysses in the early 1990s to establish the global structure of the solar wind speed during solar minimum. However, it remains unclear if the solar wind is composed of two distinct populations of solar wind from different sources (e.g., closed loops which open up to produce the slow solar wind) or if the fast and slow solar wind rely on the superradial expansion of the magnetic field to account for the observed solar wind speed variation. We investigate the solar wind in the inner corona using the Wang-Sheeley-Arge (WSA) coronal model incorporating a new empirical magnetic topology–velocity relationship calibrated for use at 0.1 AU. In this study the empirical solar wind speed relationship was determined by using Helios perihelion observations, along with results from Riley et al. (2003) and Schwadron et al. (2005) as constraints. The new relationship was tested by using it to drive the ENLIL 3-D MHD solar wind model and obtain solar wind parameters at Earth (1.0 AU) and Ulysses (1.4 AU). The improvements in speed, its variability, and the occurrence of high-speed enhancements provide confidence that the new velocity relationship better determines the solar wind speed in the outer corona (0.1 AU). An analysis of this improved velocity field within the WSA model suggests the existence of two distinct mechanisms of the solar wind generation, one for fast and one for slow solar wind, implying that a combination of present theories may be necessary to explain solar wind observations.

Virtual reality as a visualisation tool: benefits and constraints

The benefits and applications of virtual reality (VR) in the construction industry have been investigated for almost a decade. However, the practical implementation of VR in the construction industry has yet to reach maturity owing to technical constraints. The need for effective information management presents challenges: both transfer of building data to, and organisation of building information within, the virtual environment require consideration. This paper reviews the applications and benefits of VR in the built environment field and reports on a collaboration between Loughborough University and South Bank University to overcome constraints on the use of the overall VR model for whole lifecycle visualisation. The work at each research centre is concerned with an aspect of information management within VR applications for the built environment, and both data transfer and internal data organisation have been investigated. In this paper, similarities and differences between computer-aided design (CAD) and VR packages are first discussed. Three different approaches to the creation of VR models during the design stage are identified and described, with a view to providing sharing understanding across the interdiscipliary groups involved. The suitable organisation of building information within the virtual environment is then further investigated. This work focused on the visualisation of the degradation of a building, through its lifespan, with the view to provide a visual aid for developing an effective and economic project maintenance programme. Finally consideration is given to the potential of emerging standards to facilitate an integrated use of VR. The convergence towards similar data structures in VR and other construction packages may enable visualisation to be better utilised in the overall lifecycle model.