Iterative Solution Approach Research Articles

Quantifying Location-Specific Impacts of Pavement Albedo on Radiative Forcing Using an Analytical Approach

High-albedo materials reflect more solar radiation and, thereby, alter the earth's radiative balance. Increasing pavement albedo, therefore, has been considered as a technological strategy to mitigate global warming. Previous studies have evaluated this strategy using global average models. To factor this effect into life cycle assessments, location-specific models of the albedo effect for pavements are required. A parametric analytical model is developed to estimate the radiative forcing (RF) using a novel model form and an iterative solution approach. The new model is extended to estimate the corresponding global warming potential (GWP) over an analysis period of 50 years for an albedo change in a pavement surface. This was applied to quantify the GWP impacts of increasing pavement albedo in 14 cities across various climate zones in the US. For the United States, the GWP in kg CO2 equivalent per square meter of altered surface ranges from 0.8 to 1.6 per 0.01 change in albedo, a range of more than 40%. Analysis of a hypothetical albedo change to all darker pavements in the US would produce a negative RF of a magnitude equivalent to that associated with a reduction in CO2 emissions of more than 17 Mton per year.

A New Dynamical Core of the Global Environmental Multiscale (GEM) Model with a Height-Based Terrain-Following Vertical Coordinate

AbstractA new dynamical core of Environment and Climate Change Canada’s Global Environmental Multiscale (GEM) atmospheric model is presented. Unlike the existing log-hydrostatic-pressure-type terrain-following vertical coordinate, the proposed core adopts a height-based approach. The move to a height-based vertical coordinate is motivated by its potential for improving model stability over steep terrain, which is expected to become more prevalent with the increasing demand for very high-resolution forecasting systems. A dynamical core with height-based vertical coordinate generally requires an iterative solution approach. In addition to a three-dimensional iterative solver, a simplified approach has been devised allowing the use of a direct solver for the new dynamical core that separates a three-dimensional elliptic boundary value problem into a set of two-dimensional independent Helmholtz problems. The issue of dynamics–physics coupling has also been studied, and incorporating the physics tendencies within the discretized dynamical equations is found to be the most acceptable approach for the height-based vertical coordinate. The new dynamical core is evaluated using numerical experiments that include two-dimensional nonhydrostatic theoretical cases as well as 25-km resolution global forecasts. For a wide range of horizontal grid resolutions—from a few meters to up to 25 km—the results from the direct solution approach are found to be equivalent to the iterative approach for the new dynamical core. Furthermore, results from the different numerical experiments confirm that the new height-based dynamical core is equivalent to the existing pressure-based core in terms of solution accuracy.

Optimizing N-Tier Application Scalability in the Cloud

An effective cloud computing environment requires both good performance and high efficiency of computing resources. Through extensive experiments using a representative n-tier benchmark application (Rice University Bulletin Board System (RUBBoS)), we show that the soft resource allocation (e.g., thread pool size and database connection pool size) in component servers has a significant impact on the overall system performance, especially at high system utilization scenarios. Concretely, the same software resource allocation can be a good setting in one hardware configuration and then becomes an either under- or over-allocation in a slightly different hardware configuration, causing a significant performance drop. We have also observed some interesting phenomena that were caused by the non-trivial dependencies between the soft resources of servers in different tiers. For instance, the thread pool size in an Apache web server can limit the total number of concurrent requests to the downstream servers, which surprisingly decreases the Central Processing Unit (CPU) utilization of the Clustered Java Database Connectivity (C-JDBC) clustering middleware as the workload increases. To provide a globally optimal (or near-optimal) soft resource allocation of each tier in the system, we propose a practical iterative solution approach by combining a soft resource aware queuing network model and the fine-grained measurement data of every component server. Our results show that to truly scale complex distributed systems such as n-tier web applications with expected performance in the cloud, we need to carefully manage soft resource allocation in the system.

Full Field Inversion in Photoacoustic Tomography with Variable Sound Speed

To accelerate photoacoustic data acquisition, in [R. Nuster, G. Zangerl, M. Haltmeier, G. Paltauf (2010). Full field detection in photoacoustic tomography. Optics express, 18(6), 6288–6299] a novel measurement and reconstruction approach has been proposed, where the measured data consist of projections of the full 3D acoustic pressure distribution at a certain time instant T. Existing reconstruction algorithms for this kind of setup assume a constant speed of sound. This assumption is not always met in practice and thus can lead to erroneous reconstructions. In this paper, we present a two-step reconstruction method for full field detection photoacoustic tomography that takes variable speed of sound into account. In the first step, by applying the inverse Radon transform, the pressure distribution at the measurement time is reconstructed point-wise from the projection data. In the second step, a final time wave inversion problem is solved where the initial pressure distribution is recovered from the known pressure distribution at time T. We derive an iterative solution approach for the final time wave inversion problem and compute the required adjoint operator. Moreover, as the main result of this paper, we derive its uniqueness and stability. Our numerical results demonstrate that the proposed reconstruction scheme is fast and stable, and that ignoring sound speed variations significantly degrades the reconstruction.

An Iterative Solution Approach to Coupled Heat and Mass Transfer in a Steadily Fed Evaporating Water Droplet

Inspired by the thermoregulation of mammals via perspiration, cooling strategies utilizing continuously fed evaporating droplets have long been investigated in the field, yet a comprehensive modeling capturing the detailed physics of the internal liquid flow is absent. In this study, an innovative computational model is reported, which solves the governing equations with temperature-dependent thermophysical properties in an iterative manner to handle mass and heat transfer coupling at the surface of a constant shape evaporating droplet. Using the model, evaporation from a spherical sessile droplet is simulated with and without thermocapillarity. An uncommon, nonmonotonic temperature variation on the droplet surface is captured in the absence of thermocapillarity. Although similar findings were reported in previous experiments, the temperature dip was attributed to a possible Marangoni flow. This study reveals that buoyancy-driven flow is solely responsible for the nonmonotonic temperature distribution at the surface of an evaporating steadily fed spherical water droplet.

An iterative solution approach for steady state analysis of self-excited induction generator

The steady state analysis of self-excited induction generator is widely used for stand-alone renewable energy system. The impedance values of equivalent circuit vary depending on frequency. The frequency changes according to load, excitation capacitance and rotor speed. If the values of load and capacitance are known, only the frequency and slip are the two main unknowns left for analysis of the single-phase equivalent circuit. The simulation is generally performed for a desired slip value to obtain the frequency. However, with the proposed approach in this study, the values of rotor slip, and frequency of terminal voltage are both directly determined in shorter time. The proposed algorithm is verified by the experiments of three phase 1.1 kW squirrel cage induction machine.

An iterative solution approach for steady state analysis of self-excited induction generator

The steady state analysis of self-excited induction generator is widely used for stand-alone renewable energy system. The impedance values of equivalent circuit vary depending on frequency. The frequency changes according to load, excitation capacitance and rotor speed. If the values of load and capacitance are known, only the frequency and slip are the two main unknowns left for analysis of the single-phase equivalent circuit. The simulation is generally performed for a desired slip value to obtain the frequency. However, with the proposed approach in this study, the values of rotor slip, and frequency of terminal voltage are both directly determined in shorter time. The proposed algorithm is verified by the experiments of three phase 1.1 kW squirrel cage induction machine.

Multi-objective optimisation for biopharmaceutical manufacturing under uncertainty

This work addresses the multi-objective optimisation of manufacturing strategies of monoclonal antibodies under uncertainty. The chromatography sequencing and column sizing strategies, including resin at each chromatography step, number of columns, column diameters and bed heights, and number of cycles per batch, are optimised. The objective functions simultaneously minimise the cost of goods per gram and maximise the impurity reduction ability of the purification process. Three parameters are treated as uncertainties, including bioreactor titre, and chromatography yield and capability to remove impurities. Using chance constraint programming techniques, a multi-objective mixed integer optimisation model is proposed. Adapting both ε-constraint method and Dinkelbach's algorithm, an iterative solution approach is developed for Pareto-optimal solutions. The proposed model and approach are applied to an industrially-relevant example, demonstrating the benefits of the proposed model through Monte Carlo simulation. The sensitivity analysis of the confidence levels used in the chance constraints of the proposed model is also conducted.

Commitment of Fast-Responding Storage Devices to Mimic Inertia for the Enhancement of Primary Frequency Response

One distinctive feature of renewable energy resources is that they contribute little inertia to power systems. With less system inertia, power grid is less capable of resisting frequency deviation from its nominal value in the first few seconds after disturbances. However, fast-responding storage devices can mimic inertial responses through some specified control algorithm. Thus, this paper focuses on the economic aspect of resource inertia as a service to the grid operation and aims to find a minimum-cost commitment of fast-acting storage devices for the enhancement of primary frequency responses. Costs are assigned to those storage resources and a commitment cost minimization problem is formulated with nonlinear primary frequency response constraints. A tractable iterative solution approach is developed and a sensitivity-based method through a rapidly solvable linear approximation of the nonlinear constraints is proposed to reduce the computational burdens of solving the optimization problem for large-scale systems. The effectiveness of the proposed methods is demonstrated using simulation results obtained using the 118-bus IEEE reliability test system.

An iterative solution approach to a multi-objective facility location problem

This work presents a novel methodology for solving multi-objective facility location problems (mo-FLPs) with the focus on public emergency service stations. Our study is one of a few studies incorporating the objectives of three well-known problems, viz. the p-median problem (pMP), the maximal coverage location problem (MCLP) and the p-center problem (pCP). Aiming to find a set of Pareto optimal solutions and a compromise solution for all three objectives, we have developed an algorithm which solves each individual location problem sequentially. The proposed approach is mainly based on a combination of the branch & bound and iterative goal programming techniques. The performance of the algorithm is demonstrated with numerical examples.

How to Increase Energy Efficiency in Cognitive Radio Networks

In this paper, we investigate the achievable energy efficiency of cognitive radio networks where two main modes are of interest, namely, spectrum sharing (known as underlay paradigm) and spectrum sensing (or interweave paradigm). In order to improve the energy efficiency, we formulate a new multiobjective optimization problem that jointly maximizes the ergodic capacity and minimizes the average transmission power of the secondary user network while limiting the average interference power imposed on the primary user receiver. The multiobjective optimization will be solved by first transferring it into a single objective problem (SOP), namely, a power minimization problem, by using the $\varepsilon$ -constraint method. The formulated SOP will be solved using two different methods. Specifically, the minimum power allocation at the secondary transmitter in a spectrum sharing fading environment are obtained using the iterative search-based solution and augmented Lagrangian approach for single and multiple secondary links, respectively. The significance of having extra side information and also imperfect side information of cross channels at the secondary transmitter are investigated. The minimum power allocations under perfect and imperfect sensing schemes in interweave cognitive radio networks are also found. Our numerical results provide guidelines for the design of future cognitive radio networks.

Strategic workforce planning for the Australian Defence Force

This paper presents a mixed integer linear programming (MILP) model to aid in planning a large, hierarchically structured workforce. The workforce (eg, all Army commissioned officers) is classified by occupational group and rank and modelled in yearly intervals. Personnel and their movements are modelled as stocks and flows, subject to constraints representing employment conditions and resource limitations. The task is to estimate personnel numbers and flows so as to minimize a composite cost function. The model’s detailed fidelity to actual conditions, and hence its value in practice, clearly exceed previous efforts in the workforce planning domain. To overcome computational challenges that arose when an MILP solver was applied directly, the authors developed an iterative solution approach, which has yielded an attractive combination of solution quality and computational performance.

English

The capacitated multi-commodity network design problem (CMND) represents a generic network design model for applications used in designing construction and improvements to telecommunication, logistics, transportation, distribution, and production networks. This paper presents an approach combining capacity scaling and local branching for CMND. Capacity scaling is an approximate iterative solution approach for capacitated network problems based on the principle of changing arc capacities that depend on flow volumes on arcs. Local branching is a method of solving new restricted problems based on an exploration of solution neighborhoods defined as local branching constraints. Capacity scaling with a strong path flow based formulation including forcing constraints can produce high-quality solutions within a short period of time allotted for computation. By combined capacity scaling and local branching, the combined approach can offer one of the best current solutions compared to previous heuristics for CMND.

Inverse shape design via a new physical-based iterative solution strategy

Inverse shape design in the context of fluid flow problems is commonly referred to the determination of the boundary shape corresponding to a given target surface pressure. Designers naturally turn to this class of problems whenever there are concerns regarding pressure-related phenomena such as cavitation, separation, shock waves, surface loading, etc. Numerical solution is often unavoidable and, therefore, three computational tools, i.e. a grid generator, a flow field solver and a shape updater, are required in an iterative solution procedure. In all existing iterative inverse design methods, the shape updater comes from separate mathematical or physical considerations that are not derived solely from the equations governing the flow field. In this paper, the currently used strategies to solve inverse shape design problems are categorized and reviewed, and then a truly physical-based iterative inverse shape design method is introduced in which the governing equations are not only used in the flow solver, but are also employed to update the shape. To explore the features of the proposed method, it is used to solve a number of shape design problems. The previously developed direct shape design method and a typical iterative solution approach are also explained and used for the solution of the same problems for the purpose of comparison. Computational results reveal that the proposed algorithm has a two to three times faster convergence rate as compared to the iterative algorithm. The convergence rate of the proposed algorithm usually stalls after three or two orders of magnitude reduction of the residual. For the test cases in this study, the direct design method is the fastest and most accurate method as compared to other algorithms. Both in-house developed computer codes and commercial software are used as a field solver to show the general applicability of the proposed method in its current state of development.

Numerical simulation of the flow around two-dimensional partially cavitating hydrofoils

In the present study, a new approach is applied to the cavity prediction for two-dimensional (2D) hydrofoils by the potential based boundary element method (BEM). The boundary element method is treated with the source and doublet distributions on the panel surface and cavity surface by the use of the Dirichlet type boundary conditions. An iterative solution approach is used to determine the cavity shape on partially cavitating hydrofoils. In the case of a specified cavitation number and cavity length, the iterative solution method proceeds by addition or subtraction of a displacement thickness on the cavity surface of the hydrofoil. The appropriate cavity shape is obtained by the dynamic boundary condition of the cavity surface and the kinematic boundary condition of the whole foil surface including the cavity. For a given cavitation number the cavity length of the 2D hydrofoil is determined according to the minimum error criterion among different cavity lengths, which satisfies the dynamic boundary condition on the cavity surface. The NACA 16006, NACA 16012 and NACA 16015 hydrofoil sections are investigated for two angles of attack. The results are compared with other potential based boundary element codes, the PCPAN and a commercial CFD code (FLUENT). Consequently, it has been shown that the results obtained from the two dimensional approach are consistent with those obtained from the others.

When Are Deliveries Profitable?

The paper aims to optimize the final part of a firm’s value chain with regard to attended last-mile deliveries. It is assumed that to be profitable, e-commerce businesses need to maximize the overall value of fulfilled orders (rather than their number), while also limiting costs of delivery. To do so, it is essential to decide which delivery requests to accept and which time windows to offer to which consumers. This is especially relevant for attended deliveries, as delivery fees usually cannot fully compensate costs of delivery given tight delivery time windows. The literature review shows that existing order acceptance techniques often ignore either the order value or the expected costs of delivery. The paper presents an iterative solution approach: after calculating an approximate transport capacity based on forecasted expected delivery requests and a cost-minimizing routing, actual delivery requests are accepted or rejected aiming to maximize the overall value of orders given the computed transport capacity. With the final set of accepted requests, the routing solution is updated to minimize costs of delivery. The presented solution approach combines well-known methods from revenue management and time-dependent vehicle routing. In a computational study for a German metropolitan area, the potential and the limits of value-based demand fulfillment as well as its sensitivity regarding forecast accuracy and demand composition are investigated.

Optimizing throughput performance of FMIPv6 over legacy 802.11 networks using iterative scanning

Predictive handover protocols like FMIPv6 have been proposed to provide seamless handover to MNs with minimum handover latency and packet losses. An essential prerequisite of FMIPv6 protocol is the advance discovery of candidate access points while the MN is still connected to its Present Access Router, a process which is carried out by the underlying data link layer protocol. The FMIPv6 standard only specifies the seamless handover operation at the network layer and does not take into consideration the impact of the relevant mechanism, such as the advance access point discovery process, on the overall seamless operation in terms of packet losses. At the data-link layer, the MN may repeatedly preform channel scanning operations to discover the in-range candidate APs. During the scan process, a MN is unable to transmit or receive packets leading to service quality degradation.This paper will analyze the implication of the IEEE 802.11 scanning process, that is carried out in advance to the actual initiation of the FMIPv6 handover process as part of the candidate access points discovery process. The impact of such a scan process is assessed on the overall seamlessness of the FMIPv6 protocol and its impact on the user’s QoE. In this regard, an iterative channel scanning solution approach would be described, which has been proposed to offset the adverse affects of 802.11 channel scanning process on the overall seamlessness of the FMIPv6 protocol in a legacy 802.11 network, especially during the candidate AP discovery process. A detailed evaluation of the performance of our proposed scheme will be presented based on extensive simulation analysis.

Flow path and hydraulic analysis for on-farm pressurized irrigation systems

A model was developed to analyze the steady-state hydraulics of branching pipe networks as found in on-farm pressurized irrigation systems with multiple outlets at sprinklers or emitters. The model uses a new methodology to identify all flow paths based on a shortest-path algorithm. It also uses elevation and hydraulic parameters to determine which outlets are flowing and which (if any) are not. An iterative solution approach results in the calculated flow rates and pressures at all nodes in the network with a single source node, which can have a fixed head (reservoir) or a pump. The methodology provides a guaranteed steady-state hydraulic solution for the network, regardless of the available pressure, pipe layout and characteristics, or field topography.

Impact of wind integration on electricity markets: a chance-constrained Nash Cournot model

SUMMARY Wind-powered electricity generation is growing at a high rate around the world, mainly driven by the associated environmental benefits. However, because of the uncertain nature of wind energy, large-scale integration of wind-powered generators into power systems introduces uncertainty of supply in the operation and planning of electric energy systems. A chance-constrained model is presented to study the strategic behavior of power suppliers (firms) with respect to the wind generation uncertainty in an electricity market. The linear complementarity problem of a Nash-Cournot competition is extended to account for the wind generation uncertainty. An iterative solution approach is introduced to solve the resultant joint chance-constrained programming problem. Five studies were conducted to show the impact of the amount, location and standard deviation of the wind generation and transmission line limits on the suppliers’ profit in the Nash-Cournot game for different confidence levels. The value of stochastic solution index is used to evaluate the suppliers’ loss of profit. The numerical results for a three-bus test system are given to verify the formulation and the solution approach. Copyright © 2011 John Wiley & Sons, Ltd.

Modelling regional input markets with numerous processing plants: The case of green maize for biogas production in Germany

The location of first generation processing plants for biogas using bulky inputs is a prominent example of locational decisions of plants that face high per unit transport costs of feedstock and simultaneously depend to a large extent on feedstock availability. Modelling the resulting regional feedstock markets then requires a spatially explicit representation of demand. With production capacities of plants small in comparison to market size, large numbers of possible type-location combinations need to be considered, requiring considerable computation time under existing integer programming-based approaches. Therefore, in this paper we aim to present an alternative, faster and more flexible iterative solution approach to simulate location decisions for processing plants. And with greater flexibility, this approach is able to take into account spatially heterogeneous transport costs depending on total demand. The approach is implemented in a modelling framework for biogas production from green maize in Germany, which currently accounts for ca. five per cent of Germany’s agricultural area. By modifying green maize prices, demand functions are derived and intersected with regional supply functions from an agricultural model to simulate market clearing prices and quantities. The application illustrates that our approach efficiently simulates markets characterised by small-scale demand units and high, spatially heterogeneous transport costs.