Limited Measurement Data Research Articles

Large-Scale SRAM Variability Characterization in 45 nm CMOS

Increased process variability presents a major challenge for future SRAM scaling. Fast and accurate validation of SRAM read stability and writeability margins is crucial for estimating yield in large SRAM arrays. Conventional SRAM read/write metrics are characterized through test structures that are able to provide limited hardware measurement data and cannot be used to investigate cell bit fails in functional SRAM arrays. This work presents a method for large-scale characterization of read stability and writeability in functional SRAM arrays using direct bit-line measurements. A test chip is implemented in a 45 nm CMOS process. Large-scale SRAM read/write metrics are measured and compared against conventional SRAM stability metrics. Results show excellent correlation to conventional SRAM read/write metrics as well as VMIN measurements near failure.

Revealing spatial pattern dynamics in aquatic ecosystem modelling with Multi-Agent Systems in Lake Veluwe

Aquatic ecosystems are among the most complex due to the highly nonlinearity, randomness, as well as interactive multi-processes in multi-scales. Besides, highly limited understanding and very limited measurement data make the modelling of such kind of systems a very challenging task, which needs to combine domain knowledge, available data and other models. The concept of Multi-Agent Systems (MAS) in modelling spatial population dynamics of aquatic plant growth is explored in this research due to the capability of MAS in utilizing various information and data, reflecting both the interactions among different entities and entity's own properties. Expert biological knowledge, GIS maps and environmental conditions are used as input information and data in the development of MAS model. Several different aspects are included: plant agents, environmental backgrounds, and animal influences. Environmental background factors include flow pattern, light visibility, water temperature, water depth and wind, which are all abiotic factors. Plant agents represent different plant species; the ones in this research are Potamogeton pectinatus ( Pp) and Chara aspera ( Cs), while animals here refer to water birds. Based on biological knowledge and data supplied for Lake Veluwe, the MAS modelling rules were developed. Through these MAS rules, agents are linked to environmental and biological processes. The resulting spatial pattern dynamics indicates that a multi-agent approach can exhibit complex behaviour even when the individual strategies of each agent are simple. The test case for Lake Veluwe showed quite good agreement compared to the GIS maps available and the biological knowledge presented. Multi-Agent Systems show a promising approach to modelling spatial population dynamics for aquatic plants.

Measurement-Integrated Simulation of Clear Air Turbulence Using a Four-dimensional Variational Method

An unsteady flow phenomenon of clear air turbulence was investigated by numerical simulation integrating flight data of encounters with severe turbulence. To determine the initial condition of the numerical simulation based on the flight data, the four-dimensional variational method was employed. The validation of the present method was conducted by using pseudo flight data that are obtained from a reference flowfield produced by the numerical simulation. Then the four-dimensional variational method was applied to two actual severe turbulence cases to reproduce the flowfield of these turbulent flows. The validation results show reproducibility of the flowfield, even with spatially limited measurement data such as flight data, as long as the data involve primary flow features. Unsteady flowfields of turbulence were successfully simulated by using the four-dimensional variational method based on two cases of actual flight data. The results show the existence of vortices that correspond to clear air turbulence. In addition, the existing hazard indices F-factor and root-mean-square normal load were applied to the reproduced flowfield to obtain a spatial distribution of these hazard indices.

Application of data assimilation in portable operational forecasting systems—the DATools assimilation environment

The first part of the paper describes a portable and flexible data assimilation environment (DATools) for easy application of data assimilation and calibration techniques to models that are used in smaller-scale engineering applications, for example to guide temporary offshore construction works, marine surveying and salvage operations. These applications are characterized by the need for detailed forecasts of often strongly non-linear marine behaviour in shallow-shelf seas under constraints of limited field measurements and absence of large-scale computing facilities. The applications are often prepared, and their results are interpreted by end-user engineers in field offices, not well acquainted with the data assimilation theory. The DATools data assimilation environment has been designed to facilitate such applications. It presently features an ensemble Kalman filter and two particle filters. These can be coupled to any process model in a standardized way through the so-called Published Interface that is used in an increasing number of flood-forecasting applications across Europe. The design of the system and its main modules are discussed, looking at the system from a non-specialist user perspective and focusing on modularity, transparency, user guidance, intuitive and flexible uncertainty prescription. The second part of the paper describes a typical example application of data assimilation in an engineering environment for which the modelling environment has been developed: the daily forecast of current and salinity profiles to guide construction works in Osaka Bay. An ensemble Kalman filter-based steady-state Kalman filter is developed for assimilation of salinity and horizontal currents into an existing three-dimensional flow model for the highly non-linear stratified shallow bay. Calibration results are summarised for both hindcast mode and forecast mode. After assessment of the results, a second, improved model setup is described, including now assimilation of temperature and water level as well as currents and salinity. The calibration results are shown to improve further. Both applications show that significant forecast improvements can be possible with data assimilation in typical engineering applications, notwithstanding operational constraints such as limited measurement data and restricted computational infrastructure. It is argued that the described portable data assimilation environment is well suited for temporary engineering applications as it provides typical end-user functionalities for data assimilation and will so allow the user to focus on the characteristics of the physical processes and interpretation of the results instead of on details of the mathematical techniques. This should make practical data assimilation accessible for a much wider (end) user community in oceanography and offshore.

Inter-rater agreement in the assessment of exposure to carcinogens in the offshore petroleum industry

Objectives: To evaluate the reliability of an expert team assessing exposure to carcinogens in the offshore petroleum industry and to study how the information provided influenced the agreement among raters....

Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition

The proper orthogonal decomposition (POD) has been widely used in fluid dynamic applications for extracting dominant flow features. The “gappy” POD is an extension to this method that allows the consideration of incomplete data sets. In this paper, the gappy POD is extended to handle unsteady flow reconstruction problems, such as those encountered when limited flow measurement data is available. In addition, a systematic approach for effective sensor placement is formulated within the gappy framework using a condition number criterion. This criterion allows for accurate flow reconstruction results and yields sensor configurations that are robust to sensor noise. Two applications are considered. The first aims to reconstruct the unsteady flow field using a small number of surface pressure measurements for a subsonic airfoil undergoing plunging motion. The second considers estimation of POD modal content of a cylinder wake flow for active control purposes. In both cases, using the dominant POD basis vectors and a small number of sensor signals, the gappy approach is found to yield accurate flow reconstruction results.

Modelling, Identification and Control of Thermal Deformation of Machine Tool Structures, Part 1: Concept of Generalized Modelling

With the increasing demand for improved machining accuracy in recent years, the problem of thermal deformation of machine tool structures is becoming more critical than ever. In spite of the effort for improving the thermal deformation characteristics of machine tools at the design stage, there are always some residual errors that have to be compensated for during machining. The design of a generic multi-axis control system requires the development of two models to estimate the transient thermal load and to estimate the thermal deformation of the structure in real-time. To satisfy the stringent accuracy and stability requirements of these two models, a new concept of “generalized modelling” is introduced. It combines mathematical modelling with empirical calibration, and is based on the existence of a mathematical similarity between the real process and a simplified model, referred to as the fundamental generalized problem FGP. To obtain an analytical description of the heat transfer and thermal deformation processes in machine tool structures, an analytical solution of the FGP, which consists of an infinite plate with a central ring heat source, is derived using Hankel transformation. Computer-simulated test cases are presented to demonstrate the use of generalized modelling for predicting the transient thermal response in a complex machine tool structure. It was also shown how the generalized model can accurately extrapolate limited measurement data to predict the entire temperature field. The results confirmed that the generalized model can reproduce the accuracy of the finite-element solution, but two orders of magnitude faster.

Two-Dimensional Object Characterization With an Effective Model

The problem of a two-dimensional object location and characterization is considered and solved via the concept of an effective model. The nonlinearized 2D inverse problem is solved employing an effective model of a homogeneous circular cylinder. Promising numerical results are obtained for the spatial location, and determination of size and effective constitution of the objects of different topology using poor a priori information and limited measurement data.