Virtual Manufacturing of Composite Structures Applying Implicit and Explicit FE Techniques

Virtual manufacturing of composites can yield an initial early estimation of the induced residual thermal stresses that affect component fatigue life, and deformations that affect required tolerances for assembly. Based on these estimation, the designer can make early decisions, which can help in reducing cost, regarding changes in part design or material properties. In this paper, an approach is proposed to simulate the autoclave manufacturing technique for unidirectional composites. The proposed approach consists of three modules. The first module is a Thermochemical model to estimate temperature and the degree of cure distributions in the composite part during the cure cycle. The second and third modules are stress analysis using FE-Implicit and FE-Explicit respectively. User-material subroutine will be used to model the Viscoelastic properties of the material based on micromechanical theory. Estimated deformation of the composite part can be corrected during the autoclave process by modifying the process-tool design. The deformed composite surface is sent to CATIA for design modification of the process-tool.

Micro-Satellite Structure Fracture Investigation techniques

Mounting accuracy of satellite payload and
ADCS (attitude determination and control subsystem) seats
is one of the requirements to achieve the satellite mission
with satisfactory performance. Deviation of the position of
the mounting seat for Multi-Band-Earth-Imager (MBEI) is
caused by cracks in the plate of the basis unit and bracket
for attachment of MBEI. These cracks were detected during
inspection of the satellite strength mock-up after vibration
testing for air transportation phase. Most probable reason of
the cracking is fatigue damage as strength mock-up
structure was subjected to prolonged vibration loading
during various loading cases. Total vibration duration
during testing is about 56 hours. In order to study the
cracking reasons, finite element modeling of the structural
parts of the basis unit including MBEI bracket and
instrument MBEI is subjected to harmonic response to
simulate vibration loading for the case of air transportation.
Numerical results are compared with the experimental ones,
and mechanical design of the basis-plate unit is modified

Using Noise Techniques to Destabilize Composition and Improvisation

A description of the uses of computer-generated noise in my instrumental scores and guided improvisations.

Predicting organic acid concentration from UV/vis spectrometry measurements - A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.

Methodology for Validating Artifact Removal Techniques for Physiological Signals

Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a ground truth signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this ground truth, together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform. © 2012 IEEE.

Computational techniques for a simple theory of conditional preferences

A simple logic of conditional preferences is defined, with a language that allows the compact representation of certain kinds of conditional preference statements, a semantics and a proof theory. CP-nets and TCP-nets can be mapped into this logic, and the semantics and proof theory generalise those of CP-nets and TCP-nets. The system can also express preferences of a lexicographic kind. The paper derives various sufficient conditions for a set of conditional preferences to be consistent, along with algorithmic techniques for checking such conditions and hence confirming consistency. These techniques can also be used for totally ordering outcomes in a way that is consistent with the set of preferences, and they are further developed to give an approach to the problem of constrained optimisation for conditional preferences.

Assessing the Labile Arsenic Pool in Contaminated Paddy Soils by Isotopic Dilution Techniques and Simple Extractions

Arsenic (As) contamination of paddy soils threatens rice cultivation and the health of populations relying on rice as a staple crop. In the present study, isotopic dilution techniques were used to determine the chemically labile (E value) and phytoavailable (L value) pools of As in a range of paddy soils from Bangladesh, India, and China and two arable soils from the UK varying in the degree and sources of As contamination. The E value accounted for 6.2-21.4% of the total As, suggesting that a large proportion of soil As is chemically nonlabile. L values measured with rice grown under anaerobic conditions were generally larger than those under aerobic conditions, indicating increased potentially phytoavailable pool of As in flooded soils. In an incubation study, As was mobilized into soil pore water mainly as arsenite under flooded conditions, with Bangladeshi soils contaminated by irrigation of groundwater showing a greater potential of As mobilization than other soils. Arsenic mobilization was best predicted by phosphate-extractable As in the soils.

Assessing the occupational risk of dust particles in the Kenyan tanning industry using rapid image processing and microscopy techniques

The purpose of this study was to investigate the occupational hazards within the tanning industry caused by contaminated dust. A qualitative assessment of the risk of human exposure to dust was made throughout a commercial Kenyan tannery. Using this information, high-risk points in the processing line were identified and dust sampling regimes developed. An optical set-up using microscopy and digital imaging techniques was used to determine dust particle numbers and size distributions. The results showed that chemical handling was the most hazardous (12 mg m(-3)). A Monte Carlo method was used to estimate the concentration of the dust in the air throughout the tannery during an 8 h working day. This showed that the high-risk area of the tannery was associated with mean concentrations of dust greater than the UK Statutory Instrument 2002 No. 2677. stipulated limits (exceeding 10 mg m(-3) (Inhalable dust limits) and 4 mg m(-3) (Respirable dust limits). This therefore has implications in terms of provision of personal protective equipment (PPE) to the tannery workers for the mitigation of occupational risk.