Application of Novel Thermal Technology in Foods Processing

Advanced and novel thermal technologies, such as ohmic heating, dielectric heating (e.g., microwave heating and radio frequency heating), and inductive heating, have been developed to improve the effectiveness of heat processing whilst guaranteeing food safety and eliminating undesirable impacts on the organoleptic and nutritional properties of foods. Novel thermal technologies rely on heat generation directly inside foods, which has implications for improving the overall energy efficiency of the heating process itself. The use of novel thermal technologies is dependent on the complexity and inherent properties of the food materials of interest (e.g., thermal conductivity, electrical resistance, water content, pH, rheological properties, food porosity, and presence of particulates). Moreover, there is a need to address the combined use of thermal processing with emerging technologies such as pulsed electric fields, high hydrostatic pressure, and ultrasound to complement the conventional thermal processing of fluid or solid foods. This Special Issue provides readers with an overview of the latest applications of various novel technologies in food processing. A total of eight cutting-edge original research papers and one comprehensive review paper discussing novel processing technologies from the perspectives of food safety, sustainability, process engineering, (bio)chemical changes, health, nutrition, sensory issues, and consumers are covered in this Special Issue

Thermal management of heat-generating automotive powertrain hardware using spray evaporative cooling

Thermal management of heat generating powertrain hardware in automotive vehicles is examined using spray evaporative cooling, particularly taking account of the effect of vehicle vibration and agitation. The heat transfer characteristics of spray evaporative cooling of vibrating surfaces are important for both automotive and aerospace applications but this thesis content focuses on automotive applications involving highly-boosted IC engines, power electronics, batteries, and electrical machines. The fundamental physics of highly complex two-phase spray evaporative cooling is currently computationally intractable, therefore the thesis addresses (for the first time) construction and calibration of correlation models of spray evaporative cooling involving vibrating surfaces. Suitable correlation models are constructed using dimensional analysis involving the Generalized Buckingham Π-Theorem. Two nondimensional parameters are introduced in the form of Acceleration Number and Vibrational Reynold Number to account for dynamic effects resulting in models which accommodate the combined effects of vibration amplitude and frequency. The correlation models are fitted to experimentally-measured spray evaporative cooling data taken from an experimental test rig which includes instrumented test-pieces vibrated by a shaker to amplitudes and frequencies appropriate for real (on-road) vehicle conditions. The models provide dynamic predictions of heat flux in the nucleate boiling regime, and predictions of the Critical Heat Flux and its associated temperature. The experimental measurements alone show that dynamic effects do indeed influence heat transfer. Thermal management in the nucleate boiling regime using a feedback control system for flat and curved surfaces with, and without vibration, has been subsequently studied in the thesis using simulation and experimental hardware. The performance of a PID controller within a thermal management system has been examined using the calibrated correlation models to provide an approximate description of the ‘plant’ physics. A detailed examination is made of the effect of the PID controller gains on the performance of a thermal management system in terms of stability and practical response requirements with, and without vibration. The results of the study confirm that thermal management of heat-generating automotive powertrain hardware is a definite practical possibility using evaporative spray cooling

Prediction of flow and its resistance in compound open channel

Flooding situation in rivers is a complex phenomenon and affects the livelihood and economic condition of the region. This complex condition has long been identified and intensive research work has been carried out to find out its remedial solution. During flood, river overtops its banks and spreads to flood plains, called compound channel. It has been observed that the flow velocity in flood plain subsection is slower than the velocity of its course. Due to this difference in velocities between main channel and flood plain, a large shear layer produces.This large shear layer retains complex turbulent structures of different scales within it. These turbulent structures produce extra resistance to flow, which induces uncertainty in predictions of flow and its resistance. It has been also observed that, numerical turbulence models can be used to find the point to point information. Hence the analysis of turbulent structures is prevalent in this situation. Earlier, researchers have adopted various numerical, analytical and empirical models to analyze turbulent flow in compound channels, generally for low development length. Therefore, in this study an effort is made to analyze the turbulent structure by Large Eddy Simulation method (LES) to predict the flow and its resistance involved in it. The LES is carried out taking sufficient development length so that uniform turbulent flow is developed. The development length is incorporated in the computational domain. However, it is a fact that numerical simulation of compound channels with different hydraulic conditions are computationally very expensive and arduous. Therefore,this analysis is further done by using adaptive approaches such as Artificial Neural Network (ANN) and Artificial Neuro Fuzzy Inference System (ANFIS).These models are used to predict flow and its resistanceof a compound channel for different hydraulic conditions.All the proposed models are compared well with the standard modes previously developed.The proposed models are found to give better results compared to other models when applied to globa data sets

Effect of curing conditions and harvesting stage of maturity on Ethiopian onion bulb drying properties

The study was conducted to investigate the impact of curing conditions and harvesting stageson the drying quality of onion bulbs. The onion bulbs (Bombay Red cultivar) were harvested at three harvesting stages (early, optimum, and late maturity) and cured at three different temperatures (30, 40 and 50 oC) and relative humidity (30, 50 and 70%). The results revealed that curing temperature, RH, and maturity stage had significant effects on all measuredattributesexcept total soluble solids

Dynamic Modeling, Sensor Placement Design, and Fault Diagnosis of Nuclear Desalination Systems

Fault diagnosis of sensors, devices, and equipment is an important topic in the nuclear industry for effective and continuous operation of nuclear power plants. All the fault diagnostic approaches depend critically on the sensors that measure important process variables. Whenever a process encounters a fault, the effect of the fault is propagated to some or all the process variables. The ability of the sensor network to detect and isolate failure modes and anomalous conditions is crucial for the effectiveness of a fault detection and isolation (FDI) system. However, the emphasis of most fault diagnostic approaches found in the literature is primarily on the procedures for performing FDI using a given set of sensors. Little attention has been given to actual sensor allocation for achieving the efficient FDI performance. This dissertation presents a graph-based approach that serves as a solution for the optimization of sensor placement to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. This would potentially facilitate an automated sensor allocation procedure. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data, and to fit a hyper-plane to the data. The fault directions for different fault scenarios are obtained from the prediction errors, and fault isolation is then accomplished using new projections on these fault directions. The effectiveness of the use of an optimal sensor set versus a reduced set for fault detection and isolation is demonstrated using this technique. Among a variety of desalination technologies, the multi-stage flash (MSF) processes contribute substantially to the desalinating capacity in the world. In this dissertation, both steady-state and dynamic simulation models of a MSF desalination plant are developed. The dynamic MSF model is coupled with a previously developed International Reactor Innovative and Secure (IRIS) model in the SIMULINK environment. The developed sensor placement design and fault diagnostic methods are illustrated with application to the coupled nuclear desalination system. The results demonstrate the effectiveness of the newly developed integrated approach to performance monitoring and fault diagnosis with optimized sensor placement for large industrial systems

Applications of Artificial Neural Networks (ANNs) in exploring materials property-property correlations

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorThe discoveries of materials property-property correlations usually require prior knowledge or serendipity, the process of which can be time-consuming, costly, and labour-intensive. On the other hand, artificial neural networks (ANNs) are intelligent and scalable modelling techniques that have been used extensively to predict properties from materials’ composition or processing parameters, but are seldom used in exploring materials property-property correlations. The work presented in this thesis has employed ANNs combinatorial searches to explore the correlations of different materials properties, through which, ‘known’ correlations are verified, and ‘unknown’ correlations are revealed. An evaluation criterion is proposed and demonstrated to be useful in identifying nontrivial correlations. The work has also extended the application of ANNs in the fields of data corrections, property predictions and identifications of variables’ contributions. A systematic ANN protocol has been developed and tested against the known correlating equations of elastic properties and the experimental data, and is found to be reliable and effective to correct suspect data in a complicated situation where no prior knowledge exists. Moreover, the hardness increments of pure metals due to HPT are accurately predicted from shear modulus, melting temperature and Burgers vector. The first two variables are identified to have the largest impacts on hardening. Finally, a combined ANN-SR (symbolic regression) method is proposed to yield parsimonious correlating equations by ruling out redundant variables through the partial derivatives method and the connection weight approach, which are based on the analysis of the ANNs weight vectors. By applying this method, two simple equations that are at least as accurate as other models in providing a rapid estimation of the enthalpies of vaporization for compounds are obtained.School of Engineering and Materials Science of Queen Mary, University of London and China Scholarship Council (CSC), for providing Queen Mary - China Scholarship Council Joint PhD Scholarsh

Experimental Investigation and Modelling of Surface Integrity, Accuracy and Productivity Aspects in EDM of AISI D2 Steel

Electrical Discharge Machining (EDM) is one of the most popular non-traditional machining process for “difficult to machine” conducting materials and is quite extensively and successfully used in industry owing to its favourable features and advantages that it can offer. In EDM, the objective is always to get improved Material Removal Rate (MRR) along with achieving better surface quality of machined component. Furthermore, the essential requirements are as small a thermally affected region of the workpiece surface as possible and a lower radial overcut with minimal tool wear. The quality of a machined surface is becoming increasingly significant to satisfy the increasing demands of superior component performance, longevity, and reliability thus preserving the integrity of the surface is essential. In order to sustain and/or improve reliability of the components, it is always necessary to have knowledge of the effects of the manufacturing parameters on the surface integrity, precision and productivity of the EDMed components

Bibliography of Lewis Research Center technical publications announced in 1993

This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1993. All the publications were announced in the 1993 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses