Thermal food processing optimization: Algorithms and software

The algorithms and graphic user interface software package ?OPT-PROx? are developed to meet food engineering needs related to canned food thermal processing simulation and optimization. The adaptive random search algorithm and its modification coupled with penalty function?s approach, and the finite difference methods with cubic spline approximation are utilized by ?OPT-PROx? package (http://tomakechoice. com/optprox/index.html). The diversity of thermal food processing optimization problems with different objectives and required constraints are solvable by developed software. The geometries supported by the ?OPT-PROx? are the following: (1) cylinder, (2) rectangle, (3) sphere. The mean square error minimization principle is utilized in order to estimate the heat transfer coefficient of food to be heated under optimal condition. The developed user friendly dialogue and used numerical procedures makes the ?OPT-PROx? software useful to food scientists in research and education, as well as to engineers involved in optimization of thermal food processing.

InN/GaN heterojunction electrical behavior

Indium nitride (InN) has been the subject of intense research in recent years. Some of its most attractive features are its excellent transport properties such as its small band edge electron effective mass, high electron mobilities and peak drift velocities, and high frequency transient drift velocity oscillations [1]. These suggest enormous potential applications for InN in high frequency electronic devices. But to date the high unintentional bulk electron concentration (n~1018 cm-3) of undoped InN samples and the surface electron accumulation layer make it a hard task to create a reliable metalsemiconductor Schottky barrier. Some attempts have been made to overcome this problem by means of material oxidation [2] or deposition of insulators [3]. In this work we present a way to obtain an electrical rectification behaviour by means of heterojunction growth. Due to the big band gap differences among nitride semiconductors, it’s possible to create a structure with high band offsets. In InN/GaN heterojunctions, depending on the GaN doping, the magnitude of conduction and valence band offset are critical parameters which allow distinguishing among different electrical behaviours. The earliest estimate of the valence band offset at an InN–GaN heterojunction in a wurtzite structure was measured to be ~0.85 eV [4], while the Schottky barrier heights were determined to be ~ 1,4 eV [5].We grew In-face InN layer with varying thickness (between 150 nm and 1 mm) by plasma assisted molecular beam epitaxy (PA-MBE) on GaNntemplates (GaN/Al2O3), with temperatures ranging between 300°C and 450°C. The different doping in GaN template (Si doping, Fe doping and Mg doping) results in differences in band alignments of the two semiconductors changing electrical barriers for carriers and consequently electrical conduction behaviour. The processing of the devices includes metallization of the ohmic contacts on InN and GaN, for which we used Ti/Al/Ni/Au. Whereas an ohmic contact on InN is straightforward, the main issue was the fabrication of the contact on GaN due to the very low decomposition temperature of InN. A standard ohmic contact on GaN is generally obtained by high temperature rapid thermal annealing (RTA), typically done between 500ºC and 900ºC[6]. In this case, the limitation due to the presence of In-face InN imposes an upper limit on the temperature for the thermal annealing process and ohmic contact formation of about 450°C. We will present results on the morphology of the InN layers by X-Ray diffraction and SEM, and electrical measurements, in particular current-voltage and capacitance-voltage characteristics.

Optimization of InGaAsN(Sb)/GaAs quantum dots for optical emission at 1.55 µm with low optical degradation

Low optical degradation in GaInAsN(Sb)/GaAs quantum dots (QDs) p–i–n structures emitting up to 1.55 μm is presented in this paper. We obtain emission at different energies by means of varying N content from 1 to 4%. The samples show a low photoluminescence (PL) intensity degradation of only 1 order of magnitude when they are compared with pure InGaAs QD structures, even for an emission wavelength as large as 1.55 μm. The optimization studies of these structures for emission at 1.55 μm are reported in this work. High surface density and homogeneity in the QD layers are achieved for 50% In content by rapid decrease in the growth temperature after the formation of the nanostructures. Besides, the effect of N and Sb incorporation in the redshift and PL intensity of the samples is studied by post-growth rapid thermal annealing treatments. As a general conclusion, we observe that the addition of Sb to QD with low N mole fraction is more efficient to reach 1.55 μm and high PL intensity than using high N incorporation in the QD. Also, the growth temperature is determined to be an important parameter to obtain good emission characteristics. Finally, we report room temperature PL emission of InGaAsN(Sb)/GaAs at 1.4 μm.

High efficient luminescence in type-II GaAsSb-capped InAs quantum dots upon annealing

The photoluminescence efficiency of GaAsSb-capped InAs/GaAs type II quantum dots (QDs) can be greatly enhanced by rapid thermal annealing while preserving long radiative lifetimes which are ∼20 times larger than in standard GaAs-capped InAs/GaAs QDs. Despite the reduced electron-hole wavefunction overlap, the type-II samples are more efficient than the type-I counterparts in terms of luminescence, showing a great potential for device applications. Strain-driven In-Ga intermixing during annealing is found to modify the QD shape and composition, while As-Sb exchange is inhibited, allowing to keep the type-II structure. Sb is only redistributed within the capping layer giving rise to a more homogeneous composition.

Effect of Thermal Treatments on the Mechanical Properties Enhancement of High Reliability Metallic Materials by Laser Shock Processing

Laser shock processing (LSP) is increasingly applied as an effective technology for the improvement of metallic materials mechanical properties in different types of components as a means of enhancement of their fatigue life behavior. As reported in previous contributions by the authors, a main effect resulting from the application of the LSP technique consists on the generation of relatively deep compression residual stresses fields into metallic components allowing an improved mechanical behaviour, explicitly the life improvement of the treated specimens against wear, crack growth and stress corrosion cracking. Additional results accomplished by the authors in the line of practical development of the LSP technique at an experimental level (aiming its integral assessment from an interrelated theoretical and experimental point of view) are presented in this paper. Concretely, experimental results on the residual stress profiles and associated mechanical properties modification successfully reached in typical materials under different LSP irradiation conditions are presented. In this case, the specific behavior of a widely used material in high reliability components (especially in nuclear and biomedical applications) as AISI 316L is analyzed, the effect of possible “in-service” thermal conditions on the relaxation of the LSP effects being specifically characterized. I.

Thermal Scattering Libraries Processing (F4E, Task 6.2)

Presentación del trabajo realizado en el marco del proyecto F4E, sobre el procesamiento de librerías de dispersión térmica de neutrones en formato ACE para su uso con el código MCNP. Se presentan tanto los métodos y procedimientos empleados, como los resultados y diferencias entre las distintas fuentes de datos.

Effect of Thermal Treatments on the Mechanical Properties Enhancement of High Reliability Metallic Materials by Laser Shock Processing

Laser shock processing (LSP) is increasingly applied as an effective technology for the improvement of metallic materials mechanical properties in different types of components as a means of enhancement of their fatigue life behavior. As reported in previous contributions by the authors, a main effect resulting from the application of the LSP technique consists on the generation of relatively deep compression residual stresses fields into metallic components allowing an improved mechanical behaviour, explicitly the life improvement of the treated specimens against wear, crack growth and stress corrosion cracking. Additional results accomplished by the authors in the line of practical development of the LSP technique at an experimental level (aiming its integral assessment from an interrelated theoretical and experimental point of view)are presented in this paper. Concretely, experimental results on the residual stress profiles and associated mechanical properties modification successfully reached in typical materials under different LSP irradiation conditions are presented. In this case, the specific behavior of a widely used material in high reliability components (especially in nuclear and biomedical applications) as AISI 316L is analyzed, the effect of possible “in-service” thermal conditions on the relaxation of the LSP effects being specifically characterized.

Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers

Los Centros de Datos se encuentran actualmente en cualquier sector de la economía mundial. Están compuestos por miles de servidores, dando servicio a los usuarios de forma global, las 24 horas del día y los 365 días del año. Durante los últimos años, las aplicaciones del ámbito de la e-Ciencia, como la e-Salud o las Ciudades Inteligentes han experimentado un desarrollo muy significativo. La necesidad de manejar de forma eficiente las necesidades de cómputo de aplicaciones de nueva generación, junto con la creciente demanda de recursos en aplicaciones tradicionales, han facilitado el rápido crecimiento y la proliferación de los Centros de Datos. El principal inconveniente de este aumento de capacidad ha sido el rápido y dramático incremento del consumo energético de estas infraestructuras. En 2010, la factura eléctrica de los Centros de Datos representaba el 1.3% del consumo eléctrico mundial. Sólo en el año 2012, el consumo de potencia de los Centros de Datos creció un 63%, alcanzando los 38GW. En 2013 se estimó un crecimiento de otro 17%, hasta llegar a los 43GW. Además, los Centros de Datos son responsables de más del 2% del total de emisiones de dióxido de carbono a la atmósfera. Esta tesis doctoral se enfrenta al problema energético proponiendo técnicas proactivas y reactivas conscientes de la temperatura y de la energía, que contribuyen a tener Centros de Datos más eficientes. Este trabajo desarrolla modelos de energía y utiliza el conocimiento sobre la demanda energética de la carga de trabajo a ejecutar y de los recursos de computación y refrigeración del Centro de Datos para optimizar el consumo. Además, los Centros de Datos son considerados como un elemento crucial dentro del marco de la aplicación ejecutada, optimizando no sólo el consumo del Centro de Datos sino el consumo energético global de la aplicación. Los principales componentes del consumo en los Centros de Datos son la potencia de computación utilizada por los equipos de IT, y la refrigeración necesaria para mantener los servidores dentro de un rango de temperatura de trabajo que asegure su correcto funcionamiento. Debido a la relación cúbica entre la velocidad de los ventiladores y el consumo de los mismos, las soluciones basadas en el sobre-aprovisionamiento de aire frío al servidor generalmente tienen como resultado ineficiencias energéticas. Por otro lado, temperaturas más elevadas en el procesador llevan a un consumo de fugas mayor, debido a la relación exponencial del consumo de fugas con la temperatura. Además, las características de la carga de trabajo y las políticas de asignación de recursos tienen un impacto importante en los balances entre corriente de fugas y consumo de refrigeración. La primera gran contribución de este trabajo es el desarrollo de modelos de potencia y temperatura que permiten describes estos balances entre corriente de fugas y refrigeración; así como la propuesta de estrategias para minimizar el consumo del servidor por medio de la asignación conjunta de refrigeración y carga desde una perspectiva multivariable. Cuando escalamos a nivel del Centro de Datos, observamos un comportamiento similar en términos del balance entre corrientes de fugas y refrigeración. Conforme aumenta la temperatura de la sala, mejora la eficiencia de la refrigeración. Sin embargo, este incremente de la temperatura de sala provoca un aumento en la temperatura de la CPU y, por tanto, también del consumo de fugas. Además, la dinámica de la sala tiene un comportamiento muy desigual, no equilibrado, debido a la asignación de carga y a la heterogeneidad en el equipamiento de IT. La segunda contribución de esta tesis es la propuesta de técnicas de asigación conscientes de la temperatura y heterogeneidad que permiten optimizar conjuntamente la asignación de tareas y refrigeración a los servidores. Estas estrategias necesitan estar respaldadas por modelos flexibles, que puedan trabajar en tiempo real, para describir el sistema desde un nivel de abstracción alto. Dentro del ámbito de las aplicaciones de nueva generación, las decisiones tomadas en el nivel de aplicación pueden tener un impacto dramático en el consumo energético de niveles de abstracción menores, como por ejemplo, en el Centro de Datos. Es importante considerar las relaciones entre todos los agentes computacionales implicados en el problema, de forma que puedan cooperar para conseguir el objetivo común de reducir el coste energético global del sistema. La tercera contribución de esta tesis es el desarrollo de optimizaciones energéticas para la aplicación global por medio de la evaluación de los costes de ejecutar parte del procesado necesario en otros niveles de abstracción, que van desde los nodos hasta el Centro de Datos, por medio de técnicas de balanceo de carga. Como resumen, el trabajo presentado en esta tesis lleva a cabo contribuciones en el modelado y optimización consciente del consumo por fugas y la refrigeración de servidores; el modelado de los Centros de Datos y el desarrollo de políticas de asignación conscientes de la heterogeneidad; y desarrolla mecanismos para la optimización energética de aplicaciones de nueva generación desde varios niveles de abstracción. ABSTRACT Data centers are easily found in every sector of the worldwide economy. They consist of tens of thousands of servers, serving millions of users globally and 24-7. In the last years, e-Science applications such e-Health or Smart Cities have experienced a significant development. The need to deal efficiently with the computational needs of next-generation applications together with the increasing demand for higher resources in traditional applications has facilitated the rapid proliferation and growing of data centers. A drawback to this capacity growth has been the rapid increase of the energy consumption of these facilities. In 2010, data center electricity represented 1.3% of all the electricity use in the world. In year 2012 alone, global data center power demand grew 63% to 38GW. A further rise of 17% to 43GW was estimated in 2013. Moreover, data centers are responsible for more than 2% of total carbon dioxide emissions. This PhD Thesis addresses the energy challenge by proposing proactive and reactive thermal and energy-aware optimization techniques that contribute to place data centers on a more scalable curve. This work develops energy models and uses the knowledge about the energy demand of the workload to be executed and the computational and cooling resources available at data center to optimize energy consumption. Moreover, data centers are considered as a crucial element within their application framework, optimizing not only the energy consumption of the facility, but the global energy consumption of the application. The main contributors to the energy consumption in a data center are the computing power drawn by IT equipment and the cooling power needed to keep the servers within a certain temperature range that ensures safe operation. Because of the cubic relation of fan power with fan speed, solutions based on over-provisioning cold air into the server usually lead to inefficiencies. On the other hand, higher chip temperatures lead to higher leakage power because of the exponential dependence of leakage on temperature. Moreover, workload characteristics as well as allocation policies also have an important impact on the leakage-cooling tradeoffs. The first key contribution of this work is the development of power and temperature models that accurately describe the leakage-cooling tradeoffs at the server level, and the proposal of strategies to minimize server energy via joint cooling and workload management from a multivariate perspective. When scaling to the data center level, a similar behavior in terms of leakage-temperature tradeoffs can be observed. As room temperature raises, the efficiency of data room cooling units improves. However, as we increase room temperature, CPU temperature raises and so does leakage power. Moreover, the thermal dynamics of a data room exhibit unbalanced patterns due to both the workload allocation and the heterogeneity of computing equipment. The second main contribution is the proposal of thermal- and heterogeneity-aware workload management techniques that jointly optimize the allocation of computation and cooling to servers. These strategies need to be backed up by flexible room level models, able to work on runtime, that describe the system from a high level perspective. Within the framework of next-generation applications, decisions taken at this scope can have a dramatical impact on the energy consumption of lower abstraction levels, i.e. the data center facility. It is important to consider the relationships between all the computational agents involved in the problem, so that they can cooperate to achieve the common goal of reducing energy in the overall system. The third main contribution is the energy optimization of the overall application by evaluating the energy costs of performing part of the processing in any of the different abstraction layers, from the node to the data center, via workload management and off-loading techniques. In summary, the work presented in this PhD Thesis, makes contributions on leakage and cooling aware server modeling and optimization, data center thermal modeling and heterogeneityaware data center resource allocation, and develops mechanisms for the energy optimization for next-generation applications from a multi-layer perspective.

Processing and adsorption control in ZnO single nanowire photodetectors

ZnO single nanowire photodetectors have been measured in different ambient conditions in order to understand and control adsorption processes on the surface. A decrease in the conductivity has been observed as a function of time when the nanowires are exposed to air, due to adsorbed O2/H2O species at the nanowire surface. In order to have a device with stable characteristics in time, thermal desorption has been used to recover the original conductivity followed by PMMA coating of the exposed nanowire surface.

New inorganic nanotube polymer nanocomposites: Improved thermal, mechanical and tribological properties in isotactic polypropylene incorporating INT-MoS2.

Environmentally friendly molybdenum disulfide (INT-MoS2) inorganic nanotubes were introduced into an isotactic polypropylene (iPP) polymer matrix to generate novel nanocomposite materials through an advantageous melt-processing route. The effects of INT-MoS2 content on the thermal, mechanical and tribological properties were investigated. The incorporation of INT-MoS2 generates notable performance enhancements through reinforcement effects, highly efficient nucleation activity and excellent lubricating ability in comparison with other nanoparticle fillers such as nanoclays, carbon nanotubes, silicon nitrides and halloysite nanotubes. It was shown that these INT-MoS2 nanocomposites can provide an effective balance between performance, cost effectiveness and processability, and should be of some interest in the area of multifunctional polymer nanocomposite materials.

Rapid prototyping framework for visual control of autonomous micro aerial vehicles

Rapid prototyping environments can speed up the research of visual control algorithms. We have designed and implemented a software framework for fast prototyping of visual control algorithms for Micro Aerial Vehicles (MAV). We have applied a combination of a proxy-based network communication architecture and a custom Application Programming Interface. This allows multiple experimental configurations, like drone swarms or distributed processing of a drone’s video stream. Currently, the framework supports a low-cost MAV: the Parrot AR.Drone. Real tests have been performed on this platform and the results show comparatively low figures of the extra communication delay introduced by the framework, while adding new functionalities and flexibility to the selected drone. This implementation is open-source and can be downloaded from www.vision4uav.com/?q=VC4MAV-FW

Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers

Data centers are easily found in every sector of the worldwide economy. They are composed of thousands of servers, serving millions of users globally and 24-7. In the last years, e-Science applications such e-Health or Smart Cities have experienced a significant development. The need to deal efficiently with the computational needs of next-generation applications together with the increasing demand for higher resources in traditional applications has facilitated the rapid proliferation and growing of Data Centers. A drawback to this capacity growth has been the rapid increase of the energy consumption of these facilities. In 2010, data center electricity represented 1.3% of all the electricity use in the world. In year 2012 alone, global data center power demand grep 63% to 38GW. A further rise of 17% to 43GW was estimated in 2013. Moreover, Data Centers are responsible for more than 2% of total carbon dioxide emissions.