3D computational simulation and experimental characterization of polymeric stochastic network materials : case studies in reinforced eucalyptus office paper and nanofibrous materials

The properties of stochastic fibrous materials like paper and nanowebs are highly dependent on those fibers from which the network structure is made. This work contributes to a better understanding of the effect of fiber properties on the network structural properties, using an original 3D fibrous material model with experimental validation, and its application to different fibrous materials used in reinforced Eucalyptus office paper and nanofibrous networks. To establish the relationships between the fiber and the final structural material properties, an experimental laboratorial plan has been executed for a reinforced fibrous structure, and a physical based 3D model has been developed and implemented. The experimental plan was dedicated to an important Portuguese material: the reinforced Eucalyptus based office paper. Office paper is the principal Portuguese paper industry product. This paper is mainly produced from Eucalyptus globulus bleached kraft pulp with a small incorporation of a softwood pulp to increase paper strength. It is important to access the contribution of different reinforcement pulp fibers with different biometry and coarseness to the final paper properties. The two extremes of reinforcement pulps are represented by a Picea abies kraft softwood pulp, usually considered the best reinforcement fiber, and the Portuguese pine Pinus pinaster kraft pulp. Fiber flexibility was determined experimentally using the Steadman and Luner method with a computerized acquisition device. When comparing two reinforcement fibers, the information about fiber flexibility and biometry is determinant to predict paper properties. The values presented correspond to the two extremes of fibers available as reinforcement fibers, regarding wall thickness, beating ability and flexibility values. Pinus pinaster has the thickest fiber wall, and consequently it is less flexible than the thinner wall fibers: Pinus sylvestris and Picea abies. Experimental results for the evolutions of paper properties, like paper apparent density, air permeability, tensile and tear strength, together with fiber flexibility for the two reinforcement fibers, constitute valuable information, also applicable for other reinforcement fibers, with fiber walls dimensions in this range. After having quantified the influence of fiber flexibility, we identified that this is as a key physical property to be included in our structural model. Therefore, we chose to develop a 3D network model that includes fiber bending in the z direction as an important parameter. The inclusion of fiber flexibility was done for the first time by Niskanen, in a model known as the KCL-Pakka model. We propose an extension of this model, with improvements on the fiber model, as well as an original computational implementation. A simulator has been developed from scratch and the results have been validated experimentally using handmade laboratory structures made from Eucalyptus fibers (hardwood fibers), and also Pinus pinaster, Pinus Sylvestris and Picea abies fibers, which are representative reinforcement fibers. Finally, the model was modified and extended to obtain an original simulator to nanofibrous materials, which is also an important innovation. In the network model developed in this work, the structure is formed by the sequential deposition of fibers, which are modeled individually. The model includes key papermaking fiber properties like morphology, flexibility, and collapsibility and process operations such as fiber deposition, network forming or densification. For the first time, the model considers the fiber microstructure level, including lumen and fiber wall thickness, with a resolution up to 0.05μm for the paper material case and 0.05nm for the nanofibrous materials. The computational simulation model was used to perform simulation studies. In the case of paper materials, it was used to investigate the relative influence of fiber properties such as fiber flexibility, dimensions and collapsibility. The developed multiscale model gave realistic predictions and enabled us to link fiber microstructure and paper properties. In the case of nanofibrous materials, the 3D network model was modified and implemented for Polyamide-6 electrospun and cellulose nanowebs. The influence of computational fiber flexibility and dimensions was investigated. For the Polyamide-6 electrospun network experimental results were compared visually with simulation results and similar evolutions were observed. For cellulose nanowebs the simulation study used literature data to obtain the input information for the nanocellulose fibers. The design of computer experiments was done using a space filling design, namely the Latin hypercube sampling design, and the simulations results were organized and interpreted using regression trees. Both the experimental characterization, and computational modeling, contributed to study the relationships between the polymeric fibers and the network structure formed.As propriedades de materiais estocásticos constituídos por fibras, tais como o papel ou nanoredes poliméricas, dependem fortemente das fibras a partir das quais a estrutura em rede se forma. Este trabalho contribui para uma melhor compreensão da influência das propriedades das fibras nas propriedades estruturais das redes, utilizando um modelo original 3D para materiais constituídos por fibras, com validação experimental, bem como a sua aplicação aos materiais utilizados no papel de escritório de Eucalyptus, com fibras de reforço, e a redes de nanofibras. Para estabelecer as relações entre a fibra e as propriedades estruturais do material, executou-se um planeamento experimental para uma estrutura fibrosa reforçada, e desenvolveu-se e implementou-se um modelo 3D de base física. O plano experimental teve como objecto um material relevante em Portugal: o papel de escritório de Eucalyptus com fibras de reforço. O papel de escritório é o produto principal da indústria de papel Portuguesa. Este tipo de papel é produzido a partir da pasta kraft branqueada de Eucalyptus globulus, com incorporação de uma pequena quantidade de pasta de reforço, “softwood”, para melhorar a resistência do papel. É importante avaliar a contribuição de diferentes fibras de reforço, com biometria e massas linear distinta, nas diferentes propriedades finais do papel. Os dois extremos das fibras de reforço estão representados pela pasta kraft de Picea abies, usualmente considerada a melhor fibra de reforço, e a pasta kraft Portuguesa de Pinus pinaster. A flexibilidade da fibra determinou-se experimentalmente utilizando o método de Steadman e Luner, com um dispositivo de aquisição automatizado. A informação relativa à flexibilidade e biometria da fibra é fundamental para inferir sobre as propriedades do papel. Os valores determinados correspondem a valores dos extremos, paras as fibras de reforço disponíveis no mercado, no que diz respeito a espessura de parede, refinabilidade e valores de flexibilidade. Pode considerar-se a fibra de Pinus pinaster num extremo, sendo a fibra de paredes mais espessas, e consequentemente menos flexível que as fibras de paredes mais finas: Pinus sylvestris e Picea abies. Desta forma, os resultados experimentais obtidos para estas fibras, relativos à evolução de propriedades do papel, nomeadamente densidade, permeabilidade ao ar, resistência à tracção e ao rasgamento, entre outros, constituem informação importante que pode ser aplicada a outras fibras de reforço, que se situem nesta gama. Como consequência lógica da identificação da flexibilidade da fibra como uma propriedade física determinante, e após a quantificação experimental, a escolha do modelo de papel recaiu sobre um modelo que inclui a flexibilidade como propriedade chave. Assim, desenvolvemos um modelo 3D que inclui a flexão das fibras na direcção transversal, isto é, a direcção da espessura do papel, também reconhecida como direcção da coordenada z. A inclusão da flexibilidade da fibra baseia-se no modelo de Niskanen, conhecido como o modelo KCL-Pakka. Apresenta-se uma extensão deste modelo, com modificações no modelo da fibra, bem como uma implementação computacional original. Desenvolveu-se um simulador para matérias em rede, que se validou com resultados experimentais. Efectuaram-se, também, as modificações necessárias para obter um simulador para nanomateriais, o que constitui uma inovação relevante. No modelo deste trabalho, desenvolvido para materiais fibrosos em rede, as fibras modelam-se individualmente e a estrutura forma-se sequencialmente pela sua deposição e conformação à estrutura existente. O modelo inclui propriedades das fibras determinantes, tais como morfologia, flexibilidade e colapsabilidade. Bem como etapas do processo, nomeadamente a deposição das fibras e a formação da rede, isto é, a densificação da estrutura. De uma forma original, o modelo da fibra inclui a espessura do lúmen e da parede da fibra, com uma resolução de 0.05μm para as fibras do papel e 0.05nm no caso das nanofibras. O modelo computacional desenvolvido utilizou-se na realização de estudos de simulação. No caso dos materiais papeleiros, utilizou-se para investigar a influência das propriedades das fibras, tendo-se obtido previsões realistas. No caso dos nanomateriais, o modelo foi modificado e implementado para as fibras electrofiadas de Poliamida-6 e redes de nanocelulose. O plano de experiencias computacionais utilizou uma distribuição no espaço “Latin hypercube” e os resultados das simulações organizaram-se recorrendo a árvores de regressão. Tanto a caracterização experimental, como a modelação computacional, contribuíram com valiosa informação para o estudo das relações entre as fibras poliméricas e as estruturas em rede por elas formadas

CIRAS Annual Report, December 29,2000

Center for Industrial Research and Service: CIRAS annual report

CIRAS News Annual Report 2000, January 3, 2001

CIRAS professionals, the equipment they use to do their jobs, and the latest in technical support equipment. With these investments, CIRAS continues to meet the needs of Iowa manufacturers, whether in doing routine problem solving, long-range planning, or transferring newer technologies. In all of its services, but most notably in product development projects, one of the strengths of CIRAS lies in bringing ISU students into the picture, both to help reach project goals and as real-job learning experiences for the students

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Sustainable Solar Drying of Brewer’s Spent Grains: A Comparison with Conventional Electric Convective Drying

Spent grains from microbreweries are mostly formed by malting barley (or malt) and are suitable for a further valorization process. Transforming spent grains from waste to raw materials, for instance, in the production of nontraditional flour, requires a previous drying process. A natural convection solar dryer (NCSD) was evaluated as an alternative to a conventional electric convec-tive dryer (CECD) for the dehydration process of local microbrewers’ spent grains. Two types of brewer’s spent grains (BSG; Golden ale and Red ale) were dried with both systems, and sustainability indices, specific energy consumption (eC), and CO2 emissions were calculated and used to assess the environmental advantages and disadvantages of the NCSD. Then, suitable models (empirical, neural networks, and computational fluid dynamics) were used to simulate both types of drying processes under different conditions. The drying times were 30–85 min (depending on the drying temperature, 363.15 K and 333.15 K) and 345–430 min (depending on the starting daytime hour at which the drying process began) for the CECD and the NCSD, respectively. However, eC and CO2 emissions for the CECD were 1.68–1.88 · 10−3 (kW h)/kg and 294.80–410.73 kg/(kW h) for the different drying temperatures. Using the NCSD, both indicators were null, considering this aspect as an environmental benefit.Fil: Capossio, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Fabani, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Biotecnología; ArgentinaFil: Reyes Urrutia, Ramón Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Torres Sciancalepore, Rodrigo Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Deng, Yimin. Katholikie Universiteit Leuven; BélgicaFil: Baeyens, Jan. Katholikie Universiteit Leuven; Bélgica. Beijing University Of Chemical Technology; ChinaFil: Rodriguez, Rosa Ana. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Patagonia Confluencia. Instituto de Investigacion y Desarrollo En Ingenieria de Procesos, Biotecnologia y Energias Alternativas. Grupo Vinculado Instituto de Ingenieria Quimica | Universidad Nacional del Comahue. Instituto de Investigacion y Desarrollo En Ingenieria de Procesos, Biotecnologia y Energias Alternativas. Grupo Vinculado Instituto de Ingenieria Quimica.; ArgentinaFil: Mazza, German Delfor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; Argentin

Modelling volume change and deformation in food products/processes: An overview

Volume change and large deformation occur in different solid and semi-solid foods during processing, e.g., shrinkage of fruits and vegetables during drying and of meat during cooking, swelling of grains during hydration, and expansion of dough during baking and of snacks during extrusion and puffing. In addition, food is broken down during oral processing. Such phenomena are the result of complex and dynamic relationships between composition and structure of foods, and driving forces established by processes and operating conditions. In particular, water plays a key role as plasticizer, strongly influencing the state of amorphous materials via the glass transition and, thus, their mechanical properties. Therefore, it is important to improve the understanding about these complex phenomena and to develop useful prediction tools. For this aim, different modelling approaches have been applied in the food engineering field. The objective of this article is to provide a general (non-systematic) review of recent (2005–2021) and relevant works regarding the modelling and simulation of volume change and large deformation in various food products/processes. Empirical-and physics-based models are considered, as well as different driving forces for deformation, in order to identify common bottlenecks and challenges in food engineering applications.Fil: Purlis, Emmanuel. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; ArgentinaFil: Cevoli, Chiara. Università di Bologna; ItaliaFil: Fabbri, Angelo. Università di Bologna; Itali

Human reproduction in space. Late results

Objectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version