A new methodology using beam elements for the analysis of steel frames subjected to non-uniform temperatures due to fires

[EN] Non-uniform heating in structures under fire involves the appearance of 3D-phenomena and typically requires the use of complex models built with finite elements shell or solid. Although different procedures have been developed to model the complex thermo-mechanical phenomenon, there is no simple, accurate, and low-cost computational methodology involving the space-time variation of the temperature and displacement fields that opens the path advancing more easily towards modeling more complex structural problems in a fire situation. To overcome this knowledge-gap, this paper presents a new methodology that fulfills those conditions, making it possible to carry out more complex analyses that require many simulations in a short time and at low computational costs. The new methodology to obtain the thermo-mechanical response to non-uniform heating and mechanical loads is general, simple, accurate, and avoids using complex and high-cost finite elements, simplifying the structural modeling, and reducing the computational analysis cost. As a result, complex structural fire engineering problems such as probabilistic and optimization analysis can be handled much more easily, representing a significant step toward the generalized application of performance-based approaches to deal with fire effects on structures. The procedure uses simple but advanced Timoshenko¿s beam-type finite elements and represents the non-uniform temperature space-time field through a mean value of the temperature and the two mean values of the section thermal gradients which are variable in time during the fire. The methodology is satisfactorily validated with results (experimental and numerical) of the Cardington frame test and captures 3D-phenomena such as buckling, flexural-torsional buckling, and warping.Thanks are due to the Fundación Carolina, the Universitat Politècnica de València, and the Universidad Surcolombiana for the support given to this research through the 2018-2019 Ph.D. scholarship.Pallares-Muñoz, MR.; Paya-Zaforteza, I.; Hospitaler Pérez, A. (2021). A new methodology using beam elements for the analysis of steel frames subjected to non-uniform temperatures due to fires. Structures. 31:462-483. https://doi.org/10.1016/j.istruc.2021.02.008S46248331Shan, S., & Li, S. (2020). Fire-induced progressive collapse mechanisms of steel frames with partial infill walls. Structures, 25, 347-359. doi:10.1016/j.istruc.2020.03.023Shakib, H., Zakersalehi, M., Jahangiri, V., & Zamanian, R. (2020). Evaluation of Plasco Building fire-induced progressive collapse. 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Overview of Dynamic Facility Layout Planning as a Sustainability Strategy

[EN] The facility layout design problem is significantly relevant within the business operations strategies framework and has emerged as an alternate strategy towards supply chain sustainability. However, its wide coverage in the scientific literature has focused mainly on the static planning approach and disregarded the dynamic approach, which is very useful in real-world applications. In this context, the present article offers a literature review of the dynamic facility layout problem (DFLP). First, a taxonomy of the reviewed papers is proposed based on the problem formulation current trends (related to the problem type, planning phase, planning approach, number of facilities, number of floors, number of departments, space consideration, department shape, department dimensions, department area, and materials handling configuration); the mathematical modeling approach (regarding the type of model, type of objective function, type of constraints, nature of market demand, type of data, and distance metric), and the considered solution approach. Then, the extent to which recent research into DFLP has contributed to supply chain sustainability by addressing its three performance dimensions (economic, environmental, social) is described. Finally, some future research guidelines are provided.This research was funded by the Spanish Ministry of Science, Innovation and Universities Project CADS4.0, grant number RTI2018-101344-B-I00; and the Valencian Community ERDF Programme 2014-2020, grant number IDIFEDER/2018/025.Pérez-Gosende, P.; Mula, J.; Díaz-Madroñero Boluda, FM. (2020). Overview of Dynamic Facility Layout Planning as a Sustainability Strategy. Sustainability. 12(19):1-16. https://doi.org/10.3390/su12198277S1161219Ghassemi Tari, F., & Neghabi, H. (2015). A new linear adjacency approach for facility layout problem with unequal area departments. Journal of Manufacturing Systems, 37, 93-103. doi:10.1016/j.jmsy.2015.09.003Kheirkhah, A., Navidi, H., & Messi Bidgoli, M. (2015). Dynamic Facility Layout Problem: A New Bilevel Formulation and Some Metaheuristic Solution Methods. IEEE Transactions on Engineering Management, 62(3), 396-410. doi:10.1109/tem.2015.2437195Altuntas, S., & Selim, H. (2012). 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International Journal of Physical Distribution & Logistics Management, 38(5), 360-387. doi:10.1108/09600030810882816Carter, C. R., & Washispack, S. (2018). Mapping the Path Forward for Sustainable Supply Chain Management: A Review of Reviews. Journal of Business Logistics, 39(4), 242-247. doi:10.1111/jbl.12196Roy, V., Schoenherr, T., & Charan, P. (2018). The thematic landscape of literature in sustainable supply chain management (SSCM). International Journal of Operations & Production Management, 38(4), 1091-1124. doi:10.1108/ijopm-05-2017-0260Barbosa-Póvoa, A. P., da Silva, C., & Carvalho, A. (2018). Opportunities and challenges in sustainable supply chain: An operations research perspective. European Journal of Operational Research, 268(2), 399-431. doi:10.1016/j.ejor.2017.10.036Tonelli, F., Evans, S., & Taticchi, P. (2013). Industrial sustainability: challenges, perspectives, actions. International Journal of Business Innovation and Research, 7(2), 143. doi:10.1504/ijbir.2013.052576Sánchez-Flores, R. B., Cruz-Sotelo, S. E., Ojeda-Benitez, S., & Ramírez-Barreto, M. E. (2020). Sustainable Supply Chain Management—A Literature Review on Emerging Economies. Sustainability, 12(17), 6972. doi:10.3390/su12176972Ford, S., & Despeisse, M. (2016). Additive manufacturing and sustainability: an exploratory study of the advantages and challenges. Journal of Cleaner Production, 137, 1573-1587. doi:10.1016/j.jclepro.2016.04.150Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408-425. doi:10.1016/j.psep.2018.05.009Khuntia, J., Saldanha, T. J. V., Mithas, S., & Sambamurthy, V. (2018). Information Technology and Sustainability: Evidence from an Emerging Economy. 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Chinese Journal of Mechanical Engineering, 30(2), 407-418. doi:10.1007/s10033-017-0073-9Emami, S., & S. Nookabadi, A. (2013). Managing a new multi-objective model for the dynamic facility layout problem. The International Journal of Advanced Manufacturing Technology, 68(9-12), 2215-2228. doi:10.1007/s00170-013-4820-5Al Hawarneh, A., Bendak, S., & Ghanim, F. (2019). Dynamic facilities planning model for large scale construction projects. Automation in Construction, 98, 72-89. doi:10.1016/j.autcon.2018.11.021Pournaderi, N., Ghezavati, V. R., & Mozafari, M. (2019). Developing a mathematical model for the dynamic facility layout problem considering material handling system and optimizing it using cloud theory-based simulated annealing algorithm. SN Applied Sciences, 1(8). doi:10.1007/s42452-019-0865-xTuranoğlu, B., & Akkaya, G. (2018). A new hybrid heuristic algorithm based on bacterial foraging optimization for the dynamic facility layout problem. 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Scientometrics, 115(1), 1-20. doi:10.1007/s11192-017-2622-5Kulturel-Konak, S., & Konak, A. (2014). A large-scale hybrid simulated annealing algorithm for cyclic facility layout problems. Engineering Optimization, 47(7), 963-978. doi:10.1080/0305215x.2014.933825Madhusudanan Pillai, V., Hunagund, I. B., & Krishnan, K. K. (2011). Design of robust layout for Dynamic Plant Layout Problems. Computers & Industrial Engineering, 61(3), 813-823. doi:10.1016/j.cie.2011.05.014Peng, Y., Zeng, T., Fan, L., Han, Y., & Xia, B. (2018). An Improved Genetic Algorithm Based Robust Approach for Stochastic Dynamic Facility Layout Problem. Discrete Dynamics in Nature and Society, 2018, 1-8. doi:10.1155/2018/1529058McKendall, A. R., & Hakobyan, A. (2010). Heuristics for the dynamic facility layout problem with unequal-area departments. European Journal of Operational Research, 201(1), 171-182. doi:10.1016/j.ejor.2009.02.028Yang, C.-L., Chuang, S.-P., & Hsu, T.-S. (2010). A genetic algorithm for dynamic facility planning in job shop manufacturing. The International Journal of Advanced Manufacturing Technology, 52(1-4), 303-309. doi:10.1007/s00170-010-2733-0Abedzadeh, M., Mazinani, M., Moradinasab, N., & Roghanian, E. (2012). Parallel variable neighborhood search for solving fuzzy multi-objective dynamic facility layout problem. The International Journal of Advanced Manufacturing Technology, 65(1-4), 197-211. doi:10.1007/s00170-012-4160-xGuan, X., Dai, X., Qiu, B., & Li, J. (2012). A revised electromagnetism-like mechanism for layout design of reconfigurable manufacturing system. Computers & Industrial Engineering, 63(1), 98-108. doi:10.1016/j.cie.2012.01.016Jolai, F., Tavakkoli-Moghaddam, R., & Taghipour, M. (2012). A multi-objective particle swarm optimisation algorithm for unequal sized dynamic facility layout problem with pickup/drop-off locations. International Journal of Production Research, 50(15), 4279-4293. doi:10.1080/00207543.2011.613863Kia, R., Baboli, A., Javadian, N., Tavakkoli-Moghaddam, R., Kazemi, M., & Khorrami, J. (2012). Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing. Computers & Operations Research, 39(11), 2642-2658. doi:10.1016/j.cor.2012.01.012McKendall, A. R., & Liu, W.-H. (2012). New Tabu search heuristics for the dynamic facility layout problem. International Journal of Production Research, 50(3), 867-878. doi:10.1080/00207543.2010.545446Hosseini-Nasab, H., & Emami, L. (2013). A hybrid particle swarm optimisation for dynamic facility layout problem. International Journal of Production Research, 51(14), 4325-4335. doi:10.1080/00207543.2013.774486Kaveh, M., Dalfard, V. M., & Amiri, S. (2013). A new intelligent algorithm for dynamic facility layout problem in state of fuzzy constraints. Neural Computing and Applications, 24(5), 1179-1190. doi:10.1007/s00521-013-1339-5KIA, R., JAVADIAN, N., PAYDAR, M. M., & SAIDI-MEHRABAD, M. (2013). A SIMULATED ANNEALING FOR INTRA-CELL LAYOUT DESIGN OF DYNAMIC CELLULAR MANUFACTURING SYSTEMS WITH ROUTE SELECTION, PURCHASING MACHINES AND CELL RECONFIGURATION. Asia-Pacific Journal of Operational Research, 30(04), 1350004. doi:10.1142/s0217595913500048Mazinani, M., Abedzadeh, M., & Mohebali, N. (2012). Dynamic facility layout problem based on flexible bay structure and solving by genetic algorithm. The International Journal of Advanced Manufacturing Technology, 65(5-8), 929-943. doi:10.1007/s00170-012-4229-6Samarghandi, H., Taabayan, P., & Behroozi, M. (2013). Metaheuristics for fuzzy dynamic facility layout problem with unequal area constraints and closeness ratings. The International Journal of Advanced Manufacturing Technology, 67(9-12), 2701-2715. doi:10.1007/s00170-012-4685-zYu-Hsin Chen, G. (2013). A new data structure of solution representation in hybrid ant colony optimization for large dynamic facility layout problems. International Journal of Production Economics, 142(2), 362-371. doi:10.1016/j.ijpe.2012.12.012Bozorgi, N., Abedzadeh, M., & Zeinali, M. (2014). Tabu search heuristic for efficiency of dynamic facility layout problem. The International Journal of Advanced Manufacturing Technology, 77(1-4), 689-703. doi:10.1007/s00170-014-6460-9CHEN, G. Y.-H., & LO, J.-C. (2014). DYNAMIC FACILITY LAYOUT WITH MULTI-OBJECTIVES. Asia-Pacific Journal of Operational Research, 31(04), 1450027. doi:10.1142/s0217595914500274Hosseini, S., Khaled, A. A., & Vadlamani, S. (2014). Hybrid imperialist competitive algorithm, variable neighborhood search, and simulated annealing for dynamic facility layout problem. Neural Computing and Applications, 25(7-8), 1871-1885. doi:10.1007/s00521-014-1678-xKia, R., Khaksar-Haghani, F., Javadian, N., & Tavakkoli-Moghaddam, R. (2014). Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm. Journal of Manufacturing Systems, 33(1), 218-232. doi:10.1016/j.jmsy.2013.12.005Nematian, J. (2014). A robust single row facility layout problem with fuzzy random variables. The International Journal of Advanced Manufacturing Technology, 72(1-4), 255-267. doi:10.1007/s00170-013-5564-yPourvaziri, H., & Naderi, B. (2014). A hybrid multi-population genetic algorithm for the dynamic facility layout problem. Applied Soft Computing, 24, 457-469. doi:10.1016/j.asoc.2014.06.051Derakhshan Asl, A., & Wong, K. Y. (2015). Solving unequal-area static and dynamic facility layout problems using modified particle swarm optimization. Journal of Intelligent Manufacturing, 28(6), 1317-1336. doi:10.1007/s10845-015-1053-5Li, L., Li, C., Ma, H., & Tang, Y. (2015). An Optimization Method for the Remanufacturing Dynamic Facility Layout Problem with Uncertainties. Discrete Dynamics in Nature and Society, 2015, 1-11. doi:10.1155/2015/685408Ulutas, B., & Islier, A. A. (2015). Dynamic facility layout problem in footwear industry. Journal of Manufacturing Systems, 36, 55-61. doi:10.1016/j.jmsy.2015.03.004Zarea Fazlelahi, F., Pournader, M., Gharakhani, M., & Sadjadi, S. J. (2016). A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 230(12), 2264-2274. doi:10.1177/0954405415615728Hosseini, S. S., & Seifbarghy, M. (2016). A novel meta-heuristic algorithm for multi-objective dynamic facility layout problem. RAIRO - Operations Research, 50(4-5), 869-890. doi:10.1051/ro/2016057Pourvaziri, H., & Pierreval, H. (2017). Dynamic facility layout problem based on open queuing network theory. European Journal of Operational Research, 259(2), 538-553. doi:10.1016/j.ejor.2016.11.011Tayal, A., & Singh, S. P. (2016). Integrating big data analytic and hybrid firefly-chaotic simulated annealing approach for facility layout problem. Annals of Operations Research, 270(1-2), 489-514. doi:10.1007/s10479-016-2237-xKumar, R., & Singh, S. P. (2017). A similarity score-based two-phase heuristic approach to solve the dynamic cellular facility layout for manufacturing systems. Engineering Optimization, 49(11), 1848-1867. doi:10.1080/0305215x.2016.1274205Liu, J., Wang, D., He, K., & Xue, Y. (2017). Combining Wang–Landau sampling algorithm and heuristics for solving the unequal-area dynamic facility layout problem. 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Expert Systems with Applications, 98, 129-152. doi:10.1016/j.eswa.2018.01.005Wei, X., Yuan, S., & Ye, Y. (2019). Optimizing facility layout planning for reconfigurable manufacturing system based on chaos genetic algorithm. Production & Manufacturing Research, 7(1), 109-124. doi:10.1080/21693277.2019.1602486Kulturel-Konak, S. (2007). Approaches to uncertainties in facility layout problems: Perspectives at the beginning of the 21st Century. Journal of Intelligent Manufacturing, 18(2), 273-284. doi:10.1007/s10845-007-0020-1Sharma, P., & Singhal, S. (2016). Implementation of fuzzy TOPSIS methodology in selection of procedural approach for facility layout planning. The International Journal of Advanced Manufacturing Technology, 88(5-8), 1485-1493. doi:10.1007/s00170-016-8878-8Bukchin, Y., & Tzur, M. (2014). A new MILP approach for the facility process-layout design problem with rectangular and L/T shape departments. 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Variable-speed rotor helicopters: Performance comparison between continuously variable and fixed-ratio transmissions

Variable speed rotor studies represent a promising research field for rotorcraft performance improvement and fuel consumption reduction. The problems related to employing a main rotor variable speed are numerous and require an interdisciplinary approach. There are two main variable speed concepts, depending on the type of transmission employed: Fixed Ratio Transmission (FRT) and Continuously Variable Transmission (CVT) rotors. The impact of the two types of transmission upon overall helicopter performance is estimated when both are operating at their optimal speeds. This is done by using an optimization strategy able to find the optimal rotational speeds of main rotor and turboshaft engine for each flight condition. The process makes use of two different simulation tools: a turboshaft engine performance code and a helicopter trim simulation code for steady-state level flight. The first is a gas turbine performance simulator (TSHAFT) developed and validated at the University of Padova. The second is a simple tool used to evaluate the single blade forces and integrate them over the 360 degree-revolution of the main rotor, and thus to predict an average value of the power load required by the engine. The results show that the FRT does not present significant performance differences compared to the CVT for a wide range of advancing speeds. However, close to the two conditions of maximum interest, i.e. hover and cruise forward flight, the discrepancies between the two transmission types become relevant: in fact, engine performance is found to be penalized by FRT, stating that significant fuel reductions can be obtained only by employing the CVT concept. In conclusion, FRT is a good way to reduce fuel consumption at intermediate advancing speeds; CVT advantages become relevant only near hover and high speed cruise condition

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Bioadditive manufacturing of hybrid tissue scaffolds for controlled release kinetics

Development of engineered tissue scaffolds with superior control over cell-protein interactions is still very much infancy. Advancing through heterogeneous multifold scaffolds with controlled release fashion enables synchronization of regenerating tissue with the release kinetics of loaded biomolecules. This might be an engineering challenge and promising approach for improved and efficient tissue regeneration. The most critical limitations: the selection of proper protein(s) incorporation, and precise control over concentration gradient and timing should be overcome. Hence, tissue scaffolds need to be fabricated in a way that proteins or growth factors should be incorporated and released in a specific spatial and temporal orientation to mimic the natural tissue regeneration process. Spatial and temporal control over heterogeneous porous tissue scaffolds can be achieved by controlling two important parameters: (i) internal architecture with enhanced fluid transport, and (ii) distribution of scaffold base material and loaded modifiers. In this research, heterogeneous tissue scaffolds are designed considering both the parameters. Firstly, the three-dimensional porous structures of the scaffold are geometrically partition into functionally uniform porosity regions and controlled spatial micro-architecture has been achieved using a functionally gradient porosity function. The bio-fabrication of the designed internal porous architecture has been performed using a single nozzle bioadditive manufacturing system. The internal architecture scheme is developed to enhance fluid transport with continuous base material deposition Next, the hybrid tissue scaffolds are modeled with varying material characteristics to mediate the release of base material and enclosed biological modifiers are proposed based on tissue engineering requirements. The hybrid scaffolds are fabricated for spatial control of biomolecules and base material to synchronize the release kinetics with tissue regeneration. A pressure-assisted multi-chamber single nozzle bioadditive manufacturing system is used to fabricate hybrid scaffolds

Advancing Alternative Analysis: Integration of Decision Science.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts

New Hampshire University Research and Industry Plan: A Roadmap for Collaboration and Innovation

This University Research and Industry plan for New Hampshire is focused on accelerating innovation-led development in the state by partnering academia’s strengths with the state’s substantial base of existing and emerging advanced industries. These advanced industries are defined by their deep investment and connections to research and development and the high-quality jobs they generate across production, new product development and administrative positions involving skills in science, technology, engineering and math (STEM)

Automation and control in surface irrigation systems: current status and expected future trends

Surface irrigation systems are the most popular methods for irrigating crops and pastures not only in Australia but the world over. However, these systems are often labour intensive and exhibit low water use efficiency. Rising labour costs especially in the developed world and competition for scarce water resources have generated renewed interest in the automation of surface irrigation systems. This paper provides a comprehensive review of the current level of automation and control of surface irrigation systems. The automation techniques discussed utilise various devices including mechanical, electronic, pneumatic and hydraulic means. The use of telemetry is also discussed. With the almost universal access to high performance computers and fast internet, the concept of real-time control in surface irrigation is not far-fetched. Towards this end, an on-going research project at USQ aimed at modernising furrow irrigation by use of automatic control systems in real time is discusse