Multi-parameter sensitivity analysis: A design methodology applied to energy efficiency in temperate climate houses

Smith, G.B.; Aguilar, Jose L. Castro; Gentle, Angus; Chen, Dong

doi:10.1016/j.enbuild.2012.09.007

Cited by 16 publications

(6 citation statements)

References 10 publications

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“…The design of experiments (DOE) methodology is a useful complement to multivariate data analysis because it generates "structured" data tables that contain an important amount of structured variation [23,27,28]. This mathematical structure is used as a basis for multivariate modeling, what guarantees stable and robust numerical models.…”

Section: Design Of Experiments Analysis and Optimizationmentioning

confidence: 99%

“…Along these lines, there are models dealing with analytical modeling of strength in fiber reinforced gypsum composites [18], the effect of self-stress on flexural resistance [19], the effect of longitudinal reinforcement on glass fiber reinforced gypsum [20]. However, most of the studies related to gypsum composites have concentrated on analytical or laboratory tests and very few pay attention to numerical models based on the finite element modeling (FEM) and the design of experiments (DOE) methodology play [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Properties of gypsum composites containing vermiculite and polypropylene fibers: Numerical and experimental results

Gençel

Díaz

Sütçü

et al. 2014

Energy and Buildings

108

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We have added expanded vermiculite and polypropylene fibers with low thermal conductivity to lightweight gypsum. Thermal conductivity of the composites decreases on addition of vermiculite as pore-maker. Physical and mechanical properties of the composites are improved by incorporating polypropylene fibers. A nonlinear finite element model of a three point bending model and a design of experiments analysis have been developed to evaluate and optimize the additive concentrations and also to understand the effects provided by the additives on the mechanical strength. Statistical response surface method with three-level factorial was employed to evaluate the effect of addition of vermiculite and polypropylene fibers on gypsum composites. Our methodology can be applied to other nonlinear materials for property optimization.

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Section: Design Of Experiments Analysis and Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Properties of gypsum composites containing vermiculite and polypropylene fibers: Numerical and experimental results

Gençel

Díaz

Sütçü

et al. 2014

Energy and Buildings

108

View full text Add to dashboard Cite

show abstract

“…Multivariate sensitivity analysis considers the impact of different variables in the modeling process and encompasses a wide range of data through Monte Carlo simulation (Smith et al, 2012). To begin, a uniform distribution is fitted to each input variable of the model individually (Lownes and Machemehl, 2006).…”

Section: Sensitivity Analysismentioning

confidence: 99%

Land Subsidence Prediction in Coal Mining Using Machine Learning Models and Optimization Techniques

jahanmiri,

Noorian-Bidgoli

2023

Preprint

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Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertical displacement of the ground, known as subsidence. This subsidence can extend to the surface as troughs subsidence, as the movement and deformation of the hanging-wall rocks of the mining stope propagate upwards. Accurately predicting subsidence is crucial for estimating damage and protecting surface buildings and structures in mining areas. Therefore, developing a model that considers all relevant parameters for subsidence estimation is essential. In this article, we discuss the prediction of land subsidence caused by the caving of a stop's roof, focusing on coal mining using the longwall method. We consider a total of 11 parameters related to coal mining, including mining thickness and depth (related to the deposit), as well as density, cohesion, internal friction angle, elasticity modulus, bulk modulus, shear modulus, Poisson's ratio, uniaxial compressive strength, and tensile strength (related to the overburden). We utilize information collected from 14 coal mines regarding mining and subsidence to achieve this. We then explore the prediction of subsidence caused by mining using the gene expression programming (GEP) algorithm, optimized through a combination of the artificial bee colony (ABC) and ant lion optimizer (ALO) algorithms. Modeling results demonstrate that combining the GEP algorithm with optimization based on the ABC algorithm yields the best subsidence prediction, achieving a correlation coefficient of 0.96. Furthermore, sensitivity analysis reveals that mining depth and density have the greatest and least effects, respectively, on land surface subsidence resulting from coal mining using the longwall method.

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“…The effectiveness of cool roofs for the improvement of building indoor thermal comfort conditions was found to be less significant with lower thermal transmittance (U-value) roofing systems [13,17]. On the contrary, Smith et al [18] stressed that, in temperate climates, standard energy saving approaches, e.g., highly lowering U-value, are unnecessary, unless poor settings are made in other parameters. A further study carried out in hot, arid climate [19] demonstrated that the difference in heat gains through the roof with and without thermal insulation is lower when a cool roof is implemented than with other roof systems.…”

Section: Introductionmentioning

confidence: 99%

Cool Roof Impact on Building Energy Need: The Role of Thermal Insulation with Varying Climate Conditions

et al. 2019

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Cool roof effectiveness in improving building thermal-energy performance is affected by different variables. In particular, roof insulation level and climate conditions are key parameters influencing cool roofs benefits and whole building energy performance. This work aims at assessing the role of cool roof in the optimum roof configuration, i.e., combination of solar reflectance capability and thermal insulation level, in terms of building energy performance in different climate conditions worldwide. To this aim, coupled dynamic thermal-energy simulation and optimization analysis is carried out. In detail, multi-dimensional optimization of combined building roof thermal insulation and solar reflectance is developed to minimize building annual energy consumption for heating-cooling. Results highlight how a high reflectance roof minimizes annual energy need for a small standard office building in the majority of considered climates. Moreover, building energy performance is more sensitive to roof solar reflectance than thermal insulation level, except for the coldest conditions. Therefore, for the selected building, the optimum roof typology presents high solar reflectance capability (0.8) and no/low insulation level (0.00-0.03 m), except for extremely hot or cold climate zones. Accordingly, this research shows how the classic approach of super-insulated buildings should be reframed for the office case toward truly environmentally friendly buildings.

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Multi-parameter sensitivity analysis: A design methodology applied to energy efficiency in temperate climate houses

Cited by 16 publications

References 10 publications

Properties of gypsum composites containing vermiculite and polypropylene fibers: Numerical and experimental results

Properties of gypsum composites containing vermiculite and polypropylene fibers: Numerical and experimental results

Land Subsidence Prediction in Coal Mining Using Machine Learning Models and Optimization Techniques

Cool Roof Impact on Building Energy Need: The Role of Thermal Insulation with Varying Climate Conditions

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