Uncertainties In Design Parameters Research Articles

Optimizing MEP Design in Pharmaceutical Cleanroom with the Integration of 2D To 7D in BIM

Abstract: This paper examines the optimization of pharmaceutical cleanroom design using 2D to 7D Modelling in Building Information Modelling (BIM). The goal is to enhance the reliability, different methods, statistical data, and performance of pharmaceutical production plants by effectively allocating components within design constraints. Mathematical optimization techniques improve facility design, ensuring robustness and operational efficiency. The study proposes a reliable optimal design for air-conditioning systems in pharmaceutical cleanrooms, considering uncertainties in design parameters and operational strategies. Resilient cleanroom systems are developed, and adaptable to various conditions while maintaining high performance standards. Additionally, the research explores the application of experimental design methodologies in optimizing drug delivery systems, such as factorial and etc, to enhance the quality and efficiency of pharmaceutical products and processes.

A modified interval perturbation statistical energy analysis method for structural reliability analysis of vibro-acoustic systems with interval uncertain parameters

Structural reliability analysis has become a critical aspect of engineering design for vibro-acoustic systems. With the increasing complexity and uncertainty of design parameters, it is essential to develop methods and tools that can accurately account for all the sources of uncertainty. This paper presents a novel approach for structural reliability analysis of vibro-acoustic systems with interval parameters within the framework of Statistical Energy Analysis (SEA). By introducing the Sherman-Morrison-Woodbury formula and the sub-interval technique, a modified interval perturbation statistical energy analysis is proposed to predict the interval bounds of energy and stress response. Then, the non-probabilistic structural reliability is assessed by using the volume ratio theory. Case studies of a plate-cavity system and a launch fairing model, in which the interval uncertainties in external loads and material properties are considered, are presented to demonstrate the effectiveness of the proposed approach. The results show that the proposed method can effectively handle uncertainties and provide accurate estimates of the probability of failure.

Designing a Novel Porous Fixation Plate with a Gyroid Lattice Structure for Humerus Fractures Using a Probabilistic Approach

Conventional fixation plates permanently attached to the body can lead to complications, such as stress shielding and aseptic loosening, due to their contact with the bone, resulting in bone loss. The contact between these solid fixation plates and the bone surface inhibits blood flow, potentially leading to necrosis at the interface. To overcome this challenge, a porous implant featuring a gyroid lattice structure for humerus bone fixation is proposed. However, designing such an implant typically involves deterministic approaches, which do not account for uncertainties in design parameters, loadings on the plate, and additive manufacturing process parameters. Consequently, the actual conditions experienced by the plate may not be accurately modeled. Herein, both deterministic and probabilistic analyses of a porous implant with a gyroid lattice structure positioned on the humeral bone is conducted. The findings are compared to the failure probabilities of both the conventional fixation plate and the optimal plate derived from a previous study. The study reveals that uncertainties in design parameters significantly influence the plate's failure probability compared to deterministic analysis, emphasizing the importance of probability‐based analyses for a reliable plate design.

Reliability-based design optimization scheme of isolation capacity of nonlinear vibration isolators

Nonlinear vibration isolators (NVIs) are beneficial to isolate or suppress the adverse effects caused by harmful vibration. To a large extent, the isolation capacity of NVIs depends on the selection of design parameters (including structural, geometric, and excitation parameters) of vibration isolators. However, existent uncertainties of these parameters are unavoidable, which influence the reliability of the isolation capacity of NVIs. In this respect, an effective reliability-based design optimization (RBDO) scheme of isolation capacity of NVIs is presented by taking into account the parameter uncertainties of NVIs. In the proposed RBDO scheme, the force transmissibility of nonlinear isolators is regarded as the objective function, and the fundamental frequency, the dynamic displacement response amplitude, and reliability of vibration isolation performance are supplied as the constraints to search the optimal design parameters of NVIs. Considering the influence degree of design parameters uncertainties, the global reliability sensitivity analysis is employed for identifying the critical design parameters in NVIs. A surrogate model of the failure probability function is established by the adaptive learning Kriging approach to decrease the computational cost of the reliability evaluation nested in the RBDO scheme. An advanced meta-heuristic algorithm is executed to search for the design parameters for optimal isolation capacity of NVIs. The proposed scheme provides a potential direction for optimizing the isolation capacity of complicated vibration isolators.

A comprehensive review and sensitivity analysis of the factors affecting the performance of buildings equipped with Variable Refrigerant Flow system in Middle East climates

Variable Refrigerant Flow (VRF) systems are becoming increasingly popular in commercial and residential buildings due to their flexibility and efficiency. Building design and operational parameters play an important role in the process of predicting cooling loads, and these variables contain a variety of uncertainties that must be taken into account when seeking to obtain a reliable prediction. It is common practice to oversize the Heating, Ventilation, and Air Conditioning (HVAC) system installed in a building in order to reduce uncertainties in its performance. However, oversizing is undesirable due to additional capital and operating costs, inefficient space utilization, and excessive emissions of greenhouse gases. This study provided a comprehensive literature review on previous uncertainly analysis studies from six perspectives: classification of uncertainty factors, sensitivity analysis methods, building energy model for uncertainty analysis, sampling method, the most effective parameter, and distribution of uncertainty parameters., and then developed a framework to investigate the effects of uncertainty of operational and design parameters on annual cooling load, total energy consumption, HVAC electricity use per conditioned floor area, VRF system cost, and the cooling-to-total electricity consumption ratio (R) in residential buildings equipped with VRF systems located in the Middle East (ME) region (Kuwait City, Tehran, and Istanbul). Artificial Neural Network (ANN) model was also developed to predict output parameters in residential building. Latin Hypercube Sampling (LHS) simulation was also applied to generate near-random samples of uncertainty parameter values from a multidimensional distribution. It should be mentioned that a correlation was derived in this study to predict VRF cost. The study evaluated thirteen uncertainty parameters related to building characteristics, occupants, building energy systems, VRF variables, and lighting conditions. Several methods were used to evaluate the effects of input uncertainty parameters on the output variables, namely, the Standardized Regression Coefficient (SRC), Partial Correlation Coefficient (PCC), and Spearman Rank Correlation Coefficient (SRCC) or Pearson Product-Moment Correlation Coefficient (PPMCC). The results indicated that VRF cost and HVAC electricity use per area have a greater coefficient of variation compared with other output parameters. The density distribution of output parameters indicated that uncertainty parameters had the greatest impact on output parameters in Kuwait (hyper arid climate), whereas in Istanbul (humid subtropical climate), they were the least. Among the variables examined in the Sensitivity Analysis (SA), cooling setpoint had the greatest impact on residential building energy consumption in ME climates. Finally, this paper highlights emerging trends and offers recommendations for advancing future research in the realm of building energy uncertainty analysis.

Acoustic Performance of a Metamaterial Coating with Uncertainty in Design Parameters

The effect of uncertainty in design parameters on the acoustic performance of a viscoelastic metamaterial coating is investigated. The coating is composed of a layer of hard inclusions embedded in a soft host medium. The layer of inclusions in the soft medium is modelled as a homogenised layer using effective medium approximation theory. Stochastic models based on the polynomial chaos expansion (PCE) method and Monte Carlo (MC) simulations are developed. The effect of uncertainty in geometric and material parameters are quantified by examining the mean and envelopes of the absorption coefficient. Uncertainty in geometric and material parameters produce similar levels of variation in the absorption coefficient of the coating, particularly around the peaks of the absorption coefficient spectrum.

Enhancing reliability of consolidation design parameters for residual settlement estimation: a sensitivity analysis approach for existing container yard in Belawan, Medan

Data variation in geotechnical engineering poses challenges in setting up reliable design parameters used for design or analysis. This study aims to accommodate the uncertainty of consolidation design parameters, including the compression index (Cc), initial void ratio (e0), and coefficient of consolidation (Cv), to estimate the residual settlement. A case study of Belawan New Container Terminal (BNCT), which consists of two container yards situated in a reclamation area, has been presented in this paper. A robust method employing parameter sensitivity analysis and loading scenario has been carried out. Sensitivity analysis has been performed by considering ranges for each compressibility parameter consisting of low, best, and high estimates. This approach leads to nine analysis scenarios and doubles after including the service load scenario. The results are then compared with available settlement plate and topographic/bathymetric data (pre, during and post-construction). The scenarios that best fit the monitoring data are selected as the basis for estimating the residual settlement over a design lifetime of 50 years. The residual settlement is expected between 1 m and 1.27 m without service load condition, and 1.92 m to 2.46 m. with a service load of 60 kPa (equivalent to six stacks of 20-foot containers).

Optimization Numerically for Five-Pad Tilting-Pad Journal Bearings Design Based on Particle Swarm

The design of five-pad tilting-pad journal bearings (TPJB) is complicated because of the uncertainty of design parameters that greatly affects the performance of TPJB. In this study, the numerical lubrication model is established to predict the performance of TPJB. Furthermore, a new optimal method combining the lubrication model and particle swarm method with a comprehensive target is brought out, which can obtain the optimal design parameters correctly and rapidly compared with the enumeration method. The performance of TPJB is better after optimization, especially the when the temperature and frictional coefficient decreased by about 31.1% and 35.8%, respectively. Moreover, different application scenarios of TPJB need different weight factor combinations in the comprehensive target, which is valuable for different design requirements.

Reliability analysis of flexible pavements based on the quantile-value method

ABSTRACT This paper presents a simple yet efficient approach for the reliability-based design of flexible pavements, based on the concept of quantile-values. The reliability-based approaches currently adopted in the pavement design guides are simplified techniques, based on an overall reliability factor. On the other hand, simulation-based probabilistic techniques, widely discussed in literature, can be computationally expensive and tedious to implement. The ‘Simplified Effective Random Dimension Quantile Value Method’ (Simplified ERD-QVM), adopted in this study, presents an elegant technique to bridge the gap between the precision of simulation-based probabilistic techniques and the efficiency of design guide approaches. The accuracy of this technique is verified through Monte Carlo Simulations for different pavement design scenarios. The main advantages of the method are (i) the significant savings in computational times for reliability-based design of flexible pavements without the use of surrogate models (ii) the use of simple techniques already familiar to practitioners who adopt the Mechanistic-Empirical Pavement Design Guide and (iii) it is a comprehensive reliability analysis approach that addresses the drawbacks of the currently used approaches, while capturing the effect of both design parameter and model uncertainty. The methodology for integrating the Simplified ERD-QVM with the existing flexible pavement design codes is also presented.

Probabilistic Seismic Sensitivity Analyses of High-Speed Railway Extradosed Cable-Stayed Bridges

It is known that the extradosed cable-stayed bridge, a hybrid bridge, possesses the virtues of both classic cable-stayed bridges and girder bridges in mechanical behaviors. In this paper, the sensitivity of seismic fragility demand parameters (SFDP) of a high-speed railway extradosed cable-stayed bridge is studied systematically along with the consideration of structural parameter uncertainty. Based on the probability distribution and correlation of random parameters, the Latin hypercube sampling method is adopted herein. The dynamic 3D finite element model of the employed bridge is established by using powerful and attractive OpenSEES nonlinear software. A nonlinear incremental dynamic analysis is performed to consider the randomness of structural parameters using sampling analysis. Some important conclusions are drawn indicating that the structural design parameter uncertainty predominantly has influence on the SFDP for fragility analysis of bridge structures. The design parameters of extradosed cable-stayed bridges are categorized and identified as primary, secondary and insensitive parameters. The high sensitivity parameters of extradosed cable-stayed bridges for fragility analysis include friction coefficient of bearing, concrete bulk density, damping ratio, peak compressive strength of confined concrete, component size and peak strain of confined concrete. Additionally, the strength and strain of unconfined concrete cannot be ignored. Furthermore, the uncertainty of structural design parameters fails to be responsible for the cable force responses due to larger girder stiffness. The structural design parameter uncertainty has a significant influence on the responses of extradosed cable-stayed bridges for seismic fragility analysis.

Research on Carbon Emission of Prefabricated Structure in China

The comparison of carbon emissions between prefabricated and traditional cast-in-place construction methods in actual example buildings has yielded inconsistent results due to the difficulty in accounting for design parameter uncertainty. Additionally, the carbon-reduction capacity of prefabricated structures remains a topic of debate. This paper investigates the carbon emission reduction capacity of prefabricated concrete frame structures compared to traditional cast-in-place structures, with a focus on addressing design parameter uncertainty. A quantitative model of carbon emissions is established using the subproject quota method and PKPM-PC software. The study evaluates the impact of design parameters, such as slab span and seismic requirements, and calculation parameters, such as carbon emission factor and transport distance, on carbon emissions. The results indicate that prefabricated structures with a higher assembly rate exhibit a stronger emission reduction capacity, mainly due to lower demands for labor and mechanical energy consumption. The study also highlights that prefabricated structures with smaller slab spans and higher seismic requirements have lower carbon emission reduction capacities and can produce greater carbon emissions than cast-in-place structures. Furthermore, the appropriate carbon emission factor for the material used in prefabricated structures is crucial for achieving reliable carbon reduction rates. Finally, the study emphasizes the importance of considering transport as a small but significant factor in structural comparison, as changes in transport distance can significantly impact results.

A stochastic interior disturbance parameter model considering double uncertainty for probabilistic load calculation

The probabilistic method of calculating air-conditioning design load can effectively solve the oversizing problem in the deterministic method. However, the realization of the probabilistic method requires the quantification of the uncertain input parameters. Among them, the interior disturbance parameters are the key, whose uncertainties are caused by the unknown nature of interior configuration and the randomness of interior disturbance fluctuations. A stochastic interior disturbance parameter model considering double uncertainties (referred to simply as the DUSP model) was thus proposed. In this model, the uncertainty in the interior disturbance design parameters and interior disturbance time coefficients, which comes from two different sources, are described using probability distributions and occupant behavior models respectively. On this basis, the time series of interior disturbance parameters are generated by random sampling, to characterize the uncertainty of the interior disturbance parameters. Comparing the DUSP model with two models considering single-source uncertainty, the results showed that in the probabilistic load calculation, neglecting the uncertainty of the interior disturbance time coefficients may result in an overestimation of the design load by up to 9.2%; neglecting the uncertainty of the interior disturbance design parameters may result in an underestimation of the design load by up to 2.5%. Therefore, it is necessary to quantify the uncertainty in both sources simultaneously to ensure the reasonableness of the probabilistic load calculation.

Experiment, simulation, optimization design, and damage detection of composite shell of hydrogen storage vessel-A review

The composite shell of composite high pressure hydrogen storage vessel (CHPHSV) is the main structure in bearing inner pressure. Its failure will lead to leakage or burst of CHPHSV, which will cause serious safety problem and economic losses. So the security of CHPHSV has always been a key and difficult problem in academia and industry, which restricts the large-scale application of hydrogen. In this paper, the failures of composite shell of type III, type IV, and type V vessels are reviewed in experiment study, simulation method, optimization design, and damage detection, focusing on the burst modes (safe or unsafe), the influence of hydrogen-thermo-stress field, and the epistemic uncertainty. Firstly, the burst modes, failure behavior, test method, and influencing factors are summarized in the experiment study, and experiment for type V composite pressure vessel is discussed. Secondly, the simulation method is reviewed in the aspect of first ply failure (FPF) analysis and last ply failure (LPF) analysis. The damage evolution of safe burst mode and unsafe burst mode is summarized. Besides, the influence of hydrogen-thermo-stress field and the uncertainty characteristic are also reviewed. Moreover, simulations of micro-crack and permeability for type V composite pressure vessels are summarized. Then, the optimization design methods based on deterministic parameters and uncertain parameters are reviewed. Finally, the damage detection methods of the composite shell of CHPHSV are summarized. The experiment, numerical simulation, and optimization are important for design of the composite vessel, and the damage detection can significantly reduce the degree of uncertainty and maintenance safety. In the future research, precise model considering multiple damage modes, hydrogen-thermo-stress field effect should be established for simulation of safe and unsafe burst mode. Uncertain quantization should be studied for burst pressure with combination of stochastic uncertainty and epistemic uncertainty. Besides, the robust design method based on the reliability of CHPHSV considering the epistemic uncertainty of design parameters should be established for a larger safety margin.

Quantification of data and production uncertainties for tire design parameters in the frame of robustness evaluation

The design process of products and structures such as tires constitutes a multi-objective optimization to enhance the performance for a specific usage. However, the specified geometry and material parameters as well as the boundary conditions, which are basis of the performance prediction resulting from a numerical simulation, cannot be guaranteed to coincide with the real parameters of a final product in use. Most parameters are subjected to variation in production, use and service, which can lead to significant variation in performance.The objective of this contribution is to introduce a scheme of the procedure for design optimization with consideration of uncertainty, including uncertainty modeling, uncertainty analysis and robustness evaluation. Thus, the choice of the design parameters will not be solely based on the predicted value, but also on the reliability of the performance. The focus lies on a detailed description of modeling uncertainties of tire design parameters with regard to production variation as well as to non-precise data, to enable uncertainty analysis and evaluation for tire performances.For choosing appropriate uncertainty models, a distinction is made for the two types aleatoric and epistemic uncertainty with respect to origin as well as consequence. Probabilistic, possibilistic and polymorphic uncertainty modeling approaches are selected for several geometry and material parameters, based on the situation of available data as well as their sensitivity. The utilized approaches include random variables, fuzzy variables and probability-boxes (p-boxes).

Multi-objective design optimization using hybrid search algorithms with interval uncertainty for thin-walled structures

Thin-walled structures have been widely used in vehicle components due to their lightweight characteristics. To design structural parameters that are more in line with actual requirements, it is often necessary to consider the influence of uncertainty in design parameters. An effective multi-objective design optimization method using hybrid search algorithms under interval uncertainty (MDOHSA) is proposed in this study. The MDOHSA mainly consists of the modified gray​ wolf optimization (mGWO) and the pattern search (PS) algorithms. The mGWO algorithm is applied to derive a new solution set and the PS algorithm is used to quickly search the lower and upper bounds of interval responses in MDOHSA. The non-dominated strategy considering uncertainty is designed by the possibility degree of interval value. The mGWO is designed by improving the initialization population and the updating mechanism. Some standard test functions are implemented to analyze the proposed method, and the result shows that the mGWO algorithm is superior to the other analogous algorithms. In this study, the car subframe, the hexagonal fractal structure, and the bracket of an intelligent sanitation vehicle are optimized by MDOHSA. The above results show that the optimization method proposed in this study is effective. The feasible solution of the multi-objective design optimization method can be selected according to the interval deviation degree of design variables and performance requirements. This study develops an effective multi-objective design optimization method for thin-walled structures under interval uncertainty and can continue to be used for the uncertain optimization of other engineering problems.

Reliability Analysis of Critical Systems in A Fuel Booster Pump Using Advanced Simulation Techniques.

The fuel booster pump is one of the most vulnerable physical assets in an operating engine due to the harsh environmental and operational conditions. However, because of its high structural complexity and extreme operational conditions, the reliability design of the fuel booster pump becomes especially difficult, particularly by means of experiments. Thus, to overcome such a problem, advanced simulation techniques have become adequate solutions for the reliability assessment and analysis of a fuel booster pump at the design stage. In this paper, by considering the effects of the uncertainties of multiple design parameters, fatigue life distributions of the four key components (which are the sealing bolt, spline shaft, graphite ring, and inducer, respectively) in a fuel booster pump were first predicted by PoF-based reliability simulations. Then, through further sensitivity analysis on each key component, the design parameters most sensitive to the component mean fatigue life were detected from a total of 25 candidate parameters. These parameters include the “nominal diameter” and “preload” for the sealing bolt, “major and minor diameters of the small spline” for the spline shaft, “inside diameter” for the graphite ring, and “fuel pressure on the blade front surface” for the inducer, respectively. These sensitivity results were found to be in good agreement with the results from the qualitative cause analysis on each key component.

An uncertainty inversion technique using two-way neural network for parameter identification of robot arms

Due to structural complexity of robot arms, constraints of experiments, especially uncertainty of design parameters, numerical models for dynamics analysis of robot arms can produce erroneous results, which can seriously affect the performance of the designed robot arms. Reliable parameter uncertainty identification for robot arms becomes important. The current methods for uncertainty analysis have double-layered processes, in which the inner layer is for uncertainty propagation and the outer layer is an iterative optimization process. Such a nested double-layered approach limits computational efficiency. This work proposes a novel inverse method for parameter uncertainty identification using a two-way neural network. First, an element (FE) model of a robot arm is established and validated experimentally. Sensitivity analysis is then conducted using the FE model to determine a set of major parameters to be identified. A two-way neural network is next established, and the explicit formulae of direct weight inversion (DWI) use to inverse these parameters of the robot arm. Finally, the inverse result is validated by experiments. Our study show that the present inverse method can greatly improve the computational efficiency. It provides a new avenue to tackle complex inverse problems in engineering and sciences.

Effect of transformation uncertainty of soil design parameters on stochastic slope stability evaluations

Effect of transformation uncertainty of soil design parameters on stochastic slope stability evaluations

Calculation Method of Permanent Deformation of Asphalt Mixture Based on Interval Number.

There are great uncertainties in road design parameters, and the traditional point numerical calculation results cannot reflect the complexity of the actual project well. Additionally, the calculation method of road design theory based on interval analysis is more difficult in the use of uncertain design parameters. In order to simplify the calculation process of the interval parameters in the road design theory, the asphalt pavement design is taken as the analysis object, and the permanent deformation of the asphalt mixture is simplified by combining the interval analysis theory. Considering the uncertainty of the design parameters, the data with boundaries but uncertain size are expressed in intervals, and then the interval calculation formula for the permanent deformation of the asphalt mixture is derived, and the interval results are obtained. In order to avoid the dependence of interval calculation on the computer code, according to the interval calculation rule, the interval calculation method with the upper and lower end point values as point operations is proposed. In order to overcome the contradiction between interval expansion results and engineering applications, by splitting the multi-interval variable formulas, the interval variable weights are reasonably given, and the synthesis of each single interval result realizes a simplified calculation based on interval variable weight assignment. The analysis results show that the interval calculation method based on the point operation rule is accurate and reliable, and the simplified method based on the interval variable weight assignment is effective and feasible. The simplified interval calculation method proposed in this paper provides a reference for the interval application of road design theory.

Quantifying the impact of uncertainty in embedded systems mapping for NoC based architectures

We describe a modeling framework to capture and account for uncertainty in design parameters in embedded systems. We then develop an uncertainty-aware solution to the problem of mapping in embedded systems that uses Network-on-Chip (NoC) based architecture platforms. The problem of mapping is formulated as a multi-objective - reliability, performance, and energy consumption - optimization problem. To solve this problem, we propose a solution based on the NSGA-II genetic algorithm and Monte Carlo simulation techniques. The solution is implemented as a computer-aid design tool that can generate robust 3D Pareto frontiers in the solution space formed by the design objectives of reliability, performance, and energy consumption. Comparison to several state-of-the-art models and solutions for the mapping problem, indicate that significant differences in the actual values of the design attribute of interest exist when one considers uncertainty in design parameters. For example, in the case of mapping with reliability as the only objective, 10% uncertainty in design parameters can lead to a 10.06% difference in MTTF estimation. In the case of mapping with execution time and energy consumption as objectives, the difference in 2D Pareto frontiers due to 10% uncertainty in design parameters can be up to 7.9%. These differences are important because they can mislead the overall optimization process of mapping toward suboptimal solution points. The DESUU-NOC tool that implements the proposed multi-objective mapping algorithm has as a main feature and contribution of this paper the ability to generate 3D Pareto frontiers comprised of robust solution points.