Sensitivity Analysis Techniques Research Articles

Application of similarity analysis method in zero-power experimental validation

New advanced reactors require zero-power experiments to validate the correctness of design. In order to ensure sufficient validation strength, certain similarity of physical properties between zero-power experimental reactor and the target reactor is required. Based on sensitivity and uncertainty analysis techniques, three indexes for evaluating the physical similarity between reactor systems were introduced. Based on the Monte Carlo transport program MONK, a coupled similarity analysis program MCS was compiled. The results of MCS were compared with TSUNAMI’s and the two programs showed good agreement. Finally, the similarity between the CEFR in MOX fuel loading design and the BFS-119-2 zero-power experiment scheme given by IPPE was calculated by MCS. The results showed that the two systems were very similar to each other, and the physical properties of the CEFR-MOX loading design could be validate by the zero-power experiment.

Markov Regenerative Models of WebServers for Their User-Perceived Availability and Bottlenecks

The Internet world is moving toward a scenario where users and applications have very diverse service expectation, making the current best-effort model inadequate and limiting. To be able to design high-availability service systems, it is essential to consider not only the actual failure and recovery behavior of the service infrastructure, but also the behavioral aspects of its user and their subjective perceptions and reactions in the wake of failure events. In this paper, we propose to use Markov regenerative process (MRGP) models to study the availability of Internet-based services perceived by a Web user on two different online service scenarios: (1) single-user-single-host and (2) single-user-multiple-host. The MRGP models capture the interactions between the service facility and the user. We also detect its parameter bottlenecks by applying the formal sensitivity analysis technique. The trends of the users’ perceived unavailability are analyzed with the changed different parameter values, and the necessity of the sophisticated MRGP modeling is evidenced by the comparisons with the corresponding continuous time Markov chain (CTMC) models, which show that the popular convenient CTMC models tend to overestimate user-perceived service unavailability. Finally, controlled experiments are carried out on a real Web service to demonstrate the proposed approach.

Multivariate sensitivity analysis for dynamic models with both random and random process inputs

Dynamic models with both random and random process inputs are frequently used in engineering. However, sensitivity analysis (SA) for such models is still a challenging problem. This paper, therefore, proposes a new multivariate SA technique to aid the safety design of these models. The new method can decompose the SA of dynamic models into a series of SA of their principle components based on singular value decomposition, which will make the SA of dynamic models much more efficient. It is shown that the effect of both random and random process inputs on the uncertainty of dynamic output can be measured from their effects on both the distributions and directions of the principle components, based on which the individual sensitivities are defined. The generalized sensitivities are then proposed to synthesize the information that is spread between the principal components to assess the influence of each input on the entire uncertainty of dynamic output. The properties of the new sensitivities are derived and an efficient estimation algorithm is proposed based on unscented transformation. Numerical results are discussed with application to a hydrokinetic turbine blade model, where the new method is compared with the existing variance-based method.

A new convolution variational boundary element technique for design sensitivity analysis and topology optimization of anisotropic thermo-poroelastic structures

The main objective of this paper is to propose a new efficient time domain boundary element technique for design sensitivity analysis and topology optimization of anisotropic thermo-poroelastic structures. The spatial regularization scheme based on integration by parts is performed in Laplace domain in conjunction with time-domain convolution variational boundary element method (CVBEM). The generating optimization problem is solved using the Moving Asymptotes Method (MAM) with the adjoint variable method to obtain optimal material distribution, influence of anisotropy on the thermo-poroelastic stresses sensitivities. The results from the numerical model demonstrate the validity, efficiency and accuracy of the proposed technique.

Estimation of Link Choice Probabilities Using Monte Carlo Simulation and Maximum Likelihood Estimation Method

Studying the uncertainty of traffic flow takes significant importance for the transport planners because of the variation and fluctuation of temporal traffic flow on all links of the transport network. Uncertainty analysis of traffic flow requires identifying and characterizing two sets of parameters. The first set is the link choice set, which involves the Origin-Destination pairs using this link. The second set is the link choice probabilities set, which includes proportions of the travel demand for the Origin-Destination pairs in the link choice set. For this study, we developed a new methodology based on Monte Carlo simulation for link choice set and link choice probabilities in the context of route choice modeling. This methodology consists of two algorithms: In the first algorithm, we used the sensitivity analysis technique the variance-based method to identify the set of Origin-Destination pairs in each link. In the second algorithm, we used a Gaussian process based on the Maximum Likelihood framework to estimate the link choice probabilities. Furthermore, we applied the proposed methodology in a case study over multiple scenarios representing different traffic flow conditions. The results of this case study show high precision results with low errors' variances.The key contributions of this paper: First, the link choice set can be detected by using sensitivity analysis. Second, the link choice probabilities can be determined by solving an optimization problem in the Maximum likelihood framework. Finally, the prediction errors' parameters of traffic assignment model can be modeled as a Gaussian process.

Tailoring parameter distributions to specific germplasm: impact on crop model-based ideotyping

Crop models are increasingly used to identify promising ideotypes for given environmental and management conditions. However, uncertainty must be properly managed to maximize the in vivo realizability of ideotypes. We focused on the impact of adopting germplasm-specific distributions while exploring potential combinations of traits. A field experiment was conducted on 43 Italian rice varieties representative of the Italian rice germplasm, where the following traits were measured: light extinction coefficient, radiation use efficiency, specific leaf area at emergence and tillering. Data were used to derive germplasm-specific distributions, which were used to re-run a previous modelling experiment aimed at identifying optimal combinations of plant trait values. The analysis, performed using the rice model WARM and sensitivity analysis techniques, was conducted under current conditions and climate change scenarios. Results revealed that the adoption of germplasm-specific distributions may markedly affect ideotyping, especially for the identification of most promising traits. A re-ranking of some of the most relevant parameters was observed (radiation use efficiency shifted from 4th to 1st), without clear relationships between changes in rankings and differences in distributions for single traits. Ideotype profiles (i.e., values of the ideotype traits) were instead more consistent, although differences in trait values were found.

Simplification of Data Acquisition in Process Integration Retrofit of a Milk Powder Production Facility

Process integration techniques, such as Pinch Analysis, can be used to effectively detect energy saving opportunities in the industrial sector. However, their application is mainly limited to energy-intensive processes, leaving a large potential untapped in the remaining industrial sectors. One factor that discourages the application of these tools is the requirement of a large amount of reliable data, which may be difficult or time-consuming to gather. This paper presents the application of a method to simplify the data acquisition step of process integration studies on a milk powder production facility. By employing uncertainty analysis and sensitivity analysis techniques, the solution of the factor fixing problem was shown, and a subset of parameters whose accurate estimates were essential to obtain reliable analysis results, were detected. The required data reduction was significant, as only 20 out of the 41 parameters initially considered were deemed to be important. Moreover, the maximum acceptable level of inaccuracy in the definition of these parameters in order to ensure a satisfactory uncertainty level in the analysis output was presented. The output standard deviation was reduced from the initial 47.8 % to 10.0 %, relative to the mean value.

Numerical study on a porous material subject to SiC particles deposition, using OpenFOAM and sensitivity analysis technique: Effect of clogging evolution on the thermal performances

The high thermal load endured by scramjets can cause many damages to the combustion chamber walls of the engine which requires an efficient cooling of the latter. The method of transpiration cooling has shown to be an efficient technique of heat management, by sending a cold fluid through the porous walls of the combustion chamber to cool them. Various parameters could affect the hydrodynamic and thermal behaviours involved at the porous wall, such as the thermal conductivities, the material porosity, the velocity of the flow and other inputs. The present work consists of two-dimensional numerical study of the thermal behaviour of bronze-based porous medium (BR30) using an OpenFOAM house-code. The porous matrix is crossed by water as fluid cooling in laminar flow, while, constant temperatures are applied in contact of combustion chamber. The Local Thermal Non-Equilibrium (LTNE) is considered in the internal field. The developed code was validated against both experimental and numerical results from the literature. Afterwards, two clogged porous media (clogging level of 21% and 32%) are investigated from the thermal and hydrodynamic point of view. Then after, both porous mediums are compared to the nominal configuration (without clogging). The results have shown that the clogging of the material affected not only the hydrodynamic behaviour of the flow but also the thermal properties of the material. Moreover, it has been shown that the clogged materials heated quicker and more that the clean one, while the two clogged materials didn’t show a noticeable difference. To understand the effect of different parameters affecting the cooling system efficiency, a global sensitivity analysis is then conducted to determine the most important parameter by varying each one in a range of 10%. The solid thermal conductivity as well as the fluid velocity are the most influencing parameters on the temperature. The interaction of the parameters has also been studied and shown that the interactions of the fluid velocity, solid thermal conductivity as well as the porosity with other parameters have more effect on the resulting temperature.

The Ghana Stabilisation Fund: Relevance and Impact so far

Ghana in response to global best practices in managing volatile petroleum revenues included in its petroleum revenue management framework, the Ghana Stabilisation Fund (GSF). This study sought to evaluate the impact of volatile petroleum receipts on the GSF and measure the effect of discretionary capping on the ‘stabilisation’ role of the Fund since its establishment in 2011. Using qualitative comparative analysis and sensitivity analysis techniques, the study found that transfers into the GSF has been influenced to various degrees by total petroleum receipts, the variance between benchmark and actual revenues, transfers to the National Oil Company and discretion around transfers into the Fund in its initial years of operation. Out of the total of US$ 714,608,340 withdrawn from the GSF between 2014 and 2018, 86.72 percent has been transferred into the Debt Service Account/Sinking Fund (DSA/SF) and used to retire some marketable debt instruments (loans). It concludes that the GSF has impacted minimally on its primary object of cushioning or sustaining public expenditure capacity during periods of unanticipated petroleum revenue shortfalls and that, the Fund has been largely applied for debt repayment purposes than economic stabilisation over the past eight years.

A Mathematical Model for the Effects of Nitrogen and Phosphorus on Algal Blooms

The increase of nutrients in lakes typically stimulates the growth of algae in this environment. Therefore, it is important to understand the connection between nutrient concentration and algal biomass to manage the water pollution caused by excessive plant nutrients. It is worth observing that phosphorus and nitrogen are often considered as the principal limiting nutrients for aquatic algal production due to their short supply compared to cellular growth requirements. In freshwaters, phosphorus is the least abundant among the nutrients needed in large quantity by photosynthetic organisms, hence this is the primary nutrient that limits their growth. The purpose of this work is to compare the effects of nitrogen and phosphorus on the growth of algae in lakes. By using a sensitivity analysis technique, we found that the sources of phosphorus provide a greater risk for bloom of algae than that of nitrogen. Therefore, to reduce the occurrence of algal bloom more attention should be paid for the control of phosphorus input into the lake but the inflow of nitrogen cannot be ignored. The existence of a transcritical bifurcation is discussed and its direction is investigated by applying the projection method technique. Further, to make the system more realistic, time delay involved in the conversion of detritus into nutrients is considered. We show that for increasing values of time delay, the system undergoes an Andronov–Hopf-bifurcation. Some simulations are presented to verify the analytical findings. The results of our study can be helpful for the policy makers to mitigate algal blooms from lakes.

A data-driven indirect method for nonlinear optimal control

Nonlinear optimal control problems are challenging to solve due to the prevalence of local minima that prevent convergence and/or optimality. This paper describes nearest-neighbors optimal control (NNOC), a data-driven framework for nonlinear optimal control using indirect methods. It determines initial guesses for new problems with the help of precomputed solutions to similar problems, retrieved using k-nearest neighbors. A sensitivity analysis technique is introduced to linearly approximate the variation of solutions between new and precomputed problems based on their variation of parameters. Experiments show that NNOC can obtain the global optimal solution orders of magnitude faster than standard random restart methods, and sensitivity analysis can further reduce the solving time almost by half. Examples are shown on optimal control problems in vehicle control and agile satellite reorientation demonstrating that global optima can be determined with more than 99% reliability within time at the order of 10–100 milliseconds.

Global sensitivity analysis for the design of nonlinear identification experiments

Bayesian inference techniques have been used extensively in recent years for parameter estimation in nonlinear systems. Despite the many advances made in the field, highly nonlinear systems can still be challenging to identify. Of key interest is the challenge in establishing the identifiability of the model with respect to various excitation signals and, in particular, doing so prior to the collection of experimental data. Global sensitivity analysis techniques provide a perspective on this problem that is well-suited to informing the design of identification experiments for use with Bayesian inference techniques. These methods quantify the relative importance of the parameters to the model response by decomposing the variance of the response into contributions from the respective parameters. The sensitivities obtained provide a valuable indication of the information available for parameter estimation in the response of a system to a particular excitation. In this study, nonlinear model parameters are identified based on experimental responses from a nonlinear energy sink device with the unscented Kalman filter. The experimental identification results are compared with those of a Sobol’ sensitivity analysis on the system model to demonstrate how global sensitivity analysis can be used as a method to preselect experimental excitations for use with Bayesian inference techniques.

Efficient identification of critical load model parameters affecting transient stability

In this paper, an efficient sensitivity analysis (SA) technique is implemented to identify critical load model parameters affecting the transient stability of a power system. Transient stability is becoming more crucial in modern power systems due to the adoption of a deregulated market structure and an increasing penetration level of renewable generation. Research has revealed that power system loads have a significant influence on transient stability. Ranking of critical load model parameters ensures accurate power system simulation results with less effort and resources have been invested in building load models. The ranking has been made for different loading conditions in order to make the ranking applicable most of the time. Different types of load model have been applied to investigate their influence on transient stability. All simulations have been illustrated using a modified version of the 68 bus NETS-NYPS test system. The results revealed the important load model parameters and important loads, and the level of accuracy required for these critical parameters.

Pyrolysis modeling, sensitivity analysis, and optimization techniques for combustible materials: A review

Over the past years, pyrolysis models have moved from thermal models to comprehensive models with great flexibility including multi-step decomposition reactions. However, the downside is the need for a complete set of input data such as the material properties and the parameters related to the decomposition kinetics. Some of the parameters are not directly measurable or are difficult to determine and they carry a certain degree of uncertainty at high temperatures especially for materials that can melt, shrink, or swell. One can obtain input parameters by searching through the literature; however, certain materials may have the same nomenclature but the material properties may vary depending on the manufacturer, thereby inducing uncertainties in the model. Modelers have resorted to the use of optimization techniques such as gradient-based and direct search methods to estimate input parameters from experimental bench-scale data. As an integral part of the model, a sensitivity study allows to identify the role of each input parameter on the outputs. This work presents an overview of pyrolysis modeling, sensitivity analysis, and optimization techniques used to predict the fire behavior of combustible solids when exposed to an external heat flux.

Microarchitectural changes in the mandibles of ovariectomized rats: a systematic review and meta-analysis

BackgroundThis study aimed to examine radiologic microarchitectural changes in the mandibles of ovariectomized (OVX) rats through a systematic review and meta-analysis and to identify factors of the OVX rat model that influence on the bone microstructure.MethodsEligible articles were identified by searching electronic databases, including Embase, Medline, Web of Science, and KoreaMed, for articles published from January 1966 to November 2017. Two reviewers independently performed study selection, data extraction, and quality assessment. The pooled standardized mean difference (SMD) with 95% confidence intervals was calculated using a random-effects model. Subgroup analysis and meta-regression were performed to explore the effect of potential sources on the outcomes. The reliability of the results was assessed by sensitivity analysis and publication bias.ResultsOf 1160 studies, 16 studies (120 OVX and 120 control rats) were included in the meta-analysis. Compared to the control group, the OVX rats’ trabecular bone volume fraction (SMD = − 2.41, P < 0.01, I2 = 81%), trabecular thickness (SMD = − 1.73, P < 0.01, I2 = 73%) and bone mineral density (SMD = − 0.95, P = 0.01, I2 = 71%) displayed the bone loss consistent with osteoporosis. The trabecular separation (SMD = 1.66, P < 0.01, I2 = 51%) has widen in the OVX mandibular bone in comparison to the control group. However, the trabecular number showed no indication to detect the osteoporosis (SMD = − 0.45, P = 0.38, I2 = 76%). The meta-regression indicated that longer post-OVX periods led to greater changes in bone mineral density (β = − 0.104, P = 0.017). However, the rats’ age at OVX was not linked to bone microstructure change.ConclusionsUsing meta-regression and sensitivity analysis techniques, heterogeneity across the micro CT studies of OVX-induced osteoporosis was found. The major factors of heterogeneity were the region of interest and post-OVX period. Our assessment can assist in designing experiments to maximize the usefulness of OVX rat model.

Multivariate Global Sensitivity Analysis for Casing String Using Neural Network

To evaluate the safety of casing string is an important task in the oil exploitation. In this paper, the casing string with complex environment is investigated and the global sensitivity analysis (SA) technique is employed to identify the influential factors on the safety. Since the damage of casing string is of different kinds, three failure modes are mainly considered in the analysis. Then, the multivariate global SA technique is employed to identify the influential factors for the three failure modes simultaneously. Due to the full-size FE analysis of casing string which involves contact analysis of tread, being computationally expensive, a simplified model with full constraints are constructed. Then, to compute the multivariate global sensitivity efficiently, the neural network which is used to surrogate the FE model is employed to perform SA.

Energy performance of mixed-mode office buildings: Assessing typical construction design practices

Studies have demonstrated the energy savings potential of mixed-mode ventilated office buildings. Yet, it is important to widen the knowledge about how those buildings have been designed and built in practice, and which design parameters have greater influence on its energy performance. The aim of this paper was to evaluate how building envelope design parameters influence the energy performance of cellular mixed-mode office buildings, in order to identify key design variables. The analysis presents a comparison among literature research studies and typical construction practices from a sample of buildings located in the city of Sao Paulo, Brazil. According to a base case model, established based on the real buildings sample, three sensitivity analysis techniques were performed to obtain relative parameter sensitivity to thermal loads: OFAT, Morris and Monte Carlo. Results showed the importance of the window opening effective area and the reduced impact of the window-to-wall ratio on the energy performance of mixed-mode office buildings. By applying a multivariate regression model, it showed significant in predicting 78.1% of the variance in annual thermal loads. The accurate determination of annual thermal loads into mixed-mode office buildings can be used to optimize the envelope characteristics based on a combination of input data and the building geometry. Findings from this study could also be applied to other locations, provided that similar climatic environment and urban context are taken into account.

Using causal discovery to analyze emergence in agent-based models

Analyzing agent-based models is a complex task. Agent-based models typically contain complex non-linear interactions between agents and generate emergent properties that cannot easily be explained. They are most commonly analyzed using sensitivity analysis techniques. While these techniques help understanding agent-based models better, they are not a one-size-fits-all solution.This paper explores the novel use of causal discovery algorithms from the field of causality as an additional means to analyze agent-based models. We propose the AbACaD methodology: Agent-based model Analysis using Causal Discovery. In this methodology, emergence in agent-based models is analyzed using causal discovery in combination with both machine learning and sensitivity analysis techniques. AbACaD combines different causal discovery algorithms, using a novel causal graph merging algorithm, to generate a causal graph based on agent-based simulation outcomes. This graph represents the causal relationships between the model parameters and the output variables of the model, and is then exploited to improve the understanding of emergent properties in the model.To demonstrate the effectiveness of AbACaD, it is applied to two models: the El Farol bar model, and an airport security and efficiency model. New emergent properties, such as the moment agents change their strategy in the El Farol bar model were identified. Furthermore, we found queue length to be an important factor in the number of casualties in an improvised explosive device (IED) attack. These emergent properties were well identified using AbACaD, but are hard to identify with traditional analysis techniques alone.

Estimation of rocking capacity of soil-structure systems using a hybrid inverse solver

Among the geotechnical-earthquake community, the rocking concept is being acknowledged as an energy dissipation mechanism that benefits soil–structure systems during strong vibrations. Nevertheless, several involving factors such as geomaterials behavior and superstructure size can make the rocking analysis of soil–foundation–structure systems complicated. The mobilized moment and the dissipated energy can be represented as the two primary performance indicators of the soil–structure systems under strong motions. This study employs an assembled database comprised of a wide range of geomaterials with different stiffness values associated with high-rise structures with different dimensions acquired from the implementation of a dynamic nonlinear elastic-perfect plastic finite element model. This study aims to develop predictive models for the responses mentioned above using the implemented database. To predict the both mobilized moment and dissipated energy, a hybrid gene-expression programming–artificial neural network technique (GEP–ANN) was used. The results show that the GEP model can yield promising predictions with reasonable accuracy. However, the GEP model can be fine-tuned by introducing the hybrid model. The hybrid model decreases the recorded prediction errors by a factor of three for both the mobilized moment and damping ratio as compared to the GEP model. The results show that the predictive models yield a sensible performance power that minimizes the efforts needed for implementation of time-consuming finite element method. This study also deploys a local sensitivity analysis technique to assess how the input parameters are attributed to the target values.

Catalyst-free aza-Michael addition for C–N coupling in active pharmaceutical ingredient synthesis: Modelling of thermodynamic, reaction kinetics and mass transfer considerations

The present research study describes a combined experimental-modelling approach to examine the three-phased catalyst-free reversible aza-Michael addition of NH3 to the vinyl sulfone pharmaceutical intermediate in water. A series of batch experiments using gaseous NH3 were performed to thoroughly evaluate the impact of reaction temperature, NH3 pressure, starting material loading, and starting material quality on reactant solubility, convective mass transfer, and reaction kinetics in the system. A rigorous in-house mechanistic mathematical model was developed. All unknown parameters were determined through nonlinear regression fitting of experimental and simulated quantitative results. Aza-Michael addition kinetics and starting material equilibrium solubility were found to be highly temperature-dependent and had the biggest applicative effect on the maximum aza-Michael reaction rate, which was also confirmed by sensitivity analysis technique. Boundary diffusion of reactants was shown not to be the limiting controlling factor under the chosen operating conditions. Ammonia pressure was found to be important through its impact on the saturated concentration of NH3. Varying starting material load had impact solely on the time required to reach system equilibrium due to solute concentration limitations. Reverse NH3 elimination reaction kinetics was shown to be independent on changing all input operating conditions. The specific methodology grants a novel engineering approach for treating complex interrelated multi-phased heterogeneous compositions in active pharmaceutical ingredient reaction systems, which may be applied early in industrial process development, when a small continuous amount of raw data sets is available, the quantity of impurities varies, and simulations can aid intensification. It may be easily transferred to other similar environments for assistance in process development, optimization, and scale-up.