Perform Parameter Sensitivity Analysis Research Articles

Investigation of overall performance of turbofan engine with pulse detonation combustor in bypass duct

The performance model was established to study the performance of mixed exhaust turbofan engine with pulse detonation combustor (PDC) in the bypass duct. The effects of the total pressure loss of isolator and bypass circulation parameters on the characteristic of PDC and performance of engine were analyzed. Then, the sensitivity analysis of performance parameters was carried out on the influence of the components parameters. The performance of mixed exhaust turbofan engine with PDC in the bypass duct was compared with that of the conventional afterburner turbofan engine at the same design circulation parameters. The results show that to increase the total pressure loss of isolator is beneficial to the improvement of performance because of the improvement of pressure ratio; when fan pressure ratio is constant, with the increasing of bypass ratio, the specific fuel consumption and specific thrust increase; with the increasing of fan pressure ratio, when bypass ratio is 0.2 to 0.4, specific thrust increases first and then decreases. When bypass ratio is 0.4 to 0.5, specific thrust increases first and then remains constant. When bypass ratio is 0.5 to 0.9, specific thrust increases, but the rate of increase reduces. At different bypass ratio, specific fuel consumption decreases with increasing of fan pressure ratio; the performance of engine is more sensitive to the component parameters that directly affect the flow state parameters of the bypass duct; because of supercharging performance of PDC, PDC in the bypass duct only uses part of air flow to organize combustion that can make specific thrust of turbofan engine with PDC in the bypass duct and conventional afterburner turbofan engine are equivalent. Meanwhile, specific fuel consumption can be reduced by 27.7% at design point and 12.8%-26.8% at off-design points.

Mathematical modeling of drug resistance in heterogeneous cancer cell populations

Drug resistance is one of the most intractable issues associated with cancer treatment in clinical practice. Mathematical models provide an analytic framework for facilitating the understanding of resistance evolution dynamics and the design of cancer clinical trial. In this paper, we develop an elementary, compartmental mathematical model for absolute drug resistance, focusing on the effects of point mutations in genetic drivers of malignancy. A set of ordinary differential equations (ODEs) is used to describe the dynamics of competing heterogeneous cancer cell populations while taking account of pharmacokinetics. All possible equilibria and their local geometric properties are analyzed, with the result suggests that the system exhibits bistable dynamics. The existence of optimal treatment time is discussed. To identify the critical parameters which influence cellular dynamics, we also perform parameter sensitivity analysis. Finally, numerical simulations are presented to verify the feasibilities of our analytical results and to find that the pre-existence of resistant cell phenotypes contributes more than resistant mutants generated during the treatment phase.

Multi-assets Asian rainbow options pricing with stochastic interest rates obeying the Vasicek model

<abstract><p>Asian rainbow options provide investors with a new option solution as an effective tool for asset allocation and risk management. In this paper, we address the pricing problem of Asian rainbow options with stochastic interest rates that obey the Vasicek model. By introducing the Vasicek model as the change process of the stochastic interest rate, based on the non-arbitrage principle and the stochastic differential equation, the number of assets of the Asian rainbow option is expanded to $ n $ dimensions, and the pricing formulas of the Asian rainbow option with multiple ($ n $) assets under the Vasicek interest rate model are obtained. The multi-asset pricing results under stochastic interest rates provide more possibilities for Asian rainbow options. Furthermore, Monte Carlo simulation experiments show that the pricing formula is accurate and efficient under double stochastic errors. Finally, we perform parameter sensitivity analysis to further justify the pricing model.</p></abstract>

Sensitivity Analysis of Cardiac Alternans and Tachyarrhythmia to Ion Channel Conductance Using Population Modeling.

Action potential duration (APD) alternans, an alternating phenomenon between action potentials in cardiomyocytes, causes heart arrhythmia when the heart rate is high. However, some of the APD alternans observed in clinical trials occurs under slow heart rate conditions of 100 to 120 bpm, increasing the likelihood of heart arrhythmias such as atrial fibrillation. Advanced studies have identified the occurrence of this type of APD alternans in terms of electrophysiological ion channel currents in cells. However, they only identified physiological phenomena, such as action potential due to random changes in a particular ion channel's conductivity through ion models specializing in specific ion channel currents. In this study, we performed parameter sensitivity analysis via population modeling using a validated human ventricular physiology model to check the sensitivity of APD alternans to ion channel conductances. Through population modeling, we expressed the changes in alternans onset cycle length (AOCL) and mean APD in AOCL (AO meanAPD) according to the variations in ion channel conductance. Finally, we identified the ion channel that maximally affected the occurrence of APD alternans. AOCL and AO meanAPD were sensitive to changes in the plateau Ca2+ current. Accordingly, it was expected that APD alternans would be vulnerable to changes in intracellular calcium concentration.

A new coupled multiphysics model and partitioned time-stepping method for the triple-porosity-Stokes fluid flow model

This paper proposes a new coupled multiphysics model and decoupled finite element method for the realistic naturally fractured reservoir consisting of the triple-porosity medium combined with a free flow region. More specifically, the triple-porosity medium consists of three transmittable, interacting contiguous porous mediums with more-permeable macrofractures, less-permeable microfractures, and stagnant-matrix region, respectively, which is described by the dual-fracture-matrix equations. Moreover, the free fluid flow region is governed by the evolutionary Stokes equation. To couple the triple-porosity medium with the conduit region, five physically valid coupling conditions, are utilized to capture the interfacial phenomena efficiently. The variational formulation and the well-posedness of the model are reported. To lay a solid ground, a fully-implicit algorithm is proposed in a traditional format. On the other hand, based on the idea of partitioned time-stepping, the five interface conditions, and the mass exchange terms in the triple-porosity model, the second algorithm is decoupled into four levels. The decoupling technique allows a non-iterative splitting of the coupled problem into four parallel subproblems. The unconditional stability and optimal convergence analysis are derived for the partitioned time-stepping scheme. For the petroleum engineering applications, we design two numerical experiments and perform parameter sensitivity analysis, which shows the applicability and complicated flow characteristics of the triple-porosity-Stokes interface system.

Multi-Factor Modeling Method of the Load Sharing Ratio under Moving Train Loads

In railway engineering, the load sharing ratio (LSR) is the ratio of the rail seat load (RSL) to the axle load, which is affected by many factors. The LSR can be used in the design and analysis of railway track structures as well as in the research of predicting the dynamic influence of railway tunnels and the environment. The “static loading method” commonly used to study the LSR does not conform to reality; using it, it is difficult to obtain a complete LSR curve, limiting its application. Besides, there is currently a lack of LSR prediction methods considering the impact of multiple factors. Therefore, this paper proposes a “moving loading method” for investigating the LSR under moving train excitation, verified to be rational by comparing with the experimental results. At the same time, a procedure for establishing the LSR multi-factor prediction model is put forward, namely, we (1) determine the LSR function form and the fitting algorithm; (2) perform parameter sensitivity analysis to determine the main influencing parameters of the LSR function; and (3) design a quadratic regression orthogonal test to obtain the prediction formula of the LSR function coefficients. Once establishing the prediction model for a type of train-track system, the LSR of similar systems can be calculated by adjusting the main parameters of the model. Shijiazhuang Metro Line 1 using the A-type vehicle and the monolithic trackbed is taken as a case study to develop a corresponding LSR multi-factor prediction model by the moving loading method and the procedure mentioned above. The results indicate that the proposed method performs well and can be adopted to enhance the accuracy of track design or tunnel and environmental vibration prediction.

Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water

Remote sensing can detect and map algal blooms. The HydroLight (Sequoia Scientific Inc., Bellevue, Washington, DC, USA) model generates the reflectance profiles of various water bodies. However, the influence of model parameters has rarely been investigated for inland water. Moreover, the simulation time of the HydroLight model increases as the amount of input data increases, which limits the practicality of the HydroLight model. This study developed a graphical user interface (GUI) software for the sensitivity analysis of the HydroLight model through multiple executions. The GUI software stably performed parameter sensitivity analysis and substantially reduced the simulation time by up to 92%. The GUI software results for lake water show that the backscattering ratio was the most important parameter for estimating vertical reflectance profiles. Based on the sensitivity analysis results, parameter calibration of the HydroLight model was performed. The reflectance profiles obtained using the optimized parameters agreed with observed profiles, with R2 values of over 0.98. Thus, a strong relationship between the backscattering coefficient and the observed cyanobacteria genera cells was identified.

Prediction and sensitivity analysis under different performance indices of R1234ze ORC with Taguchi's multi-objective optimization

In this study, parametric optimization and sensitivity analysis of performance parameters were performed for the Organic Rankine Cycle (ORC) system at 120 °C heat source temperature. R1234ze, which is called the new-generation fluid, was used in the ORC design. Performance parameters have been selected considering Energy, Exergy, Economy (Turbine performance) and Environmental (Thermodynamic sustainability indices) factors. Six performance indices used in the orthogonal design with Taguchi-ANOVA. These are; thermal efficiency, turbine power, exergy efficiency, total irreversibility, Volume Flow Ratio (VFR) and Environmental Effect Factor (EEF). Such control factors as ΔTPP,e - ΔTPP,c -Tc,i -Tsup – ƞt – ƞp were selected for the statistical analysis. Sensitivity levels were determined under all performance indices for the designed ORC. It has been determined that indices affect control factors differently. According to the results of the parametric optimization, it was determined that the parameter affecting the ORC performance the most was ΔTPP,e. The effect of ΔTPP,e on ORC performance is stated as 39.72%. EES numerical analysis results were compared with the derived predictive equations using different statistical methods with regression method. When these equations obtained for all objective functions are evaluated, the average MAPE, RRMSE and R2 values were determined as 4.1%, 4.29% and 94.1%, respectively.

Option predictive clustering trees for multi-target regression

Decision trees are one of the most widely used predictive modelling methods primarily because they are readily interpretable and fast to learn. These nice properties come at the price of predictive performance. Moreover, the standard induction of decision trees suffers from myopia: a single split is chosen in each internal node which is selected in a greedy manner; hence, the resulting tree may be sub-optimal. To address these issues, option trees have been proposed which can include several alternative splits in a new type of internal nodes called option nodes. Considering all of this, an option tree can be also regarded as a condensed representation of an ensemble. In this work, we propose to learn option trees for multi-target regression (MTR) based on the predictive clustering framework. The resulting models are thus called option predictive clustering trees (OPCTs). Multi-target regression is concerned with learning predictive models for tasks with multiple numeric target variables.We evaluate the proposed OPCTs on 11 benchmark MTR data sets. The results reveal that OPCTs achieve statistically significantly better predictive performance than a single predictive clustering tree (PCT) and are competitive with bagging and random forests of PCTs. By limiting the number of option nodes, we can achieve a good trade-off between predictive power and efficiency (model size and learning time).We also perform parameter sensitivity analysis and bias-variance decomposition of the mean squared error. Our analysis shows that OPCTs can reduce the variance of PCTs nearly as much as ensemble methods do. In terms of bias, OPCTs occasionally outperform other methods. Finally, we demonstrate the potential of OPCTs for multifaceted interpretability and illustrate the potential for inclusion of domain knowledge in the tree learning process.

Design of Fuel Cell Systems for Aviation: Representative Mission Profiles and Sensitivity Analyses

The global transition to a clean and sustainable energy infrastructure does not stop at aviation. The European Commission defined a set of environmental goals for the “Flight Path 2050”: 75% CO2 reduction, 90% NOx reduction, and 65% perceived noise reduction. Hydrogen as an energy carrier fulfills these needs, while it would also offer a tenable and flexible solution for intermittent, large-scale energy storage for renewable energy networks. If hydrogen is used as an energy carrier, there is no better device than a fuel cell to convert its stored chemical energy. In order to design fuel cell systems for passenger aircraft, it is necessary to specify the requirements that the system has to fulfill. In this paper, a statistical approach to analyze these requirements is presented, which accounts for variations in the flight mission profile. Starting from a subset of flight data within the desired class (e.g., mid-range inter-European flights) a stochastic model of the random mission profile is inferred. This model allows for subsequent predictions under uncertainty as part of the aircraft design process. By using Monte Carlo-based sampling of flight mission profiles, the range of necessary component sizes, as well as optimal degrees of hybridization with a battery, is explored, and design options are evaluated. Furthermore, Monte Carlo-based sensitivity analysis of performance parameters explores the potential of future technological developments. Results suggest that the improvement of the specific power of the fuel cell is the deciding factor for lowering the energy system mass. The specific energy of the battery has a low influence but acts in conjunction with the specific power of the fuel cell.

Synchronization of Pacemaking in the Sinoatrial Node: A Mathematical Modeling Study

Computational studies using mathematical models of the sinoatrial node (SAN) cardiac action potential (AP) have provided important insight into the fundamental nature of cell excitability, cardiac arrhythmias, and potential therapies. While the impact of ion channel dynamics on SAN pacemaking has been studied, the governing dynamics responsible for regulating spatial and temporal control of SAN synchrony remain elusive. Here we attempt to develop methods to explore cohesion in a network of coupled spontaneously active SAN cells. We present the updated version of a previously published graphical user interface LongQt: a cross-platform, threaded application for advanced cardiac electrophysiology studies that does not require advanced programming skills. We incorporated additional features to the existing LongQt platform that allows users to (1) specify heterogeneous gap junction conductivity across a multicellular grid, and (2) set heterogeneous ion channel conductance across a multicellular grid. We developed two methods of characterizing the synchrony of SAN tissue based on alignment of activation in time and similarity of voltage peaks among clusters of functionally related cells. In pairs and two-dimensional grids of coupled cells, we observed a range of conductivities (0.00014 1/Ω-cm to 0.00033 1/Ω-cm) in which the tissue was more susceptible to developing asynchronous spontaneous pro-arrhythmic behavior (e.g. spiral wave formation). We performed parameter sensitivity analysis to determine the relative impact of ion channel conductances on cycle length, diastolic and peak voltage and synchrony measurements in isolated and coupled cell pairs. We also defined measurements of evaluating synchrony based on peak AP voltage and the rate of wave propagation. Cell-to-cell coupling had a nonlinear effect on the relationship between ion channel conductances, AP properties and synchrony measurements. Our simulations demonstrate that conductivity plays an important role in regulating synchronous firing of heterogeneous SAN tissue, and demonstrate how to evaluate the role of coupling and ion channel conductance in pairs and grids of SAN cells. We anticipate that the approach outlined here will facilitate identification of key cell- and tissue-level factors responsible for cardiac disease.

Disentangling the effects of climate and urban growth on streamflow for sustainable urban development: A stochastic approach to flow regime attribution

In urban watersheds, climate variability and change, urban growth, and stormwater management can act concurrently over time to shape and alter the streamflow dynamics. Yet, when assessing the impacts of urbanization on streamflow, these factors are rarely taken simultaneously into consideration. There is thus an emerging need for approaches that allow disentangling the hydrological impacts of land cover change from those due to climate, in the context of long-term, historical changes in urban landscapes. This is here termed flow regime attribution. We demonstrate in this study the ability of a stochastic mechanistic model to perform flow regime attribution. The modeling approach is applied to the Watts Branch watershed, located in metropolitan Washington D.C., United States. To carry out the flow regime attribution, the model is used to compute streamflow indicators of hydrological alteration and perform parameter sensitivity analysis. The application of the model shows that in Watts Branch urban growth drives the long-term temporal trend in streamflow. The mean and variance of streamflow increase at the end of the gauging period by 2 and 7 times, respectively, their value relative to an only climate (no urban growth) scenario. The results show that climate mainly amplifies or dampens the temporal trend according to wet/dry variations in annual rainfall. Further, the model facilitates the attribution process by allowing the derivation of streamflow indicators that directly depend on the model parameters. The proposed modeling approach may be useful for assessing the long-term flow behavior of urban watersheds, and informing sustainable urban development decisions.

Analysing the dynamics of a model for alopecia areata as an autoimmune disorder of hair follicle cycling

Alopecia areata (AA) is a CD8$^{+}$ T cell-dependent autoimmune disease that disrupts the constantly repeating cyclic transformations of hair follicles (HFs). Among the three main HF cycle stages-growth (anagen), regression (catagen) and relative quiescence (telogen)-only anagen HFs are attacked and thereby forced to prematurely enter into catagen, thus shortening active hair growth substantially. After having previously modelled the dynamics of immune system components critically involved in the disease development (Dobreva et al., 2015), we here present a mathematical model for AA which incorporates HF cycling and illustrates the anagen phase interruption in AA resulting from an inflammatory autoimmune response against HFs. The model couples a system describing the dynamics of autoreactive immune cells with equations modelling the hair cycle. We illustrate states of health, disease and treatment as well as transitions between them. In addition, we perform parameter sensitivity analysis to assess how different processes, such as proliferation, apoptosis and input from stem cells, impact anagen duration in healthy versus AA-affected HFs. The proposed model may help in evaluating the effectiveness of existing treatments and identifying new potential therapeutic targets.

The ubiquitous self-organizing map for non-stationary data streams

The Internet of things promises a continuous flow of data where traditional database and data-mining methods cannot be applied. This paper presents improvements on the Ubiquitous Self-Organized Map (UbiSOM), a novel variant of the well-known Self-Organized Map (SOM), tailored for streaming environments. This approach allows ambient intelligence solutions using multidimensional clustering over a continuous data stream to provide continuous exploratory data analysis. The average quantization error and average neuron utility over time are proposed and used to estimating the learning parameters, allowing the model to retain an indefinite plasticity and to cope with changes within a multidimensional data stream. We perform parameter sensitivity analysis and our experiments show that UbiSOM outperforms existing proposals in continuously modeling possibly non-stationary data streams, converging faster to stable models when the underlying distribution is stationary and reacting accordingly to the nature of the change in continuous real world data streams.

Mathematical model for alopecia areata

Alopecia areata (AA) is an autoimmune disease, and its clinical phenotype is characterized by the formation of distinct hairless patterns on the scalp or other parts of the body. In most cases hair falls out in round patches. A well-established hypothesis for the pathogenesis of AA states that collapse of hair follicle immune privilege is one of the essential elements in disease development. To investigate the dynamics of alopecia areata, we develop a mathematical model that incorporates immune system components and hair follicle immune privilege agents whose involvement in AA has been confirmed in clinical studies and experimentally. We perform parameter sensitivity analysis in order to determine which inputs have the greatest effect on outcome variables. Our findings suggest that, among all processes reflected in the model, immune privilege guardians and the pro-inflammatory cytokine interferon-γ govern disease dynamics. These results agree with the immune privilege collapse hypothesis for the development of AA.

Optimal Budget Allocation Across Search Advertising Markets

One critical operational decision facing online advertisers when they engage in sponsored search advertising is concerned with the allocation of a limited advertising budget. In particular, dealing with multi-keyword search markets over multiple decision periods poses significant decision-making challenges. In this paper, we develop a novel budget allocation optimization model with multiple search advertising markets and a finite time horizon. One key element of our modeling work is developing a customized advertising response function when considering distinctive features of sponsored search, including the quality score and the dynamic advertising effort. We derive a feasible solution to our budget model and study its properties. Computational experiments are conducted on real-world data to evaluate our budget model and perform parameter sensitivity analysis. Experimental results indicate that our budget allocation strategy significantly outperforms several baseline strategies. In addition, the identified properties derived from the solution process illuminate critical managerial insights for advertisers in sponsored search.

Disrupted calcium release as a mechanism for atrial alternans associated with human atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia, but our knowledge of the arrhythmogenic substrate is incomplete. Alternans, the beat-to-beat alternation in the shape of cardiac electrical signals, typically occurs at fast heart rates and leads to arrhythmia. However, atrial alternans have been observed at slower pacing rates in AF patients than in controls, suggesting that increased vulnerability to arrhythmia in AF patients may be due to the proarrythmic influence of alternans at these slower rates. As such, alternans may present a useful therapeutic target for the treatment and prevention of AF, but the mechanism underlying alternans occurrence in AF patients at heart rates near rest is unknown. The goal of this study was to determine how cellular changes that occur in human AF affect the appearance of alternans at heart rates near rest. To achieve this, we developed a computational model of human atrial tissue incorporating electrophysiological remodeling associated with chronic AF (cAF) and performed parameter sensitivity analysis of ionic model parameters to determine which cellular changes led to alternans. Of the 20 parameters tested, only decreasing the ryanodine receptor (RyR) inactivation rate constant (kiCa) produced action potential duration (APD) alternans seen clinically at slower pacing rates. Using single-cell clamps of voltage, fluxes, and state variables, we determined that alternans onset was Ca2+-driven rather than voltage-driven and occurred as a result of decreased RyR inactivation which led to increased steepness of the sarcoplasmic reticulum (SR) Ca2+ release slope. Iterated map analysis revealed that because SR Ca2+ uptake efficiency was much higher in control atrial cells than in cAF cells, drastic reductions in kiCa were required to produce alternans at comparable pacing rates in control atrial cells. These findings suggest that RyR kinetics may play a critical role in altered Ca2+ homeostasis which drives proarrhythmic APD alternans in patients with AF.

Quantitative Contributions to Electrical Propagation in 1D Cable Model

From previous experimental and theoretical studies, it is well-known that gap junctional coupling, Na+ channel density, and Na+ channel localization influence the speed of electrical conduction velocity (CV) in multicellular cardiac tissue. However, two recent findings complicate our understanding of electrical conduction: (1) Na+ channels located at the intercalated disk region have different properties than those in the cell center; (2) in disease states, changes in gap junctional coupling occur in tandem with alterations in Na+ channel density, Na+ channel properties, and subcellular structure, which makes it difficult to determine the effects of each change. To gain a greater quantitative understanding of the factors that influence CV, we implemented a one-dimensional mathematical model of electrically coupled human ventricular myocytes. The model accounts for the possibility that electrical fields in the narrow clefts between adjacent cells can influence conduction. To determine how each model variable affects CV, we performed parameter sensitivity analysis under two conditions: (1) normal gap junctional conductance (400 kΩ resistance between cells), and (2) severely reduced gap junctional conductance (40 MΩ resistance between cells). Our simulation results predict the following: (1) Increasing the width of the cleft between adjacent cells increases CV under normal gap junctional conductance conditions but decreases CV when gap junctional coupling is low; (2) reducing cell coupling decreases CV under normal gap junctional conductance conditions, but further changes to coupling have little effect under low gap junctional conductance conditions, (3) Na+ channel activation kinetics and voltage dependence have a greater quantitative effect on CV with low than with normal gap junctional conductance. The simulations generate predictions that can be tested experimentally, and the results provide insight into changes in CV observed in disease.

Robust Design Optimization of Dual Thrust Solid Propellant Motors due to Burning Rate Uncertainties

Solid rocket motor design becomes a clamant task where fusion of design and non-design parameters may lead to various design configurations, especially under uncertainties. This paper proposes a robust design optimization method for the performance of a dual thrust solid rocket motor under investigation of the propellant burning rate and grain geometry uncertainties. It was found that due to uncertainties, the burning rate varies erratically during burning surface area regression, which may lead to catastrophic failure. The present approach aims at uncertainty quantification associated with burning rate and its influence on dual thrust motor performance. A first-order orthogonal design is applied to estimate the worst case deviation coupled with motor internal ballistics. The robustness assessment is measured directly by a mean-variance and average difference approach. A sensitivity analysis of performance parameters is performed to analyze the effects of variation of the design parameters. A hybrid genetic algorithm and simulated annealing approach is used as optimizer. The robust design solution obtained in the form of insensitive design and performance parameters shows the effectiveness and efficiency of the proposed methodology.

Uncertainty-based computational robust design optimisation of dual-thrust propulsion system

This paper proposes a robust design optimisation methodology for performance evaluation of high-fidelity propulsion systems under both aleatory and epistemic uncertainties. In this paper, uncertainty-based robust design methodology is applied using first-order orthogonal design to estimate worst case of the uncertainties. The effectiveness and computational efficiency of proposed formulation is obtained by decoupling computation of both types of uncertainties in the optimisation process loop. The system robustness is measured by directly minimising the variance of target mean and standard deviation and adhering to performance constraints. A parametric sensitivity analysis of performance parameters due to input parameters variations is also performed to identify the most significant design variables. A hybrid genetic algorithm and simulated annealing approach are used as an optimiser. By using the proposed method, the objective function to maximise the booster phase to sustain phase thrust ratio and total impulse could be achieved while adhering to the motor performance constraints to provide an insensitive solution.