Input Velocity Research Articles

Design, Simulation, and Implementation of MRAC Controller with PMAC Target Hardware for Controlling 7.5 m Antenna

The article presents the design of an adaptive controller, simulation, code generation, and porting of the algorithm into target hardware, movement of the 7.5 m antenna, testing, verification, and validation of theoretical and experimental results of the antenna system. The conventional servo control methods based on PID controllers are being used for tracking X-band missions. As the need for higher data rates is increasing, missions are switching to the Ka band, as a result the satellite ground stations will have to track the satellite in the Ka band where the antenna band beam width is narrowed by a factor of 3 when compared to the X-band antenna beamwidth. These challenges had driven the designer to design and implement a Model Reference Adaptive Controller over PID to improve and achieve the tracking accuracy of less than 25m° from the present 30 m°. Thus, consistent and constant performance is achieved throughout the trajectory tracking of the mission using PID + MRAC (Model Reference Adaptive Control). MRAC uses a reference model that specifies the desired response of the antenna system and adjusts the controller parameters such that the plant follows the reference to achieve the desired performance. The entire drive chain modules and their parameters are considered in the design of the plant, namely MRAC controller, PID controller, Power Drive electronic (Rectifier and Inverter), Servo Motor, mechanical torque coupling, Gear reducer, Slew Ring Bearing (SRB), and absolute encoder modules of each axis for position feedback from the antenna. Model Order Reduction of higher-order actual Plant model to the standard second-order system is optimized. The modelled and analysed outcomes of the MRAC scheme using the Modified MIT are demonstrated. The developed model is tested against step, ramp, parabolic, satellite trajectory, velocity and acceleration inputs in MATLAB/Simulink and validated with practical/ experimental test results with antenna load at the satellite ground Station. The achieved results indicate that the PID + MRAC controller has significant and consistent performance improvements when compared to the PID controller alone.

Machine Learning and Wavelet Transform: A Hybrid Approach to Predicting Ammonia Levels in Poultry Farms.

Ammonia (NH3) is a major pollutant in poultry farms, negatively impacting bird health and welfare. High NH3 levels can cause poor weight gain, inefficient feed conversion, reduced viability, and financial losses in the poultry industry. Therefore, accurate estimation of NH3 concentration is crucial for environmental protection and human and animal health. Three widely used machine learning (ML) algorithms-extreme learning machine (ELM), k-nearest neighbor (KNN), and random forest (RF)-were initially used as base algorithms. The wavelet transform (WT) with ten levels of decomposition was then applied as a preprocessing method. Three statistical metrics, including the mean absolute error (MAE) and the correlation coefficient (R), were used to evaluate the predictive accuracies of algorithms. The results indicate that the RF algorithms perform robustly individually and in combination with the WT. The RF-WT algorithm performed best using the air temperature, relative humidity, and air velocity inputs with a MAE of 0.548 ppm and an R of 0.976 for the testing dataset. In summary, applying WT to the inputs significantly improved the predictive power of the ML algorithms, especially for inputs that initially had a low correlation with the NH3 values.

Adaptive Saturated Obstacle Avoidance Trajectory Tracking Control for Euler–Lagrange Systems With Velocity Constraints

ABSTRACTThis article pays attention to the obstacle avoidance trajectory tracking control problem for uncertain Euler–Lagrange (EL) systems subject to velocity constraints and input saturation. The existing obstacle avoidance results do not consider velocity constraints under input saturation, which means that an EL system may not be able to obtain sufficient control inputs to avoid a collision with an obstacle if it has a high speed when approaching the obstacle. Therefore, the velocity constraints in the obstacle avoidance tracking control are considered in this paper. A novel velocity constraint function that depends on the distance between the system and the obstacle is proposed. Integral‐multiplicative Lyapunov‐barrier functions (LBFs) are constructed and incorporated into the backstepping procedure to design an adaptive fuzzy obstacle avoidance tracking control scheme. Moreover, an auxiliary dynamic system is designed by constructing a bounded nonlinear vector related to an auxiliary variable to compensate for the effects of saturation. Through the Lyapunov method and boundedness analysis for the barrier function, it is shown that the protocol achieves obstacle avoidance for the EL system without violating the velocity constraints inside the obstacle detection region, while also guaranteeing the ultimate uniform boundedness of all the closed‐loop signals. Numerical simulations are presented to demonstrate the efficacy of the proposed control strategy.

A variable damping viscous damper for control of buildings under wind loading

High-rise buildings are susceptible to low-frequency vibrations induced by wind loading, and the traditional constant damping viscous damper (CDVD) falls short in effectively suppressing vibrations of high-rise buildings under wind loadings because of its constant damping coefficient. To address this, a novel variable damping viscous damper (VDVD) is first designed and fabricated. The characterization tests were conducted to evaluate the variable damping characteristics under different velocities. The control performance of the VDVD in high-rise buildings under wind loadings with varying wind pressures is simulated and analysed, as comparisons with CDVD. The test results show that the damping coefficient of the VDVD increases with the input velocity. Furthermore, it exhibits the capability to achieve both constant and variable damping through the adjustment of the pre-pressure force in the spring. Compared with cases using CDVD, the VDVD demonstrates a significantly greater reduction in structural response, with cumulative dissipated energy ratios ranging from 1.0 to 3.14 as wind pressure increases. The VDVD offers excellent potential for various engineering applications in terms of new concepts and new devices for vibration control and mitigation induced by wind loadings.

SpinDoctor-IVIM: A virtual imaging framework for intravoxel incoherent motion MRI

Intravoxel incoherent motion (IVIM) imaging is increasingly recognised as an important tool in clinical MRI, where tissue perfusion and diffusion information can aid disease diagnosis, monitoring of patient recovery, and treatment outcome assessment. Currently, the discovery of biomarkers based on IVIM imaging, similar to other medical imaging modalities, is dependent on long preclinical and clinical validation pathways to link observable markers derived from images with the underlying pathophysiological mechanisms. To speed up this process, virtual IVIM imaging is proposed. This approach provides an efficient virtual imaging tool to design, evaluate, and optimise novel approaches for IVIM imaging. In this work, virtual IVIM imaging is developed through a new finite element solver, SpinDoctor-IVIM, which extends SpinDoctor, a diffusion MRI simulation toolbox. SpinDoctor-IVIM simulates IVIM imaging signals by solving the generalised Bloch-Torrey partial differential equation. The input velocity to SpinDoctor-IVIM is computed using HemeLB, an established Lattice Boltzmann blood flow simulator. Contrary to previous approaches, SpinDoctor-IVIM accounts for volumetric microvasculature during blood flow simulations, incorporates diffusion phenomena in the intravascular space, and accounts for the permeability between the intravascular and extravascular spaces. The above-mentioned features of the proposed framework are illustrated with simulations on a realistic microvasculature model.

An Eulerian formulation for the computational modeling of phase-contrast MRI.

Computational simulation of phase-contrast MRI (PC-MRI) is an attractive way to physically interpret properties and errors in MRI-reconstructed flow velocity fields. Recent studies have developed PC-MRI simulators that solve the Bloch equation, with the magnetization transport being modeled using a Lagrangian approach. Because this method expresses the magnetization as spatial distribution of particles, influences of particle densities and their spatial uniformities on numerical accuracy are well known. This study developed an alternative method for PC-MRI modeling using an Eulerian approach in which the magnetization is expressed as a spatially smooth continuous function. The magnetization motion was described using the Bloch equation with an advection term and computed on a fixed grid using a finite difference method, and k-space sampling was implemented using the spoiled gradient echo sequence. PC-MRI scans of a fully developed flow in straight and stenosed cylinders were acquired to provide numerical examples. Reconstructed flow in a straight cylinder showed excellent agreement with input velocity profiles and mean errors were less than 0.5% of the maximum velocity. Numerical cases of flow in a stenosed cylinder successfully demonstrated the velocity profiles, with displacement artifacts being dependent on scan parameters and intravoxel dephasing due to flow disturbances. These results were in good agreement with those obtained using the Lagrangian approach with a sufficient particle density. The feasibility of the Eulerian approach to PC-MRI modeling was successfully demonstrated.

Analysing fluid flow and heat transfer comparatively in flow passage systems: Evaluating thermal impacts and geometric configurations

This research thoroughly examines heat transfer and fluid flow in a passage flow system, highlighting the difficulties posed by different geometric arrangements and temperature conditions that may affect the system's performance. The main aim is to evaluate the impacts of different geometric characteristics on the velocity, pressure, and temperature profiles inside the flow route. These factors encompass cavity dimensions, tube diameter, and input conditions. An inclusive comparison of three different geometric designs at controlled temperatures is conducted using computational fluid dynamics (CFD) simulations. The findings indicate that the optimal geometric parameters improve thermal performance, with certain arrangements displaying improvements in heat transfer rates up to 30 %. In this regard, the higher cavity dimensions and suitable input velocities are exposed as advantages. The first sample exhibited a higher velocity of 0.024 m s-1 due to its simpler geometry and favorable heating conditions, while the third sample demonstrated a higher temperature of 465 K due to its complex cavity shape and multiple heating sources. This study suggests that enhancing efficiency in heat management applications necessitates a strategic design approach for passage flow systems, which must account for flow characteristics and geometric specifications. This research would provide insightful information for designers and engineers looking at enhancing fluid flow systems across a range of industrial applications.

Autonomous Underwater Vehicle Path Planning Based on Improved Salp Swarm Algorithm

Aiming at the problem of path planning for autonomous underwater vehicle (AUV) to cope with the influence of obstacles and eddies in complex marine environments, a path planning method based on an improved salp swarm algorithm (ISSA) is proposed. Firstly, the motion model of the AUV and eddy current model are constructed, including the relationship between position, velocity, attitude, and control inputs. Secondly, the improved SSA is proposed, which introduces the Levy flight strategy to enhance the algorithm’s optimization seeking ability and adds a nonlinear convergence factor to enhance the convergence ability of the algorithm. The stability and robustness of the improved algorithm are verified by test functions. Finally, the ISSA is applied to AUV path planning, which optimizes the AUV travel distance, improves the search efficiency and accuracy, and avoids the local optimum of the algorithm. The ISSA enhances the adaptive ability and robustness of the algorithm by introducing a dynamic adjustment strategy and feedback mechanism. Experimental verification is carried out using a simulated marine environment. The results show that the ISSA is better than the traditional algorithm in terms of path length as well as algorithm stability, and can effectively improve the navigation performance of AUV.

Estimating orientation in natural scenes: A spiking neural network model of the insect central complex.

The central complex of insects contains cells, organised as a ring attractor, that encode head direction. The 'bump' of activity in the ring can be updated by idiothetic cues and external sensory information. Plasticity at the synapses between these cells and the ring neurons, that are responsible for bringing sensory information into the central complex, has been proposed to form a mapping between visual cues and the heading estimate which allows for more accurate tracking of the current heading, than if only idiothetic information were used. In Drosophila, ring neurons have well characterised non-linear receptive fields. In this work we produce synthetic versions of these visual receptive fields using a combination of excitatory inputs and mutual inhibition between ring neurons. We use these receptive fields to bring visual information into a spiking neural network model of the insect central complex based on the recently published Drosophila connectome. Previous modelling work has focused on how this circuit functions as a ring attractor using the same type of simple visual cues commonly used experimentally. While we initially test the model on these simple stimuli, we then go on to apply the model to complex natural scenes containing multiple conflicting cues. We show that this simple visual filtering provided by the ring neurons is sufficient to form a mapping between heading and visual features and maintain the heading estimate in the absence of angular velocity input. The network is successful at tracking heading even when presented with videos of natural scenes containing conflicting information from environmental changes and translation of the camera.

Enhancing mixing efficiency of a circular electroosmotic micromixer with cross-reciprocal electrodes

Enhancing mixing efficiency in microscale processes for sensitive biomedical, pharmaceutical, and chemical applications is crucial, particularly when operating under low-velocity constraints. This study presents a comprehensive investigation into the impact of various factors on microfluidic mixing within a circular mixing chamber micromixer, utilizing electroosmotic principles. The governing equations are solved numerically using the finite element technique-based solver. This research examines the effects of microchamber diameter (D), inlet velocity (uo), alternating current (AC) voltage amplitude (ϕo), and AC frequency (f) on fluid mixing dynamics. Several key findings are noted from this study. The reduction of the circular microchamber diameter decreases the linear distance between cross-reciprocally placed microelectrodes, resulting in increased electroosmosis force and mixing efficiency. The voltage amplitude within the specified range shows increased mixing efficiency when fluid species are combined at appropriate velocity and AC frequency. The highest mixing efficiency of 98.84% is achieved with the following parameters: flow velocity (uo) of 150 μm/s, AC frequency of 4 Hz, voltage amplitude of 500 mV, and microchamber diameter of 20 μm. At a frequency of 12 Hz and voltage amplitude of 500 mV, the mixing efficiency exceeds 94.66% across a wide range of input velocities (100–200 μm/s), enabling versatile control in microfluidic devices. The nonlinear interaction between electroosmotic flow and microchamber geometry significantly contributes to this enhanced mixing efficiency. These results demonstrate the potential for optimizing microfluidic mixing processes through careful parameter tuning, particularly in applications requiring high efficiency at low flow rates. Thus, this study provides valuable insights for designing more effective microfluidic systems in various scientific and industrial fields.

Interactive Design of Stylized Walking Gaits for Robotic Characters

Procedural animation has seen widespread use in the design of expressive walking gaits for virtual characters. While similar tools could breathe life into robotic characters, existing techniques are largely unaware of the kinematic and dynamic constraints imposed by physical robots. In this paper, we propose a system for the artist-directed authoring of stylized bipedal walking gaits, tailored for execution on robotic characters. The artist interfaces with an interactive editing tool that generates the desired character motion in realtime, either on the physical or simulated robot, using a model-based control stack. Each walking style is encoded as a set of sample parameters which are translated into whole-body reference trajectories using the proposed procedural animation technique. In order to generalize the stylized gait over a continuous range of input velocities, we employ a phase-space blending strategy that interpolates a set of example walk cycles authored by the animator while preserving contact constraints. To demonstrate the utility of our approach, we animate gaits for a custom, free-walking robotic character, and show, with two additional in-simulation examples, how our procedural animation technique generalizes to bipeds with different degrees of freedom, proportions, and mass distributions.

Discrete-Time Visual Servoing Control with Adaptive Image Feature Prediction Based on Manipulator Dynamics.

In this paper, a practical discrete-time control method with adaptive image feature prediction for the image-based visual servoing (IBVS) scheme is presented. In the discrete-time IBVS inner-loop/outer-loop control architecture, the time delay caused by image capture and computation is noticed. Considering the dynamic characteristics of a 6-DOF manipulator velocity input system, we propose a linear dynamic model to describe the motion of a robot end effector. Furthermore, for better estimation of image features and smoothing of the robot's velocity input, we propose an adaptive image feature prediction method that employs past image feature data and real robot velocity data to adopt the prediction parameters. The experimental results on a 6-DOF robotic arm demonstrate that the proposed method can ensure system stability and accelerate system convergence.

Mid-Infrared Supercontinuum Generation in Highly Nonlinear Chalcogenide Fibers

Interest in mid-infrared broadband laser light sources has surged due to applications in trace gas detection, free-space communications, and countermeasures. Progress in supercontinuum generation leverages fiber-based near-infrared and bulk-optic mid-infrared pump sources. In this paper, the Generalized Nonlinear Schrödinger Equation has been solved, using the Split Step Fourier Method, to simulate the pulse propagation and mid-infrared supercontinuum generation, inside a fiber composed of highly nonlinear As2Se3/As2S3 chalcogenide glass. The effect of various parameters, including fiber nonlinearity, Group Velocity Dispersion (GVD), input power and pulse-width, anomalous and normal dispersion pumping regime, etc. on the output supercontinuum bandwidth has been extensively studied. A tapered chalcogenide fiber is modeled to facilitate continuous simultaneous modification of the GVD and the Kerr nonlinearity parameter. Pumping the waveguides with 230-fs secant pulses at a peak power of 4.2-kW yields a mid-IR supercontinuum extending from [Formula: see text] to [Formula: see text] micrometers.

Vortex-p: A Helmholtz-Hodge and Reynolds decomposition algorithm for particle-based simulations

Astrophysical turbulent flows display an intrinsically multi-scale nature, making their numerical simulation and the subsequent analyses of simulated data a complex problem. In particular, two fundamental steps in the study of turbulent velocity fields are the Helmholtz-Hodge decomposition (compressive+solenoidal; HHD) and the Reynolds decomposition (bulk+turbulent; RD). These problems are relatively simple to perform numerically for uniformly-sampled data, such as the one emerging from Eulerian, fix-grid simulations; but their computation is remarkably more complex in the case of non-uniformly sampled data, such as the one stemming from particle-based or meshless simulations. In this paper, we describe, implement and test vortex-p, a publicly available tool evolved from the vortex code, to perform both these decompositions upon the velocity fields of particle-based simulations, either from smoothed particle hydrodynamics (SPH), moving-mesh or meshless codes. The algorithm relies on the creation of an ad-hoc adaptive mesh refinement (AMR) set of grids, on which the input velocity field is represented. HHD is then addressed by means of elliptic solvers, while for the RD we adapt an iterative, multi-scale filter. We perform a series of idealised tests to assess the accuracy, convergence and scaling of the code. Finally, we present some applications of the code to various SPH and meshless finite-mass (MFM) simulations of galaxy clusters performed with OpenGadget3, with different resolutions and physics, to showcase the capabilities of the code.

Optimal hybrid strategy in adaptive cruise control system for enhanced autonomous vehicle stability and safety

The adaptive cruise control system relies heavily on the vehicle's longitudinal control algorithm. Traditional longitudinal control algorithms typically depend on precise vehicle dynamic modeling or complex controller parameter calibrations. In the research, an optimal hybrid strategy for the control of basic adaptive cruise control system is proposed. The proposed hybrid strategy is the combination of both the Model Predictive Control (MPC) and Coati Optimization Algorithm (COA) and hence it is named as MPCCOA strategy. The fundamental goal of the proposed work is that a vehicle should maintain its current velocity when it approaches another vehicle that is driving at a steady velocity. The inputs for the proposed strategy are vehicle speed and acceleration. The inter-vehicle distance error, velocity error and acceleration are regulated based on the upper and lower limit of acceleration command with the help of proposed strategy. For accomplishing the required steadiness with the limitations in the given input and actual velocity with constant previous vehicle velocity, the proposed controller is utilized. The proposed controller is used to control the traction force of the host vehicle such that the vehicle speed follows the optimal speed trajectory as good as possible while ensuring constraints like distance to a preceding vehicle or speed limits. The performance of the proposed is implemented in the MATLAB platform and compared with existing approaches. The proposed technique demonstrates superior performance metrics, achieving the collision rate, rate of reaching maximum deceleration and ratio of exceeding comfortable deceleration or jerk are 1.27 %, 3.131 % and 9.052 %, respectively.

Computational fluid dynamics based multi-species transport simulation of auxiliary energy systems for friction stir welding of dissimilar materials

This research compares auxiliary energy-assisted friction stir welding (FSW) techniques with conventional FSW when joining dissimilar materials. Specifically, it conducts numerical modeling and experimental validation for the effectiveness of plasma-assisted FSW and induction-assisted FSW for DH36 steel and 6061-T6 aluminum alloy. Fully coupled 3D computational fluid dynamics (CFD) models, incorporating the multi-species transport method, were developed, where the species mass fractions of the workpieces are transported through diffusion, convection, and reaction sources for individual species. Based on the temperature validation, the dedicated heat flux based on the rectangular heat flux and Gaussian heat flux distribution were considered for induction coil and plasma arc heating on the DH36 steel side, respectively. The established conventional and auxiliary energy-assisted FSW models were validated against experimentally observed temperature fields and the joints’ material features. Results indicate that the assistance of plasma and induction auxiliary energy sources increased the temperature field, strain rate, and flow velocity without forming stagnant zones on the steel side caused by reduced dynamic viscosity. In plasma arc-assisted FSW, the steel could not extrude effectively from the base steel sheet due to deficient heat and flow velocity input; therefore, defect-prone coarse steel fragments were blended with the Al matrix. In induction-assisted FSW, the uninterrupted steel layer was extruded from the steel side and placed on the Al side, which was caused by enhanced heat build-up and flow velocity. Moreover, induction-assisted FSW achieved symmetric material flow on both advancing and retreating sides, resulting in defect-free welds.

Transient cold-front-water through y-shaped aluminium ducts: nature of turbulence, non-equilibrium thermodynamics, and velocity at the converged and diverged outlets

Abstract The interaction between water motion efficiency, outlet control mechanisms, and energy dynamics management hinges significantly on turbulence characteristics. However, understanding the influence of input velocities and duct features on outlets remains elusive. This study employs the realizable k − ɛ viscous model and Reynolds-averaged Navier–Stokes equations (RANS equations) to explore transient water dynamics encountering a cold front through ducts leading to convergence or divergence. Using Ansys Fluent 2023R2 and the waterlight workflow, meticulous meshing of the ducts is executed to capture flow intricacies accurately. Grid independence, suitable boundary conditions, and solver settings are carefully considered to ensure reliable results for investigating four key research questions. Duct bending introduces non-uniformities in velocity distribution, impacting exit velocity and altering flow characteristics and turbulence. In Case III, centrifugal forces from a 90° bend result in higher outlet velocities at the convergent exit and secondary flow patterns like swirls and vortexes. Additionally, entrance velocities influence Reynolds numbers, affecting mixing, heat transfer coefficients, and flow regimes, thereby optimizing thermal conductivity. This comprehensive investigation sheds light on optimizing water dynamics and energy management across various duct configurations, offering valuable insights into efficient flow control and thermal performance enhancement.

ANALISIS ALIRAN UDARA PADA PIPA KOMPOR BURNER

A used oil burner stove is a type of stove that uses used oil as fuel to produce fire and heat which is used in the cooking process. This concept is more commonly found in areas with limited access to conventional energy sources such as electricity or gas. Apart from that, there is a blower which has an important function to maintain sufficient and high-speed air flow towards the combustion axis, thereby helping to increase oil burning efficiency. This test was carried out using Solidworks software with the CFD or Computational Fluid Dynamic feature which has the function of analyzing fluid flow on an object by entering the required parameters. In this paper, a burner stove pipe with an initial cross-sectional area of 2" inches and a final cross-sectional area of 1/2" inches is used where an initial velocity input is given with independent variables of 2 m/s, 4 m/s and 6 m/s and the output is obtained or The results on the stove burner pipe were 17.9 m/s, 37.3 m/s and 54.6 m/s.

Time-Optimal Trajectory Planning and Tracking for Autonomous Vehicles.

This article presents a hierarchical control framework for autonomous vehicle trajectory planning and tracking, addressing the challenge of accurately following high-speed, at-limit maneuvers. The proposed time-optimal trajectory planning and tracking (TOTPT) framework utilizes a hierarchical control structure, with an offline trajectory optimization (TRO) module and an online nonlinear model predictive control (NMPC) module. The TRO layer generates minimum-lap-time trajectories using a direct collocation method, which optimizes the vehicle's path, velocity, and control inputs to achieve the fastest possible lap time, while respecting the vehicle dynamics and track constraints. The NMPC layer is responsible for precisely tracking the reference trajectories generated by the TRO in real time. The NMPC also incorporates a preview algorithm that utilizes the predicted future travel distance to estimate the optimal reference speed and curvature for the next time step, thereby improving the overall tracking performance. Simulation results on the Catalunya circuit demonstrated the framework's capability to accurately follow the time-optimal raceline at an average speed of 116 km/h, with a maximum lateral error of 0.32 m. The NMPC module uses an acados solver with a real-time iteration (RTI) scheme, to achieve a millisecond-level computation time, making it possible to implement it in real time in autonomous vehicles.

Uncertainty quantification of the lattice Boltzmann method focussing on studies of human-scale vascular blood flow

Uncertainty quantification is becoming a key tool to ensure that numerical models can be sufficiently trusted to be used in domains such as medical device design. Demonstration of how input parameters impact the quantities of interest generated by any numerical model is essential to understanding the limits of its reliability. With the lattice Boltzmann method now a widely used approach for computational fluid dynamics, building greater understanding of its numerical uncertainty characteristics will support its further use in science and industry. In this study we apply an in-depth uncertainty quantification study of the lattice Boltzmann method in a canonical bifurcating geometry that is representative of the vascular junctions present in arterial and venous domains. These campaigns examine how quantities of interest—pressure and velocity along the central axes of the bifurcation—are influenced by the algorithmic parameters of the lattice Boltzmann method and the parameters controlling the values imposed at inlet velocity and outlet pressure boundary conditions. We also conduct a similar campaign on a set of personalised vessels to further illustrate the application of these techniques. Our work provides insights into how input parameters and boundary conditions impact the velocity and pressure distributions calculated in a simulation and can guide the choices of such values when applied to vascular studies of patient specific geometries. We observe that, from an algorithmic perspective, the number of time steps and the size of the grid spacing are the most influential parameters. When considering the influence of boundary conditions, we note that the magnitude of the inlet velocity and the mean pressure applied within sinusoidal pressure outlets have the greatest impact on output quantities of interest. We also observe that, when comparing the magnitude of variation imposed in the input parameters with that observed in the output quantities, this variability is particularly magnified when the input velocity is altered. This study also demonstrates how open-source toolkits for validation, verification and uncertainty quantification can be applied to numerical models deployed on high-performance computers without the need for modifying the simulation code itself. Such an ability is key to the more widespread adoption of the analysis of uncertainty in numerical models by significantly reducing the complexity of their execution and analysis.