Simulation-based Predictions Research Articles

Digital Twin Technology in the Gas Industry: A Comparative Simulation Study

Continuous innovation is essential in the urban gas industry to achieve the stability of energy supply and sustainability. The continuous increase in the global demand for energy indicates that the urban gas industry plays a crucial role in terms of stability, the economy, and the environmental friendliness of the energy supply. However, price volatility, supply chain complexity, and strengthened environmental regulations are certain challenges faced by this industry. In this study, we intend to overcome these challenges by elucidating the application of digital twin technology and by improving the performance of the prediction models in the gas industry. The real-time data and simulation-based predictions of pressure fluctuations were integrated in terms of pressure control equipment. We determined the contribution of this data integration to enhancing the operational efficiency, safety, and sustainable development in the gas industry. The summary of the results highlights the superior predictive performance of the autoregressive integrated moving average (ARIMA) model. It exhibited the best performance across all evaluation indices—mean absolute percentage error (MAPE), root mean square error (RMSE), and the coefficient of determination (R2)—when compared with the raw data. Specifically, the ARIMA model demonstrated the lowest RMSE value of 0.01575, the lowest MAPE value of 0.00609, and the highest R2 value of 0.94993 among the models evaluated. This indicates that the ARIMA model outperformed the other models in accurately predicting the outcomes. These findings validate that the integration of digital twin technology and prediction models can innovatively improve the maintenance strategy, operational efficiency, and risk prediction in the gas industry. Predictive maintenance models can help prevent significant industrial risks, such as gas leak accidents. Moreover, the integration of digital twin technology and predictive maintenance models can significantly enhance the safety and sustainability in the gas industry. The proposed innovative method of implementing digital twin technology and improved prediction models lays a theoretical foundation for sustainable development that can be applied to other industries with high energy consumption.

An Empirical Model For Intrinsic Alignments: Insights From Cosmological Simulations

We extend current models of the halo occupation distribution (HOD) to include a flexible, empirical framework for the forward modeling of the intrinsic alignment (IA) of galaxies. A primary goal of this work is to produce mock galaxy catalogs for the purpose of validating existing models and methods for the mitigation of IA in weak lensing measurements. This technique can also be used to produce new, simulation-based predictions for IA and galaxy clustering. Our model is probabilistically formulated, and rests upon the assumption that the orientations of galaxies exhibit a correlation with their host dark matter (sub)halo orientation or with their position within the halo. We examine the necessary components and phenomenology of such a model by considering the alignments between (sub)halos in a cosmological dark matter only simulation. We then validate this model for a realistic galaxy population in a set of simulations in the Illustris-TNG suite. We create an HOD mock with Illustris-like correlations using our method, constraining the associated IA model parameters, with the between our model’s correlations and those of Illustris matching as closely as 1.4 and 1.1 for orientation–position and orientation–orientation correlation functions, respectively. By modeling the misalignment between galaxies and their host halo, we show that the 3-dimensional two-point position and orientation correlation functions of simulated (sub)halos and galaxies can be accurately reproduced from quasi-linear scales down to . We also find evidence for environmental influence on IA within a halo. Our publicly-available software provides a key component enabling efficient determination of Bayesian posteriors on IA model parameters using observational measurements of galaxy-orientation correlation functions in the highly nonlinear regime.

Design Lab: a simulation-based approach for the design of sustainable maritime energy systems

The process of achieving decarbonization in the maritime industry relies on tackling complex issues related to ship design. Designers require tools that can integrate the processes of providing relevant operational profile, configuring a target design, evaluating the design and exploring possibilities. Design Lab framework innovatively addresses this need by creating realistic operational profiles, simulating vessel performance and machinery systems, and providing comprehensive system evaluations. The framework promotes a comprehensive design process that starts by creating an operational profile. This profile is used to simulate the vessel’s propulsion power considering statistical weather conditions. Then, machinery systems are configured and simulations are performed. The performance of these systems is evaluated against key performance indicators such as total cost of ownership, carbon intensity indicator, etc., and the process is iterated with new design candidates. A case study of a hydrogen-fueled RoPax vessel is presented to validate the framework and demonstrate its capabilities. By focusing on simulation-based predictions and performance indicators, it provides a quantitative assessment, thereby supporting the decision-making process for stakeholders.

Accurate Simulation-Based Predictions of Assembly Force and Deformation for Warped Thin-Walled Components Characterized by Geometric and Topological Complexities

Accurate Simulation-Based Predictions of Assembly Force and Deformation for Warped Thin-Walled Components Characterized by Geometric and Topological Complexities

AAp-MSMD: Amino Acid Preference Mapping on Protein-Protein Interaction Surfaces Using Mixed-Solvent Molecular Dynamics.

Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI surfaces are expected to accelerate the design of peptide drugs. Mixed-solvent molecular dynamics (MSMD), which adds probe molecules or fragments of functional groups as solutes to the hydration model, detects the binding hotspots and cryptic sites induced by small molecules. The detection results vary depending on the type of probe molecule; thus, they provide important information for drug design. For rational peptide drug design using MSMD, we proposed MSMD with amino acid residue probes, named amino acid probe-based MSMD (AAp-MSMD), to detect hotspots and identify favorable amino acid types on protein surfaces to which peptide drugs bind. We assessed our method in terms of hotspot detection at the amino acid probe level and binding free energy prediction with amino acid probes at the PPI site for the complex structure that formed the PPI. In hotspot detection, the max-spatial probability distribution map (max-PMAP) obtained from AAp-MSMD detected the PPI site, to which each type of amino acid can bind favorably. In the binding free energy prediction using amino acid probes, ΔGFE obtained from AAp-MSMD roughly estimated the experimental binding affinities from the structure-activity relationship. AAp-MSMD, with amino acid probes, provides estimated binding sites and favorable amino acid types at the PPI site of a target protein.

Stability and Performance of an Undersea Kite Operating in a Turbulent Flow Field

In this article, we examine the effects of flow disturbances resulting from turbulence on the dynamic behavior of an underwater energy-harvesting kite system that executes periodic figure-8 flight. Due to the periodic nature of the kite’s operation, we begin by assessing orbital stability using the Floquet analysis and stroboscopic intersection analysis of a Poincaré section, with the former analysis performed on a simplified “unifoil” model and the latter performed on a six-degree-of-freedom (6-DOF)/flexible tether model. With periodic stability established, a frequency-domain analysis based on a linearization about the kite’s path is used to predict the quality of flight path tracking as a function of the turbulence frequency. To validate the accuracy of these simulation-based predictions under flow disturbances, we compare the predictions of the kite’s behavior against the results of small-scale tow testing experiments performed in a controlled pool environment.

Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: Application to planar soft tissues

Data-based approaches are promising alternatives to the traditional analytical constitutive models for solid mechanics. Herein, we propose a Gaussian process (GP) based constitutive modeling framework, focusing on planar, hyperelastic and incompressible soft tissues. Specifically, the strain energy density of soft tissues is modeled as a GP, which can be regressed to experimental stress–strain data obtained from biaxial stretching experiments. Moreover, the GP model can be weakly constrained to be convex. A key advantage of a GP-based model is that, in addition to the mean value, it provides a probability density (i.e. associated uncertainty) for the strain energy density. To simulate the effect of this uncertainty, a non-intrusive stochastic finite element analysis (SFEA) framework is proposed. The proposed framework is verified against an artificial dataset based on the Gasser–Ogden–Holzapfel model and applied to a real experimental dataset of a porcine aortic valve leaflet tissue. The results show that the proposed framework can be trained with limited experimental data and fits the data better than several existing models. The SFEA framework provides a straightforward way of using the experimental data and quantifying the resulting uncertainty in simulation-based predictions.

Optimization of trial duration to predict long-term HbA1c change with therapy: A pharmacometrics simulation-based evaluation.

Glycated hemoglobin (HbA1c) is the main biomarker of diabetes drug development. However, because of its delayed turnover, trial duration is rarely shorter than 12 weeks, and being able to predict long-term HbA1c with precision using data from shorter studies would be beneficial. The feasibility of reducing study duration was therefore investigated in this study, assuming a model-based analysis. The aim was to investigate the predictive performance of 24- and 52-week extrapolations using data from up to 4, 6, 8 or 12 weeks, with six previously published pharmacometric models of HbA1c. Predictive performance was assessed through simulation-based dose-response predictions and model averaging (MA) with two hypothetical drugs. Results were consistent across the methods of assessment, with MA supporting the results derived from the model-based framework. The models using mean plasma glucose (MPG) or nonlinear fasting plasma glucose (FPG) effect, driving the HbA1c formation, showed good predictive performance despite a reduced study duration. The models, using the linear effect of FPG to drive the HbA1c formation, were sensitive to the limited amount of data in the shorter studies. The MA with bootstrap demonstrated strongly that a 4-week study duration is insufficient for precise predictions of all models. Our findings suggest that if data are analyzed with a pharmacometric model with MPG or FPG with a nonlinear effect to drive HbA1c formation, a study duration of 8 weeks is sufficient with maintained accuracy and precision of dose-response predictions.

Electric-Field-Induced Spatially Dynamic Heterogeneity of Solvent Motion and Cation Transference in Electrolytes

While electric fields primarily result in migration of charged species in electrolytic solutions, the solutions are dynamically heterogeneous. Solvent molecules within the solvation shells of the cation will be dragged by the field while free solvent molecules will not. We combine electrophoretic NMR measurements of ion and solvent velocities under applied electric fields with molecular dynamics simulations to interrogate different solvation motifs in a model liquid electrolyte. Measured values of the cation transference number (t_{+}^{0}) agree quantitatively with simulation-based predictions over a range of electrolyte concentrations. Solvent-cation interactions strongly influence the concentration-dependent behavior of t_{+}^{0}. We identify a critical concentration at which most of the solvent molecules lie within solvation shells of the cations. The dynamic heterogeneity of solvent molecules is minimized at this concentration where t_{+}^{0} is approximately equal to zero.

Optimal control of a NASCAR – specification race car

This paper adapts the minimum-lap-time simulation technologies developed in Formula One to NASCAR. The adaptation process requires recognition of the variable lateral curvature of some tracks, as well as the asymmetries of NASCAR-specification race cars that have been configured for high-speed oval tracks that are dominated by left-hand cornering. These asymmetries include different suspension characteristics on the left- and right-hand ends of each axle, different left- and right-hand tyres on each axle, as well as asymmetries in the car's aerodynamic performance. The results compare optimal control calculations, industrial simulation-based predictions and race-captured telemetry data. For the most part, these various data sources are in good agreement, with the importance of variable lateral camber highlighted. It is demonstrated how race car drivers have to find the best compromise between the conflicting objectives of exploiting the additional camber available on the outside of variable-camber tracks and increasing the travelled distance and racing line cornering curvature.

Sensitivity Analysis Based NVH Optimization in Permanent Magnet Synchronous Machines Using Lumped Unit Force Response

Acoustic noise and vibration sources in electric machines are mainly of electromagnetic and structural origin. A lumped structural unit response-based sensitivity analysis procedure is proposed in this article, which isolates electromagnetic and structural impacts brought by variation of different design parameters in a fractional slot concentrated winding, surface mounted permanent magnet synchronous machine (SPMSM). The impact of ten design parameters on average torque, torque ripple, synchronous inductance, dominant spatial-temporal order airgap force, cogging torque, friction torque, dominant mode frequency and structural unit response is studied in detail for a 12 slot/10 pole (12s10p) SPMSM. Analysis reveals that on a 12s10p SPMSM, slot opening has a very high impact on dominant airgap force component. A multilevel nonlinear regression model-based fast optimization strategy is introduced considering electromagnetic and structural design objectives and constraints following the sensitivity analysis. The optimized design is prototyped to validate the proposed design approach. Impact hammer-based modal analysis, no load, load and run-up tests are performed on the prototype for experimental validation. Ranging from structural tests to electromagnetic tests, the experimental results closely follow and validate the simulation-based predictions.

Differentiable Predictions for Large Scale Structure with SHAMNet

In simulation-based models of the galaxy-halo connection, theoretical predictions for galaxy clustering and lensing are typically made based on Monte Carlo realizations of a mock universe. In this paper, we use Subhalo Abundance Matching (SHAM) as a toy model to introduce an alternative to stochastic predictions based on mock population, demonstrating how to make simulation-based predictions for clustering and lensing that are both exact and differentiable with respect to the parameters of the model. Conventional implementations of SHAM are based on iterative algorithms such as Richardson-Lucy deconvolution; here we use the JAX library for automatic differentiation to train SHAMNet, a neural network that accurately approximates the stellar-to-halo mass relation (SMHM) defined by abundance matching. In our approach to making differentiable predictions for large scale structure, we map parameterized PDFs onto each simulated halo, and calculate gradients of summary statistics of the galaxy distribution by using autodiff to propagate the gradients of the SMHM through the statistical estimators used to measure one- and two-point functions. Our techniques are quite general, and we conclude with an overview of how they can be applied in tandem with more complex, higher-dimensional models, creating the capability to make differentiable predictions for the multi-wavelength universe of galaxies.

Using a Modeling Approach to Inform Progress Towards Stoat Eradication From the Orkney Islands

Invasive non-native species eradication attempts are typically large and expensive projects that benefit from the support of quantitative tools, such as population models, to be completed within the scheduled and funded time. Managed ecosystems are constantly changing due to population and ecosystem dynamics. Accordingly, any model predictions need to be updated, using different sources of data, to inform the project about the progress toward eradication. The stoat Mustela erminea was introduced to the hitherto predatory land mammal free Orkney archipelago around 2010. In 2016, a project aiming to eradicate stoats to preserve ecologically and economically important native wildlife was designed and implemented. It entailed a “knockdown” phase followed by a “mopping-up” phase to remove stoats that escaped capture. We used data from this project to iteratively predict the progress toward eradication. We applied spatially explicit individual-based models to estimate the proportion of stoats being exposed to capture, and then compared these simulation-based predictions with removal data, allowing us to estimate changes in the population size through time. We also used sighting data from members of the public to refine eradication probability. We were also able to demonstrate how the initially wide uncertainty gradually diminished as more evidence accumulated. The information derived from different types of data and quantitative models allowed us to track the effectiveness of current trapping approaches and to help to inform project managers about when the project achieved the knockdown phase milestone. Our analyses confirmed that the expected magnitude of the initial knockdown phase has been achieved in some areas, but also revealed spatial and temporal heterogeneity in the distribution of captures, most likely caused by the sequential trapping and stoat movement and trap shy stoats exposed to capture but not caught. This heterogeneity calls for additional data sources (e.g., from camera traps or detection dogs) to estimate the proportion of trap-shy individuals and the size of the untrapped population, and ultimately the feasibility of eradication.

Effect of Uncertainty on Target Registration Error in Image-guided Renal Interventions: From Simulation to in-vitro Assessment.

It is imperative that image-guided intervention (IGI) systems provide accurate and precise navigation information to enable the user to trust the system and not place unwarranted confidence in the guidance capabilities of the system. Unfortunately, the actual error associated with the overall targeting capabilities of an IGI system is not readily known. Here we are primarily interested in the application of image-guided surgery in the context of renal interventions. We built a simulation pipeline to study the uncertainty propagation through an optically tracked IGI system to gain insight into the overall accuracy of the system. Our simulation pipeline models several stages, including stylus calibration, tool tracking, patient tracking, and image to patient registration. In the effort to realistically estimate tracking noise and user-associated fiducial localization error (FLE), we conducted several experiments using the optical tracking system. Our simulation suggested that a wider cone angle results in a more accurate tool calibration, which improves further with the collection of additional samples. Furthermore, our simulations also suggested that the image-to-patient registration was the most significant contributor to navigation uncertainty, followed by the fiducial localization error. Lastly, we also observed a 0.72 correlation between the Target Registration Error (TRE) estimated at target fiducials and the distance between the the centroids of the registration and target fiducial landmarks. To validate the simulation predictions, we also conducted several in vitro experiments using a 3D printed patient specific kidney phantom and compared the simulation-based registration predictions with those observed experimentally in vitro. The experiments confirmed the registration metrics (Fiducial Registration Error and TRE) predicted by the simulations, given several specific combinations of fiducial landmarks used to perform the image to patient registration.

Impact of the vertical resolution of corner-point grids on CO2 plume migration predictions for layered aquifers

Design of subsurface CO2 storage sites largely relies on numeric simulation-based predictions of plume extent and progressive immobilization. In most cases, sensitivity analyses are performed with corner-point grid representations of the geo-model and first-order IFD methods using two-point flux approximation (TPFA). Here, we have conducted a comprehensive analysis of the impact of the vertical resolution of such grids on the predicted plume extent and capacity in a simplified layered aquifer system. Four different CO2 mobility and buoyancy scenarios were analyzed. To minimize grid-orientation effects, the analysis was performed for predominantly grid-axes parallel flow through regularly gridded cross-sectional models with uniform cell size and variable cell width-over-height ratios. The analysis of the role of vertical grid resolution indicates a first-order correlation between plume extent and this parameter. The results also reveal that capillary forces reduce plume extent and enhance aquifer storage for low permeability contrasts between layers. Furthermore, model sensitivity to grid resolution scales with the magnitude of the permeability contrast between layers. Inspection of these results reveals that an underestimation of CO2 mobility at the top of the plume is the root cause of the observed plume retardation. A comparison with two alternative simulators that discretize mobility as piecewise linear within cells as opposed to piecewise constant and are less resolution sensitive confirms this interpretation.

Indicators for assessing the robustness of metapopulations against habitat loss

Habitat loss and fragmentation resulting from environmental changes are main drivers of global biodiversity loss, as the survival of metapopulations relies on the ability of individuals to disperse among suitable habitat patches. To prioritize conservation efforts, methods are needed for evaluating the robustness of metapopulations against habitat loss. We therefore investigate this robustness for different degrees of habitat loss, for different types of habitat loss (random, peripheral, and contagious) and of habitat networks, and for species differing in their local-extinction risks and dispersal ranges. In particular, we analyse several standard network types (with random, regular, small-world, or scale-free structure) and compare them with several alternative network types derived from real-world two-dimensional habitat landscapes (with random, clustered, or contiguous habitat allocation). Furthermore, we investigate how well 29 different graph-theoretic metrics of habitat networks can serve as indicators of metapopulation robustness against habitat loss – as this approach, where feasible, allows replacing complex simulation-based predictions with simple indicator-based predictions. We find that responses of species to habitat loss on the considered landscape-based habitat networks qualitatively differ from those on the considered standard habitat networks. This suggests that results obtained for the latter, albeit widely examined in the literature, can be unrepresentative and misleading. As expected, species with high risks of local extinction and short dispersal ranges are particularly vulnerable to habitat loss, across all considered types of habitat loss and habitat networks. The graph-theoretic network metric that best explains the robustness of metapopulations against habitat loss depends on the considered types of species, habitat networks, and habitat loss. None of the examined metrics give consistently reliable predictions under all circumstances. For sensitive species, characterized by high local-extinction risks and short dispersal ranges, a network’s average clique size, redundancy, average degree, connectance, clustering coefficient, and average closeness centrality are the best indicators of metapopulation robustness. For landscape-based habitat networks, a network’s average clique size, beta coefficient, clustering coefficient, redundancy, and cyclomatic number work best. For contagious habitat loss, the network type has a particularly strong impact on the species-specific robustness against habitat loss. In summary, our study introduces a method for evaluating the robustness of metapopulations against habitat loss and shows that a network’s clustering coefficient, under a wide range of circumstances, is a particularly reliable indicator of this robustness.

Proactive planning of bandwidth resource using simulation-based what-if predictions for Web services in the cloud

Resource planning is becoming an increasingly important and timely problem for cloud users. As more Web services are moved to the cloud, minimizing network usage is often a key driver of cost control. Most existing approaches focus on resources such as CPU, memory, and disk I/O. In particular, CPU receives the most attention from researchers, but the bandwidth is somehow neglected. It is challenging to predict the network throughput of modern Web services, due to the factors of diverse and complex response, evolving Web services, and complex network transportation. In this paper, we propose a methodology of what-if analysis, named Log2Sim, to plan the bandwidth resource of Web services. Log2Sim uses a lightweight workload model to describe user behavior, an automated mining approach to obtain characteristics of workloads and responses from massive Web logs, and traffic-aware simulations to predict the impact on the bandwidth consumption and the response time in changing contexts. We use a real-life Web system and a classic benchmark to evaluate Log2Sim in multiple scenarios. The evaluation result shows that Log2Sim has good performance in the prediction of bandwidth consumption. The average relative error is 2% for the benchmark and 8% for the real-life system. As for the response time, Log2Sim cannot produce accurate predictions for every single service request, but the simulation results always show similar trends on average response time with the increase of workloads in different changing contexts. It can provide sufficient information for the system administrator in proactive bandwidth planning.

Accuracy of Molecular Simulation-Based Predictions of koff Values: A Metadynamics Study.

The koff values of ligands unbinding to proteins are key parameters for drug discovery. Their predictions based on molecular simulation may under- or overestimate experiment in a system- and/or technique-dependent way. Here we use an established method-infrequent metadynamics, based on the AMBER force field-to compute the koff of the ligand iperoxo (in clinical use) targeting the muscarinic receptor M2. The ligand charges are calculated by either (i) the Amber standard procedure or (ii) B3LYP-DFT. The calculations using (i) turn out not to provide a reasonable estimation of the transition-state free energy. Those using (ii) differ from experiment by 2 orders of magnitude. On the basis of B3LYP DFT QM/MM simulations, we suggest that the observed discrepancy in (ii) arises, at least in part, from the lack of electronic polarization and/or charge transfer in biomolecular force fields. These issues might be present in other systems, such as DNA-protein complexes.

Phase Modulated Radio-Over-Fiber for Efficient 5G Fronthaul Uplink

Analog radio-over-fiber technology is gaining interest as a potential candidate for radio signal transport over the future fronthaul section of the 5th generation (and beyond) radio access network. In this paper, we propose a radio-over-fiber fronthaul with intensity modulation in the downlink and phase modulation with interferometric detection in the uplink, for simplified and power efficient remote units. We conduct an experimental investigation and verification of theoretical and simulation models of the performance of the phase-modulated uplink and demonstrate the ability of such an architecture to transport single-channel and multi-channel 5G-type radio waveforms. Experimentally verified data rates of 4.3 Gbps and simulation-based predictions, using a well matched-to-measurements model of the uplink, of 12.4 Gbps are presented, with error-vector magnitude performance well within relevant standard specifications for 64-QAM.

PH-Dependent Protonation of the Phl p 6 Pollen Allergen Studied by NMR and cpH-aMD.

We use state-of-the-art NMR experiments to measure apparent pKa values in the native protein environment and employ a cutting-edge combination of enhanced sampling and constant pH molecular dynamics (MD) simulations to rationalize strong pKa shifts. The major timothy grass pollen allergen Phl p 6 serves as an ideal model system for both methods due to its high number of titratable residues despite its comparably small size. We present a proton transition analysis as intuitive tool to depict the captured protonation state ensemble in atomistic detail. Combining microscopic structural details from MD simulations and macroscopic ensemble averages from NMR shifts leads to a comprehensive view on pH dependencies of protonation states and tautomers. Overall, we find striking agreement between simulation-based pKa predictions and experiment. However, our analyses suggest subtle differences in the underlying molecular origin of the observed pKa shifts. From accelerated constant pH MD simulations, we identify immediate proximity of opposite charges, followed by vicinity of equal charges as major driving forces for pKa shifts. NMR experiments on the other hand, suggest only a weak relation of pKa shifts and close contacts to charged residues, while the strongest influence derives from the dipolar character of α helices. The presented study hence pinpoints opportunities for improvements concerning the theoretical description of protonation state and tautomer probabilities. However, the coherence in the resulting apparent pKa values from simulations and experiment affirms cpH-aMD as a reliable tool to study allergen dynamics at varying pH levels.