Simulating High-Throughput Cryptocurrency Payment Channel Networks.
Abstract not provided.
Cordi, Christopher Neal↗
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Results for “Network simulation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Engagement: Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations
We present our SciDAC FASTMath-HEP partnership results for tuning generative adversarial models (GANs) for high energy physics applications. The GANs are used in hybrid simulations to accelerate otherwise time-consuming computations. We optimize for both, prediction accuracy and variability with the goal to find GAN architectures that are reliable and robust.
high energy physics↗
Simulating matrix thermal contraction and stochastic fracture networks in enhanced geothermal systems using PFLOTRAN
Explore the source record for details and available documents.
Gatz-Miller, Hannah Sarah [Sandia National Laborat↗
Molecular transport enhancement in pure metallic carbon nanotube porins
Nanofluidic channels impose extreme confinement on water and ions, giving rise to unusual transport phenomena strongly dependent on the interactions at the channel–wall interface. Yet how the electronic properties of the nanofluidic channels influence transport efficiency remains largely unexplored. Here we measure transport through the inner pores of sub-1 nm metallic and semiconducting carbon nanotube porins. We find that water and proton transport are enhanced in metallic nanotubes over semiconducting nanotubes, whereas ion transport is largely insensitive to the nanotube bandgap value. Molecular simulations using polarizable force fields highlight the contributions of the anisotropic polarizability tensor of the carbon nanotubes to the ion–nanotube interactions and the water friction coefficient. We also describe the origin of the proton transport enhancement in metallic nanotubes using deep neural network molecular dynamics simulations. Finally, these results emphasize the complex role of the electronic properties of nanofluidic channels in modulating transport under extreme nanoscale confinement.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Quartz Dissolution Effects on Flow Channelization and Transport Behavior in Three‐Dimensional Fracture Networks
We perform a set of reactive transport simulations in three-dimensional fracture networks to characterize the impact of geochemical reactions on flow channelization. Flow channelization, a frequently observed phenomenon in porous and fractured subsurface rock formations, results from the spatially variable hydraulic resistance offered by a geological structure. In addition to geo-structural features such as network connectivity, geometry, and hydraulic resistance, geochemical reactions, for example, dissolution and precipitation, can dynamically inhibit or enhance flow channelization. These geochemical processes can change the fracture permeability leading to increased flow channelization, which are localized connected regions of high volumetric flow rates that are seemingly ubiquitous in the subsurface. In our simulations, fractures partially filled with quartz are gradually dissolved until quasi-steady state conditions are obtained. We compare the flow field's initial unreacted and final dissolved states in terms of flow and transport observations. We observe that the dissolved fracture networks provide less resistance to flow and exhibit increased flow channelization when compared to their unreacted counterparts. However, there is substantial variability in the magnitude of these changes which implies that the channelization strongly depends on the network structure. In turn, we identify the interplay between the particular network structure and the impact of geochemical dissolution on flow channelization. The presented results indicate that geological systems that have been weathering or reactive for longer times in older landscapes are likely to have increased flow channelization compared to their equivalent but younger counterparts, which implies a time dependence on flow channelization in fractured media.
Hyman, Jeffrey D.↗
CSK hopping pattern model for visible light communication networks
This paper proposes a new channel access method for visible light communications. The transmitter uses the CSK hopping pattern mode to load data to the R, G, and B light-emitting diodes of LEDs and establishes the network access point (AP) of the LEDs for the illumination coverage area. The coordinator controls the work of each AP to ensure that it simultaneously uses different color patterns to distinguish its respective networks. When the receiver (mobile terminal) detects a particular color pattern in the illumination coverage area, it establishes a communication access network process with the AP that is transmitting the particular color pattern. The advantage of this method is that interference between adjacent APs can be avoided. In addition, due to the simplification of the access network, the proposed CSK hopping model can shorten delays in the terminal’s access network. Through modeling and simulation, it is found that this method can effectively support network power and reduce network delay. Moreover, the fairness and stability of terminals that access the network are improved to a certain extent.
42 ENGINEERING↗
Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project
Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.
04 OIL SHALES AND TAR SANDS↗
Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data
Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Planck 2013 results. XXV. Searches for cosmic strings and other topological defects
Planck data have been used to provide stringent new constraints on cosmic strings and other defects. We describe forecasts of the CMB power spectrum induced by cosmic strings, calculating these from network models and simulations using line-of-sight Boltzmann solvers. We have studied Nambu-Goto cosmic strings, as well as field theory strings for which radiative effects are important, thus spanning the range of theoretical uncertainty in the underlying strings models. We have added the angular power spectrum from strings to that for a simple adiabatic model, with the extra fraction defined as f 10 at multipole = 10. This parameter has been added to the standard six parameter fit using COSMOMC with flat priors. For the Nambu-Goto string model, we have obtained a constraint on the string tension of G μ/c 2 < 1.5 × 10 -7 and f 10 < 0.015 at 95% confidence that can be improved to G μ/c 2 < 1.3 × 10-7 and f 10 < 0.010 on inclusion of high-ℓ CMB data. For the Abelian-Higgs field theory model we find, G μ AH /c 2 < 3.2 × 10 -7 and f 10 < 0.028. The marginalised likelihoods for f 10 and in the f 10 -Ω b h 2 plane are also presented. We have additionally obtained comparable constraints on f 10 for models with semilocal strings and global textures. In terms of the effective defect energy scale these are somewhat weaker at G μ/c 2 < 1.1 × 10 -6 . We have made complementarity searches for the specific non-Gaussian signatures of cosmic strings, calibrating with all-sky Planck resolution CMB maps generated from networks of post-recombination strings. We have validated our non-Gaussian searches using these simulated maps in a Planck -realistic context, estimating sensitivities of up to Δ G μ/c 2 ≈ 4 × 10 -7 . We have obtained upper limits on the string tension at 95% confidence of G μ/c 2 < 9.0 × 10 -7 with modal bispectrum estimation and G μ/c 2 < 7.8 × 10 -7 for real space searches with Minkowski functionals. These are conservative upper bounds because only post-recombination string contributions have been included in the non-Gaussian analysis.
79 ASTRONOMY AND ASTROPHYSICS↗
Highly resolved Eulerian simulations of fuel spray transients in single and multi-hole injectors: Nozzle flow and near-exit dynamics
In high pressure fuel injectors, needle opening and closing transients cause complex off-design fluid dynamics behaviors that profoundly impact the spray and mixture formation processes. These dynamics are completely different from what is known to occur in steady state conditions. In this study, diesel spray transients have been investigated in single-hole and 3-hole nozzles, encompassing internal and external nozzle flow and including needle motion, performing highly resolved (2.5 μm) computational fluid dynamics (CFD) simulations. We focused on end-of-injection (EOI) and start-of-injection (SOI) processes, in order to provide insights in to the physics. The liquid fuel, vapor and gas species are modeled with a single-fluid multiphase mixture approach, with diffuse interface, and with large eddy simulations (LES) of the turbulence. Occurrence of phase change due to cavitation is accounted for, and the spray dispersion is described with a turbulent dispersion model. Detailed needle motion data and orifice internal surface are available from xray synchrotron source measurements carried out at Argonne National Laboratory, and shared through the Engine Combustion Network (ECN) community. Simulations are compared against x-ray phase contrast imaging and radiography of the internal and near-exit flow, in addition to optical microscopy data of the near-exit sprays. Simulation results are found to agree well with available experimental data, and are able to realistically capture local and global features. The simulations allow to gain insight into the physics of gas ingestion and dribbles at EOI, for different hole diameters, operating conditions and number of holes. At SOI, timing of liquid appearance out of the injector and spray tip penetration are adequately predicted, by using the EOI flow field as in-nozzle initialization, and by prescribing the measured tip needle displacement with an informed effective valve opening point inferred from the x-ray observations. Lastly, the variation of spreading angle over time is also discussed in detail for the multi-hole case, including hole-to-hole variations. Due to real geometry features and asymmetric needle motion with eccentric components, it is found that the three holes exhibit swirling flows of increasing intensity as the lift decreases, causing the near cone angle to open and spread, in a quasi-hollow cone structure. These features are not observed in axial single-hole injectors because of their relative simplicity and intrinsic symmetry.
42 ENGINEERING↗
Development of Transient Coupled Chemical-Thermal-Fluids Simulation for Unsealed Aluminum-Clad Spent Nuclear Fuel Storage Canister
This study examines a preliminary three-dimensional (3D) multi-physics computational fluid dynamics (CFD) model of the unsealed canisters containing aluminum-clad spent nuclear fuel from INL’s ATR in the irradiated fuel storage facility. This 3D CFD model considers fully resolved fuel plates in an unsealed canister with small mass exchange with surrounding air as time evolves in the simulation and contains a network of chemical reactions to consider due to several of the side reactions occurring in the presence of water vapor, nitrogen, oxygen and carbon dioxide rather than in helium backfilled DOE standard canisters. Prior to the CFD modeling, the full set of chemical reactions under these conditions was analyzed, reduced, and refit to a more manageable number of equations for the computational efficiency of the 3D CFD simulation. To deal with the long time-scale of interest for tracking the possible buildup of trace species such as nitric acid, NOx, and hydrogen, a segregated time stepping algorithm is used, wherein the velocity field is only updated periodically, and assume to be in a pseudo steady state. This study first examines the theory of the model, workflow to establish radiolysis reaction network, and initial simulations of the evolutions of thermal fields, hydrogen gas and nitric acid concentrations within unsealed canisters in conditions relevant to the INL INTEC CPP-603 facility over a long period of time.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Development of Transient Coupled Chemical-Thermal-Fluids Simulation for Unsealed Aluminum-Clad Spent Nuclear Fuel Storage Canister
This study examines a preliminary three-dimensional (3D) multi-physics computational fluid dynamics (CFD) model of the unsealed canisters containing aluminum-clad spent nuclear fuel from INL’s ATR in the irradiated fuel storage facility. This 3D CFD model considers fully resolved fuel plates in an unsealed canister with small mass exchange with surrounding air as time evolves in the simulation and contains a network of chemical reactions to consider due to several of the side reactions occurring in the presence of water vapor, nitrogen, oxygen and carbon dioxide rather than in helium backfilled DOE standard canisters. Prior to the CFD modeling, the full set of chemical reactions under these conditions was analyzed, reduced, and refit to a more manageable number of equations for the computational efficiency of the 3D CFD simulation. To deal with the long time-scale of interest for tracking the possible buildup of trace species such as nitric acid, NOx, and hydrogen, a segregated time stepping algorithm is used, wherein the velocity field is only updated periodically, and assume to be in a pseudo steady state. This study first examines the theory of the model, workflow to establish radiolysis reaction network, and initial simulations of the evolutions of thermal fields, hydrogen gas and nitric acid concentrations within unsealed canisters in conditions relevant to the INL INTEC CPP-603 facility over a long period of time.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Development of Transient Coupled Chemical-Thermal-Fluids Simulation for Unsealed Aluminum-Clad Spent Nuclear Fuel Storage Canister - 19273
This study examines a preliminary three-dimensional (3D) multi-physics computational fluid dynamics (CFD) model of the unsealed canisters containing aluminum-clad spent nuclear fuel from INL's ATR in the irradiated fuel storage facility. This 3D CFD model considers fully resolved fuel plates in an unsealed canister with small mass exchange with surrounding air as time evolves in the simulation and contains a network of chemical reactions to consider due to several of the side reactions occurring in the presence of water vapor, nitrogen, oxygen and carbon dioxide rather than in helium backfilled DOE standard canisters. Prior to the CFD modeling, the full set of chemical reactions under these conditions was analyzed, reduced, and refit to a more manageable number of equations for the computational efficiency of the 3D CFD simulation. To deal with the long time-scale of interest for tracking the possible buildup of trace species such as nitric acid, NOx, and hydrogen, a segregated time stepping algorithm is used, wherein the velocity field is only updated periodically, and assumed to be in a pseudo steady state. This study first examines the theory of the model, workflow to establish radiolysis reaction network, and initial simulations of the evolutions of thermal fields, hydrogen gas and nitric acid concentrations within unsealed canisters in conditions relevant to the INL INTEC CPP-603 facility over a long period of time. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Fast, High-fidelity Lyα Forests with Convolutional Neural Networks
Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources. Here, we train a convolutional neural network to use a cheaper N-body-only simulation to reconstruct the baryon hydrodynamic variables (density, temperature, and velocity) on scales relevant to the Lyα forest, using data from Nyx simulations. We show that our method enables rapid estimation of these fields at a resolution of ~20 kpc, and captures the statistics of the Lyα forest with much greater accuracy than existing approximations. Because our model is fully convolutional, we can train on smaller simulation boxes and deploy on much larger ones, enabling substantial computational savings. Furthermore, as our method produces an approximation for the hydrodynamic fields instead of Lyα flux directly, it is not limited to a particular choice of ionizing background or mean transmitted flux.
79 ASTRONOMY AND ASTROPHYSICS↗
Statistically informed upscaling of damage evolution in brittle materials
The presence and growth of micro-cracks degrade the strength of brittle solids, greatly impacting the overall material response. Hence, the evolution of these micro-cracks must be accounted for in models describing the relationship between stress and strain so that accurate predictions of material failure can be made. The evolution of individual cracks and crack networks can be simulated with high-fidelity microscale models utilizing highly resolved meshes that can be computationally expensive to a point in which it limits the simulation scale. Hence, for many engineering applications that require simulations of large components, continuum-scale models, which cannot explicitly resolve individual cracks and thus lose important physical information, are required. In this work, we bridge these two scales by developing and implementing a continuum-scale effective moduli constitutive model that is informed by crack statistics generated from a high-fidelity model resolved using a finite-discrete element method (FDEM) implementation. Using statistical information describing the evolution of crack lengths and orientations, this model can capture the effects of brittle damage evolution without the need to resolve individual cracks. We have successfully captured the stress-strain behavior of the high-fidelity simulations using the statistics-based constitutive model shown through direct comparison of stress-strain curves. The curves match within error bars present in the strain-softening portions of the stress-strain curve of the high-fidelity results due to the statistical variation of the initial pre-existing crack network. In conclusion, the stress-strain curves are also compared to experimental results for similar loading conditions and show good qualitative agreement.
36 MATERIALS SCIENCE↗
National Hydrogen Scenarios: How Many Stations, Where, and When?
This report examines development scenarios for hydrogen station networks needed to support a national rollout of fuel cell electric vehicles (FCEVs) in the United States. It was developed within the framework of the U.S. public-private collaboration known as H2USA, with approximately 50 partners committed to enabling hydrogen infrastructure for FCEV adoption. In this report, an internally consistent analytic modeling method simulates how station networks might develop across cities and regions, assuming that successful planning and coordination activities result in a rollout of FCEVs and stations similar to California's. These scenarios are not forecasts of future market outcomes. Rather, they provide insight to various public and private stakeholders engaged in the planning and coordination required to help facilitate their own station networks, while reducing hydrogen station investment risks.
08 HYDROGEN↗
Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization
The study proposes a comprehensive Transportation Mobility (TransitMo) framework covering conceptual design, model formulation, optimization, simulation, and impact analysis of the transportation mobility system. TransitMo is composed of a transportation digital twin developed in Simulation of Urban MObility (SUMO) and an Intelligent Traffic Management and Control Center (ITMCC) that identifies the best ways to improve the movement of people within urban areas using various modes of transportation. This study encompasses advanced modeling techniques, algorithms, and strategic testing to optimize energy efficiency and mobility in a multimodal shared mobility network. TransitMo’s practical applications are exemplified through a city-scaled simulation network in Chattanooga, TN, employing demographic data to analyze historical traffic patterns and forecast future demands. Central to this methodology are three models: the User Preference Model (UP), the Energy Consumption Model (EC), and the System Optimization Model (SO). These models work in concert to iteratively devise the optimal travel incentives and minimize the total system cost in a real-time manner. In conclusion, test results verified that the proposed adaptive incentive program and optimized bus scheduling can improve network performance by increasing public transit ridership.
42 ENGINEERING↗
A Graphical Approach to Diagnosing the Validity of the Conditional Independence Assumptions of a Bayesian Network Given Data
Bayesian networks have attained widespread use in data analysis and decision making. Well studied topics include: efficient inference, evidence propagation, parameter learning from data for complete and incomplete data scenarios, expert elicitation for calibrating Bayesian network probabilities, and structure learning. It is not uncommon for the researcher to assume the structure of the Bayesian network or to glean the structure from expert elicitation or domain knowledge. In this scenario, the model may be calibrated through learning the parameters from relevant data. There is a lack of work on model diagnostics for fitted Bayesian networks; this is the contribution of this paper. We key on the definition of (conditional) independence to develop a graphical diagnostic method which indicates if the conditional independence assumptions imposed when one assumes the structure of the Bayesian network are supported by the data. We develop the approach theoretically and describe a Monte Carlo method to generate uncertainty measures for the consistency of the data with conditional independence assumptions under the model structure. We describe how this theoretical information and the data are presented in a graphical diagnostic tool. We demonstrate the approach through data simulated from Bayesian networks under different conditional independence assumptions. We also apply the diagnostic to a real world data set. The results indicate that our approach is a reasonable way of visualizing and inspecting the conditional independence assumption of a Bayesian network given data.
Bayesian Networks · Structural Model Diagnostics ·↗