Modeling Of Systems Research Articles

RESEARCH METHODS AND GEOINFORMATION MODELS OF CHANGES IN LAND RESOURCES OF ALMATY REGION UNDER THE INFLUENCE OF HOUSEHOLD WASTE

This article deals with the solution of scientific problems, including the development of holistic conceptual models for presenting, processing and analyzing geographic information, and comprehensively assesses the extent of the impact of regional systems on many dangerous natural and artificial processes. This makes it possible to resolve the contradiction between the need to analyze the state of various territorial systems in order to prevent the occurrence of emergencies due to the influence of natural and man-made processes.On the one hand, providing information and support in decision-making, and on the other, ensuring the provision of cartographic models of transport systems risk assessment of dangerous natural and man-made processes and their mapping. Integrated environmental assessment and modeling of industrial systems. A method for mapping payment systems and assessing the health of the population. Since the city of Almaty is a large metropolis, the ecological situation is very poor. This directly affects the ecology of the City, Public Health. We manage, process data by processing GIS data. In solving upcoming environmental problems, in developing new solutions, a special data card will come to the rescue. This will lead to solving problems from this point of view. It is possible to create monitoring maps by combining space surveys and statistical data. One of the most effective aspects of GIS data is that it allows you to optimally consider problems and work in any area. From this point of view, the GIS industry is developing rapidly. In the country, this industry is developing rapidly and is gaining a lot of demand.

Interpretable large-scale belief rule base for complex industrial systems modeling with expert knowledge and limited data

Complex system modeling technology is a hot topic. Nowadays, many complex industrial systems present three characteristics: multiple input indicators, limited data and interpretability requirements. With good interpretability, belief rule base (BRB) serves as an efficient tool for modeling complex systems. However, as the number of input indicators of industrial systems increases, BRB suffers from the combinatorial explosion problem, which makes it hard to generate large-scale BRB and optimize it while maintaining its interpretability. For this purpose, an interpretable large-scale BRB is proposed for complex systems with limited data, where expert knowledge can be utilized effectively. First, a framework for generating an initial large-scale BRB using expert knowledge and limited data is developed, including the determination of attribute weight, basic belief degree and rule weight. Afterwards, a new parameter optimization model is designed to reduce the burden of parameter optimization and maintain the interpretability of BRB, where the Adaptive Moment Estimation (Adam) algorithm is adopted to further improve the efficiency of large-scale parameter optimization. Finally, a health assessment case of an inertial navigation system (INS) verifies the proposed method.

Specific Characteristics of Numerical Simulation of Mechatronic Systems with PWM-Controlled Drives

This paper explores the features of numerical simulation used to analyze the dynamic behaviour of drives controlled by pulse-width modulators. Modern motor control systems commonly employ pulse-width modulation. Effective numerical modelling of such systems presents unique challenges because the models employed are continuous-event and have hybrid behaviour due to the presence of nonlinear links with discontinuities of the first kind. Therefore, it is essential to have special integration methods with variable steps, which should be factored in when developing the model. This paper shows how these problems are solved when modelling an electric drive with a DC motor using the SimInTech software.

Gravity.jl: Fast and accurate gravitational lens modeling in Julia

We present Gravity.jl, a new proprietary software for the modeling of gravitational lens systems. Gravity.jl is written in the Julia programming language, and is designed to be fast, accurate, and flexible. It can be used to model gravitational lens systems composed of multiple lensing planes, and to perform Bayesian inference on the lens model parameters. In this paper we present the theoretical and statistical ideas behind the code, and we describe its main features. In this first paper of the series, we focus on the modeling of point-like and small extended sources, for which we can linearize the lens equation. We show a practical use of Gravity.jl on a galaxy-scale lens, and we compare the results with those obtained with other codes. We also show how Gravity.jl can be used to perform Bayesian inference on cosmological parameters.

Seasonal dynamics of chemistry in an Arctic glacier-fed river

Arctic rivers, intricately linked to fjord systems, wield significant influence over the geochemical and biological dynamics of the upper Arctic Ocean, providing it with freshwater, nutrients, suspended particles, and potentially harmful pollutants. To comprehend the full picture of the Arctic ecosystem, it is crucial to understand how these rivers vary across regions and seasons, especially considering ongoing climate changes. However, comprehensive studies that address long-term observations and seasonal variations in Arctic rivers' geochemical composition remain scarce. In this study, we present comprehensive long-term investigations of the seasonal variations in elemental concentrations in Bayelva, a high Arctic glacier-fed river. By analyzing 224 surface water samples, collected during different seasons between 2011 and 2020, we elucidate the diverse influences of marine, geological and atmospheric factors on the river chemistry. Our findings underscore the importance of marine-influenced snowmelt in the early flow season, which leads to elevated concentrations of marine and trace elements in the runoff water at the onset of melting, with concentrations subsequently decreasing as the snow melting continues. Glacial meltwater dominates the river chemistry during the peak flow season, during which elemental concentrations are at their lowest. Late flow season exhibits high elemental concentrations, primarily driven by weathering processes. Additionally, heavy rain and freezing events play a crucial role in sudden alterations in river chemistry. We highlight the dynamic response of Arctic river systems to such environmental drivers as key players in transporting essential micro- and macro-nutrients, as well as potentially harmful pollutants to adjacent fjord systems. Given their susceptibility to climate change, continuous monitoring is essential for understanding future changes. This study provides data for a better foundation for accurate climate modeling of biogeochemical systems in the Arctic environment.

Dynamic modeling and real-time performance analysis of multi-product batch manufacturing systems with perishable products

Production lines handling perishable goods in industries such as petrochemical and food processing demand meticulous precision in timing. These systems are governed by stringent timeframes, where products must strictly adhere to defined residence times. Exceeding these time constraints leads to product scrapping, posing a significant threat to overall productivity. Modeling such intricate systems presents a formidable challenge, and success hinges upon a deep understanding of the complex interplay between production and batch scrapping dynamics. In response to this challenge, this paper presents a novel model for a multiproduct batch production line with perishable products. Furthermore, the real-time performance metric of Permanent Production Loss (PPL) is extended to this system using a method of PPL back-adjustment in time. To validate the fidelity of the proposed mathematical model, numerical case studies are conducted, comparing the developed model to a discrete event simulation. The results demonstrate that the model accurately represents the system at hand. The expressions for PPL evaluation and attribution are also validated using case studies.

Cumulative Effects Assessment in Community Watersheds at Different Spatial Scales: A Review of Indicators

Cumulative effect assessments (CEA) for water systems are becoming more necessary as pressures from multiple stressors impact communities, watersheds, and basins. CEA is an important tool for shifting from fragmented to holistic, integrated management as per the Integrated Water Resources Management (IWRM) paradigm. Through a review of scientific literature, this study analyzed the patterns and use of indicators for upstream-downstream linkages. This study found that five categories of spatial scale approaches are used: the national/regional scale, basin scale, watershed scale, local scale, and the specially defined multi-scale approach. It was found that CEA has been applied using a high-level, qualitative approach for national, regional, and multi-scale studies – these undertakings inform strategic planning and sustainable decision-making. Basin, watershed, and local scale CEA studies have focused on quantitative modeling of environmental and human systems. These assessments emphasized the interconnectedness of water systems, as well as the role of policy development and stakeholders in improving system outcomes. Four indicator categories were found to span all five spatial scales: water quality (occurred in 64% of the literature), water quantity (53%), land use (56%), and landscape characteristics (67%). These indicator categories provide a common foundation for understanding stressor interactions, impact response, and upstream-downstream linkages. However, further research is needed on temporal scales and the integration of time into CEA for watersheds. This review found that CEA, a tool which is useful for a variety of scales, can help find a balance between present and future needs and further community sustainability using a holistic and integrated approach.

Enhancing Long-Term Water Quality Modeling by Addressing Base Demand, demand Patterns, and Temperature Uncertainty Using Unsupervised Machine Learning Techniques.

Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, these uncertainties can result in inaccurate simulations. This study proposes a novel framework that leverages unsupervised machine learning, specifically a Gaussian Mixture Model (GMMs), to represent and integrate these uncertainties in the simulation. By classifying historical water demand into fuzzy clusters, the framework allows for certain linguistic inputs (e.g., "high" or "low" demand) to be used in water quality simulations. The framework also incorporates representative hourly demand patterns and temperature-dependent chlorine decay constants based on historical data correlations. Validations were conducted on the Anytown network using WNTR-EPANET, comparing simulated chlorine residuals with Validation data from 181 steady-state simulations. The simulation through the framework achieved a Jensen-Shannon Divergence (JSD) of less than 0.008 across all demand clusters, indicating high similarity between predicted and actual probability distributions . In comparison to other simulation scenarios tested, which exhibited increased variability (JSD > 0.18), the proposed framework demonstrated improved accuracy in representing chlorine residual distributions. The methodology is adaptable to other systems, if similar historical datasets containing key variables, such as flow rates and temperature, are provided. While the framework offers a more flexible and accurate approach to handling uncertainties in WDS, its effectiveness is contingent upon the availability of robust historical demand and temperature data for decay constant calibration.

Dynamically coupled modelling of ground source heat pump systems considering groundwater flow and unbalanced seasonal building thermal loads

The ground source heat pump (GSHP) system is a promising technology to exploit shallow geothermal energy via ground heat exchangers, GHEs (e.g., deep boreholes or piles) for space heating or cooling. Although GSHP systems have been widely used in temperate regions owing to their environmentally clean characteristics and excellent energy-saving performance, their use in cooling-dominated areas, such as subtropical or tropical regions (e.g., Hong Kong or Singapore), remains limited. The operation of GSHP systems in these regions may be subjected to unbalanced seasonal thermal loads on buildings and induce heat accumulation in soils surrounding GHEs, leading to potential deterioration in the long-term performance (e.g., coefficient of performance, COP) of GSHP systems. To properly evaluate the long-term COP of GSHP systems in cooling-dominated areas, a dynamically coupled simulation approach is proposed in this study. The proposed method integrates building thermal loads with ground heat transfer under groundwater seepage flow, within a unified framework. The effectiveness of this approach is demonstrated through a case study where, in the absence of groundwater seepage flow, the soil temperature increased by 17.2 °C. In contrast, when groundwater seepage flow was considered, the GSHP system not only maintained a high long-term cooling COP but also achieved up to 30 % electricity savings. These findings demonstrate the feasibility and sustainability of applying GSHP systems in cooling-dominated areas with the aid of groundwater seepage flow.

FOS: A fully integrated open-source program for Fast Optical Spectrum calculations of nanoparticle media

FOS, which means light in Greek, is an open-source program for Fast Optical Spectrum calculations of nanoparticle media. This program takes the material properties and a description of the system as input, and outputs the spectral response including the reflectance, absorptance, and transmittance. Previous open-source codes often include only one portion of what is needed to calculate the spectral response of a nanoparticulate medium, such as Mie theory or a Monte Carlo method. FOS is designed to provide a convenient fully integrated format to remove the barrier as well as providing a significantly accelerated implementation with compiled Python code, parallel processing, and pre-trained machine learning predictions. This program can accelerate optimization and high throughput design of optical properties of nanoparticle or nanocomposite media, such as radiative cooling paint and solar heating liquids, allowing for the discovery of new materials and designs. FOS also enables convenient modeling of lunar dust coatings, combustion particulates, and many other particulate systems. In this paper we discuss the methodology used in FOS, features of the program, and provide four case studies.

One-step TaqMan® RT-qPCR detection of sugar beet-infecting poleroviruses in Myzus persicae from yellow water pan traps opens up new possibilities for early risk assessment of virus yellows disease

Virus yellows disease (VY) is a major threat to sugar beet production in Europe. Beet chlorosis virus (BChV) and beet mild yellowing virus (BMYV) are of particular economic importance and are both persistently transmitted by the aphid vector Myzus persicae. As part of integrated pest management strategies, M. persicae influx into sugar beet fields is recorded weekly using yellow water pan traps. To date, only ELISA and RT-PCR assays have been described for BChV and BMYV detection in individual aphids.In this study, we describe for the first time two one-step TaqMan® RT-qPCR assays designed for the specific detection of BChV and BMYV in M. persicae after 7d incubation in water pan trap medium. Both viruses were reproducibly detected in individual aphids. After 7d incubation in trap medium, both viruses were reproducibly detected in individual aphids, as well as in one viruliferous aphid in a pool of 99 non-viruliferous aphids. Significant correlations can be shown between different mixing ratios of viruliferous to non-viruliferous aphids and Ct values of total RNA templates, allowing the percentage of viruliferous aphids in yellow water pan traps to be estimated using a standard curve.The described methodology provides a high sensitivity combined with a high sample throughput and can be used, after evaluation in the field, for practical monitoring, risk modelling and development of decision support systems for VY.

A synthesis of the REE-Fe-polymetallic mineral system of the REE-line, Bergslagen, Sweden: New mineralogical and textural-paragenetic constraints

Rare earth elements (REE) have gained increasing significance for numerous technologies, particularly in today’s rapidly expanding “green transition” applications such as wind generators and electric vehicle traction engines. Among the more well-known REE mineralisation types in Sweden, together with alkaline intrusions and apatite-iron oxide ores, is the classic yet enigmatic REE-Fe-polymetallic mineral system of Bastnäs-type. The mineralisation type is regional in context and occurs in a discontinuous SW-NE-striking belt (the REE-line) in the west-central part of the Palaeoproterozoic Bergslagen ore province, Sweden. This contribution is aimed at integrating and synthesising existing geological mineralogical, and textural features with new observations from both well-known and several lesser-known, underexplored or previously unrecognised REE-enriched occurrences within this belt, and to discuss key features within the context of mineral systems modelling. A considerable diversity in both the style and abundance of REE mineralisation as well as in discrete REE mineralogy is evident both regionally across the entire REE-line and locally within different ore districts or mine fields. These variations also extend laterally within or across different stratigraphic levels, and within different host rocks, primarily skarn-altered metacarbonates but also variably altered felsic metavolcanic rocks. Many of the mineralisations share similar textural features, which record a protracted evolution with multiple stages of formation or replacement of REE-minerals. The earliest recognised REE assemblages feature fine-grained cerite-(CeCa) with minor bastnäsite-(Ce) – bastnäsite-(La) or fluorbritholite-(Ce) – fluorbritholite-(Y) or locally britholite-(Ce) – britholite-(Y) minerals. Such assemblages typically display anhedral-granoblastic textures appearing in folded assemblages, all suggesting recrystallisation and ductile deformation during regional metamorphism of REE-minerals that had formed during an early stage of the Svecokarelian orogeny. Overprinting overgrowths and cross-cutting vein-like features of allanite-group minerals likely represent different stage(s) of REE mineralisation and (re)-mobilisation during this orogenic evolution. Several of the REE-enriched occurrences contain variably abundant and diverse polymetallic Cu-Mo-Bi-(Co) sulphide mineralisation that typically occur in late paragenetic positions and show a prevalence to REE-rich assemblages, often dominated by different allanite-group minerals. Sulphide and REE mineralisation are locally strongly associated with metamorphic minerals formed during metamorphism of variably Mg-(Fe)-altered metavolcanic rocks. The diversity in style and intensity of the REE mineralisations, along with variations in textures and specific mineralogy, suggest slight differences in the ore-forming conditions or environment at the time of mineralisation. Additionally, these differences may also reflect variations in the preservation or modification processes that operated later in the evolution of the Svecokarelian orogeny, also featuring remobilisation of REEs and sulphidic minerals. The insights gained from all available evidence synthesised herein, from micro-scale to province-scale, help define key proxies for prospectivity mapping. The combined evidence supports that the primary ore-forming stages in the mineral system coincided with felsic volcanism and associated sub-volcanic to plutonic processes at around 1.9 Ga.

Stability Conditions and Stiffness Variability of General Tensegrity Systems with Kinematic Joints

Abstract Tensegrity systems represent promising candidate mechanisms with in-situ stiffness variability through changing the cables' prestress levels. However, prestress-based stiffness behaviors of tensegrity systems with arbitrary kinematic joints have not been analyzed systematically. This paper adopts the natural absolute coordinates for static modeling of tensegrity systems consisting of rigid members and tension elements. Then, a generic stiffness analysis method is developed to formulate the reduced-basis tangent stiffness matrix, which is found to include three parts: positive semi-definite material and geometric stiffness matrices, and an indefinite constraint stiffness matrix. Based on these findings, a systematic stability-checking procedure is derived to determine prestress and super stability, which are qualitative indicators of the softening and stiffening effects in different tensegrity systems. Then, we proceed to quantify the range of prestress-based stiffness variability by formulating semidefinite programming problems that numerically pinpoint the maximum and zero stiffness points. Furthermore, this paper reveals the composable nature of multiple self-stress states, enabling the composability of stiffness properties in mechanism designs. Several numerical examples verify the efficacy and versatility of the proposed method and demonstrate interesting stiffness behaviors of tensegrity systems with kinematic joints.

Toward Polaritonic Molecular Orbitals for Large Molecular Systems.

A comprehensive understanding of electron-photon correlation is essential for describing the reshaping of molecular orbitals in quantum electrodynamics (QED) environments. The strong coupling QED Hartree-Fock (SC-QED-HF) theory tackles these aspects by providing consistent molecular orbitals in the strong coupling regime. The previous implementation, however, has significant convergence issues that limit the applicability. In this work, we introduce two second-order algorithms that significantly reduce the computational requirements, thereby enhancing the modeling of large molecular systems in QED environments. Furthermore, the implementation will enable the development of correlated methods based on a reliable molecular orbital framework as well as multi-level methodologies able to model the inclusion of solvent effects in this kind of complex systems.

Radiation Detectors and Sensors in Medical Imaging.

Medical imaging instrumentation design and construction is based on radiation sources and radiation detectors/sensors. This review focuses on the detectors and sensors of medical imaging systems. These systems are subdivided into various categories depending on their structure, the type of radiation they capture, how the radiation is measured, how the images are formed, and the medical goals they serve. Related to medical goals, detectors fall into two major areas: (i) anatomical imaging, which mainly concerns the techniques of diagnostic radiology, and (ii) functional-molecular imaging, which mainly concerns nuclear medicine. An important parameter in the evaluation of the detectors is the combination of the quality of the diagnostic result they offer and the burden of the patient with radiation dose. The latter has to be minimized; thus, the input signal (radiation photon flux) must be kept at low levels. For this reason, the detective quantum efficiency (DQE), expressing signal-to-noise ratio transfer through an imaging system, is of primary importance. In diagnostic radiology, image quality is better than in nuclear medicine; however, in most cases, the dose is higher. On the other hand, nuclear medicine focuses on the detection of functional findings and not on the accurate spatial determination of anatomical data. Detectors are integrated into projection or tomographic imaging systems and are based on the use of scintillators with optical sensors, photoconductors, or semiconductors. Analysis and modeling of such systems can be performed employing theoretical models developed in the framework of cascaded linear systems analysis (LCSA), as well as within the signal detection theory (SDT) and information theory.

Managing and editing UML documents: structure, integration, automation

Unified Modeling Language (UML) plays an important role in the modern software development process by offering standardized methods for visual modeling, analysis, and design of complex systems. This paper discusses the main aspects of storing, editing, and integrating UML documents with integrated development environments (IDEs). The main focus is on comparing the two main formats for storing UML models – XML Metadata Interchange (XMI) and JavaScript Object Notation (JSON). XMI, being an official standard developed by Object Management Group, provides a high degree of detail and compatibility with various UML tools, making it preferable for large and complex projects that require support for the full UML specification. At the same time, JSON is simple and flexible, which makes it suitable for projects where speed of development and ease of integration are important, although it is inferior to XMI in describing complex aspects of UML models. The paper analyzes in detail the methods of editing UML documents, including manual editing and automated approaches, using APIs and scripts. Manual editing is useful when you need to make small changes to the structure of a UML document, but it can be time-consuming and error-prone when working with large projects. Automation, on the other hand, provides effective tools for mass editing and generating UML elements, which significantly speeds up development and minimizes the likelihood of errors, especially since modern tools such as StarUML offer the necessary tools for programmatically modifying UML documents by integrating them into development processes. Combining of UML documents with integrated development environments also plays a key role in increasing development efficiency, as this allows automating code generation based on UML models, maintaining synchronization between code and diagrams, and facilitating visualization and documentation of architectural solutions. Despite the significant advantages, there are challenges associated with limited support for all UML features and potential conflicts between models and code. This only emphasizes the need for further research in this area. Prospects for further research include the development of new methods and tools for working with UML documents, improving their integration with development environments, and using artificial intelligence to automate the analysis and design of UML models. Attention should also be paid to the using of UML with continuous integration processes, which can significantly increase the flexibility and adaptability of software development. Thus, the study of the structure of storing, editing, and integrating UML documents is an important area for optimizing the design and development of modern software systems.

A sandwich-type Winkler-Pasternak double elastic foundation beam model for analysis of nut column load transfer systems of shiplifts

The rack and pinion shiflifts feature its capability of fortification against extreme accidents of unbalance of ship chamber. This requires the load transfer systems of nut columns to be able to transfer the huge and centralized loads from the safety mechanisms to the tower columns in a distributive bearing way. Aiming at exploring a design-oriented computation method for the nut column load transfer systems (NCLTSs), a sandwich-type semi-infinite coupling Winkler-Pasternak double elastic foundation beam model (DEFBM) has been established, with a distributive assumption of the axial constrain force on the interfaces between the nut columns, mortar beds, adjusting beams and the second stage concrete based on the physical model test, has been proposed. A set of formulars for describing the distribution of deflections, internal forces and stress of nut columns and adjusting beams have been derived. A case study of the Three-Gorges shiplift has been conducted to demonstrate the approach of calculation of internal forces and stress by applying the formulas based on sandwich-type Winkler-Pasternak DEFBM, showing the convenience and rationality of the method to parametric layout design of the nut columns and adjusting beams in NCLTSs. Not only to NCLTSs and rack load transfer systems of shiplifts, the sandwich-type Winkler-Pasternak DEFBM also supplies the reference to the modeling and analysis of the rail-embedment structural systems broadly applied in hydraulic engineering.

A novel approach to thermal insulation modelling in soft and medium vacuum insulation systems

The accuracy of vacuum-dependent models for predicting thermal performance of Multi-Layer Insulation (MLI) and other layered insulation systems is critical for the development of novel solutions in the aerospace and energy sectors, particularly long distance superconductors and cryogenic transfer lines. This paper presents a review of the current state of the art in cryogenic vacuum insulation systems and their associated modelling techniques and test methods. Current modelling techniques, namely the Lockheed and McIntosh MLI models, are compared to cryogenic, boil-off calorimeter test data for 3 types of MLI from the current literature. Both current models provide acceptable accuracy at high vacuum pressures but deviate from the test data when gas conduction becomes the dominant heat transfer mechanism (Kn≤1). Neither of the current models follow the characteristic S-curve observed by researchers during insulation tests. This paper presents the introduction of a novel modelling approach for layered insulation systems though changes to the current state of the art, specifically at soft (pvac≤7.5×105 mTorr) and medium vacuum (pvac≤7.5×102 mTorr) pressures, by substituting the gas conduction term in both equations with alternative terms based on the system Knudsen number (Kn) and molecule mean free path (l). This results in a stronger pressure dependence across the vacuum regime. Both modified models exhibited the characteristic S-curve with significantly reduced errors over the entire range.

Connections between precast hollow concrete columns and foundations using UHPC and rebar lap splices

Hollow concrete columns (HCC) have been widely used in accelerated bridge construction worldwide. The column-foundation connections are vital in such precast systems to ensure seismic resilience. In this paper, a novel HCC-foundation connection using UHPC and rebar lap splices was proposed, which could facilitate the construction process. Cyclic tests were carried out on four specimens to examine their seismic performance. Test results showed that the proposed connections exhibited satisfying capacities, ductility, and energy dissipation comparable to monolithic connections under lateral cyclic loads. The use of a precast UHPC shell did not deteriorate the performance of the connection with no interface separation observed. The decrease in joint height would, however, bring about anchor rebar slippage and reduce energy dissipation. A validated fiber element model of the specimens was established for efficient seismic modeling of precast systems with the proposed connection in actual projects. Force transfer mechanisms and design methods for the connections were further deduced. The proposed connection has been verified to be comparable to cast-in-place connections and is thus suitable for use in precast column-foundation connections. Several design recommendations were derived for engineering reference.

New Thermodynamic Models for Anhydrous Alkaline-Silicate Magmatic Systems

Abstract A new thermodynamic model for silicate melt in the Na2O–CaO–K2O–FeO–MgO–Al2O3–SiO2–TiO2–Fe2O3–Cr2O3 model system is presented, building on the tholeiitic through to granitic melt model of Holland et al. (2018) [Journal of Petrology, 59, 881–900] but extending for the first time into anhydrous alkaline systems. The new melt model is accompanied by new thermodynamic models for nepheline, kalsilite, leucite, melilite and ilmenite. Collectively these models enable pseudosection modelling of alkaline-silicate magmatic systems, providing a new tool for investigating this geologically- and economically-important compositional space. The models are calibrated with respect to experimental data on phase relations among minerals and melt, and the fit is benchmarked here via detailed comparison with seven experimental datasets, which encompass a range of pressure (0–22 kbar), temperature (680–1350°C), oxygen fugacity (log fO2 ΔFMQ-3 to +1), total alkali (3–16 wt %) and silica (37–70 wt %) conditions. The calculated pseudosections successfully reproduce experimental crystallisation sequences and phase compositions, indicating that the thermodynamic models are well calibrated across this spectrum of conditions. Redox buffered experimental conditions are simulated using oxygen buffered pseudosections. Contouring of oxygen buffered pseudosections with XFe3+ (mol. Fe3+/Fetotal), or pseudosections of varying XFe3+ with ΔFMQ, reveals (i) often complex and non-intuitive relationships between these two representations of oxidation state, and (ii) substantial variation in ferric iron over narrow temperature intervals in some oxygen buffered sets of experiments. An implication is that simulating oxygen buffering is vital when benchmarking thermodynamic models using experimental results. Furthermore, because natural igneous systems likely feature a near-constant XFe3+, it is important to assess experimental results in this framework when making inferences about natural systems, recognising that oxygen fugacity is a consequence not a control of phase equilibria in nature. Overall, our new models provide a novel tool to explore the role of variables, such as pressure, fractional crystallisation and crustal assimilation in the petrogenesis of alkaline-silicate magmatic systems and their associated mineralisation.