Explore the vast range of titles available.

The verification of mathematical models for multistage reciprocating compressors is crucial for ensuring their accuracy and reliability. In this study, we used different machine learning (ML) models to verify the results of MATLAB-based models of single-stage reciprocating compressors, multistage reciprocating compressors without intercoolers, and multistage reciprocating compressors with intercoolers to simulate the real-world operating conditions of a reciprocating compressor. The verification focuses on key performance indicators, such as the pressure–volume (PV) graph, outlet temperature graph, volumetric efficiency, and pressure ratio graph. The MATLAB model computes thermodynamic parameters, such as the power required, outlet pressure, and outlet temperature for various operating conditions. The MATLAB model produced the following results for single-stage compressor: the outlet pressure increased by 1.6 times the inlet pressure of the compressor, the volume reduced by 20% of the volume at the inlet of the single-stage compressor, and the outlet temperature increased by 30% of the inlet temperature. In the case of a multistage compressor without an intercooler, the outlet pressure increased by about 3.3–3.6 times the inlet pressure of the compressor; the volume reduced by 60% of the volume at the inlet, and the outlet temperature increased by 35% in comparison to the inlet temperature of the multistage compressor without an intercooler. Subsequently, in the case of a multistage compressor with an intercooler at the first stage of compression, the pressure increased by three times the inlet pressure; at the second stage of compression, the pressure increased by six times the inlet pressure of the compressor, the volume was reduced by approximately 80%, and the intercooler maintained the increase in outlet temperature by 30%, limiting it and preventing excessive expansion of air in the compressor and increasing the efficiency of the compressor by 12% in comparison to the multistage compressor without an intercooler. In addition, the results generated by all the machine learning models used in the study were in correlation with the results generated by the MATLAB model for all three compressors, with an accuracy of approximately 90% or more for almost all the models implemented for prediction. By comparing the predicted outputs from the ML model with the MATLAB-generated results, the accuracy and consistency of the simulation were assessed. This study aims to bridge the gap between traditional mathematical modeling and modern data-driven validation techniques to ensure robustness in compressor performance predictions.

A comprehensive introduction to the foundations of model checking, a fully automated technique for finding flaws in hardware and software; with extensive examples and both practical and theoretical exercises.

The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC. Upper-air comparison is included for forecast verification against both rawinsondes and aircraft reports, the latter allowing hourly verification. In general, the RAP produces superior forecasts to those from the RUC, and its skill has continued to increase from 2012 up to RAP version 3 as of 2015. In addition, the RAP can improve on persistence forecasts for the 1–3-h forecast range for surface, upper-air, and ceiling forecasts.

The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core—the Finite Volume Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25 km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the stochastically perturbed physics tendencies (SPPT) scheme and stochastic kinetic energy backscatter (SKEB) scheme. Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error–spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over North America. GEFSv12 was implemented on 23 September 2020.

Finite element (FE) model studies have made important contributions to our understanding of functional biomechanics of the lumbar spine. However, if a model is used to answer clinical and biomechanical questions over a certain population, their inherently large inter-subject variability has to be considered. Current FE model studies, however, generally account only for a single distinct spinal geometry with one set of material properties. This raises questions concerning their predictive power, their range of results and on their agreement with in vitro and in vivo values. Eight well-established FE models of the lumbar spine (L1-5) of different research centers around the globe were subjected to pure and combined loading modes and compared to in vitro and in vivo measurements for intervertebral rotations, disc pressures and facet joint forces. Under pure moment loading, the predicted L1-5 rotations of almost all models fell within the reported in vitro ranges, and their median values differed on average by only 2° for flexion-extension, 1° for lateral bending and 5° for axial rotation. Predicted median facet joint forces and disc pressures were also in good agreement with published median in vitro values. However, the ranges of predictions were larger and exceeded those reported in vitro, especially for the facet joint forces. For all combined loading modes, except for flexion, predicted median segmental intervertebral rotations and disc pressures were in good agreement with measured in vivo values. In light of high inter-subject variability, the generalization of results of a single model to a population remains a concern. This study demonstrated that the pooled median of individual model results, similar to a probabilistic approach, can be used as an improved predictive tool in order to estimate the response of the lumbar spine.

In this paper, which is a continuation of the discrete-time paper (Björk and Murgoci in Finance Stoch. 18:545–592, 2004 ), we study a class of continuous-time stochastic control problems which, in various ways, are time-inconsistent in the sense that they do not admit a Bellman optimality principle. We study these problems within a game-theoretic framework, and we look for Nash subgame perfect equilibrium points. For a general controlled continuous-time Markov process and a fairly general objective functional, we derive an extension of the standard Hamilton–Jacobi–Bellman equation, in the form of a system of nonlinear equations, for the determination of the equilibrium strategy as well as the equilibrium value function. The main theoretical result is a verification theorem. As an application of the general theory, we study a time-inconsistent linear-quadratic regulator. We also present a study of time-inconsistency within the framework of a general equilibrium production economy of Cox–Ingersoll–Ross type (Cox et al. in Econometrica 53:363–384, 1985 ).

Forecast verification and evaluation is a critical aspect of forecast development and improvement, day-to-day forecasting, and the interpretation and application of forecasts. In recent decades, the verification field has rapidly matured, and many new approaches have been developed. However, until recently, a stable set of modern tools to undertake this important component of forecasting has not been available. The Model Evaluation Tools (MET) was conceived and implemented to fill this gap. MET (https://dtcenter.org/community-code/model-evaluation-tools-met) was developed by the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Air Force (USAF) and is supported via the Developmental Testbed Center (DTC) and collaborations with operational and research organizations. MET incorporates traditional verification methods, as well as modern verification capabilities developed over the last two decades. MET stands apart from other verification packages due to its inclusion of innovative spatial methods, statistical inference tools, and a wide range of approaches to address the needs of individual users, coupled with strong community engagement and support. In addition, MET is freely available, which ensures that consistent modern verification capabilities can be applied by researchers and operational forecasting practitioners, enabling the use of consistent and scientifically meaningful methods by all users. This article describes MET and the expansion of MET to an umbrella package (METplus) that includes a database and display system and Python wrappers to facilitate the wide use of MET. Examples of MET applications illustrate some of the many ways that the package can be used to evaluate forecasts in a meaningful way.

Air pollution is a global problem, which needs to be understood and controlled to ensure a healthy environment and inform sustainable development. Urban areas have been established as one of the main contributors to air pollution, and, as such, urban air quality is the subject of an increasing volume of research. One of the principal means of studying air pollution dispersion is to use computational fluid dynamics (CFD) models. Subject to careful verification and validation, these models allow for analysts to predict air flow and pollution concentration for various urban morphologies under different environmental conditions. This article presents a detailed review of the use of CFD to model air pollution dispersion in an urban environment over the last decade. The review extracts and summarises information from nearly 90 pieces of published research, categorising it according to over 190 modelling features, which are thematically systemised into 7 groups. The findings from across the field are critically compared to available urban air pollution modelling guidelines and standards. Among the various quantitative trends and statistics from the review, two key findings stand out. The first is that, despite the existence of best practice guidelines for pollution dispersion modelling, anywhere between 12% and 34% of the papers do not specify one or more aspects of the utilised models, which are required to reproduce the study. The second is that none of the articles perform verification and validation according to accepted standards. The results of this review can, therefore, be used by practitioners in the field of pollution dispersion modelling to understand the general trends in current research and to identify open problems to be addressed in the future.

PurposeAs an advanced calculation methodology, reliability-based multidisciplinary design optimization (RBMDO) has been widely acknowledged for the design problems of modern complex engineering systems, not only because of the accurate evaluation of the impact of uncertain factors but also the relatively good balance between economy and safety of performance. However, with the increasing complexity of engineering technology, the proposed RBMDO method gradually cannot effectively solve the higher nonlinear coupled multidisciplinary uncertainty design optimization problems, which limits the engineering application of RBMDO. Many valuable works have been done in the RBMDO field in recent decades to tackle the above challenges. This study is to review these studies systematically, highlight the research opportunities and challenges, and attempt to guide future research efforts.Design/methodology/approachThis study presents a comprehensive review of the RBMDO theory, mainly including the reliability analysis methods of different uncertainties and the decoupling strategies of RBMDO.FindingsFirst, the multidisciplinary design optimization (MDO) preliminaries are given. The basic MDO concepts and the corresponding mathematical formulas are illustrated. Then, the procedures of three RBMDO methods with different reliability analysis strategies are introduced in detail. These RBMDO methods were proposed for the design optimization problems under different uncertainty types. Furtherly, an optimization problem for a certain operating condition of a turbine runner blade is introduced to illustrate the engineering application of the above method. Finally, three aspects of future challenges for RBMDO, namely, time-varying uncertainty analysis; high-precision surrogate models, and verification, validation and accreditation (VVA) for the model, are discussed followed by the conclusion.Originality/valueThe scope of this study is to introduce the RBMDO theory systematically. Three commonly used RBMDO-SORA methods are reviewed comprehensively, including the methods' general procedures and mathematical models.

We present MCMAS, a model checker for the verification of multi-agent systems. MCMAS supports efficient symbolic techniques for the verification of multi-agent systems against specifications representing temporal, epistemic and strategic properties. We present the underlying semantics of the specification language supported and the algorithms implemented in MCMAS, including its fairness and counterexample generation features. We provide a detailed description of the implementation. We illustrate its use by discussing a number of examples and evaluate its performance by comparing it against other model checkers for multi-agent systems on a common case study.