Computer simulation and analysis techniques for reliable circuit design

The design of antennas in complex environments, such as urban areas, underground settings, or environments with high interference, presents significant challenges. Advanced simulation techniques are crucial in optimizing antenna designs to meet the specific requirements of these environments. This article explores various advanced simulation methods used in antenna design, such as electromagnetic simulation, ray-tracing, and computational electromagnetic techniques, to improve the performance and efficiency of antennas in complex environments. It also highlights the role of these techniques in improving the accuracy, reliability, and cost-effectiveness of antenna systems

Cumulative effects are defined as the joint and aggregated effects of many factors and processes. Their consideration in landscape and environmental assessments are integral at both the project and strategic level, and they can help to bridge the different spatial and temporal scales. The primary challenge of conducting cumulative effect assessments (CEAs) is the difficulty in understanding the complicated nature of cumulative effects. We used three criteria for a systematic understanding of the barriers to addressing cumulative effects that are critical for improving knowledge systems for sustainable development and environmental assessment: salience, credibility, and legitimacy, and analyzed three cases through a variety of studies and resources: the Middle Humber in the U.K., the Transboundary Crown of the Continent in the U.S. and Canada, and the Great Sandhills in Canada, to understand how CEAs have been applied and obstructed in terms of the three criteria. In addition, a series of focus group interviews with experts and practitioners were performed to illuminate the critical barriers based on the criteria for addressing CEA in the context of South Korea. Based on the lessons, we suggest several key strategies such as securing a cooperative consulting process, and active and transparent partnerships; using a strategic environmental assessment as a framework; understanding and incorporating stakeholder knowledge; using advanced computer modeling and simulation techniques including effective visualization tools; and preparing a simple model design and understandable scientific information.

The spectrum of approaches to performance prediction of parallel systems ranges from low-cost, low-precision compile-time methods to high-cost, high-precision methods with an equal variety of underlying modeling formalisms (e.g., task graphs, queueing networks, simulation languages) which are mutually incompatible. A performance prediction technique is described which unifies a number of existing approaches within one formalism. As the analysis is based on a model reduction calculus the approach allows for a flexible trade-off between precision and cost, effectively ranging from simulation to static analysis. >

The technique of real-time simulation for studying air traffic control problems has been employed for many years. This technique has enabled investigators to simulate live air traffic situations for the purpose of evaluating new concepts and their relationship to the air traffic controller. The modern high-speed digital computer has increased the scope of these real-time simulation studies to include large scale air traffic control systems employing such computers in the control process.

Petrophysical variables, such as porosity and permeability in reservoir rocks, result from complex geological processes, and are characterized by considerable amounts of heterogeneity in their spatial distribution. The challenging task for geologists and engineers is to represent such heterogeneity in numerical reservoir models for flow simulation, leading to better predictions of reservoir performance. Stochastic simulation techniques have commonly been adopted to construct numerical reservoir models in recent years. These techniques provide a range of equi-probable images or realizations of variable under consideration with a realistic level of heterogeneity. The Sequential Gaussian Simulation (SGS) is one of the popular conditional simulation techniques employed in reservoir characterization. The SGS algorithm provides a simulated value at any location by first determining the probability distribution at that location, and then drawing a number at random from this distribution. The SGS technique was adopted to generate a three-dimensional, high-resolution reservoir model of porosity within the Upper Jurassic multi-zonal Arab-D carbonate reservoir in the Harmaliyah oil field, located in the Eastern Province of Saudi Arabia. The study was based on the well-log derived porosity measurements from 44 vertical wells. The generated models display the general pattern of the porosity distribution within the reservoir and provide the basic input for the flow simulation. As expected, the simulation model appears to have reproduced a realistic level of heterogeneity. Comparison of the histogram and statistical parameters of the input data with those of the simulated values reveals a close match in both the distribution patterns and the parameters.

Numerical simulation and production prediction assessment of Takigami geothermal reservoir

Numerical modelling and experimental investigation of drug release from layered silicone matrix systems

We present a numerical model, EODES-3, for the simulation and performance prediction of electro-optic imaging systems in ocean environments. A radiative transfer equation solver under the small-angle scattering approximation forms the foundation for the model. The model accounts for solar illumination, depth-dependent optical properties, and a full spectrum of system related parameters. All numerical computations have been thoroughly verified, and a detailed model of a specific laser line scan (LLS) system has been validated against at-sea test data. The model provides performance predictions for mission planning and simulated images for operator training and system evaluation.

Digital twin technology is the talking point of academia and industry. When defining a digital twin, new modeling paradigms and computational methods are needed. Developments in the Internet of Things and advanced simulation and modeling techniques have provided new strategies for building complex digital twins. The digital twin is a virtual entity representation of the physical entity, such as a product or a process. This virtual entity is a collection of computationally complex knowledge models that embeds all the information of the physical world. To that end, this article proposes a graph-based representation of the virtual entity. This graph-based representation provides a method to visualize the parameter and their interactions across different modeling domains. However, the virtual entity graph becomes inherently complex with multiple parameters for a complex multidimensional physical system. This research contributes to the body of knowledge with a novel graph-based model reduction method that simplifies the virtual entity analysis. The graph-based model reduction method uses graph structure preserving algorithms and Dempster–Shaffer Theory to provide the importance of the parameters in the virtual entity. The graph-based model reduction method is validated by benchmarking it against the random forest regressor method. The method is tested on a turbo compressor case study. In the future, a method such as graph-based model reduction needs to be integrated with digital twin frameworks to provide digital services by the twin efficiently.

This study analyzes the post-pandemic dynamics and investment potential of diverse clean energy equities, including solar, wind, nuclear, and other renewable assets, highlighting nuanced differences and investment opportunities within this critical sector. The analysis reveals that nuclear energy portfolios (NLR) exhibit notable resilience, sustaining growth amidst significant market volatility. Within the mean-variance portfolio optimization (MVO) framework, this study identifies strategic investments that balance risk and return, underscoring NLR’s role as a stabilizing force and return enhancer, as evidenced by its predominant allocation in both Minimum Variance and Tangency Portfolios. Employing advanced stochastic modeling and simulation techniques, the research uses a uniform distribution to generate random portfolio weights, ensuring comprehensive and unbiased exploration of the feasible solution space, thereby enhancing the robustness of the portfolio optimization process. The findings also illustrate the diversification merits of integrating clean energy equities into broader portfolios comprising traditional stocks and bonds, with nuclear-focused equity significantly enhancing the efficient frontier. Results underscore the superiority of the nuclear energy exchange-traded fund (ETF) both as a standalone investment and as a crucial component of diversified portfolios, highlighting its contribution to investment performance and risk management. This approach offers insights for investors and policymakers navigating the intersection of finance, sustainability, and economic growth post-pandemic.

This paper presents recent advances in cardiovascular fluid mechanics that define the current state of the art. These studies include complex multimodal investigations with advanced measurement and simulation techniques. We first discuss the complex flows within the total cavopulmonary connection in Fontan patients. We emphasize the quantification of energy losses by studying the importance of caval offsets as well as the differences among various Fontan surgical protocols. In our studies of the fluid mechanics of prosthetic heart valves, we reveal for the first time the full three-dimensional complexity of flow fields in the vicinity of bileaflet and trileaflet valves and the microscopic hinge flow dynamics. We also present results of these valves functioning in a patient-specific native aorta geometry. Our in vitro mitral valve studies show the complex mechanism of the native mitral valve apparatus. We demonstrate that the different components of the mitral valve have independent and synergistically complex functions that allow the valve to operate efficiently. We also show how valve mechanics change under pathological and repair conditions associated with enlarged ventricles. Finally, our ex vivo studies on the interactions between the aortic valve and its surrounding hemodynamic environment are aimed at providing insights into normal valve function and valve pathology. We describe the development of organ- and tissue-culture systems and the biological response of the tissue subjected to their respective simulated mechanical environment. The studies noted above have enhanced our understanding of the complex fluid mechanics associated with the cardiovascular system and have led to new translational technologies.

Flapping flight is the most power-demanding mode of locomotion, associated with a suite of anatomical specializations in extant adult birds. In contrast, many developing birds use their forelimbs to negotiate environments long before acquiring “flight adaptations,” recruiting their developing wings to continuously enhance leg performance and, in some cases, fly. How does anatomical development influence these locomotor behaviors? Isolating morphological contributions to wing performance is extremely challenging using purely empirical approaches. However, musculoskeletal modeling and simulation techniques can incorporate empirical data to explicitly examine the functional consequences of changing morphology by manipulating anatomical parameters individually and estimating their effects on locomotion. To assess how ontogenetic changes in anatomy affect locomotor capacity, we combined existing empirical data on muscle morphology, skeletal kinematics, and aerodynamic force production with advanced biomechanical modeling and simulation techniques to analyze the ontogeny of pectoral limb function in a precocial ground bird (Alectoris chukar). Simulations of wing-assisted incline running (WAIR) using these newly developed musculoskeletal models collectively suggest that immature birds have excess muscle capacity and are limited more by feather morphology, possibly because feathers grow more quickly and have a different style of growth than bones and muscles. These results provide critical information about the ontogeny and evolution of avian locomotion by (i) establishing how muscular and aerodynamic forces interface with the skeletal system to generate movement in morphing juvenile birds, and (ii) providing a benchmark to inform biomechanical modeling and simulation of other locomotor behaviors, both across extant species and among extinct theropod dinosaurs.

As the need for early detection and mitigation of potential threats from near-Earth objects grows, this study presents a comprehensive approach to predicting hazardous asteroids using machine learning techniques. Given the increasing importance of protecting our planet from potential impact events, accurately classifying and predicting hazardous asteroids is cru- cial. The study proposes a comprehensive system for predicting and mitigating asteroid hazards using advanced data processing, machine learning, and simulation techniques. The proposed study collect real-time and historical asteroid data, process and clean this data, extract relevant features, and train machine learning models to predict potentially hazardous asteroids. Additionally, Monte Carlo simulations are employed to assess the collision risks of these asteroids with Earth. A forecasting module predicts future asteroid behavior based on historical and simulated data. The system also includes an alert mechanism that notifies relevant stakeholders when a potentially hazardous asteroid is detected

Pharmacometrics, enabled by advanced pharmacostatistical modeling and simulation technique, has gained significant importance as a tool for model-based drug development and for pharmacotherapy. This introductory chapter provides an overview of the models and mathematical framework that are commonly used in the field. Both pharmacokinetic and pharmacodynamic models are discussed; the physiologically based pharmacokinetic (PBPK) approach is emphasized through illustrations and examples. We have shown how systemic and tissue clearances are related through mathematical proofs using the PBPK approach. As for the pharmacodynamic section, models for continuous and noncontinuous responses were explained, as well as a disease progression model. We also provided select examples of systems pharmacology models that investigate physiological and biochemical processes. This chapter also discusses the software tools and their specific application. As pharmacometrics and quantitative pharmacology has evolved as a discipline, more and more different types of statistical and mathematical models have been utilized. The advancement of computing software and systems facilitate these complex computations.

Maximizing solar photovoltaic energy efficiency: MPPT techniques investigation based on shading effects