Cost Reduction Research Articles

Prioritization of investment order in Brazil among renewable energy sources: an analysis using the SWARA-MOORA-3NAG method

Purpose This paper aims to comprehensively analyze renewable energy alternatives in Brazil, focusing on identifying the most suitable option for investment in the country’s sustainable development. Design/methodology/approach The study adopts the step-wise weight assessment ratio analysis-multiobjective optimization by ratio analysis −3NAG (a combination of three normalization methods) methodology, a multicriteria decision-making approach, to evaluate and rank renewable energy sources based on key criteria such as resource availability, cost-effectiveness, job creation potential and environmental impact. Findings The analysis reveals that solar energy emerges as the preferred choice for Brazil, offering significant advantages over other alternatives such as hydroelectric, wind and biomass energy. Solar energy’s distributed generation capability, cost reduction trends and positive environmental impact contribute to its favorable position in meeting Brazil’s energy needs. Research limitations/implications While the study provides valuable insights into renewable energy selection, there are limitations regarding the criteria’ scope and the exclusion of specific renewable energy options. Future research could explore sensitivity analyses and incorporate additional criteria to enhance the study’s comprehensiveness. Originality/value This research contributes to the existing literature by thoroughly analyzing renewable energy alternatives in Brazil using a robust multicriteria decision-making methodology. The study’s findings provide actionable guidance for policymakers, businesses and stakeholders seeking to promote sustainable energy development in the country.

Value co-creation of carbon-neutral tourism route: a case in Thailand

ABSTRACT This participatory action research initiative was undertaken in Thailand to promote carbon-neutral tourism (CNT). The research involved the engagement of multiple stakeholders in the value chain to co-create a valuable CNT route for Nong Khao sub-district tourism in Kanchanaburi Province. A service design approach was applied in this study, consisting of four phases: building engagement and awareness, exploring customer needs, co-creating a route, and assessing the results. The findings revealed that tourists exhibited a strong preference towards the CNT trip option. The implementation of the activities resulted in an increased net profit margin of 165.31% due to cost reduction, sale-price growth, and larger tourist numbers. The research provides important value co-creation of CNT management guidelines for community tourism.

A novel dynamic predictive maintenance framework for gearboxes utilizing nonlinear Wiener process

Abstract In the context of advancing industrial automation, gearboxes, as pivotal components in power transmission systems, have a direct bearing on the operational efficiency and safety of the entire machinery. This study introduces a novel dynamic predictive maintenance framework for gearboxes using a nonlinear Wiener process. Comprehensive experiments validate the framework, demonstrating significant reductions in maintenance costs and improvements in reliability. First, a full-life degradation experiment was executed on the gearbox, leveraging the root mean square (RMS) value of the vibration signal as an indicator of system degradation. Subsequently, the signals from four vibration sensors were synthesized and normalized through Kernel Principal Component Analysis (KPCA), thereby enabling a more nuanced representation of the gearbox's degradation profile. The degradation trajectory was then modeled using a nonlinear Wiener process framework. The Wiener process's parameters and state variables were iteratively refined utilizing an online filtering algorithm grounded in Bayesian inference. This facilitated the derivation of the probability density function for the remaining useful life (RUL), thereby enabling a robust prediction of the gearbox's RUL. Finally, to minimize maintenance costs per unit of time, an optimization model for dynamic maintenance decision-making was formulated. The optimal maintenance timing was ascertained by solving this model. The empirical findings of this investigation demonstrate the efficacy of the proposed approach in executing dynamic predictive maintenance for gearboxes. This research endeavors to furnish novel theoretical underpinnings and pragmatic directives for the field of predictive maintenance in the context of gearboxes.

Predictive heating load management and energy flexibility analysis in residential sector using an archetype gray-box modeling approach: Application to an experimental house in Québec

This paper presents a methodology to develop archetype gray-box models and use them in an economic model-based predictive control algorithm to simulate optimal heating load management in response to a newly-introduced static time-of-use tariff for Québec’s residential sector, rate Flex-D. The methodology is evaluated through a case study, wherein in situ measurements from a two-storey unoccupied research house of Hydro-Québec are used to develop an 11R6C network with a heuristic zoning-by-floor approach and compute the sequence of optimal electric heating input for the next control horizon. Properly-tuned economic model-based predictive control under rate Flex-D shows potential for an approximately 30% reduction in daily heating cost compared to the reference operation, with a minimal average deviation of indoor air temperature from the reference setpoint. Also, the analysis of the response’s sensitivity to weather forecast uncertainties indicates that the most influential uncontrolled input directing the performance of economic model-based predictive control is the structure price signal, rendering the impact of uncertainty in the weather forecast negligible.

Digital trade liberalization and quality upgrading: evidence from Chinese intermediate imports

ABSTRACT This article investigates how the import quality of intermediate products responds to digital trade liberalization using Chinese Customs data and the composite index of digital trade liberalization obtained from OECD database for 2014–2019. The results reveal that digital trade liberalization has significantly improved the quality of Chinese manufacturing intermediate imports. Further mechanism tests show that digital trade liberalization will boost the improvements through trade cost reduction effects. For the influence path, digital trade liberalization will promote the quality upgrading through both intensive and extensive margins. The heterogeneity analyses find that imports from countries with high economic developments experienced larger quality promotion than those from lower economic area. Meanwhile, the quality upgrading effects of digital trade liberalization are mainly reflected in Chinese non-comparative industries, such as downstream, digital-intensive, R&D-intensive and technology-intensive industries. Moreover, compared to homogeneous products and those by processing trade mode, the quality of intermediate imports for differentiated products by general trade are more responsive to the liberalization of digital trade barriers.

Method “Monte Carlo” in healthcare

In public health, simulation modeling stands as an invaluable asset, enabling the evaluation of new systems without their physical implementation, experimentation with existing systems without operational adjustments, and testing system limits without real-world repercussions. In simulation modeling, the Monte Carlo method emerges as a powerful yet underutilized tool. Although the Monte Carlo method has not yet gained widespread prominence in healthcare, its technological capabilities hold promise for substantial cost reduction and risk mitigation. In this review article, we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts. We underscore the significance of experiential insights derived from simulated experimentation, especially in resource-constrained scenarios where time, financial constraints, and limited resources necessitate innovative and efficient approaches. As public health faces increasing challenges, incorporating the Monte Carlo method presents an opportunity for enhanced system construction, analysis, and evaluation.

Artificial Intelligence Enabled Supply Chain Management: Unlocking New Opportunities and Challenges

This paper delves deeper into the potential of Artificial Intelligence (AI)-enabled Supply Chain Management (SCM) as a groundbreaking technology capable of revolutionizing supply chain operations and ushering in a new era of possibilities. In today's dynamic business landscape, where agility and efficiency are paramount, AI plays a pivotal role in redefining how supply chains operate. The journey commences with an in-depth exploration of AI's fundamental concepts and its manifold applications within SCM, shedding light on its adaptability across various aspects of the supply chain, from demand forecasting to inventory optimization. Moreover, this paper illuminates the myriad benefits that AI brings to SCM practitioners. These advantages encompass heightened operational efficiency through real-time data analysis, cost reduction through predictive maintenance and optimized routing and a superior customer experience resulting from improved demand prediction and personalized service offerings. However, acknowledging the transformative power of AI in SCM, we must also acknowledge the hurdles in its implementation. This paper underscores the significant challenges that organizations may face while integrating AI into their SCM processes, ranging from data quality issues and concerns regarding privacy and security to the need for domain-specific human expertise. To address these hurdles effectively, the paper proposes a comprehensive framework. This framework encompasses a holistic strategy that aligns AI initiatives with organizational goals, governance and ethics considerations to ensure responsible AI deployment, and a clear roadmap that guides the implementation journey from inception to full integration. In conclusion, this paper offers valuable insights into the opportunities and challenges that AI-powered SCM presents in the ever-evolving business landscape. By providing practical recommendations, it equips organizations with the knowledge and tools needed to successfully harness the potential of AI in their supply chain operations, ultimately paving the way for enhanced competitiveness and sustainability in the future.

Healthcare Data Analytics and Predictive Modelling: Enhancing Outcomes in Resource Allocation, Disease Prevalence and High-Risk Populations

Purpose: This study aims to explore the role of healthcare data analytics and predictive modeling in enhancing healthcare outcomes, specifically in resource allocation, disease forecasting, and identifying high-risk populations. Methodology: The research employs a comprehensive approach, utilizing various sources of healthcare data such as electronic health records (EHRs) and public health databases. Advanced analytical techniques, including machine learning, artificial intelligence, and big data analytics, are applied to derive actionable insights. Findings: The study reveals that predictive modeling significantly enhances resource optimization, enables accurate disease prevalence forecasting, and improves the identification of high-risk populations. Case studies demonstrate how these technologies lead to more efficient healthcare delivery, cost reduction, and better patient care outcomes. Unique Contribution to Theory, Policy, and Practice: This research contributes to the theoretical understanding of healthcare data analytics by integrating advanced predictive modeling techniques with real-world healthcare applications. It offers valuable insights for policymakers on the importance of investing in data infrastructure and promoting data-driven decision-making. Practically, the study provides healthcare organizations with actionable strategies to implement predictive analytics for improved resource allocation and patient care.

Hydrocarbon‐Based Ionomer/PTFE‐Reinforced Composite Membrane Through Multibar Coating Technique for Durable Fuel Cells

AbstractFor cost reduction and environmental‐friendly manufacturing, it is highly demanded to replace the current perfluorinated sulfonic acid‐based membrane in polymer electrolyte membrane fuel cells (PEMFCs) with inexpensive and readily available hydrocarbon‐based (HC) membranes. However, HC membranes suffer from profound dimensional changes caused by swelling and shrinking during operation, especially in automotive applications. These changes lead to severe mechanical degradation and shorten the service life of PEMFC. Herein, a multibar coating system is developed to manufacture HC/polytetrafluoroethylene (PTFE) composite membrane. This system facilitates capillary‐rise infiltration with the aid of an optimal amount of residual alcohol solvent on the PTFE. To address compatibility issues between PTFE and HC‐ionomer solutions, the effects of residual alcohol solvent on tuning the PTFE surface are investigated by controlling systemic parameters and performing diverse mechanical, optical, and electrochemical measurements. Based on its enhanced mechanical toughness (≈30.04%) and superior impregnation properties, the constructed HC/PTFE composite membrane exhibited more than seven‐fold improvement in mechanical durability under repeated accelerated wet–dry conditions compared with an unsupported pristine HC membrane while also mitigating performance loss (≈5.84%).

A volume‐of‐solid implicit volume penalty method for moving‐body flows

AbstractAn original Immersed Boundary Method for solving moving body flows is proposed. This method couples (i) a Lagrangian Volume‐of‐Solid description of the solid object avoiding conservation issues and (ii) a robust implicit volume penalty forcing embedded in a low‐Mach number projection method to account for the solid's impact on the fluid dynamics. A new composite velocity field is introduced to describe both solid and fluid domains in a single set of governing equations. The accuracy of the method has been assessed on several academic cases, involving stationary or moving bodies and with different mesh resolutions. The predicted forces on the solid are in excellent agreement with body‐fitted reference cases. The system of equations is also proven to be fully mass conservative. Application of the method on a two‐dimensional vertical axis turbine case shows a reduction in computational cost compared to a body‐fitted method.

A Deep Learning-Based Approach for Identifying Defects in Solar Panels

In the solar energy sector, the task of monitoring and maintaining large photovoltaic (PV) system portfolios is essential for ensuring optimal performance and reliability. Prominent solar energy companies face challenges with their current fault detection methods, which are inefficient and resource- intensive. This paper addresses the critical need for improved fault detection in solar PV systems to maximize uptime and minimize maintenance costs. We employed advanced data preprocessing and augmentation techniques using Roboflow and developed a YOLOv8 segmentation model in Google Colab with GPU. This model was then deployed using Streamlit, providing a robust solution for identifying faulty solar modules. The proposed approach significantly enhances fault detection accuracy, achieving a minimum accuracy rate of 85%, thus ensuring reliable operation of the PV systems. Additionally, the implementation of this model contributes to a 15% reduction in system downtime and a 10% reduction in maintenance costs. By leveraging advanced machine learning techniques, our solution transforms the maintenance process, making it more efficient and cost-effective. Consequently, this work not only improves the reliability and performance of solar PV systems but also supports the broader goal of sustainable energy through more efficient resource usage.

Sustainable Business Practices through IoT: Energy Efficiency and Cost Reductions

This is the age of industrial transformation that is being driven by the Internet of Things (IoT). It offers very unique chance to maximize productivity excellence and foster economic expansion while having the opportunity to meet environmental concerns. This comprehensive study looks into not only the IoT contribution to energy efficiency and cost reduction but also potential use in areas of administrative operations with the same objective of energy efficiency and cost reduction. With the aid of rigorous mixed methods research that brings together qualitative data from multiple industries, against the backdrop of carefully selected case studies showing the actual benefits of IoT installations, the paper seeks to give a clear picture of the practical advantages of IoT-powered organizations. The research presents information regarding how IoT technologies function to optimize energy applications, enhance processes, and cut down waste through their ability to collect data in real-time and manage systems. Besides, it will analyze the problems companies may run into while adding IoT solutions, such as technological complexity, initial expenditure, and security matters. On the other hand, the research also designs the tactical moves that the companies can opt for as an alternative to completely eradicate these barriers and successfully deploy the IoT technology. Through the systematic study of the role of the Internet of Things on green business practices policymakers, business leaders, and technologists will gain valuable insights which can be applied in whatever levels they are working from. This policy is based on a systematic approach to IoT adoption, which, inter alia, suggests the organization of the project, statistical analysis, and improvement of the present situation to get the best economic and environmental state of affairs. This will further the comprehension of the power of IoT technology in both developing sustainability and increasing profitability, all indications that it is not just a technological advantage but rather a basic instrument for the future sustainability of business practices.

The Implementation of Enhanced Recovery After Spine Surgery in High and Low/Middle-income Countries: A Systematic Review and Meta-Analysis

In this review article, we explore the implementation and outcomes of enhanced recovery after spine surgery (spine ERAS) across different World Bank country-income levels. A systematic literature search was conducted through PubMed, Embase, Scopus, and CINAHL databases for articles on the implementation of spine ERAS in both adult and pediatric populations. Study characteristics, ERAS elements, and outcomes were analyzed and meta-analyses were performed for length of stay (LOS) and cost outcomes. The number of spine ERAS studies from low-middle-income countries (LMICs) increased since 2017, when the first spine ERAS implementation study was published. LMICs were more likely than high-income countries (HICs) to conduct studies on patients aged ≥18 years (odds ratio [OR], 6.00; 95% CI, 1.58-42.80), with sample sizes 51 to 100 (OR, 4.50; 95% CI, 1.21-22.90), and randomized controlled trials (OR, 7.25; 95% CI, 1.77-53.50). Preoperative optimization was more frequently implemented in LMICs than in HICs (OR, 2.14; 95% CI, 1.06-4.41), and operation time was more often studied in LMICs (OR 3.78; 95% CI, 1.77-8.35). Implementation of spine ERAS resulted in reductions in LOS in both LMIC (−2.06; 95% CI, −2.47 to −1.64 d) and HIC (−0.99; 95% CI, −1.28 to −0.70 d) hospitals. However, spine ERAS implementation did result in a significant reduction in costs. This review highlights the global landscape of ERAS implementation in spine surgery, demonstrating its effectiveness in reducing LOS across diverse settings. Further research with standardized reporting of ERAS elements and outcomes is warranted to explore the impact of spine ERAS on cost-effectiveness and other patient-centered outcomes.

Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques

This study presents an advanced methodology for improving electronic assembly quality through real-time, inline inspection utilizing state-of-the-art artificial intelligence (AI) and deep learning technologies. The primary goal is to ensure compliance with stringent manufacturing standards, notably IPC-A-610 and IPC-J-STD-001. Employing the existing infrastructure of pick-and-place machines, this system captures high-resolution images of electronic components during the assembly process. These images are analyzed instantly by AI algorithms capable of detecting a variety of defects, including damage, corrosion, counterfeit, and structural irregularities in components and their leads. This proactive approach shifts from conventional reactive quality assurance methods by integrating real-time defect detection and strict adherence to industry standards into the assembly process. With an accuracy rate exceeding 99.5% and processing speeds of about 5 ms per component, this system enables manufacturers to identify and address defects promptly, thereby significantly enhancing manufacturing quality and reliability. The implementation leverages big data analytics, analyzing over a billion components to refine detection algorithms and ensure robust performance. By pre-empting and resolving defects before they escalate, the methodology minimizes production disruptions and fosters a more efficient workflow, ultimately resulting in considerable cost reductions. This paper showcases multiple case studies of component defects, highlighting the diverse types of defects identified through AI and deep learning. These examples, combined with detailed performance metrics, provide insights into optimizing electronic component assembly processes, contributing to elevated production efficiency and quality.

Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process.

The bio-pharmaceutical industry heavily relies on mammalian cells for the production of bio-therapeutic proteins. The complexity of implementing and high cost-of-goods of these processes are currently limiting more widespread patient access. This is driving efforts to enhance cell culture productivity and cost reduction. Upstream process intensification (PI), using perfusion approaches in the seed train and/or the main bioreactor, has shown substantial promise to enhance productivity. However, developing optimal process conditions for perfusion-based processes remain challenging due to resource and time constraints. Model-based optimization offers a solution by systematically screening process parameters like temperature, pH, and culture media to find the optimum conditions in silico. To our knowledge, this is the first experimentally validated model to explain the perfusion dynamics under different operating conditions and scales for process optimization. The hybrid model accurately describes Chinese hamster ovary (CHO) cell culture growth dynamics and a neural network model explains the production of mAb, allowing for optimization of media exchange rates. Results from six perfusion runs in Ambr® 250 demonstrated high accuracy, confirming the model's utility. Further, the implementation of dynamic media exchange rate schedule determined through model-based optimization resulted in 50% increase in volumetric productivity. Additionally, two 5 L-scale experiments validated the model's reliable extrapolation capabilities to large bioreactors. This approach could reduce the number of wet lab experiments needed for culture process optimization, offering a promising avenue for improving productivity, cost-of-goods in bio-pharmaceutical manufacturing, in turn improving patient access to pivotal medicine.

Exergy and Energy Analysis of Forced Convective Thin-layer Solar Drying of Tiger Nuts (Cyperus esculantus L.)

Aims: To study the exergy and energy analysis for thin-layer solar drying of tiger nuts; and also to determine the drying kinetics for the thin-layer solar drying of tiger nuts. Study Design: Yellow variety of tiger nuts were dried in a forced convection solar dryer to determine the exergy and energy indices. Place and Duration of Study: Federal University of Technology, Owerri, Nigeria between October, 2022 to March, 2023. Methodology: The experiment was conducted at three different air velocities of 1.5, 2.5, and 3.5 m s-1 and two different loads of 70 and 140 g. The tiger nuts were dried from average initial moisture content of 52% (wb) to final moisture content of 10.2% (wb), at an average temperature range of 50.5 to 51.2 ºC. Energy utilization, exergy loss rate, exergy efficiency, improvement potential, and sustainability index were determined. Results: The results of energy utilization, exergy loss rate and improvement potential ranged from 2.1 to 5.09 W, 0.25 to 0.93 W and 0.08 to 0.54 W respectively; however, these values increased with increasing air velocities and loads. The results of exergy efficiency and sustainability index ranged from 41.8 to 68.49% and 1.72 to 3.17 respectively; these values increased with decreasing air velocities and loads. Graphical plots of moisture content with drying time showed that the moisture content of the tiger nuts decreased with increasing drying time. The characteristic nature of the curve is indicative of the fact that there was an initial higher rate of drying, which gradually declined with drying time. The results also showed that the drying rates of the tiger nuts ranged from 6.03 to 8.44 %MC/hr; and these values varied with the different air velocities and loads. Conclusion: The results from this research will serve as a guide in reduction of energy costs for the optimum design of convective dryers.

Clinical Outcomes of Continuation and Maintenance Electroconvulsive Therapy

Large-scale evidence for the efficacy of continuation and maintenance electroconvulsive therapy (c/mECT) is lacking. To provide an exhaustive and naturalistic insight into the real-world outcomes and the cost-effectiveness of c/mECT in a large dataset. This cohort study included all patients in the Danish National Patient Registry who initiated treatment with ECT from 2003 through 2022. The data were analyzed from October 2023 to February 2024. ECT. An algorithm to identify c/mECTs in the dataset was developed: (>3 treatments with ≥7 and <90 days between adjacent treatments, occurring within a time frame of 180 days [cECT] or more [mECT] after an acute [aECT] series). The association of c/mECT with subsequent 6- to 12-month risk of hospitalization or suicidal behavior using Cox proportional hazard regression with multiple adjustments and aECT only as a reference, propensity score matching, and self-controlled case series analysis using a Poisson regression model. A cost-effectiveness analysis based on hospitalization and ECT expenses was made. A total of 19 944 individuals were treated with ECT (12 157 women [61%], 7787 men [39%]; median [IQR] age, 55 [41-70] years). Of these, 1533 individuals (7.7%) received c/mECT at any time point (1017 [5.1%] cECT only and 516 [2.6%] mECT). Compared with patients receiving aECT only, c/mECT patients more frequently experienced schizophrenia (odds ratio [OR], 2.14; 95% CI, 1.86-2.46) and schizoaffective disorder (OR, 2.42; 95% CI, 1.90-3.09) and less frequently unipolar depression (OR, 0.56; 95% CI, 0.51-0.62). In all models, c/mECT was associated with a lower rate of hospitalization after finishing aECT (eg, 6-month adjusted hazard ratio, 0.68; 95% CI, 0.60-0.78 [Cox regression]; 6-month incidence rate ratio, 0.51; 95% CI, 0.41-0.62 [Poisson regression]). There was no significant difference in the risk of suicidal behavior. Compared with the periods before the end of aECT, c/mECT was associated with a substantial reduction in total treatment costs. In a nationwide and naturalistic setting, c/mECT after aECT was infrequently used but associated with a lower risk of readmission than aECT alone. The totality of the evidence indicates that c/mECT should be considered more often to prevent relapse after successful aECT in patients whose condition does not respond sufficiently to other interventions.

Labor-Saving in Order Picking- A Real Time Study Advancing Lean Operations

Purpose: Efficient warehouse operations rely heavily on optimizing order picking processes, which are known to constitute a significant portion of overall operational costs. The aim is to enhance operational efficiency and cost-effectiveness by minimizing non-value-added tasks in warehouse operations. Materials and Methods: This study investigates the transition from an Assisted-Picker model to a Solo-Picker model at a prominent 3PL service provider in India. The analysis compares two approaches for picking orders—pairing laborers versus employing a Solo-Picker—using metrics such as travel time and picking time. Findings: Results reveal that while the Assisted-Picker model may initially show faster completion times for individual orders, the Solo-Picker model proves more efficient overall. This is attributed to the elimination of idle time associated with helpers and enhanced labor utilization efficiency. Real-time productivity data collected during the study period demonstrate a remarkable 44% increase in productivity with the Solo-Picker model, resulting in substantial Labor-Savings and significant reductions in operational costs. Implications to Theory, Practice and Policy: This study highlights the critical importance of reevaluating traditional labor allocation strategies in warehouse environments. By identifying and eliminating non-value-added work, warehouses can move closer to lean and efficient supply chain management practices, ultimately enhancing overall productivity and cost-efficiency.

A novel phaselet-based approach for fault detection and location in HVDC lines

Recently, the popularity of direct current (DC) transmission has surged due to a 30 % reduction in power electronics equipment costs over the last decade. High-Voltage Direct Current (HVDC) transmission lines experience fluctuations in both nominal and faulty states, posing challenges for fault location methods, particularly those based on traveling waves, resulting in imprecise outcomes with an average error margin of ±2 %. This paper investigates a novel fault detection and location approach utilizing wavelet and phaselet transforms. The proposed method focuses on traveling waves in HVDC transmission lines, where the phaselet transform effectively eliminates undesired fluctuations in line currents, improving fault detection accuracy by up to 15 %. By leveraging the traveling time in the faulty line, the exact fault location is calculated with an accuracy of 98.5 %, independent of transmission line parameters, making it applicable to any DC line. Additionally, the method's precision is minimally affected by fault resistance, with <0.5 % deviation even in nonlinear scenarios. Sensitivity analysis conducted on various parameters, including mother wavelet patterns, suggests the optimal solution for each fault type, enhancing detection speed by 20 %. The proposed method is validated using the Cigre HVDC benchmark, confirming its accuracy, speed, and robustness in fault detection and location in HVDC lines. The main contribution of this paper is an accurate, fast, and robust phaselet-based algorithm that reduces fault location errors to <1 % and is applicable to all types of bipolar and monopolar HVDC systems, delivering reliable results for both ground and line faults.

On the adaptive solution of phase‐field problems with A‐stable explicit last‐stage diagonally implicit Runge–Kutta (ELDIRK) methods

AbstractRunge–Kutta algorithms with adaptive step size control provide reliable tools for the solution of initial value problems with diagonally implicit Runge‐Kutta (DIRK) methods as the most common approach. In this contribution, the new low‐order explicit last‐stage diagonally implicit Runge–Kutta (ELDIRK) methods are investigated, combining implicit schemes with an additional explicit evaluation as an explicit last stage. This results in Butcher tableaus with two solutions of different convergence orders suitable for embedded methods, where the higher‐order solution is achieved by additional explicit evaluations. Thus, the iterative solution of non‐linear systems is omitted for the additional stage, presenting a major reduction in computational cost for the determination of a local error estimate. The key contribution is the application of the novel Butcher tableaux to phase‐field problems, solved with the finite‐element method, leading to substantial numerical investigations with an efficient approach for DIRK schemes. The most important aspects are the investigations of stability properties which lead to the novel class of A‐stable ELDIRK methods. In accordance, the study of the convergence orders is presented. A local error estimator is presented, such that adaptive step size control for the new low‐order embedded schemes based on an empirical approach for error estimation is achieved. A suitable parallel algorithm is presented with conclusive two‐dimensional phase‐field simulations based on a Kobayashi‐Warren‐Carter model. The higher‐order convergence suggested by the novel schemes is confirmed, and their effective results are demonstrated, resulting in a valuable semi‐explicit addition to the family of Runge–Kutta time integration schemes.