Search results

This study aims to study the thermal identification issue by harvesting both solar energy and atmospheric thermal updraft for a solar-powered unmanned aerial vehicle (SUAV) and its electric energy performance under continuous soaring conditions.

The authors develop a specific dynamic model for SUAVs in both soaring and cruise modes. The support vector machine regression (SVMR) is adopted to estimate the thermal position, and it is combined with feedback control to implement the SUAV soaring in the updraft. Then, the optimal path model is built based on the graph theory considering the existence of several thermals distributed in the environment. The procedure is proposed to estimate the electricity cost of SUAV during flight as well as soaring, and making use of dynamic programming to maximize electric energy.

The simulation results present the integrated control method could allow SUAV to soar with the updraft. In addition, the proposed approach allows the SUAV to fly to the destination using distributed thermals while reducing the electric energy use.

Two simplified dynamic models are constructed for simulation considering there are different flight mode. Besides, the data-driven-based SVMR method is proposed to support SUAV soaring. Furthermore, instead of using length, the energy cost coefficient in optimization problem is set as electric power, which is more suitable for SUAV because its advantage is to transfer the three-dimensional path planning problem into the two-dimensional.

This paper aims to establish a lattice Boltzmann (LB) method for solid-liquid phase transition (SLPT) from the pore scale to the representative elementary volume (REV) scale. By applying this method, detailed information about heat transfer and phase change processes within the pores can be obtained, while also enabling the calculation of larger-scale SLPT problems, such as shell-and-tube phase change heat storage systems.

Three-dimensional (3D) pore-scale enthalpy-based LB model is developed. The computational input parameters at the REV scale are derived from calculations at the pore scale, ensuring consistency between the two scales. The approaches to reconstruct the 3D porous structure and determine the REV of metal foam were discussed. The implementation of conjugate heat transfer between the solid matrix and the solid−liquid phase change material (SLPCM) for the proposed model is developed. A simple REV-scale LB model under the local thermal nonequilibrium condition is presented. The method of bridging the gap between the pore-scale and REV-scale enthalpy-based LB models by the REV is given.

This coupled method facilitates detailed simulations of flow, heat transfer and phase change within pores. The approach holds promise for multiscale calculations in latent heat storage devices with porous structures. The SLPT of the heat sinks for electronic device thermal control was simulated as a case, demonstrating the efficiency of the present models in designing and optimizing SLPT devices.

A coupled pore-scale and REV-scale LB method as a numerical tool for investigating phase change in porous materials was developed. This innovative approach allows for the capture of details within pores while addressing computations over a large domain. The LB method for simulating SLPT from the pore scale to the REV scale was given. The proposed method addresses the conjugate heat transfer between the SLPCM and the solid matrix in the enthalpy-based LB model.

Among the various applications involving the use of microwave energy, its growing utility within the mining industry is particularly noteworthy. Conventional grinding processes are often overburdened by energy inefficiencies that are directly related to machine wear, pollution and rising project costs. In this work, we numerically investigate the effects of microwave pretreatment through a series of compression tests as a means to help mitigate these energy inefficiencies.

We investigate the effects of microwave pretreatment on various rock samples, as quantified by uniaxial compression tests. In particular, we assign sample heterogeneity based on a Gaussian statistical distribution and invoke a damage model for elemental tensile and compressive stresses based on the maximum tensile stress and the Mohr–Coulomb theories, respectively. We further couple the electromagnetic, thermal and solid displacement relations using finite element modeling.

(1) Increased power intensity during microwave pretreatment results in decreased axial compressive stress. (2) Leveraging statistics to induce variable compressive and tensile strength can greatly facilitate sample heterogeneity and prove necessary for damage modeling. (3) There exists a nonlinear trend to the reduction in smax with increasing power levels, implying an optimum energy output efficiency to create the maximum degradation-power cost relationship.

Previous research in this area has been largely limited to two-dimensional thermo-electric models. The onset of high-performance computing has allowed for the development of high-fidelity, three-dimensional models with coupled equations for electromagnetics, heat transfer and solid mechanics.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

This study asks whether external auditors enable the transfer of policies to the United Nations organizations that they audit and, if so, what types of policies are transferred.

The empirical research is based on a content analysis of 512 external auditor recommendations from 28 pre- and post-accrual reports of 14 UN bodies.

We find that external auditors do enable policy transfer and that such involvements may, at times, veer into non-neutral policy spaces.

We did not analyze all UN organizations with accruals-based accounting. We also did not engage in a longer longitudinal study.

Our findings raise new questions about international organization accountability, the technocratic and policy-specific influences of external auditors, and open a debate about whether attempted policy transfers can be neutral.

The world’s largest group of international organizations is affiliated with the UN. External auditors help ensure that member-state monies are appropriately utilized. Our study is the first to compare pre- and post-accrual external auditor recommendations for 14 UN bodies. It is also the first to notate and study the attempted policy transfers from external auditors to the audited UN bodies.

This study aims to investigate how the Chinese Belt and Road Initiative (BRI) and Chinese outward foreign direct investments (FDI) impact the Belt and Road countries (BRCs). It draws on postcolonial theory to investigate the (geo)political objectives behind the financial and economic means.

In line with the nature of postcolonial studies, the study applies a discourse analysis integrating it with empirical data on indebtedness and trade.

This study finds that FDI and the BRI, as a development project, need to be considered a double-edged sword for the receiving countries. The authors provide evidence that China has instrumentalized financial and economic means to gain political influence and pursue geopolitical ambitions. Moreover, investments into sensitive sectors (e.g. energy, infrastructure), combined with the BRCs’ inability to pay back loans, could eventually lead to China gaining control of these assets.

The study investigates the financial and economic means that are instrumentalized to gain political influence while not considering flows of technology and know-how. It also limits itself to the study of FDI coming from one specific country, i.e. China. Therefore, no comparison and evaluation are made of FDI from other countries, such as the USA or European countries.

By revealing noncommercial objectives and geopolitical ambitions that China pursues through the BRI, the authors derive policy implications for the BRCs, third countries and China.

The study contributes to postcolonial theory and neocolonialism by investigating how China uses financial and economic means to achieve noncommercial objectives and pursue geopolitical ambitions. Additionally, the authors enhance the understanding of FDI by highlighting more subtle aspects of the complex and contextual nature of FDI as a social phenomenon, which have been overlooked thus far. The authors challenge the predominant positive framing of FDI and provide a counterpoint to the way FDI is often coined.

The organization of services can affect the adoption of sustainable practices within the relationship between a buyer (e.g. a shipper) and a supplier (e.g. a logistics service provider–LSP). The purpose of this paper is to analyse, within this relationship, the mechanisms affecting collaboration between shippers and LSPs towards adopting green logistics practices to reduce the negative environmental effects of logistics processes. The authors take the perspective of small and medium enterprises (SMEs), which represent – although less investigated than large enterprises – a relevant field of investigation given their impact on the environmental sustainability of logistics processes.

The authors conducted a multiple case-study investigation on a set of dyads involving shippers and LSPs. The authors explored the antecedents shaping the approach to sustainability in logistics and, adopting the absorptive capacity (AC) theory, the learning and knowledge transfer processes leading to the adoption of green practices.

Collaboration between shippers and LSPs for better sustainability in logistics seems not to work when relationships are limited to simple annual (or pluriannual) contracts, and when shippers do not show ambition to improve the level of sustainability of their logistics processes (regardless of whether they show an interest in general sustainability matters). On the other hand, successful cases show higher commitment in the dyadic relationship with respect to improving logistics sustainability, good levels of communication and a more structured process of knowledge sharing, enabled by IT integration, shared performance monitoring, and creation of inter-organizational teams.

While most of the existing research focuses on the perspective of shippers or LSPs, this work is original since it explores collaborative mechanisms within a buyer-supplier relationship simultaneously taking the perspective of both parties, according to the lens of the AC. It identifies directions for improving collaboration within the shipper-LSP relationship in the context of SMEs to foster the adoption of collaborative green logistics practices to impact sustainability positively.

Megaprojects have large impact on the environment and stakeholders should take collective action to ensure that these projects are developed in a socially responsible manner. Hitherto, it is not known whether group and subjective norms and social identity could compel stakeholders to take socially responsible collective actions in megaprojects. The aim of this study is to design and test a model to boost stakeholders' intention to take socially responsible collective action in the context of mega water transfer projects in China.

A quasi-experimental causal research design was adopted to establish cause–effect relationships among the dependent variable (we-intention) and independent variables (subjective norms, group norms, social identity and desire). This study adopts the belief–desire–intention model and social influence theory to empirically investigate how to boost the stakeholders' intention to participate in socially responsible collective action. An online questionnaire survey was conducted and data was collected from 365 respondents who were involved in mega water transfer projects in China. The partial least squares structural equation modeling technique was employed to analyze the data.

The results from partial least squares analyses indicate that the presence of subjective norms, group norms and social identity (collectively known as social influence process) could increase stakeholders' intention to take socially responsible collective action. In addition, the desire to be socially responsible also boosts stakeholders' intention to take collective action. Desire partially mediates the relationship between social influence process and intention to take socially responsible collective action.

This study adds to existing knowledge by discovering social influence process as an antecedent to taking socially responsible collective action in megaprojects. Strong group norms and subjective norms could propel stakeholders to be more socially responsible. The study also adds to knowledge by discovering that stakeholders' desire to fulfill social responsibility also leads them to take concrete actions. Implications and recommendations are provided on how to manipulate different types of social influence processes to facilitate stakeholders to adopt socially responsible collective action in the process of managing megaprojects.

Project-based learning is one of the most effective methods of transferring academic knowledge and skills to real-world situations in higher education. However, its effectiveness is not much investigated focusing on the students' narrative. This study aims at evaluating the students' experience and perspective on adopting project-based learning in master by research and doctoral programmes for proactive skills development.

This study evaluates the self-reflection of 10 postgraduate students and their supervisor who have participated in developing a software tool for solar photovoltaics (PV) integrated building envelope design, management and the related education.

Findings reveal that the students have effectively improved their knowledge on the subject via collaborating with the industry, self-learning/observation, peer learning, problem-solving and teamwork. Dividing the project into student-led tasks has improved the decision-making and leadership skills, risks identification, planning and time management skills. The overall experience has (1) built up confidence in students, (2) enhanced their creativity and critical thinking and (3) improved their proactive skills and context knowledge.

A clear research gap can be seen in exploring the effectiveness of project-based learning for master by research and doctoral programmes, which mainly focus on extensive research. These programmes do not necessarily focus on developing students' proactive skills, which is the main requirement if they intend to work in the construction industry. This paper addresses the above research gap by demonstrating the effectiveness of project-based learning for developing the proactive skills in a research-intensive learning environment.

This research fills a gap in process science by defining and explaining entropy and the increase of entropy in processes.

This is a theoretical treatment that begins with a conceptual understanding of entropy in thermodynamics and information theory and extends it to the study of degradation and improvement in a transformation process.

A transformation process with three inputs: demand volume, throughput and product design, utilizes a system composed of processors, stores, configuration, human actors, stored data and controllers to provide a product. Elements of the system are aligned with the inputs and each other with a purpose to raise standard of living. Lack of alignment is entropy. Primary causes of increased entropy are changes in inputs and disordering of the system components. Secondary causes result from changes made to cope with the primary causes. Improvement and innovation reduce entropy by providing better alignments and new ways of aligning resources.

This is the first detailed theoretical treatment of entropy in a process science context.