Hasil untuk "Mechanical industries"

Christina E Hoicka, Anna Berka, Sara Chitsaz et al.

Place-based approaches to renewable energy transitions tailor solutions to specific social, cultural, economic and ecological contexts inherent to particular localities. Drawing on transformative energy justice frameworks and approaches, we argue that place-based framings and interpretations of impacts of community renewable energy projects provide the means to center Indigenous worldviews, observations and experiences of justice associated with these projects. This co-created study draws on interviews with knowledge holders in 14 First Nations across the Province of British Columbia (BC), Canada. Interview participants shared experiences and observations on both the process (community engagement) and outcome (impacts and benefits) dimensions of 36 operational and planned renewable energy projects, pointing to a rich diversity of social, political, material, economic, ecological and relational impacts. Across a wide range of project sizes and technologies, the findings indicate that deep community engagement and the collective decisions for allocation of revenues mediate the positive and transformative impacts experienced by the community. Taken collectively, these findings show that First Nations approaches to developing projects are place-based, ensuring a wide range of impacts to the community that can collectively contribute to transformative change. In the broader context of systematic neglect of social, environmental and justice-oriented values in public policy making, and amidst widespread failure of ‘decide-announce-defend’ approaches to achieving social acceptance for renewable energy projects, this study demonstrates what distinguishes place-based approaches in practice, and how they deliver transformative outcomes for First Nations. Policy, project and resource allocation decisions should reflect the diverse impacts and transformative outcomes of renewable energy projects in First Nations contexts. We conclude that embedding place-based approaches in institutional arrangements, policy and project design is critical to providing economic opportunities to First Nations without discrimination under the United Nations Declaration on the Rights of Indigenous People, alongside meeting BC’s power needs and decarbonization goals.

Adji Putra Abriantoro, Manlian Ronald Adventus Simanjuntak

The effect of concentration of sodium hydroxide (NaOH) on physical and mechanical properties of geopolymer paving blocks consist of fly ash type F and glass powder. A quantitative experimental method was used both the NaOH molarity (1M, 2M, 4M, 6M, 8M and 10M) was studied and the compressive strength, flexure strength and water absorption were taken as key parameters. The ratio of alkali activator to binder was 0.35, and the ratio of Sodium Silicate/NaOH was 1.5. The best performance at 4M molarity yielded compressive strength of 35.60 MPa and flexural strength of 4.29 MPa, due to optimal geopolymerization and denser microstructure. The mechanical performance of the geopolymer paving blocks was compromised at NaOH molarities higher than 4M due to the emergence of a micropore, which increased the porosity and water absorption from 6.55% at 1M to 9.14% at 10M. These results confirm that increasing NaOH molarity is vital for producing geopolymer paving block with high performance. Hence, this study contributes to sustainable construction by using waste products of industries for producing construction and building materials.

Qiangsheng Bu, Zhigang Ye, Shuyi Zhuang et al.

Abstract Probabilistic prediction of photovoltaic (PV) output power is crucial to maintain the stable operation and reliability of the power grid and to develop effective operational strategies and short-term plans. Therefore, a new short-term PV power generation forecasting approach via improved variational mode decomposition (I-VMD)-based dynamic model fusion and adaptive boundary optimization is proposed in this paper. First of all, a correlation analysis is conducted on various meteorological data that affect PV output. Then, feature extraction of selected meteorological data is carried out by using I-VMD and kernel principal component analysis (KPCA). After that, based on the improved stacked generalization (I-Stacking) ensemble learning framework, a new multi-model fusion method is proposed to construct a forecasting model for short-term PV power generation. Finally, an adaptive boundary optimization method for prediction errors is proposed to enhance the PIs overall performance. Through numerical comparison and analysis with the conventional methods, the performance of the proposed method is validated.

Paul Ducarme, Bart Weber, Martin van Hecke et al.

Nonlinearities and instabilities in mechanical structures have shown great promise for embedding advanced functionalities. However, simulating structures subject to nonlinearities can be challenging due to the complexity of their behavior, such as large shape changes, effect of pre-tension, negative stiffness and instabilities. While traditional finite element analysis is capable of simulating a specific nonlinear structure quantitatively, it can be costly and cumbersome to use due to the high number of degrees of freedom involved. We propose a framework to facilitate the exploration of highly nonlinear structures under quasistatic conditions. In our framework, models are simplified by introducing `flexels', elements capable of intrinsically representing the complex mechanical responses of compound structures. By extending the concept of nonlinear springs, flexels can be characterized by multi-valued response curves, and model various mechanical deformations, interactions and stimuli, e.g., stretching, bending, contact, pneumatic actuation, and cable-driven actuation. We demonstrate that the versatility of the formulation allows to model and simulate, with just a few elements, complex mechanical systems such as pre-stressed tensegrities, tape spring mechanisms, interaction of buckled beams and pneumatic soft gripper actuated using a metafluid. With the implementation of the framework in an easy-to-use Python library, we believe that the flexel formulation will provide a useful modeling approach for understanding and designing nonlinear mechanical structures.

Francesc Sabater, Ouns El Harzli, Geert-Jan Besjes et al.

Optimization via decoded quantum interferometry (DQI) has recently gained a great deal of attention as a promising avenue for solving optimization problems using quantum computers. In this paper, we apply DQI to an industrial optimization problem in the automotive industry: the vehicle option-package pricing problem. Our main contributions are 1) formulating the industrial problem as an integer linear program (ILP), 2) converting the ILP into instances of max-XORSAT, and 3) developing a detailed quantum circuit implementation for belief propagation, a heuristic algorithm for decoding LDPC codes. Thus, we provide a full implementation of the DQI algorithm using Belief Propagation, which can be applied to any industrially relevant ILP by first transforming it into a max-XORSAT instance. We also evaluate the effectiveness of our implementation by benchmarking it against both Gurobi and a random sampling baseline.

Paola Leo, Gilda Renna, Neetesh Soni et al.

In this study, the effect of increasing exposure time on the microstructures, porosity, mechanical properties and corrosion behavior of selective laser melted sample Al-Si10-Mg powder was investigated. The samples were processed at the same power (375 W) and scan speed (2000 mm/s), but with increasing exposure time. Exposure time equal to 40, 50 and 60 µs was applied. The features of the analyzed samples show that with increasing exposure time, greater efficiency of the heat input was obtained, with a larger size of the melt pool and Si particles and lower porosity. Specifically, at the highest exposure time the melt pool showed an increase of 19% in width and 48% in depth, while the volume percentage of the voids decreased by 50% with respect to the lowest exposure time. Moreover, with the coarser microstructure being associated with a lower level of voids, the average hardness is similar for the analyzed samples. Corrosion resistance was evaluated, being one of the most important properties that may affect the service performance of Al-Si10-Mg alloy in the aerospace, marine and automotive industries. The potentiodynamic curves of the samples show that the voids occurrence is more significant with respect to the scale of the microstructure on corrosion behavior, with the sample processed at the highest exposure time being the more resistant to corrosion. The experimental techniques used in the present study were Optical Microscope (OM), Scanning Electron Microscope (SEM), hardness and X-Ray Computed Tomography.

Mahlon Kida Marvin, Zakiyyu Muhammad Sarkinbaka

Abstract Nigeria currently relies on 80% thermal energy generation. However, studies have shown that less than 60% of the population have access to power. To address this issue, Nigeria has developed an energy transition plan to achieve net-zero emissions by utilizing eco-friendly and sustainable renewable energy sources. However, the effectiveness of renewable energy resources is often hampered by seasonal variations, which limit the amount of energy that can be produced to meet growing demand. One effective solution to this challenge is long-term energy storage, particularly during periods of low demand. Power-to-X (PtX) technology offers a promising approach by enabling long-term sustainable energy generation and storage for future use when renewable energy availability decreases during peak demands. This study critically reviews the latest advancements in renewable PtX technology and evaluates its potential application within Nigeria's energy sector. Furthermore, it explores the potential obstacles to the widespread adoption of PtX technology in Nigeria. Despite Nigeria’s significant potential for implementing PtX initiatives, the country currently falls behind in technology deployment and viable production pathways for sustainable PtX implementation. This shortfall is primarily due to lack of policies, frameworks, and financing schemes to support infrastructural development, especially for long-term energy storage. Given the intermittent nature of renewable energy, a transition strategy that includes adequate storage capacity is crucial. Although green hydrogen, a key component of PtX, has substantial potential as an energy carrier in Nigeria, its immediate use is limited by high production costs. Nonetheless, ongoing efforts to diversify Nigeria’s energy mix through infrastructure and policy developments could eventually establish a roadmap for PtX implementation, promoting long-term energy sustainability and distribution efficiency.

Carlos Roberto Pinheiro Junior, João Luís Nunes Carvalho, Lucas Pecci Canisares et al.

Abstract Biofuels are essential to ensure the energy transition and mitigating of climate change. However, understanding the impact of land use change (LUC) and management practices on soil organic carbon (SOC) stocks is fundamental to ensuring well‐founded policymaking and assessing the sector's carbon footprint. Here, we conducted a meta‐analysis (511 pairwise observations) to obtain Brazil's SOC stock change factors (SOCscf) for LUC and management practices in sugarcane fields. Our results showed that converting native vegetation to sugarcane reduced the SOC stock in all assessed periods. The conversion from annual crops to sugarcane showed a reduction in SOC stock in the first 10 years but with a recovery over time. The conversion of pasture to sugarcane reduced the SOC stock only in the 10–20‐year period and had a neutral effect in other periods evaluated. However, our dataset showed high variability in SOCscf, with many observations indicating an increase in SOC stock, which is related to degraded pastures. We observed that the SOC accumulation rate for each ton of sugarcane straw was affected by the interaction between soil texture and precipitation. Regarding straw management, a low removal rate (< 34%) did not affect the SOC stock, while moderate (34%–66%) and high (> 66%) removal resulted in losses of 5.0% (SOCscf 0.950) and 9.9% (SOCscf 0.901), respectively. Our results also showed that reduced tillage and vinasse application increased SOC stocks by 24.0% (SOCscf 1.24) and 10.0% (SOCscf 1.10) respectively, proving to be good strategies to support C sequestration in sugarcane fields. Finally, we highlight that our results can contribute to the improvement of public policies and also be used in future life cycle assessment (LCA) and modeling studies, as they provide robust data to establishing regional SOCscf induced by LUC and management practices, enhancing the reliability of the C footprint assessment of biofuel production.

Shuo Wang, Junsheng Wang, Chengpeng Xue et al.

Abstract Aluminum‐lithium (Al‐Li) alloy is one of the most promising lightweight structural materials in the aeronautic and aerospace industries. The key to achieving their excellent mechanical properties lies in tailoring T1 strengthening precipitates; however, the nucleation of such nanoparticles remains unknown. Combining atomic resolution HAADF‐STEM with first‐principles calculations based on the density functional theory (DFT), here, we report a counterintuitive nucleation mechanism of the T1 that evolves from an Eshelby inclusion with unstable stacking faults. This precursor is accelerated by Ag‐Mg clusters to reduce the barrier, forming the structural framework. In addition, these Ag‐Mg clusters trap the free Cu and Li to prepare the chemical compositions for T1. Our findings provide a new perspective on the phase transformations of complex precipitates through solute clusters in terms of geometric structure and chemical bonding functions.

Marta Bertola, Elena Magenau, Enrico Martani et al.

Abstract Miscanthus, a C4 perennial rhizomatous grass, is a low‐input energy crop suitable for marginal land, which cultivation can improve soil quality and promote soil organic carbon (SOC) sequestration. In this study, four promising Miscanthus hybrids were chosen to evaluate their short‐term potential, in six European marginal sites, to sequester SOC and improve physical, chemical, and biological soil quality in topsoil. Overall, no differences among Miscanthus hybrids were detected in terms of impacts on soil quality and SOC sequestration. SOC sequestration rate after 4 years was of +0.4 Mg C ha−1 year−1, but land‐use transition from former cropland or grassland showed contrasting SOC sequestration trajectories. In unfertilized marginal lands, cultivation of high‐yielding Miscanthus genotypes caused a depletion of K (−216 kg ha−1 year−1), followed by Ca (−56 kg ha−1 year−1), Mg (−102 kg ha−1 year−1) and to a lesser extent of N. On the contrary, the biological turnover of organic matter increased the available P content (+164 kg P2O5 ha−1 year−1). SOC content was identified as the main driver of changes in biological soil quality. High input of labile plant C stimulated an increment of microbial biomass and enzymatic activity. Here, a novel approach was applied to estimate C input to soil from different Miscanthus organs. Despite the high estimated plant C input to soil (0.98 Mg C ha−1 year−1), with significant differences among sites and Miscanthus hybrids, it was not identified as a driver of SOC sequestration. On the contrary, initial SOC and nutrients (N, P) content, as well as their elemental stoichiometric ratios with C, were the key factors controlling SOC dynamics. Introducing Miscanthus on marginal lands impacts positively soil biological quality over the short term, but targeted fertilization plans are needed to secure crop yield over the long term as well as the C sink capacity of this perennial cropping system.

Abhinav Garg, Naman Shukla, Maarten Wormer

Time series forecasting in the air cargo industry presents unique challenges due to volatile market dynamics and the significant impact of accurate forecasts on generated revenue. This paper explores a comprehensive approach to demand forecasting at the origin-destination (O\&D) level, focusing on the development and implementation of machine learning models in decision-making for the air cargo industry. We leverage a mixture of experts framework, combining statistical and advanced deep learning models to provide reliable forecasts for cargo demand over a six-month horizon. The results demonstrate that our approach outperforms industry benchmarks, offering actionable insights for cargo capacity allocation and strategic decision-making in the air cargo industry. While this work is applied in the airline industry, the methodology is broadly applicable to any field where forecast-based decision-making in a volatile environment is crucial.

Nusrat Zahan, Yasemin Acar, Michel Cukier et al.

Cyber attacks leveraging or targeting the software supply chain, such as the SolarWinds and the Log4j incidents, affected thousands of businesses and their customers, drawing attention from both industry and government stakeholders. To foster open dialogue, facilitate mutual sharing, and discuss shared challenges encountered by stakeholders in securing their software supply chain, researchers from the NSF-supported Secure Software Supply Chain Center (S3C2) organize Secure Supply Chain Summits with stakeholders. This paper summarizes the Industry Secure Supply Chain Summit held on November 16, 2023, which consisted of \panels{} panel discussions with a diverse set of \participants{} practitioners from the industry. The individual panels were framed with open-ended questions and included the topics of Software Bills of Materials (SBOMs), vulnerable dependencies, malicious commits, build and deploy infrastructure, reducing entire classes of vulnerabilities at scale, and supporting a company culture conductive to securing the software supply chain. The goal of this summit was to enable open discussions, mutual sharing, and shedding light on common challenges that industry practitioners with practical experience face when securing their software supply chain.

Tianyu Zhang, Chuanyu Xue, Jiachen Wang et al.

Industrial Internet of Things (IIoT) technologies have revolutionized industrial processes, enabling smart automation, real-time data analytics, and improved operational efficiency across diverse industry sectors. IIoT testbeds play a critical role in advancing IIoT research and development (R&D) to provide controlled environments for technology evaluation before their real-world deployment. In this article, we conduct a comprehensive literature review on existing IIoT testbeds, aiming to identify benchmark performance, research gaps and explore emerging trends in IIoT systems. We first review the state-of-the-art resource management solutions proposed for IIoT applications. We then categorize the reviewed testbeds according to their deployed communication protocols (including TSN, IEEE 802.15.4, IEEE 802.11 and 5G) and discuss the design and usage of each testbed. Driven by the knowledge gained during this study, we present suggestions and good practices for researchers and practitioners who are planning to design and develop IIoT testbeds for connectivity research.

Malte Schneevogt, Karsten Binninger, Noah Klarmann

This paper explores the potential application of Deep Reinforcement Learning in the furniture industry. To offer a broad product portfolio, most furniture manufacturers are organized as a job shop, which ultimately results in the Job Shop Scheduling Problem (JSSP). The JSSP is addressed with a focus on extending traditional models to better represent the complexities of real-world production environments. Existing approaches frequently fail to consider critical factors such as machine setup times or varying batch sizes. A concept for a model is proposed that provides a higher level of information detail to enhance scheduling accuracy and efficiency. The concept introduces the integration of DRL for production planning, particularly suited to batch production industries such as the furniture industry. The model extends traditional approaches to JSSPs by including job volumes, buffer management, transportation times, and machine setup times. This enables more precise forecasting and analysis of production flows and processes, accommodating the variability and complexity inherent in real-world manufacturing processes. The RL agent learns to optimize scheduling decisions. It operates within a discrete action space, making decisions based on detailed observations. A reward function guides the agent's decision-making process, thereby promoting efficient scheduling and meeting production deadlines. Two integration strategies for implementing the RL agent are discussed: episodic planning, which is suitable for low-automation environments, and continuous planning, which is ideal for highly automated plants. While episodic planning can be employed as a standalone solution, the continuous planning approach necessitates the integration of the agent with ERP and Manufacturing Execution Systems. This integration enables real-time adjustments to production schedules based on dynamic changes.

Guodong Shao, Guntars O. Martinson, Marife D. Corre et al.

Abstract Monoculture croplands are a major source of global anthropogenic emissions of nitrous oxide (N2O), a potent greenhouse gas that contributes to ozone depletion. Agroforestry has the potential to reduce N2O emissions. Presently, there is no systematic comparison of soil N2O emissions between cropland agroforestry and monoculture systems in Central Europe. We investigated the effects of converting the monoculture cropland system into the alley cropping agroforestry system on soil N2O fluxes at three sites (each site has paired agroforestry and monoculture) in Germany, where agroforestry combined crop rows and poplar short‐rotation coppice (SRC). We measured soil N2O fluxes monthly over 2 years (March 2018–January 2020) using static vented chambers. Annual soil N2O emissions from agroforestry ranged from 0.21 to 2.73 kg N ha−1 year−1, whereas monoculture N2O emissions ranged from 0.34 to 3.00 kg N ha−1 year−1. During the rotation of corn crop, with high fertilization rates, agroforestry reduced soil N2O emissions by 9% to 56% compared to monocultures. This was mainly caused by low soil N2O emissions from the unfertilized agroforestry tree rows. Soil N2O fluxes were predominantly controlled by soil mineral N in both agroforestry and monoculture systems. Our findings suggest that optimized fertilizer input will further enhance the potential of agroforestry for mitigating N2O emissions.

Kaiqing Yao, Chonghu Song, Hong Fang et al.

In the rapid development of modern materials, there is a great need for novel energy-saving, time-saving, cost-saving, and facile approaches to fabricate light, low-density, and high-porosity aerogels with excellent mechanical and thermal performance. In this work, a freeze-extraction method combined with normal vacuum-drying (VD), using short electrospun polyimide (PI) fibers as a supporting skeleton, was developed to prepare high-performance PI fibrous aerogels (PIFAs) without the need for a special drying process. The resulting PIFAs exhibit low density (≤ 52.8 mg·cm−3) and high porosity (> 96%). The PIFAs are highly fatigue resistant, with cycling compression for at least 20 000 cycles and a low energy-loss coefficient. A thermal conductivity of 40.4 mW·m−1·K−1 was obtained for a PIFA with a density of 39.1 mg·cm−3. Further modification of the PIFAs with polysilazane led to enhanced fire resistance and a high residue (> 70%) in a nitrogen atmosphere. These excellent properties make PIFAs and their composites promising candidates for lightweight construction, thermal insulating, and fireproof layers for the construction industry, aviation, and aerospace industries, as well as for high-temperature reaction catalyst carriers. In addition, the proposed freezing-extraction/VD approach can be extended to other material systems to provide savings in energy, time, and costs.

Wei Guo

The high nitrogen austenitic stainless steel is widely used as power generation and geological exploration equipment materials because of its excellent strength, corrosion resistance and non-magnetic. In this paper, the mechanical behavior and microstructure evolution of P550 steel in the range of 900 °C–1200 °C and 0.001–10 s ^−1 deformation conditions were studied by physical and heat treatment simulations, metallographic observations and thermal processing maps. The results showed that the flow curves quickly reach the peak and then soften to a steady state, which indicates dynamic recrystallization (DRX) behavior. DRX is easy to occur when the deformation temperature is above 1080 °C. The activation energy of the forged P550 stainless steel was calculated as 519 kJ mol ^−1 . There is a positive correlation between the peak stress, DRX critical stress, strain and Z value of the tested steel. The instability of the tested steel is easy to produce in the high strain rate region and low temperature region during hot working. Crack germinates and expands preferentially at the ‘necklace structure’ of inadequate dynamic recrystallization. Under the deformation state of 0.001 s ^−1 , coarse crystals and mixed crystals are easily emerged during subsequent heat treatment. Combining the hot working map, the maximum deformation resistance and the grain evolution behavior during hot working and heat treatment, the suggested working window is T = 1020 °C–1200 °C and έ = 0.01–1 s ^−1 .

Jihang Li, Dongping Zhan, Zhouhua Jiang et al.

Ultra-high strength steels are the crucial irreplaceable materials in the fields of aerospace, national defense and military industries. Unfortunately, it is challenging to achieve the requirements of vital components because of the strength-toughness trade-off of ultra-high strength steels. In this review, three types of conventional ultra-high strength martensitic steels are systematically summarized. These are: (i) low alloy ultra-high strength steels; (ii) ultra-high strength maraging steels; and (iii) Co–Ni secondary hardening steels. The main influencing factors of mechanical properties of ultra-high strength steels and the exploration on strengthening/toughening are discussed. In particular, the design concept based on the synergistic precipitation of low lattice misfit nano-particles and the formation of martensite matrix with high-density dislocations is a promising approach to develop novel ultra-high strength steels. Finally, some suggestions on the traditional design methods and future development of ultra-high strength steels are put forward and an outlook on future work is offered. This provides guidance for the development of novel ultra-high strength steels with excellent comprehensive performance.

Luana Dias Campos, Valfredo de Almeida Santos Junior, Júlia Demuner Pimentel et al.

Collagen is one of the main components of the extracellular matrix of the dermis and articular cartilage and influences the body’s mechanical, organizational, and tissue formation properties. Produced from food industry by-products, it is considered a nutraceutical product widely used as an ingredient or supplement in food, pharmaceutical, and cosmetic industries. This study aimed to conduct a literature review on the scientific evidence regarding the beneficial effects of collagen consumption in the treatment of skin and orthopedic diseases. Literature data have shown that hydrolyzed collagen supplementation promotes skin changes, such as decreased wrinkle formation; increased skin elasticity; increased hydration; increased collagen content, density, and synthesis, which are factors closely associated with aging-related skin damage. Regarding orthopedic changes, collagen supplementation increases bone strength, density, and mass; improves joint stiffness/mobility, and functionality; and reduces pain. These aspects are associated with bone loss due to aging and damage caused by strenuous physical activity. Thus, this review addresses the economic and health potential of this source of amino acids and bioactive peptides extracted from food industry by-products.

Yasunori Minami, Nao Matsuyama, Yasuo Takeichi et al.

Polyetheretherketone (PEEK) is an important super engineering plastic utilized in industries owing to its thermal stability and mechanical strength, however, its robustness hinders chemical recycling. Here, the authors report the depolymerization of insoluble PEEK using sulfur nucleophiles via carbon–oxygen bond cleavage and then treatment with organic halides to form various dithiofunctionalized benzophenones and hydroquinone monomers.